Semi-Automatik Klassifikation Plugin Dokumentation¶

Introduction¶

Developed by Luca Congedo, the Semi-Automatic Classification Plugin (SCP) is a free open source plugin for QGIS that allows for the semi-automatic classification (also known as supervised classification) of remote sensing images. It provides several tools for the download of free images, the preprocessing, the postprocessing, and the raster calculation (please see What can I do with the SCP?).

The overall objective of SCP is to provide a set of intertwined tools for raster processing in order to make an automatic workflow and ease the land cover classification, which could be performed also by people whose main field is not remote sensing. The first version of the SCP was written by Luca Congedo in 2012 for the ACC Dar Project in order to create a tool for the classification of land cover in an affordable and automatic fashion (read this working paper). Following versions of SCP were developed as personal commitment to the remote sensing field and open source software. SCP version 5 (codename: Kourou) is developed in the frame of Luca Congedo’s PhD in Landscape and Environment at Sapienza University of Rome.

http://www.youtube.com/watch?v=K2mIa66e6h0

This user manual provides information about the Plugin Installation of SPC and the Die Schnittstelle von SCP, with detailed information about all the functions. In addition, the Brief Introduction to Remote Sensing illustrates the basic concepts and definitions which are required for using the SCP.

Basic Tutorials are available for learning the main functions of SCP and Thematic Tutorials illustrate specific tools.

You are kindly invited to contribute to SCP (see How to contribute to SCP) and join the Facebook group or the Google+ Community. Several thousand people have already joined and posted hundreds of questions and comments. Also, please read the Frequently Asked Questions.

For more information and tutorials visit the official site

From GIS to Remote Sensing

How to cite:

Congedo Luca (2016). Semi-Automatic Classification Plugin Documentation. DOI: http://dx.doi.org/10.13140/RG.2.2.29474.02242/1

License:

Except where otherwise noted, content of this work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Semi-Automatic Classification Plugin is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, version 3 of the License. Semi-Automatic Classification Plugin is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with Semi-Automatic Classification Plugin. If not, see http://www.gnu.org/licenses/.

Translators:

Language: Author

Plugin Installation¶

Das Semi-Automatik Klassifikation Plugin benötigt die Installation von GDAL, OGR, NumPy, SciPy und `Matplotlib <http://matplotlib.org/>`_(bereits in QGIS enthalten).
Dieses Kapitel beschreibt die Installation des Semi-Automatik Klassifikation Plugins auf den unterstützten Betriebssystemen.

Installation in Windows 32 bit¶

QGIS download and installation¶

Download the latest QGIS version 32 bit from here (the direct download of QGIS 2.8 from this link);
Execute the QGIS installer with administrative rights, accepting the default configuration.

Now, QGIS 2 is installed.

Semi-Automatic Classification Plugin installation¶

Run QGIS 2;
From the main menu, select Plugins > Manage and Install Plugins;

From the menu All, select the Semi-Automatic Classification Plugin and click the button Install plugin;

TIP: in case of issues or an offline installation is required see How to install the plugin manually? and How to install the plugin from the official SCP repository?.

The SCP should be automatically activated; however, be sure that the Semi-Automatic Classification Plugin is checked in the menu Installed (the restart of QGIS could be necessary to complete the SCP installation);

Configuration of the plugin¶

Now, the Semi-Automatic Classification Plugin is installed and a dock and a toolbar should be added to QGIS. Also, a SCP menu is available in the Menu Bar of QGIS. It is possible to move the SCP Tools and the dock according to your needs, as in the following image.

_images/SemiAutomaticClassificationPlugin.jpg

Installation in Windows 64 bit¶

QGIS download and installation¶

Download the latest QGIS version 64 bit from here (the direct download of QGIS 2.8 from this link);
Execute the QGIS installer with administrative rights, accepting the default configuration.

Now, QGIS 2 is installed.

Semi-Automatic Classification Plugin installation¶

Run QGIS 2;
From the main menu, select Plugins > Manage and Install Plugins;

From the menu All, select the Semi-Automatic Classification Plugin and click the button Install plugin;

TIP: in case of issues or an offline installation is required see How to install the plugin manually? and How to install the plugin from the official SCP repository?.

The SCP should be automatically activated; however, be sure that the Semi-Automatic Classification Plugin is checked in the menu Installed (the restart of QGIS could be necessary to complete the SCP installation);

Configuration of the plugin¶

The configuration of available RAM is recommended in order to reduce the processing time. From the SCP Menü select Settings > Processing .

In the Settings, set the Available RAM (MB) to a value that should be half of the system RAM. For instance, if your system has 2GB of RAM, set the value to 1024MB.

Installation in Ubuntu Linux¶

QGIS download and installation¶

Open a terminal and type:
```
sudo apt-get update
```
Press Enter and type the user password;

Type in a terminal:

sudo apt-get install qgis python-matplotlib python-scipy

Press Enter and wait until the software is downloaded and installed.

Now, QGIS 2 is installed.

Semi-Automatic Classification Plugin installation¶

Run QGIS 2;
From the main menu, select Plugins > Manage and Install Plugins;

From the menu All, select the Semi-Automatic Classification Plugin and click the button Install plugin;

TIP: in case of issues or an offline installation is required see How to install the plugin manually? and How to install the plugin from the official SCP repository?.

The SCP should be automatically activated; however, be sure that the Semi-Automatic Classification Plugin is checked in the menu Installed (the restart of QGIS could be necessary to complete the SCP installation);

Configuration of the plugin¶

The configuration of available RAM is recommended in order to reduce the processing time. From the SCP Menü select Settings > Processing .

In the Settings, set the Available RAM (MB) to a value that should be half of the system RAM. For instance, if your system has 2GB of RAM, set the value to 1024MB.

Installation in Debian Linux¶

QGIS download and installation¶

Open a terminal and type:
```
sudo apt-get update
```
Press Enter and type the user password;

Type in a terminal:

sudo apt-get install qgis python-matplotlib python-scipy

Press Enter and wait until the software is downloaded and installed.

Now, QGIS 2 is installed.

Semi-Automatic Classification Plugin installation¶

Run QGIS 2;
From the main menu, select Plugins > Manage and Install Plugins;

From the menu All, select the Semi-Automatic Classification Plugin and click the button Install plugin;

TIP: in case of issues or an offline installation is required see How to install the plugin manually? and How to install the plugin from the official SCP repository?.

The SCP should be automatically activated; however, be sure that the Semi-Automatic Classification Plugin is checked in the menu Installed (the restart of QGIS could be necessary to complete the SCP installation);

Configuration of the plugin¶

The configuration of available RAM is recommended in order to reduce the processing time. From the SCP Menü select Settings > Processing .

In the Settings, set the Available RAM (MB) to a value that should be half of the system RAM. For instance, if your system has 2GB of RAM, set the value to 1024MB.

Installation in Mac OS¶

QGIS download and installation¶

Download and install the latest version of QGIS and GDAL from here .
In addition, download and install the python modules Numpy, Scipy, and Matplotlib from this link .

Now, QGIS 2 is installed.

Semi-Automatic Classification Plugin installation¶

Run QGIS 2;
From the main menu, select Plugins > Manage and Install Plugins;

From the menu All, select the Semi-Automatic Classification Plugin and click the button Install plugin;

TIP: in case of issues or an offline installation is required see How to install the plugin manually? and How to install the plugin from the official SCP repository?.

The SCP should be automatically activated; however, be sure that the Semi-Automatic Classification Plugin is checked in the menu Installed (the restart of QGIS could be necessary to complete the SCP installation);

Configuration of the plugin¶

The configuration of available RAM is recommended in order to reduce the processing time. From the SCP Menü select Settings > Processing .

In the Settings, set the Available RAM (MB) to a value that should be half of the system RAM. For instance, if your system has 2GB of RAM, set the value to 1024MB.

Die Schnittstelle von SCP¶

SCP Menü¶

SCP Menü

Das SCP Menü erlaubt die Auswahl der Hauptfunktionen des Main Interface Window, des Spectral Signature Plot und des Scatter Plot.

Band set;
Download images;
Tools;
Preprocessing;
Postprocessing;
Band calc;
Spectral Signature Plot;
Scatter Plot;
Batch;
Settings;
Benutzerhandbuch: öffnet das Benutzerhandbuch im Browser;
Online help: open the Online help in a web browser; also, a Facebook group and a Google+ Community are available for sharing information and asking for help about SCP;
Plugin anzeigen: zeigt alle SCP Werkzeugleisten und Dock, wenn diese vorher geschlossen wurden;

SCP Tools¶

SCP Werkzeuge

The toolbar SCP Tools allows for the selection of the main functions of the Main Interface Window, the Spectral Signature Plot, and the Scatter Plot.

Band set;
Download images;
Tools;
Preprocessing;
Postprocessing;
Band calc;
Spectral Signature Plot;
Scatter Plot;
Batch;
Settings;
Benutzerhandbuch: öffnet das Benutzerhandbuch im Browser;
Online help: open the Online help in a web browser; also, a Facebook group and a Google+ Community are available for sharing information and asking for help about SCP;

Working toolbar¶

Working toolbar

The Working toolbar allows for displaying the Input image, creating temporary ROIs and classification previews.

The functions are described in detail in the following paragraphs, using these conventions:

= Input date

[I] = Input text

= List

= Input number

= Optional

= Configuration stored in the active project of QGIS

= Configuration stored in QGIS registry

= Slider

= Table

Image control¶

: show the Main Interface Window;
: zoom the map to the extent of Input image;
RGB= : use the button to show/hide the Input image in the map; from the list select a Color Composite that is applied to the Input image; new color composites can be entered typing the band numbers separated by - or ; or , (e.g. RGB = 4-3-2 or RGB = 4;3;2 or RGB = 4,3,2);
: display the input image stretching the minimum and maximum values according to cumulative count of current map extent;
: display the input image stretching the minimum and maximum values according to standard deviation of current map extent;

Temporary ROI¶

A temporary ROI is a temporary polygon displayed in the map, which can be saved permanently in the Training input. A temporary ROI can be drawn manually or using a Region Growing Algorithm.

: zoom the map to the extent of temporary ROI;
ROI: use the button to show/hide the temporary ROI and the Training input in the map;
: activate the pointer to create a temporary ROI by drawing a polygon in the map; left click on the map to define the ROI vertices and right click to define the last vertex closing the polygon; press the keyboard button CTRL to add a multipart polygon; press the keyboard buttons CTRL + Z for removing the last multipart polygon;
: activate the pointer to create a temporary ROI using the region growing algorithm; left click on the map for creating the ROI; right click on the map for displaying the spectral signature of a pixel of the Input image in the Spectral Signature Plot; press the keyboard button CTRL to add a multipart polygon (new parts are not created if overlapping to other parts); press the keyboard buttons CTRL + Z for removing the last multipart polygon;
: create a temporary ROI using the region growing algorithm at the same seed pixel as the previous one; it is useful after changing the region growing parameters;

Region growing parameters: the following parameters are required for the ROI creation using a region growing algorithm on the Input image:

Dist : set the interval which defines the maximum spectral distance between the seed pixel and the surrounding pixels (in radiometry unit);
Min : set the minimum area of a ROI (in pixel unit); this setting overrides the Range radius until the minimum ROI size is reached; if Rapid ROI on band is checked, then ROI will have at least the size defined Min ROI size; if Rapid ROI on band is unchecked, then ROI could have a size smaller than Min ROI size;
Max : set the maximum width of a ROI (i.e. the side length of a square, centred at the seed pixel, which inscribes the ROI) in pixel unit;

Classification preview¶

Classification preview allows for displaying temporary classifications (i.e. classification previews). Classification previews are useful for testing the algorithm in a small area of the Input image, before classifying the entire image which can be time consuming (see Classification output).

Classification preview is performed according to the parameters defined in Classification algorithm.

In addition to the classification raster, an Algorithm raster can be displayed, which is useful for assessing the distance of a pixel classified as class X from the corresponding spectral signature X. In Classification previews, black pixels are distant from the corresponding spectral signature (i.e. probably a new ROI, or spectral signature, should be collected in that area) and white pixels are closer to the corresponding spectral signature (i.e. probably the spectral signature identifies correctly those pixels).

After the creation of a new preview, old previews are placed in QGIS Layers inside a layer group named Class_temp_group (custom name can be defined in Temporary group name) and are deleted when the QGIS session is closed.

WARNING: Classification previews are automatically deleted from disk when the QGIS session is closed; a QGIS message (that can be ignored) could ask for the path of missing layers when opening a previously saved project.

: zoom the map to the extent of the last Classification preview;
Preview: use the button to show/hide the last Classification preview in the map;
: activate the pointer for the creation of a Classification preview; left click the map to start the classification process and display the classification preview; right click to start the classification process and show the Algorithm raster of the preview;
: create a new Classification preview centred at the same pixel as the previous one;
T : change dynamically the classification preview transparency, which is useful for comparing the classification to other layers;
S : size of the preview in pixel unit (i.e. the side length of a square, centred at the clicked pixel);
: remove from QGIS the classification previews that are archived in the Class_temp_group;

The SCP dock allows for the definition of inputs, the creation of ROIs (Regions Of Interest) and spectral signatures, and the classification of an input image.

The Input image, to be classified, can be a multi-band raster or a set of single bands defined in the Band set.

The Training input, created with SCP, stores the ROI polygons and spectral signatures used for the land cover classification of the Input image.

ROIs are polygons used for the definition of the spectral characteristics of land cover classes. SCP allows for the creation of temporary ROI polygons using a region growing algorithm or drawn manually. Using the region growing algorithm the image is segmented around a pixel seed including spectrally homogeneous pixels. Temporary ROI polygons can be saved in the Training input along with the spectral signatures of the ROI. It is worth pointing out that classification is always based on spectral signatures.

In SCP, land cover classes (and ROIs) are defined with a system of Classes (Class ID) and Macroclasses (Macroclass ID) (see Classes and Macroclasses) that are used for the classification process; each Macroclass ID is related to a Macroclass Information (e.g. macroclass name) and each Class ID is related to a Class Information (e.g. class name), but only Macroclass ID and Class ID are used for the classification process.

Training input is composed of a vector part (i.e. a shapefile) and a spectral signature part which are independent. The attribute table of the vector contains four fields as in the following table.

Training input fields

Description	Field name	Field type
Macroclass ID	MC_ID	int
Macroclass Information	MC_info	string
Class ID	C_ID	int
Class Information	C_info	string

Spectral signatures of classes are calculated from the ROIs and saved in the Training input. In addition, spectral signatures can be imported from other sources (see Import signatures).

The use of the Macroclass ID or Class ID for classifications is defined with the option Use MC ID or C ID in the Classification algorithm. It is worth highlighting that when using Macroclass ID all the spectral signatures are evaluated separately and each pixel is classified with the corresponding MC ID (i.e. there is no combination of signatures before the classification).

The classification can be performed for the entire image ( Classification output ) or a part of it, creating a Classification preview.

The functions are described in detail in the following paragraphs, using these conventions:

= Input date

= Input text

= List

= Input number

= Optional

= Configuration stored in the active project of QGIS

= Configuration stored in QGIS registry

= Slider

= Table

SCP input ¶

SCP input

Input image ¶

This section allows for the selection of the image to be classified. Raster files must be already loaded in QGIS.

Input image can be a multi-band raster or a set of single bands defined in the Band set. If a multi-band raster is selected, raster bands are listed in the Band set.

: open one or more raster files and add them Band set;
: open the Band set;
Input image : select the input image from a list of multi-spectral images loaded in QGIS; if the Band set is defined, then this list will contain the item << band set >>;
: refresh layer list;

Training input ¶

The training input is a file .scp created in SCP (i.e. a zip file containing a shapefile and an xml file) used for storing ROIs and spectral signatures.

Warning: Signature list files saved with previous versions of SCP are not compatible with SPC 5; however you can import a ROI shapefile using the tool Import shapefile.

ROIs and spectral signatures are displayed in the ROI Signature list. ROIs and spectral signatures can be imported from other sources (see Import signatures) and exported (see Export signatures). ROIs are displayed in QGIS as vector file (in order to prevent data loss, you should not edit this layer using QGIS functions).

: open a training input file; ROIs and spectral signatures are loaded in ROI Signature list; the vector part of the training input is loaded in QGIS;
: create an empty training input file (.scp); the vector part of the training input is loaded in QGIS; also a backup file is created (a file .scp.backup in the same directory as the file .scp) when the training input file is saved;
Training input : it displays the path to the training input file;
: open the Download images;
: open the Tools;
: open the Preprocessing;
: open the Postprocessing;
: open the Band calc;
: open the Settings;
: open the online user manual in a web browser;
: open the Online help in a web browser; also, a Facebook group and a Google+ Community are available for sharing information and asking for help about SCP;

SCP news ¶

This section displays news about the SCP and related services. News are downloaded on startup (internet connection required). It can be enabled or disabled in the settings Dock.

Classification dock ¶

The Classification dock is designed to manage the spectral signatures, and classify the Input image.

ROI Signature list ¶

ROI Signature list

The ROI Signature list displays the ROI polygons and spectral signatures contained in the Training input. If an item is a ROI polygon, double click the item to zoom to that ROI in the map. Items in the table can be highlighted with the mouse left click.

Changes in the ROI Signature list are applied to the file Training input only when the QGIS project is saved. ROIs can be edited, deleted and merged from this table.

WARNING: In order to avoid data loss, do not edit the vector Training input using the QGIS tools. Use only the tools of SCP for managing the Training input.

ROI Signature list:
- S: selection checkbox; only the spectral signatures checked in this list are used for the classification process;
- Type: type of the item:
  
  R = only ROI polygon;
  
  S = only spectral signature;
  
  B = both ROI and spectral signature;
- MC ID: ROI Macroclass ID [int]; it can be edited with a single click; MC Info is displayed in Macroclasses; if the ID of a spectral signature is set 0, then pixels belonging to this signature are labelled as unclassified;
- C ID: ROI Class ID [int]; it can be edited with a single click;
- C Info: ROI Class Information [text]; it can be edited with a single click;
- Color: C ID color; double click to select a color for the class that is used in the classification; if the ID of a spectral signature is set 0, then pixels belonging to this signature are labelled as unclassified;
: delete highlighted ROIs and signatures;
: merge highlighted spectral signatures or ROIs obtaining a new signature calculated as the average of signature values for each band (covariance matrix is excluded);
: calculate spectral signatures of highlighted ROIs;
: show the ROI spectral signature in the Spectral Signature Plot; spectral signature is calculated from the Input image;
: open the Scatter Plot;
: open the tab Export signatures;
: open the tab Import signatures;

ROI creation ¶

ROI creation

ROI creation is complementary to the Working toolbar and it allows for saving ROIs to the Training input defining classes and macroclasses. A Band set must be defined before the ROI creation, and ROI polygons must be inside the area of the Band set.

MC ID : ROI Macroclass ID [int]; the corresponding MC Info is loaded if already defined in Macroclasses;
MC Info : ROI Macroclass information [text]; style and information for macroclasses are defined in Macroclasses;
C ID : ROI Class ID [int];
C Info : ROI Class information [text];
: delete the last saved ROI from the Training input;
Calculate sig. : if checked, while saving a ROI, the spectral signature thereof is calculated (from Input image pixels under ROI polygon) and saved to Training input (calculation time depends on the band number of Input image);
: save the temporary ROI to the Training input using the defined classes and macroclasses; ROI is displayed in the ROI Signature list;
Display : if the ROI creation pointer is active (see Working toolbar), the pixel value of selected vegetation index is displayed on the map; vegetation indices available in the combo box are:
- NDVI (Normalized Difference Vegetation Index); NDVI requires the near-infrared and red bands;
- EVI (Enhanced Vegetation Index); EVI requires the blue, near-infrared and red bands converted to reflectance; wavelengths must be defined in the Band set;
- Custom; use the custom expression defined in the following line Expression;
Expression : set a custom expression; expression is based on the Band set; bands are defined as bandset#b + band number (e.g. bandset#b1 for the first band of the Band set); for example NDVI for a Landsat image would be ( bandset#b4 - bandset#b3 ) / ( bandset#b4 + bandset#b3 );
Rapid ROI band : if checked, temporary ROI is created with region growing using only one Input image band (i.e.region growing is rapider); the band is defined by the Band set number; if unchecked, ROI is the result of the intersection between ROIs calculated on every band (i.e. region growing is slower, but ROI is spectrally homogeneous in every band);
Automatic refresh ROI: calculate automatically a new temporary ROI while Region growing parameters in the Working toolbar are being changed;
Automatic plot: calculate automatically the temporary ROI spectral signature and display it in the Spectral Signature Plot (MC Info of this spectral signature is set tempo_ROI);

Macroclasses ¶

Macroclasses

Macroclasses allows for the definition of Macroclass names and colors (used to display the results of Classification preview and Classification output). According to Classification algorithm, classifications performed using C ID have the colors defined for classes in the ROI Signature list; classifications performed using MC ID have the colors defined in the Macroclasses.

MC IDs are automatically added to this table when a new ROI is saved to the ROI Signature list (if the MC ID is not already in the list). Settings are stored in Training input.

Macroclasses :
- MC ID: Macroclass ID [int]; it can be edited with a single click;
- MC Info: Macroclass Information [text]; it can be edited with a single click;
- Color: MC ID color; double click to select a color for the class that is used in the classification;
: add a new row to the table;
: delete the highlighted rows from the table;

Classification style¶

In addition, a previously saved classification style (QGIS .qml file) can be loaded and used for classification style.

Load qml : select a .qml file overriding the colors defined for C ID or MC ID;
: reset style to default (i.e. use the colors defined for C ID or MC ID);

Classification algorithm ¶

Classification algorithm

The Classification algorithm includes several functions for the classification process used also during the Classification preview.

Use MC ID C ID : if MC ID is checked, the classification is performed using the Macroclass ID (code MC ID of the signature); if C ID is checked, the classification is performed using the Class ID (code C ID of the signature);
: open the Algorithm band weight for the definition of band weights;

Algorithm¶

Classification is performed using the selected algorithm.

: available Classification Algorithms are:
Threshold : it allows for the definition of a classification threshold (applied to all the spectral signatures); for advanced settings see Signature threshold; if threshold is equal to 0, then no threshold is considered and all image pixels are classified; otherwise:
- for Minimum Distance, pixels are unclassified if distance is greater than threshold value;
- for Maximum Likelihood, pixels are unclassified if probability is less than threshold value (max 100);
- for Spectral Angle Mapping, pixels are unclassified if spectral angle distance is greater than threshold value (max 90);
: open the Signature threshold for the definition of signature thresholds;

Land Cover Signature Classification¶

Land Cover Signature Classification is a classification that can be used as alternative or in combination with the Algorithm (see LCS threshold). Pixels belonging to two or more different classes (or macroclasses) are classified as Class overlap with raster value = -1000.

Use LCS Algorithm only overlap: if LCS is checked, the Land Cover Signature Classification is used; if Algorithm is checked, the selected Algorithm is used for unclassified pixels of the Land Cover Signature Classification; if only overlap is checked, the selected Algorithm is used only for class overlapping pixels of the Land Cover Signature Classification; unclassified pixels of the Land Cover Signature Classification are left unclassified;
: open the LCS threshold;

Classification output ¶

Classification output

Classification output allows for the classification of the Input image according to the parameters defined in Classification algorithm.

Classification raster is a file .tif (a QGIS style file .qml is saved along with the classification); also other outputs can be optionally calculated. Outputs are loaded in QGIS after the calculation.

Apply mask : if checked, a shapefile can be selected for masking the classification output (i.e. the area outside the shapefile is not classified);
: reset the mask shapefile;
Create vector : if checked, in addition to the classification raster, a classification shapefile is saved in the same directory and with the same name as the Classification output; conversion to vector can also be performed at a later time (see Classification to vector);
Classification report : if checked, a report about the land cover classification is calculated and saved as a .csv file in the same directory and with the same name (with the suffix _report) as the Classification output; report can also be performed at a later time (see Classification report);
Save algorithm files : if checked, the Algorithm raster is saved, in addition to the classification raster, in the same directory as the Classification output; a raster for each spectral signature used as input (with the suffix _sig_MC ID_C ID) and a general algorithm raster (with the suffix _alg_raster) are created;
: choose the output destination and start the image classification;

Main Interface Window¶

The Main Interface Window is composed of several tabs and subtabs. The functions are described in detail in the following paragraphs, using these conventions:

= Input date

= Input text

= List

= Input number

= Optional

= Configuration stored in the active project of QGIS

= Configuration stored in QGIS registry

= Slider

= Table

Download images ¶

The tab Download images includes the tools for searching and downloading free remote sensing images. An internet connection is required.

Landsat download ¶

Landsat download

This tab allows for searching and downloading the whole archive of Landsat Satellite images (from 1 MSS to 8 OLI), acquired from the 80s to present days. Search is performed through the CMR Search API developed by NASA.

Landsat images are freely available through the services: EarthExplorer , Google Earth Engine , and the Amazon Web Services (AWS) (for Landsat 8). This tool attempts to download images first from Amazon Web Services and Google Earth Engine ; only if images are not available, the download is performed through the service EarthExplorer in order to prevent the server from becoming saturated.

Images are downloaded as compressed archives (this tool allows for the download of single bands for Landsat 8 images provided by the Amazon Web Services). Also, automatic conversion to reflectance of downloaded bands is available.

Login https://ers.cr.usgs.gov/¶

USGS EROS credentials (https://ers.cr.usgs.gov) are required for downloads from EarthExplorer . Login using your USGS EROS credentials or register for free at https://ers.cr.usgs.gov/register .

User : enter the user name;
Password : enter the password;
remember: remember user name and password in QGIS;

Search area¶

Define the search area by entering the coordinates (longitude and latitude) of an Upper Left (UL) point and Lower Right (LR) point, or interactively drawing an area in the map.

The definition of a search area is required before searching the images.

UL X (Lon) : set the UL longitude;
UL Y (Lat) : set the UL latitude;
LR X (Lon) : set the LR longitude;
LR Y (Lat) : set the LR latitude;
Show: show or hide the search area drawn in the map;
: define a search area by drawing a rectangle in the map; left click to set the UL point and right click to set the LR point; the area is displayed in the map;

Search¶

Define the search settings such as date of acquisition, maximum cloud cover, or specify Landsat satellites.

Satellites : set the Landsat satellites;
Date from : set the starting date of acquisition;
to : set the ending date of acquisition;
Max cloud cover (%) : maximum cloud cover in the image;
Results : maximum number of images returned by the search;
Filter : set a filter such as the Image ID of Landsat images (e.g. LC81910312015006LGN00); it is possible to enter multiple Image IDs separated by comma or semicolon (e.g. LC81910312015006LGN00, LC81910312013224LGN00 ); filtered images must be inside the search area;
Find : find the images in the search area; results are displayed inside the table in Landsat images; results are added to previous results;

Tip: Search results (and the number thereof) depend on the defined area extent and the range of dates. In order to get more results, perform multiple searches defining smaller area extent and narrow acquisition dates (from and to).

Landsat images¶

Image list: found images are displayed in this table, which includes the following fields;
- ImageID: the Landsat Image ID;
- AcquisitionDate: date of acquisition of Landsat image;
- CloudCover: percentage of cloud cover in the image;
- Path: WRS path of the image;
- Row: WRS row of the image;
- min_lat: minimum latitude of the image;
- min_lon: minimum longitude of the image;
- max_lat: maximum latitude of the image;
- max_lon: maximum longitude of the image;
- USGScollection: USGS collection code of the image;
- Preview: URL of the image preview;
- collection: collection code of the image;
: display preview of highlighted images in the map; preview is roughly georeferenced on the fly;
: remove highlighted images from the list;
: remove all images from the list;

Download options¶

Download options

Landsat 8 bands

This tab allows for the selection of single bands (only for Landsat 8 images provided by the Amazon Web Services).

Band X: select bands for download;
: select or deselect all bands;

Download¶

Download the Landsat images in the Landsat images. During the download it is recommended not to interact with QGIS.

Download is performed according to image availability from the services EarthExplorer , Google Earth Engine , or the Amazon Web Services (AWS) . If the image is not available for download it is possible to check the availability thereof on http://earthexplorer.usgs.gov/ .

Only if preview in Layers: if checked, download only those images listed in Landsat images which are also listed in the QGIS layer panel;
Preprocess images: if checked, bands are automatically converted after the download, according to the settings defined in Landsat;
Load bands in QGIS: if checked, bands are loaded in QGIS after the download;
: export the download links to a text file;
: start the download process of all the images listed in Landsat images;

Sentinel-2 download ¶

Sentinel-2 download

Sentinel-2 is a European satellite launched in 2015, developed in the frame of Copernicus land monitoring services, which acquires 13 spectral bands (see Sentinel-2 Satellite). This tab allows for searching and downloading the free Sentinel-2 images (Level-1C) from the Sentinels Scientific Data Hub (using the Data Hub API ). Images are mainly downloaded from the Amazon S3 AWS if available.

Sentinel-2 satellite has a swath width of 290km. Sentinel-2 Level-1C images are delivered in granules (also called tiles) with a side of 100km in UTM/WGS84 projection. This tool allows for the selection and download of granules and bands.

Tip: In case of errors please see Error [50] ‘Internet error’. Unable to download Sentinel-2 images. Why? and Error [56] ‘SSL connection error’. Unable to download Sentinel-2 images. Why?.

Login Sentinels¶

In order to access to Sentinel data a free registration is required at https://scihub.copernicus.eu/userguide/1SelfRegistration (other services may require different registrations). After the registration, enter the user name and password for accessing data.

Service : enter the service URL (default is https://scihub.copernicus.eu/apihub); other mirror services that share the same infrastructure can be used (such as https://scihub.copernicus.eu/dhus , https://finhub.nsdc.fmi.fi , https://data.sentinel.zamg.ac.at);
: reset the default service https://scihub.copernicus.eu/s2);
User : enter the user name;
Password : enter the password;
remember: remember user name and password in QGIS;

Search area¶

Define the search area by entering the coordinates (longitude and latitude) of an Upper Left (UL) point and Lower Right (LR) point, or interactively drawing an area in the map. The definition of a search area is required before searching the images.

UL X (Lon) : set the UL longitude;
UL Y (Lat) : set the UL latitude;
LR X (Lon) : set the LR longitude;
LR Y (Lat) : set the LR latitude;
Show: show or hide the search area drawn in the map;
: define a search area by drawing a rectangle in the map; left click to set the UL point and right click to set the LR point; the area is displayed in the map;

Search¶

Define search settings such as the date of acquisition or search for specific Sentinel images using the Image ID or name.

Date from : set the starting date of acquisition;
to : set the ending date of acquisition;
Max cloud cover (%) : maximum cloud cover in the image;
Results : maximum number of images returned by the search;
Filter : set a filter such as the Image Name of Sentinel images (e.g. S2A_OPER_PRD_MSIL1C_PDMC_20160419T190217_R022_V20160419T101026);
Find : find the images in the search area; results are displayed inside the table in Sentinel images; results are added to previous results;

Tip: Search results (and the number thereof) depend on the defined area extent and the range of dates. In order to get more results, perform multiple searches defining smaller area extent and narrow acquisition dates (from and to).

Sentinel images¶

Image list: found images are displayed in this table, which includes the following fields;
- ImageName: the Sentinel Image Name;
- Granule: the single granule name;
- AcquisitionDate: date of acquisition of Sentinel image;
- Zone: tile zone according to the US-MGRS naming convention;
- CloudCover: percentage of cloud cover in the image;
- min_lat: minimum latitude of the image;
- min_lon: minimum longitude of the image;
- max_lat: maximum latitude of the image;
- max_lon: maximum longitude of the image;
- Size: the size of the image (unused);
- Preview: URL of the image overview;
- GranulePreview: URL of the granule preview; if available, preview is downloaded from the Amazon Web Services ;
- ImageID: the Sentinel Image ID;
: display preview of highlighted granules in the map;
: display overview of highlighted images in the map; overview is roughly georeferenced on the fly; overviews could not be available when using mirror services;
: remove highlighted images from the list;
: remove all images from the list;

Tip: download this zip file containing the shapefile of Sentinel-2 granules for identifying the zone; load this shapefile in QGIS, select the granules in your search area and open the attribute table to see the zone name.

Download options¶

Download options

This tab allows for the selection of single bands.

Band X: select bands for download;
Ancillary data: if checked, the metadata files (a .xml file whose name contains MTD_SAFL1C and a .xml file whose name contains MTD_L1C) and the cloud mask file (a .gml file whose name contains MSK_CLOUDS) are downloaded;
: select or deselect all bands;

Download¶

Download the Sentinel-2 images in the Sentinel images. Bands selected in Download options are downloaded.

During the download it is recommended not to interact with QGIS.

Only if preview in Layers: if checked, download only those images listed in Sentinel images which are also listed in the QGIS layer panel;
Preprocess images: if checked, bands are automatically converted after the download, according to the settings defined in Sentinel-2;
Load bands in QGIS: if checked, bands are loaded in QGIS after the download;
: export the download links to a text file;
: start the download process of all the images listed in Sentinel images;

ASTER download ¶

ASTER download

This tab allows for searching and downloading the whole archive of free images L1T acquired by ASTER Satellite since 2000. Search is performed through the CMR Search API developed by NASA. The ASTER L1T data products are retrieved from the online Data Pool, courtesy of the NASA Land Processes Distributed Active Archive Center (LP DAAC), USGS/Earth Resources Observation and Science (EROS) Center, Sioux Falls, South Dakota, https://lpdaac.usgs.gov/data_access/data_pool.

Also, automatic conversion to reflectance of downloaded bands is available.

Login https://urs.earthdata.nasa.gov ¶

EOSDIS Earthdata credentials (https://urs.earthdata.nasa.gov ) are required for download. Login using your EOSDIS Earthdata credentials or register for free at https://urs.earthdata.nasa.gov/users/new .

Warning: Before downloading ASTER images, you must approve LP DAAC Data Pool clicking the following https://urs.earthdata.nasa.gov/approve_app?client_id=ijpRZvb9qeKCK5ctsn75Tg

User : enter the user name;
Password : enter the password;
remember: remember user name and password in QGIS;

Search area¶

Define the search area by entering the coordinates (longitude and latitude) of an Upper Left (UL) point and Lower Right (LR) point, or interactively drawing an area in the map.

The definition of a search area is required before searching the images.

UL X (Lon) : set the UL longitude;
UL Y (Lat) : set the UL latitude;
LR X (Lon) : set the LR longitude;
LR Y (Lat) : set the LR latitude;
Show: show or hide the search area drawn in the map;
: define a search area by drawing a rectangle in the map; left click to set the UL point and right click to set the LR point; the area is displayed in the map;

Search¶

Define the search settings such as date of acquisition, maximum cloud cover, or specify ASTER satellites.

Satellites : set the ASTER satellites (unused);
Date from : set the starting date of acquisition;
to : set the ending date of acquisition;
Max cloud cover (%) : maximum cloud cover in the image;
Results : maximum number of images returned by the search;
Filter : set a filter such as the Image ID of ASTER images; it is possible to enter multiple Image IDs separated by comma or semicolon; filtered images must be inside the search area;
Find : find the images in the search area; results are displayed inside the table in ASTER images; results are added to previous results;

Tip: Search results (and the number thereof) depend on the defined area extent and the range of dates. In order to get more results, perform multiple searches defining smaller area extent and narrow acquisition dates (from and to).

ASTER images¶

Image list: found images are displayed in this table, which includes the following fields;
- ImageID: the ASTER Image ID;
- AcquisitionDate: date of acquisition of ASTER image;
- CloudCover: percentage of cloud cover in the image;
- ImageDisaplyID: the ASTER Image ID;
- DayNightFlag: flag for acquisition during day or night;
- min_lat: minimum latitude of the image;
- min_lon: minimum longitude of the image;
- max_lat: maximum latitude of the image;
- max_lon: maximum longitude of the image;
- Service: download service of the image;
- Preview: URL of the image preview;
- collection: collection code of the image;
: display preview of highlighted images in the map; preview is roughly georeferenced on the fly;
: remove highlighted images from the list;
: remove all images from the list;

Download¶

Download the ASTER images in the ASTER images. During the download it is recommended not to interact with QGIS.

Only if preview in Layers: if checked, download only those images listed in ASTER images which are also listed in the QGIS layer panel;
Preprocess images: if checked, bands are automatically converted after the download, according to the settings defined in ASTER;
Load bands in QGIS: if checked, bands are loaded in QGIS after the download;
: export the download links to a text file;
: start the download process of all the images listed in ASTER images;

MODIS download ¶

MODIS download

This tab allows for searching and downloading the archive of free MODIS Products acquired since 2000 (in particular MOD09GQ, MYD09GQ, MOD09GA, MYD09GA, MOD09Q1, MYD09Q1, MOD09A1, MYD09A1). Search is performed through the CMR Search API developed by NASA. MODIS products are retrieved from the online Data Pool, courtesy of the NASA Land Processes Distributed Active Archive Center (LP DAAC), USGS/Earth Resources Observation and Science (EROS) Center, Sioux Falls, South Dakota, https://lpdaac.usgs.gov/data_access/data_pool.

Also, automatic reprojection of downloaded bands is available.

Login https://urs.earthdata.nasa.gov ¶

Warning: Before downloading MODIS images, you must approve LP DAAC Data Pool clicking the following https://urs.earthdata.nasa.gov/approve_app?client_id=ijpRZvb9qeKCK5ctsn75Tg

User : enter the user name;
Password : enter the password;
remember: remember user name and password in QGIS;

Search area¶

Define the search area by entering the coordinates (longitude and latitude) of an Upper Left (UL) point and Lower Right (LR) point, or interactively drawing an area in the map.

The definition of a search area is required before searching the images.

UL X (Lon) : set the UL longitude;
UL Y (Lat) : set the UL latitude;
LR X (Lon) : set the LR longitude;
LR Y (Lat) : set the LR latitude;
Show: show or hide the search area drawn in the map;
: define a search area by drawing a rectangle in the map; left click to set the UL point and right click to set the LR point; the area is displayed in the map;

Search¶

Define the search settings such as date of acquisition, maximum cloud cover, or specify MODIS product.

Products : set the MODIS products;
Date from : set the starting date of acquisition;
to : set the ending date of acquisition;
Max cloud cover (%) : maximum cloud cover in the image (unused);
Results : maximum number of images returned by the search;
Filter : set a filter such as the Image ID of MODIS images; it is possible to enter multiple Image IDs separated by comma or semicolon; filtered images must be inside the search area;
Find : find the images in the search area; results are displayed inside the table in MODIS images; results are added to previous results;

Tip: Search results (and the number thereof) depend on the defined area extent and the range of dates. In order to get more results, perform multiple searches defining smaller area extent and narrow acquisition dates (from and to).

MODIS images¶

Image list: found images are displayed in this table, which includes the following fields;
- ImageID: the MODIS Image ID;
- AcquisitionDate: date of acquisition of MODIS image;
- CloudCover: percentage of cloud cover in the image;
- ImageDisaplyID: the MODIS Image ID;
- DayNightFlag: flag for acquisition during day or night;
- min_lat: minimum latitude of the image;
- min_lon: minimum longitude of the image;
- max_lat: maximum latitude of the image;
- max_lon: maximum longitude of the image;
- Service: download service of the image;
- Preview: URL of the image preview;
- collection: collection code of the image;
: display preview of highlighted images in the map; preview is roughly georeferenced on the fly;
: remove highlighted images from the list;
: remove all images from the list;

Download¶

Download the MODIS images in the MODIS images. During the download it is recommended not to interact with QGIS.

Only if preview in Layers: if checked, download only those images listed in MODIS images which are also listed in the QGIS layer panel;
Preprocess images: if checked, bands are automatically converted after the download, according to the settings defined in MODIS;
Load bands in QGIS: if checked, bands are loaded in QGIS after the download;
: export the download links to a text file;
: start the download process of all the images listed in MODIS images;

Tools ¶

The tab Tools includes several tools for manipulating ROIs and spectral signatures.

Multiple ROI Creation ¶

Multiple ROI Creation

This tab allows for the automatic creation of ROIs, useful for the rapid classification of multi-temporal images, or for accuracy assessment. Given a list of point coordinates and ROI options, this tool performs the region growing of ROIs. Created ROIs are automatically saved to the Training input.

Create random points¶

Number of points : set a number of points that will be created when Create points is clicked;
inside grid : if checked, the input image area is divided in cells where the size thereof is defined in the combobox (image unit, usually meters); points defined in Number of random points are created randomly within each cell;
min distance : if checked, random points have a minimum distance defined in the combobox (image unit, usually meters); setting a minimum distance can result in fewer points than the number defined in Number of points;
Create points : create random points inside the input image area;

Point coordinates and ROI definition¶

Point coordinates and ROI definition: table containing the following fields;
- X : point X coordinate (float);
- Y : point Y coordinate (float);
- MC ID: ROI Macroclass ID (int);
- MC Info: ROI Macroclass information (text);
- C ID: ROI Class ID (int);
- C Info: ROI Class information (text);
- Min : the minimum area of a ROI (in pixel unit);
- Max : the maximum width of a ROI (in pixel unit);
- Dist : the interval which defines the maximum spectral distance between the seed pixel and the surrounding pixels (in radiometry unit);
- Rapid ROI band : if a band number is defined, ROI is created only using the selected band, similarly to Rapid ROI band in ROI creation ;
: add a new row to the table; all the table fields must be filled for the ROI creation;
: delete the highlighted rows from the table;
: import a point list from text file to the table; every line of the text file must contain values separated by tabs of X, Y, MC ID, MC Info, Class ID, C Info, Min, Max, Dist, and optionally the Rapid ROI band;
: export the point list to text file;

Run¶

Calculate sig.: if checked, the spectral signature is calculated while the ROI is saved to Training input;
: start the ROI creation process for all the points and save ROIs to the Training input;

Import signatures ¶

The tab Import signatures allows for importing spectral signatures from various sources.

Import library file¶

Import library file

This tool allows for importing spectral signatures from various sources: a previously saved Training input (.scp file); a USGS Spectral Library (.asc file); a previously exported CSV file. In case of USGS Spectral Library, the library is automatically sampled according to the image band wavelengths defined in the Band set, and added to the ROI Signature list;

Select a file : open a file to be imported in the Training input;

Import shapefile¶

Import shapefile

This tool allows for importing a shapefile, selecting the corresponding fields of the Training input.

Select a shapefile : open a shapefile;
MC ID field : select the shapefile field corresponding to MC ID;
MC Info field : select the shapefile field corresponding to MC Info;
C ID field : select the shapefile field corresponding to C ID;
C Info field : select the shapefile field corresponding to C Info;
Calculate sig.: if checked, the spectral signature is calculated while the ROI is saved to Training input;
Import shapefile : import all the shapefile polygons as ROIs in the Training input;

Download USGS Spectral Library¶

Download USGS Spectral Library

The tab Download USGS Spectral Library allows for the download of the USGS spectral library (Clark, R.N., Swayze, G.A., Wise, R., Livo, E., Hoefen, T., Kokaly, R., Sutley, S.J., 2007, USGS digital spectral library splib06a: U.S. Geological Survey, Digital Data Series 231).

The libraries are grouped in chapters including Minerals, Mixtures, Coatings, Volatiles, Man-Made, Plants, Vegetation Communities, Mixtures with Vegetation, and Microorganisms. An internet connection is required.

Select a chapter : select one of the library chapters; after the selection, chapter libraries are shown in Select a library;
Select a library : select one of the libraries; the library description is displayed in the frame Library description;
Import spectral library : download the library and add the sampled spectral signature to the ROI Signature list using the parameters defined in ROI creation; the library is automatically sampled according to the image band wavelengths defined in the Band set, and added to the ROI Signature list;

Tip: Spectral libraries downloaded from the USGS Spectral Library can be used with Minimum Distance or Spectral Angle Mapping algorithms, but not Maximum Likelihood because this algorithm needs the covariance matrix that is not included in the spectral libraries.

Export signatures ¶

Export signatures

This tool allows for exporting the signatures highlighted in the ROI Signature list.

Export as SCP file : create a new .scp file and export highlighted ROIs and spectral signatures as SCP file (* .scp);
Export as shapefile : export highlighted ROIs (spectral signature data excluded) as a new shapefile (* .shp);
Export as CSV file : open a directory, and export highlighted spectral signatures as individual CSV files (* .csv) separated by semicolon ( ; );

Algorithm band weight ¶

Algorithm band weight

This tab allows for the definition of band weights that are useful for improving the spectral separability of materials at certain wavelengths (bands). During the classification process, band values and spectral signature values are multiplied by the corresponding band weights, thus modifying the spectral distances.

Band weight¶

Band weight: table containing the following fields;
- Band number : number of the band in the Band set;
- Band name : name of the band;
- Weight : weight of the band; this value can be edited;

Automatic weight¶

: reset all band weights to 1;
Set weight : set the defined value as weight for all the highlighted bands in the table;

Signature threshold ¶

Signature threshold

This tab allows for the definition of a classification threshold for each spectral signature. All the signatures contained in the Training input are listed. This is useful for improving the classification results, especially when spectral signatures are similar. Thresholds of signatures are saved in the Training input.

If threshold is 0 then no threshold is applied. Depending on the selected Classification algorithm the threshold value is evaluated differently:

for Minimum Distance, pixels are unclassified if distance is greater than threshold value;
for Maximum Likelihood, pixels are unclassified if probability is less than threshold value (max 100);
for Spectral Angle Mapping, pixels are unclassified if spectral angle distance is greater than threshold value (max 90).

Signature threshold¶

Signature threshold: table containing the following fields;
- MC ID: signature Macroclass ID;
- MC Info: signature Macroclass Information;
- C ID: signature Class ID;
- C Info: signature Class Information;
- MD Threshold: Minimum Distance threshold; this value can be edited;
- ML Threshold: Maximum Likelihood threshold; this value can be edited;
- SAM Threshold: Spectral Angle Mapping threshold; this value can be edited;
: reset all signatures thresholds to 0 (i.e. no threshold used);

Automatic thresholds¶

Set threshold : set the defined value as threshold for all the highlighted signatures in the table;
Set threshold = σ * : for all the highlighted signatures, set an automatic threshold calculated as the distance (or angle) between mean signature and (mean signature + (σ * v)), where σ is the standard deviation and v is the defined value; currently works for Minimum Distance and Spectral Angle Mapping;

LCS threshold ¶

LCS threshold

This tab allows for setting the signature thresholds used by Land Cover Signature Classification. All the signatures contained in the Training input are listed; also, signature thresholds are saved in the Training input.

Overlapping signatures (belonging to different classes or macroclasses) are highlighted in orange in the table LC Signature threshold; the overlapping check is performed considering MC ID or C ID according to the setting Use MC ID C ID in Classification algorithm. Overlapping signatures sharing the same ID are not highlighted.

LC Signature threshold¶

LC Signature threshold: table containing the following fields;
- MC ID: signature Macroclass ID;
- MC Info: signature Macroclass Information;
- C ID: signature Class ID;
- C Info: signature Class Information;
- Color [overlap MC_ID-C_ID]: signature color; also, the combination MC ID-C ID is displayed in case of overlap with other signatures (see Land Cover Signature Classification);
- Min B X: minimum value of band X; this value can be edited;
- Max B X: maximum value of band X; this value can be edited;
: show the ROI spectral signature in the Spectral Signature Plot; spectral signature is calculated from the Input image;

Automatic thresholds¶

Set thresholds automatically for highlighted signatures in the table LC Signature threshold; if no signature is highlighted, then the threshold is applied to all the signatures.

Min Max : set the threshold based on the minimum and maximum of each band;
σ * : set an automatic threshold calculated as (band value + (σ * v)), where σ is the standard deviation of each band and v is the defined value;
From ROI : set the threshold using the temporary ROI pixel values, according to the following checkboxes:
- +: if checked, signature threshold is extended to include pixel signature;
- –: if checked, signature threshold is reduced to exclude pixel signature;
From pixel : set the threshold by clicking on a pixel, according to the following checkboxes:
- +: if checked, signature threshold is extended to include pixel signature;
- –: if checked, signature threshold is reduced to exclude pixel signature;

RGB list ¶

RGB list

This tab allows for managing the RGB Color Composite used in the list RGB= of the Image control.

RGB list¶

RGB list: table containing the following fields;
- RGB: RGB combination; this field can be manually edited;
: move highlighted RGB combination upward;
: move highlighted RGB combination downward;
: automatically sort RGB combinations by name;
: add a row to the table;
: remove highlighted rows from the table;
: clear all RGB combinations from RGB list;
: export the RGB list to a file (i.e. .csv);
: import a previously saved RGB list from file (i.e. .csv);

Automatic RGB¶

Band combinations : add the combinations of all bands (i.e. permutation) to the RGB list (e.g. 1-2-3, 1-2-4, ..., 3-2-1);

Preprocessing ¶

The tab Preprocessing provides several tools for data manipulation which are useful before the actual classification process.

Landsat ¶

Landsat

This tab allows for the conversion of Landsat 1, 2, and 3 MSS and Landsat 4, 5, 7, and 8 images from DN (i.e. Digital Numbers) to the physical measure of Top Of Atmosphere reflectance (TOA), or the application of a simple atmospheric correction using the DOS1 method (Dark Object Subtraction 1), which is an image-based technique (for more information about the Landsat conversion to TOA and DOS1 correction, see Image conversion to reflectance). Pan-sharpening is also available; for more information read Pan-sharpening.

Once the input is selected, available bands are listed in the metadata table.

Landsat conversion to TOA reflectance and brightness temperature¶

Directory containing Landsat bands : open a directory containing Landsat bands; names of Landsat bands must end with the corresponding number; if the metadata file is included in this directory then Metadata are automatically filled;
Select MTL file : if the metadata file is not included in the Directory containing Landsat bands, select the path of the metadata file in order to fill the Metadata automatically;
Brightness temperature in Celsius: if checked, convert brightness temperature to Celsius (if a Landsat thermal band is listed in Metadata); if unchecked temperature is in Kelvin;
Apply DOS1 atmospheric correction: if checked, the DOS1 Correction is applied to all the bands (thermal bands excluded);
Use NoData value (image has black border) : if checked, pixels having NoData value are not counted during conversion and the DOS1 calculation of DNmin; it is useful when image has a black border (usually pixel value = 0);
Perform pan-sharpening: if checked, a Brovey Transform is applied for the Pan-sharpening of Landsat bands;
Create Band set and use Band set tools: if checked, the Band set is created after the conversion; also, the Band set is processed according to the tools checked in the Band set;

Metadata¶

All the bands found in the Directory containing Landsat bands are listed in the table Metadata. If the Landsat metadata file (a .txt or .met file with the suffix MTL) is provided, then Metadata are automatically filled. For information about Metadata fields read this page and this one .

Satellite : satellite name (e.g. Landsat8);
Date : date acquired (e.g. 2013-04-15);
Sun elevation : Sun elevation in degrees;
Earth sun distance : Earth Sun distance in astronomical units (automatically calculated if Date is filled;
: remove highlighted bands from the table Metadata;
Metadata: table containing the following fields;
- Band: band name;
- RADIANCE_MULT: multiplicative rescaling factor;
- RADIANCE_ADD: additive rescaling factor;
- REFLECTANCE_MULT: multiplicative rescaling factor;
- REFLECTANCE_ADD: additive rescaling factor;
- RADIANCE_MAXIMUM: radiance maximum;
- REFLECTANCE_MAXIMUM: reflectance maximum;
- K1_CONSTANT: thermal conversion constant;
- K2_CONSTANT: thermal conversion constant;
- LMAX: spectral radiance that is scaled to QCALMAX;
- LMIN: spectral radiance that is scaled to QCALMIN;
- QCALMAX: minimum quantized calibrated pixel value;
- QCALMIN: maximum quantized calibrated pixel value;

Run¶

: select an output directory and start the conversion process; only bands listed in the table Metadata are converted; converted bands are saved in the output directory with the prefix RT_ and automatically loaded in QGIS;

Sentinel-2 ¶

Sentinel-2

This tab allows for the conversion of Sentinel-2 images to the physical measure of Top Of Atmosphere reflectance (TOA), or the application of a simple atmospheric correction using the DOS1 method (Dark Object Subtraction 1), which is an image-based technique (for more information about conversion to TOA and DOS1 correction, see Image conversion to reflectance).

Once the input is selected, available bands are listed in the metadata table.

Sentinel-2 conversion¶

Directory containing Sentinel-2 bands : open a directory containing Sentinel-2 bands; names of Sentinel-2 bands must end with the corresponding number; if the metadata file is included in this directory then Metadata are automatically filled;
Select metadata file : select the metadata file which is a .xml file whose name contains MTD_MSIL1C);
Apply DOS1 atmospheric correction: if checked, the DOS1 Correction is applied to all the bands;
Use NoData value (image has black border) : if checked, pixels having NoData value are not counted during conversion and the DOS1 calculation of DNmin; it is useful when image has a black border (usually pixel value = 0);
Create Band set and use Band set tools: if checked, the Band set is created after the conversion; also, the Band set is processed according to the tools checked in the Band set;

Metadata¶

All the bands found in the Directory containing Sentinel-2 bands are listed in the table Metadata. If the Sentinel-2 metadata file (a .xml file whose name contains MTD_MSIL1C) is provided, then Metadata are automatically filled. For information about Metadata fields read this informative page .

Satellite : satellite name (e.g. Sentinel-2A);
: remove highlighted bands from the table Metadata;
Metadata: table containing the following fields;
- Band: band name;
- Quantification value: value for conversion to TOA reflectance;
- Solar irradiance: solar irradiance of band;

Run¶

: select an output directory and start the conversion process; only bands listed in the table Metadata are converted; converted bands are saved in the output directory with the prefix RT_ and automatically loaded in QGIS;

ASTER ¶

ASTER

This tab allows for the conversion of ASTER L1T images to the physical measure of Top Of Atmosphere reflectance (TOA), or the application of a simple atmospheric correction using the DOS1 method (Dark Object Subtraction 1), which is an image-based technique (for more information about conversion to TOA and DOS1 correction, see Image conversion to reflectance).

Once the input is selected, available bands are listed in the metadata table.

ASTER conversion¶

Select file ASTER L1T : select an ASTER image (file .hdf);
Apply DOS1 atmospheric correction: if checked, the DOS1 Correction is applied to all the bands;
Use NoData value (image has black border) : if checked, pixels having NoData value are not counted during conversion and the DOS1 calculation of DNmin; it is useful when image has a black border (usually pixel value = 0);
Create Band set and use Band set tools: if checked, the Band set is created after the conversion; also, the Band set is processed according to the tools checked in the Band set;

Metadata¶

All the bands found in the Select file ASTER L1T are listed in the table Metadata. For information about Metadata fields visit the ASTER page .

Date : date acquired (e.g. 20130415);
Sun elevation : Sun elevation in degrees;
Earth sun distance : Earth Sun distance in astronomical units (automatically calculated if Date is filled;
UTM zone : UTM zone code of the image;
UPPERLEFTM : coordinates of the upper left corner of the image;
: remove highlighted bands from the table Metadata;
Metadata: table containing the following fields;
- Band: band name;
- UnitConversionCoeff: value for radiance conversion;
- PixelSize: solar irradiance of band;

Run¶

: select an output directory and start the conversion process; only bands listed in the table Metadata are converted; converted bands are saved in the output directory with the prefix RT_ and automatically loaded in QGIS;

MODIS ¶

MODIS

This tab allows for the conversion of MODIS images to .tif format, and the reprojection to WGS 84.

Once the input is selected, available bands are listed in the metadata table.

MODIS conversion¶

Select file MODIS : select a MODIS image (file .hdf);
Reproject to WGS 84: if checked, reproject bands to WGS 84, required for use in SCP;
Use NoData value (image has black border) : if checked, pixels having NoData value are not counted during conversion and the DOS1 calculation of DNmin; it is useful when image has a black border (usually pixel value = 0);
Create Band set and use Band set tools: if checked, the Band set is created after the conversion; also, the Band set is processed according to the tools checked in the Band set;

Metadata¶

All the bands found in the Select file MODIS are listed in the table Metadata. For information about Metadata fields visit the MODIS page .

ID : ID of the image;
: remove highlighted bands from the table Metadata;
Metadata: table containing the following fields;
- Band: band name;
- UnitConversionCoeff: value for conversion;

Run¶

: select an output directory and start the conversion process; only bands listed in the table Metadata are converted; converted bands are saved in the output directory with the prefix RT_ and automatically loaded in QGIS;

Clip multiple rasters ¶

Clip multiple rasters

This tab allows for cutting several image bands at once, using a rectangle defined with point coordinates or a boundary defined with a shapefile.

Raster list¶

: refresh layer list;
: select all the rasters;

Clip coordinates¶

Set the Upper Left (UL) and Lower Right (LR) point coordinates of the rectangle used for clipping; it is possible to enter the coordinates manually. Alternatively use a shapefile.

UL X : set the UL X coordinate;
UL Y : set the UL Y coordinate;
LR X : set the LR X coordinate;
LR Y : set the LR Y coordinate;
Show: show or hide the clip area drawn in the map;
: define a clip area by drawing a rectangle in the map; left click to set the UL point and right click to set the LR point; the area is displayed in the map;
Use shapefile for clipping : if checked, use the selected shapefile (already loaded in QGIS) for clipping; UL and LR coordinates are ignored;
Use temporary ROI for clipping: if checked, use a temporary ROI (see ROI creation) for clipping; UL and LR coordinates are ignored;
: refresh layer list;
NoData value : if checked, set the value for NoData pixels (e.g. pixels outside the clipped area);
Output name prefix : set the prefix for output file names (default is clip);

Run¶

: choose an output destination and clip selected rasters; only rasters selected in the Raster list are clipped and automatically loaded in QGIS;

Split raster bands ¶

Split raster bands

Split a multiband raster to single bands.

Raster input¶

Select a multiband raster : select a multiband raster already loaded in QGIS;
: refresh layer list;
Output name prefix : set the prefix for output file names (default is split);

Run¶

: choose the output destination and split selected raster; output bands are automatically loaded in QGIS;

Stack raster bands ¶

Stack raster bands

Stack raster bands into a single file.

Raster list¶

: refresh layer list;
: select all the rasters;

Run¶

: choose the output destination and stack selected rasters; output is automatically loaded in QGIS;

PCA ¶

PCA

This tab allows for the PCA (Principal Component Analysis) of bands loaded in the Band set.

Principal Component Analysis of Band set¶

Number of components : if checked, set the number of calculated components; if unchecked, all the components are calculated;
Use NoData value : if checked, set the value of NoData pixels, ignored during the calculation;

Run¶

: select an output directory and start the calculation process; principal components are calculated and saved as raster files; also, the details about the PCA are displayed in the tab Output and saved in a .txt file in the output directory;

Vector to raster ¶

Vector to raster

This tab allows for the conversion of a vector to raster format.

Select the vector : select a vector already loaded in QGIS;
: refresh layer list;
Use the value field of the vector : if checked, the selected field is used as attribute for the conversion; pixels of the output raster have the same values as the vector attribute;
Use constant value : if checked, the polygons are converted to raster using the selected constant value;
Select the type of conversion : select the type of conversion between Center of pixels and All pixels touched:
- Center of pixels: during the conversion, vector is compared to the reference raster; output raster pixels are attributed to a polygon if pixel center is within that polygon;
- All pixels touched: during the conversion, vector is compared to the reference raster; output raster pixels are attributed to a polygon if pixel touches that polygon;
Select the reference raster : select a reference raster; pixels of the output raster have the same size and alignment as the reference raster;
: refresh layer list;

Run¶

: choose the output destination and start the conversion to raster;

Postprocessing ¶

The tab Postprocessing provides several functions that can be applied to the classification output.

Accuracy ¶

Accuracy

This tab allows for the validation of a classification (read Accuracy Assessment ). Classification is compared to a reference raster or reference shapefile (which is automatically converted to raster). If a shapefile is selected as reference, it is possible to choose a field describing class values.

Several statistics are calculated such as overall accuracy, user’s accuracy, producer’s accuracy, and Kappa hat. The output is an error raster that is a .tif file showing the errors in the map, where pixel values represent the categories of comparison (i.e. combinations identified by the ErrorMatrixCode in the error matrix) between the classification and reference. Also, a text file containing the error matrix (i.e. a .csv file separated by tab) is created with the same name defined for the .tif file.

Input¶

Select the classification to assess : select a classification raster (already loaded in QGIS);
: refresh layer list;
Select the reference shapefile or raster : select a raster or a shapefile (already loaded in QGIS), used as reference layer (ground truth) for the accuracy assessment;
: refresh layer list;
Shapefile field : if a shapefile is selected as reference, select a shapefile field containing numeric class values;

Run¶

: choose the output destination and start the calculation; the error matrix is displayed in the tab Output and the error raster is loaded in QGIS;

Land cover change ¶

Land cover change

The tab Land cover change allows for the comparison between two classifications in order to assess land cover changes. Output is a land cover change raster (i.e. a file .tif showing the changes in the map, where each pixel represents a category of comparison (i.e. combinations) between the two classifications, which is the ChangeCode in the land cover change statistics) and a text file containing the land cover change statistics (i.e. a file .csv separated by tab, with the same name defined for the .tif file).

Input¶

Select the reference classification : select a reference classification raster (already loaded in QGIS);
: refresh layer list;
Select the new classification : select a new classification raster (already loaded in QGIS), to be compared with the reference classification;
: refresh layer list;
Report unchanged pixels: if checked, report also unchanged pixels (having the same value in both classifications);

Run¶

: choose the output destination and start the calculation; the land cover change statistics are displayed in the tab Output (and saved in a text file) and the land cover change raster is loaded in QGIS;

Classification report ¶

Classification report

This tab allows for the calculation of class statistics such as number of pixels, percentage and area (area unit is defined from the image itself).

Input¶

Select the classification : select a classification raster (already loaded in QGIS);
: refresh layer list;
Use NoData value : if checked, NoData value will be excluded from the report;

Run¶

: choose the output destination and start the calculation; the report is saved in a text file and displayed in the tab Output;

Cross classification ¶

Cross classification

This tab allows for the calculation of a cross classification raster and matrix. Classification is compared to a reference raster or reference shapefile (which is automatically converted to raster). This is useful for calculating the area for every combination between reference classes and classification values. If a shapefile is selected as reference, it is possible to choose a field describing class values.

The output is a cross raster that is a .tif file where pixel values represent the categories of comparison (i.e. combinations identified by the CrossMatrixCode) between the classification and reference. Also, a text file containing the cross matrix (i.e. a .csv file separated by tab) is created with the same name defined for the .tif file.

Input¶

Select the classification : select a classification raster (already loaded in QGIS);
: refresh layer list;
Use NoData value : if checked, NoData value will be excluded from the calculation;
Select the reference shapefile or raster : select a raster or a shapefile (already loaded in QGIS), used as reference layer;
: refresh layer list;
Shapefile field : if a shapefile is selected as reference, select a shapefile field containing numeric class values;

Run¶

: choose the output destination and start the calculation; the cross matrix is displayed in the tab Output and the cross raster is loaded in QGIS;

Classification to vector ¶

Classification to vector

This tab allows for the conversion of a classification raster to vector shapefile.

Select the classification : select a classification raster (already loaded in QGIS);
: refresh layer list;

Symbology¶

Use code from Signature list : if checked, color and class information are defined from ROI Signature list:
- MC ID: use the ID of macroclasses;
- C ID: use the ID of classes;

Run¶

: choose the output destination and start the conversion;

Reclassification ¶

Reclassification

This tab allows for the reclassification (i.e. assigning a new class code to raster pixels). In particular, it eases the conversion from C ID to MC ID values.

Select the classification : select a classification raster (already loaded in QGIS);
: refresh layer list;

Values¶

calculate C ID to MC ID values: if checked, the reclassification table is filled according to the ROI Signature list when Calculate unique values is clicked;
Calculate unique values : calculate unique values in the classification and fill the reclassification table;
Values: table containing the following fields;
- Old value: set the expression defining old values to be reclassified; Old value can be a value or an expressions defined using the variable name raster (custom names can be defined in Variable name for expressions ), following Python operators (e.g. raster > 3 select all pixels having value > 3 ; raster > 5 | raster < 2 select all pixels having value > 5 or < 2 ; raster >= 2 & raster <= 5 select all pixel values between 2 and 5);
- New value: set the new value for the old values defined in Old value;
: add a row to the table;
: remove highlighted rows from the table;

Symbology¶

Use code from Signature list : if checked, color and class information are defined from ROI Signature list:
- MC ID: use the ID of macroclasses;
- C ID: use the ID of classes;

Run¶

: choose the output destination and start the calculation; reclassified raster is loaded in QGIS;

Edit raster ¶

Edit raster

This tab allows for the direct editing of pixel values in a raster. Only pixels beneath ROI polygons or vector polygons are edited.

Attention: the input raster is directly edited; it is recommended to create a backup copy of the input raster before using this tool in order to prevent data loss.

This tool can rapidly edit large rasters, especially when editing polygons are small, because pixel values are edited directly. In addition, the SCP Edit Toolbar is available for easing the raster editing using multiple values.

Select the input raster : select a raster (already loaded in QGIS);
: refresh layer list;

Edit raster values¶

Edit values using ROI polygons: if checked, raster is edited using temporary ROI polygons in the map;
Edit values using a vector : if checked, raster is edited using all the polygons of selected vector;
: refresh layer list;

Edit options¶

Use the value field of the vector : if checked, raster is edited using the selected vector (in Edit values using a vector) and the polygon values of selected vector field;
Use constant value : if checked, raster is edited using the selected constant value;
Use expression : if checked, raster is edited according to the entered expression; the expression must contain one or more where; the following example expression where(raster == 1, 2, raster) is already entered, which sets 2 where raster equals 1, and leaves unchanged the values where raster is not equal to 1;

Run¶

: undo the last raster edit (available only when using ROI polygons);
: edit the raster;

Classification sieve ¶

Classification sieve

This tab allows for the replacement of isolated pixel values with the value of the largest neighbour patch (based on GDAL Sieve ). It is useful for removing small patches from a classification.

Select the classification : select a raster (already loaded in QGIS);
: refresh layer list;
Size threshold : size of the patch to be replaced (in pixel unit); all patches smaller the the selected number of pixels will be replaced by the value of the largest neighbour patch;
Pixel connection : select the type of pixel connection:
- 4: in a 3x3 window, diagonal pixels are not considered connected;
- 8: in a 3x3 window, diagonal pixels are considered connected;

Run¶

: choose the output destination and start the calculation;

Classification erosion ¶

Classification erosion

This tab allows for removing the border of a class patch (erosion), defining the class values to be eroded and the number of pixels from the border. It is useful for classification refinement.

Select the classification : select a raster (already loaded in QGIS);
: refresh layer list;
Class values : set the class values to be eroded; class values must be separated by , and - can be used to define a range of values (e.g. 1, 3-5, 8 will select classes 1, 3, 4, 5, 8); if the text is red then the expression contains errors;
Size in pixels : number of pixels to be eroded from the border;
Pixel connection : select the type of pixel connection:
- 4: in a 3x3 window, diagonal pixels are not considered connected;
- 8: in a 3x3 window, diagonal pixels are considered connected;

Run¶

: choose the output destination and start the calculation;

Classification dilation ¶

Classification dilation

This tab allows for dilating the border of a class patch, defining the class values to be dilated and the number of pixels from the border. It is useful for classification refinement.

Select the classification : select a raster (already loaded in QGIS);
: refresh layer list;
Class values : set the class values to be dilated; class values must be separated by , and - can be used to define a range of values (e.g. 1, 3-5, 8 will select classes 1, 3, 4, 5, 8); if the text is red then the expression contains errors;
Size in pixels : number of pixels to be dilated from the border;
Pixel connection : select the type of pixel connection:
- 4: in a 3x3 window, diagonal pixels are not considered connected;
- 8: in a 3x3 window, diagonal pixels are considered connected;

Run¶

: choose the output destination and start the calculation;

Band calc ¶

Band calc

The Band calc allows for the raster calculation for bands (i.e. calculation of pixel values) using NumPy functions . Raster bands must be already loaded in QGIS. Input rasters must be in the same projection.

In addition, it is possible to calculate a raster using decision rules.

Band list ¶

Band list: table containing a list of single band rasters (already loaded in QGIS);
- Variable: variable name defined automatically for every band (e.g. raster1, raster2);
- Band name: band name (i.e. the layer name in QGIS);
: refresh image list;

Expression ¶

Enter a mathematical expression for raster bands. In particular, NumPy functions can be used with the prefix np. (e.g. np.log10(raster1) ). For a list of NumPy functions see the NumPy page .

The expression can work with both Variable and Band name (between double quotes). Also, bands in the Band set can be referenced directly; for example bandset#b1 refers to band 1 of the Band set. Double click on any item in the Band list for adding its name to the expression. In addition, the following variables related to Band set the are available:

“#BLUE#”: the band with the center wavelength closest to 0.475 \(\mu m\);
“#RED#”: the band with the center wavelength closest to 0.65 \(\mu m\);
“#NIR#”: the band with the center wavelength closest to 0.85 \(\mu m\);

Variables for output name are available:

#BANDSET#: the name of the first band in the Band set;
#DATE#: the current date and time (e.g. 20161110_113846527764);

If text in the Expression is green, then the syntax is correct; if text is red, then the syntax is incorrect and it is not possible to execute the calculation.

It is possible to enter multiple expressions separated by newlines such as the following example:

"raster1" + "raster2"

"raster3" - "raster4"

The above example calculates two new rasters in the output directory with the suffix _1 (e.g. calc_raster_1 ) for the first expression and _2 (e.g. calc_raster_2 ) for the second expression. Also, it is possible to define the output name using the symbol @ followed by the name, such as the following example:

"raster1" + "raster2" @ calc_1

"raster3" - "raster4" @ calc_2

The following buttons are available:

+: plus;
-: minus;
*: product;
/: ratio;
^: power;
V: square-root;
(: open parenthesis;
): close parenthesis;
>: greater then;
<: less then;
ln: natural logarithm;
π: pi;
==: equal;
!=: not equal;
sin: sine;
asin: inverse sine;
cos: cosine;
acos: inverse cosine;
tan: tangent;
atan: inverse tangent;
where: conditional expression according to the syntax where( condition , value if true, value if false) (e.g. where("raster1" == 1, 2, "raster1"));
exp: natural exponential;
nodata: NoData value of raster (e.g. nodata("raster1")); it can be used as value in the expression (e.g. where("raster1" == nodata("raster1"), 0, "raster1"));

Index calculation ¶

Index calculation allows for entering a spectral index expression (see Spectral Indices).

Index calculation : list of spectral indices:
- NDVI: if selected, the NDVI calculation is entered in the Expression ( (( "#NIR#" - "#RED#") / ( "#NIR#" + "#RED#") @ NDVI) );
- EVI: if selected, the EVI calculation is entered in the Expression ( 2.5 * ( "#NIR#" - "#RED#" ) / ( "#NIR#" + 6 * "#RED#" - 7.5 * "#BLUE#" + 1) @ EVI );
: open a text file (.txt) containing custom expressions to be listed in Index calculation; the text file must contain an expression for each line; each line must be in the form expression_name; expression (separated by ;) where the expression_name is the expression name that is displayed in the Index calculation; if you open an empty text file, the default values are restored; following an example of text content:
```
NDVI; ( "#NIR#" - "#RED#" ) / ( "#NIR#" + "#RED#" ) @NDVI
EVI; 2.5 * ( "#NIR#" - "#RED#" ) / ( "#NIR#" + 6 * "#RED#" - 7.5 * "#BLUE#" + 1) @EVI
SR; ( "#NIR#" / "#RED#" ) @SR
```

Decision rules ¶

Decision rules allows for the calculation of an output raster based on rules. Rules are conditional statements based on other rasters; if the Rule is true, the corresponding Value is assigned to the output pixel.

Rules are verified from the first to the last row in the table; if the first Rule is false, the next Rule is verified for that pixel, until the last rule. If multiple rules are true for a certain pixel, the value of the first Rule is assigned to that pixel. The NoData value is assigned to those pixels where no Rule is true.

Decision rules: table containing the following fields;
- Value: the value assigned to pixels if the Rule is true;
- Rule: the rule to be verified (e.g. "raster1" > 0); multiple conditional statements can be entered separated by ; (e.g. "raster1" > 0; "raster2" < 1 which means to set the Value where raster1 > 0 and raster2 < 1);
: move highlighted rule up;
: move highlighted rule down;
: add a new row to the table;
: delete the highlighted rows from the table;
: clear the table;
: export the rules to a text file that can be imported later;
: import rules from a text file;

Output raster ¶

The output raster is a .tif file, with the same spatial resolution and projection of input rasters; if input rasters have different spatial resolutions, then the highest resolution (i.e. minimum pixel size) is used for output raster.

Use NoData value : if checked, set the value of NoData pixels in output raster;
Extent: if the following options are unchecked, the output raster extent will include the extents of all input rasters;
- Intersection: if checked, the extent of output raster equals the intersection of input raster extents (i.e. minimum extent);
- Same as : if checked, the extent of output raster equals the extent of “Map extent” (the extent of the map currently displayed) or a selected layer;
Align: if checked, and Same as is checked selecting a raster, the calculation is performed using the same extent and pixel alignment of selected raster;
: if Expression is active and text is green, choose the output destination and start the calculation based on Expression; if Decision rules is active and text is green, choose the output destination and start the calculation based on Decision rules;

Band set ¶

Band set

This tab allows for the definition of a set of single band rasters (Band set) used as Input image. The Center wavelength of bands should be defined in order to use several functions of SCP.

If a Band set of single band rasters is defined, then the item << band set >> will be listed in the Working toolbar as input image.

The Band set definition is saved with the QGIS project.

Band list ¶

List of single band rasters loaded in QGIS.

: open one or more raster file (single band) which are added to the Band set and loaded in QGIS;
: refresh list of raster bands loaded in QGIS;
: select all raster bands;
: add selected rasters to the Band set.

Band set definition ¶

Definition of bands composing the input image .

If the Center wavelength of bands is not defined, the band number is used and some SCP tools will be disabled. It is possible to define a multiplicative rescaling factor and additive rescaling factor for each band (for instance using the values in Landsat metadata), which are used on the fly (i.e. pixel value = original pixel value * multiplicative rescaling factor + additive rescaling factor) during the processing.

Band set definition: table containing the following fields;
- Band name : name of the band; name cannot be edited;
- Center wavelength : center of the wavelength of the band;
- Multiplicative Factor : multiplicative rescaling factor;
- Additive Factor : additive rescaling factor;
: move highlighted bands upward;
: move highlighted bands downward;
: sort automatically bands by name, giving priority to the ending numbers of name;
: remove highlighted bands from the Band set;
: clear all bands from Band set;
: export the Band set to a file;
: import a previously saved Band set from file;
Quick wavelength settings : rapid definition of band center wavelength for the following satellite sensors:
- ASTER;
- GeoEye-1;
- Landsat 8 OLI;
- Landsat 7 ETM+;
- Landsat 5 TM;
- Landsat 4 TM;
- Landsat 1, 2, and 3 MSS;
- MODIS;
- Pleiades;
- QuickBird;
- RapidEye;
- Sentinel-2;
- SPOT 4;
- SPOT 5;
- SPOT 6;
- WorldView-2 and WorldView-3;
Wavelength unit : select the wavelength unit among:
- Band number: no unit, only band number;
- \(\mu m\): micrometres;
- nm: nanometres;

Band set tools ¶

It is possible to perform several processes directly on Band set.

Create virtual raster of band set: if checked, create a virtual raster of bands;
Create raster of band set (stack bands): if checked, stack all the bands and create a unique .tif raster;
Build band overviews: if checked, build raster overviews (i.e. pyramids) for improving display performance; overview files are created in the same directory as bands;
Band calc expression: if checked, calculate the Expression entered in Band calc; it is recommended the use of Band set variables in the expression (e.g. bandset#b1 );
: choose the output destination and start the process;

Batch ¶

Batch

This tab allows for the automatic execution (batch) of several SCP functions using a scripting interface.

Batch ¶

Enter a batch expression; each function must be in a new line. Functions have the following structure:

function name; function options

Each functions has options, identified by a name, with the following structure:

option name: option argument

Options must be separated by the character ; . Each function option represents an option in the corresponding interface of SCP; option arguments of type text must be between the character ' ; in case of checkboxes, the value 1 represents checked, while the value 0 represents unchecked. A new line beginning with # can be used for commenting.

According to the function, some of the options are mandatory while other options can be omitted from the expression. Option names that contain path require the full path to a file. Some options that require multiple arguments such as lists; lists must be separated by , .

If the expression contains errors, the text is red.

: clear the expression;
: export the batch expression to a file;
: import a previously saved batch expression from file;

Functions: the following functions are available with the corresponding options;

Accuracy: calculate accuracy (accuracy;classification_file_path : '';reference_file_path : '';shapefile_field_name : '';output_raster_path : '');
ASTER: ASTER conversion (aster_conversion;input_raster_path : '';celsius_temperature : 0;apply_dos1 : 0;use_nodata : 1;nodata_value : 0;create_bandset : 1;output_dir : '');
Band calc: band calculation (band_calc;expression : '';output_raster_path : '';extent_same_as_raster_name : '';extent_intersection : 1;set_nodata : 0;nodata_value : 0);
Classification output: perform classification (classification;use_macroclass : 0;algorithm_name : 'Minimum Distance';use_lcs : 0;use_lcs_algorithm : 0;use_lcs_only_overlap : 0;apply_mask : 0;mask_file_path : '';vector_output : 0;classification_report : 0;save_algorithm_files : 0;output_classification_path : '');
Classification dilation: dilation of a classification (classification_dilation;input_raster_path : '';class_values : '';size_in_pixels : 1;pixel_connection : 4;output_raster_path : '');
Classification erosion: erosion of a classification (classification_erosion;input_raster_path : '';class_values : '';size_in_pixels : 1;pixel_connection : 4;output_raster_path : '');
Classification report: report of a classification (classification_report;input_raster_path : '';use_nodata : 0;nodata_value : 0;output_report_path : '');
Classification sieve: classification sieve(classification_sieve;input_raster_path : '';size_threshold : 2;pixel_connection : 4;output_raster_path : '');
Classification to vector: convert classification to vector (classification_to_vector;input_raster_path : '';use_signature_list_code : 1;code_field : 'C_ID';output_vector_path : '');
Clip multiple rasters: clip multiple rasters (clip_multiple_rasters;input_raster_path : '';output_dir : '';use_shapefile : 0;shapefile_path : '';ul_x : '';ul_y : '';lr_x : '';lr_y : '';nodata_value : 0;output_name_prefix : 'clip');
Cross classification: cross classification (cross_classification;classification_file_path : '';use_nodata : 0;nodata_value : 0;reference_file_path : '';shapefile_field_name : '';output_raster_path : '');
Edit raster: edit raster values using a shapefile); (edit_raster_using_shapefile;input_raster_path : '';input_vector_path : '';vector_field_name : '';constant_value : 0;expression : 'where(raster == 1, 2, raster)');
Land cover change: calculate land cover change (land_cover_change;reference_raster_path : '';new_raster_path : '';output_raster_path : '');
Landsat: Landsat conversion (landsat_conversion;input_dir : '';mtl_file_path : '';celsius_temperature : 0;apply_dos1 : 0;use_nodata : 1;nodata_value : 0;pansharpening : 0;create_bandset : 1;output_dir : '');
MODIS: MODIS conversion (modis_conversion;input_raster_path : '';reproject_wgs84 : 1;use_nodata : 1;nodata_value : -999;create_bandset : 1;output_dir : '');
PCA: Principal Component Analysis (pca;use_number_of_components : 0, number_of_components : 2;use_nodata : 1;nodata_value : 0;output_dir : '');
Reclassification: raster reclassification (reclassification;input_raster_path : '';value_list : 'oldVal-newVal;oldVal-newVal';use_signature_list_code : 1;code_field : 'MC_ID';output_raster_path : '');
Sentinel-2: Sentinel-2 conversion (sentinel_conversion;input_dir : '';mtd_safl1c_file_path : '';apply_dos1 : 0;use_nodata : 1;nodata_value : 0;create_bandset : 1;output_dir : '');
Split raster bands: split raster to single bands (split_raster_bands;input_raster_path : '';output_dir : '';output_name_prefix : 'split');
Stack raster bands: stack rasters into a single file (stack_raster_bands;input_raster_path : '';output_raster_path : '');
Vector to raster: convert vector to raster (vector_to_raster;vector_file_path : '';use_value_field : 1;vector_field_name : '';constant_value : 1;reference_raster_path : '';type_of_conversion : 'Center of pixels';output_raster_path : '');

In addition, the following functions are available:

Add raster to QGIS: add a raster to QGIS (add_raster;input_raster_path : '';input_raster_name : '');
Create Band set: create a Band set (create_bandset;raster_path_list : '';center_wavelength : '';wavelength_unit : 1;multiplicative_factor : '';additive_factor : '');
Open training input: open a training input file (open_training_input;training_file_path : '');
Set working directory: set a working directory (argument is the path to a directory) (!working_dir!;'');

If a working directory is defined, !working_dir! can be entered in other functions where a path is required (e.g. add_raster;input_raster_path : '!working_dir!/raster1.tif';input_raster_name : 'raster1.tif'); An example of batch expression is:

!working_dir!; '/home/user/Desktop/temp/'
add_raster;input_raster_path : '!working_dir!/raster1.tif';input_raster_name : 'raster1.tif'
band_calc;expression : 'where("raster1.tif" > 1, 1,0)';output_raster_path : '!working_dir!/calc1.tif';set_nodata : 1;nodata_value : 0
band_calc;expression : '"raster1.tif" * "calc1.tif"';output_raster_path : '!working_dir!/calc2.tif';extent_intersection : 0

Run ¶

: if text in the batch expression is green, start the batch processes;

Settings ¶

The tab Settings allows for the customization of SCP.

Interface ¶

Customization of the interface.

Interface

Field names of training input¶

Set the names of fields in the Training input . Changing field names should usually be avoided.

MC ID field : name of the Macroclass ID field (default is MC_ID);
MC Info field : name of the Macroclass Information field (default is MC_info);
C ID field : name of the Class ID field (default is C_ID);
C Info field : name of the Class Information field (default is C_info);
: reset field names to default;

ROI style¶

Change ROI color and transparency for a better visualization of temporary ROIs on the map.

ROI color : button for changing ROI color;
Transparency : change ROI transparency;
: reset ROI color and transparency to default;

Variable name for expressions¶

Set the variable name used in expressions of the Reclassification and Edit raster .

Variable name : set variable name (default is raster);
: reset variable name to default;

Temporary group name¶

Set the temporary group name in QGIS Layers used for temporary layers .

Group name : set group name (default is Class_temp_group);
: reset group name to default;

Dock¶

Download news on startup: if checked, news about the SCP and related services are downloaded on startup and displayed in Dock;

Processing ¶

Processing

Classification process¶

Play sound when finished : if checked, play a sound when the classification process is completed;
Use virtual rasters for temp files : if checked, create virtual rasters for certain temporary files, instead of creating real rasters; it is useful for reducing disk space usage during calculations;
Raster compression : if checked, a lossless compression (DEFLATE or PACKBITS) is applied to raster outputs in order to save disk space; it is recommended to check this option, however compressed files are sometimes larger than files without compression;

RAM¶

Available RAM (MB) : set the available RAM (in MB) that is used during the processes in order to improve the SCP performance; this value should be half of the system RAM (e.g. 1024MB if system has 2GB of RAM); in case of errors, set a value lower than 512MB;

Temporary directory¶

: select a new temporary directory where temporary files are saved during processing; the path to the current temporary directory is displayed; default is a system temporary directory;
: reset to default temporary directory;

Debug ¶

Debug

Debugging utilities for the creation of a Log file (i.e. recording of SCP activities for reporting issues) and testing SCP dependencies.

If you found a plugin error, please read How can I report an error? .

Log file¶

Records events in a log file : if checked, start recording events in a Log file;
: export the Log file (i.e. a .txt file);
: clear the content of Log file;

Test¶

Test dependencies : test SCP dependencies (GDAL, GDAL subprocess, NumPy, SciPy, Matplotlib, Internet connection); results are displayed in a window;

Spectral Signature Plot¶

The window Spectral Signature Plot includes several functions for displaying spectral signature values as a function of wavelength (defined in the Band set). Signatures can be added to the Spectral Signature Plot through the SCP dock.

The window Spectral Signature Plot includes also some functions useful for the definition of value ranges used by the Land Cover Signature Classification (see LCS threshold).

Overlapping signatures (belonging to different classes or macroclasses) are highlighted in orange in the table Plot Signature list; the overlapping check is performed considering MC ID or C ID according to the setting Use MC ID C ID in Classification algorithm. Overlapping signatures sharing the same ID are not highlighted.

Spectral Signature Plot

The functions are described in detail in the following paragraphs, using these conventions:

= Input date

= Input text

= List

= Input number

= Optional

= Configuration stored in the active project of QGIS

= Configuration stored in QGIS registry

= Slider

= Table

Plot Signature list¶

Signature list:
- S: checkbox field; if checked, the spectral signature is displayed in the plot;
- MC ID: signature Macroclass ID;
- MC Info: signature Macroclass Information;
- C ID: signature Class ID;
- C Info: signature Class Information;
- Color [overlap MC_ID-C_ID]: signature color; also, the combination MC ID-C ID is displayed in case of overlap with other signatures (see Land Cover Signature Classification);
- Min B X: minimum value of band X; this value can be edited;
- Max B X: maximum value of band X; this value can be edited;
: remove highlighted signatures from this list;
: add highlighted spectral signatures to ROI Signature list;
: calculate the spectral distances of spectral signatures displayed in the plot; distances are reported in the tab Spectral distances;

Automatic thresholds¶

Set thresholds automatically for highlighted signatures in the table Plot Signature list; if no signature is highlighted, then the threshold is applied to all the signatures.

Min Max : set the threshold based on the minimum and maximum of each band;
σ * : set an automatic threshold calculated as (band value + (σ * v)), where σ is the standard deviation of each band and v is the defined value;
: undo the last automatic thresholds;
From ROI : set the threshold using the temporary ROI pixel values, according to the following checkboxes:
- +: if checked, signature threshold is extended to include pixel signature;
- –: if checked, signature threshold is reduced to exclude pixel signature;
From pixel : set the threshold by clicking on a pixel, according to the following checkboxes:
- +: if checked, signature threshold is extended to include pixel signature;
- –: if checked, signature threshold is reduced to exclude pixel signature;

Plot¶

Left click and hold inside the plot to move the view of the plot. Use the mouse wheel to zoom in and out the view of the plot. Right click and hold inside the plot to zoom in a specific area of the plot. Legend inside the plot can be moved using the mouse.

Plot commands:

: automatically fit the plot to data;
: save the plot image to file (available formats are .jpg, .png, and .pdf);
: activate the cursor for interactively changing the value range of highlighted signatures in the plot; click the plot to set the minimum or maximum value of a band (also for several signatures simultaneously); cursor is deactivated when moving outside the plot area;
Plot value range: if checked, plot the value range for each signature (semi-transparent area);
Band lines: if checked, display a vertical line for each band (center wavelength);
Grid: if checked, display a grid;
Max characters : set the maximum length of text in the legend;
x y: display x y coordinates of mouse cursor inside the plot;

Spectral Signature: Example of spectral signature plot

Signature details¶

Spectral Signature: Signature details

Display the details about spectral signatures (i.e. Wavelength, Values, and Standard deviation). In case of signatures calculated from ROIs, the ROI size (number of pixels) is also displayed.

Spectral Signature: Example of signature details

Spectral distances¶

_images/spectral_signature_distances.jpg

Spectral Signature: Spectral distances

Display spectral distances of signatures (see Plot Signature list), which are useful for assessing ROI separability (see Spectral Distance).

The following spectral distances are calculated :

Jeffries-Matusita Distance: range [0 = identical, 2 = different]; useful in particular for Maximum Likelihood classifications;
Spectral Angle: range [0 = identical, 90 = different]; useful in particular for Spectral Angle Mapping classifications;
Euclidean Distance: useful in particular for Minimum Distance classifications;
Bray-Curtis Similarity: range [0 = different, 100 = identical]; useful in general;

Values are displayed in red if signatures are particularly similar.

Spectral Signature: Example of spectral distances

Scatter Plot¶

The window Scatter plot displays pixel values for two raster bands as points in the 2D space. Scatter plots are useful for assessing ROI separability between two bands.

Scatter Plot

The functions are described in detail in the following paragraphs, using these conventions:

= Input date

= Input text

= List

= Input number

= Optional

= Configuration stored in the active project of QGIS

= Configuration stored in QGIS registry

= Slider

= Table

Scatter list¶

Scatter list:
- S: checkbox field; if checked, the spectral signature is displayed in the plot;
- MC ID: signature Macroclass ID;
- MC Info: signature Macroclass Information;
- C ID: signature Class ID;
- C Info: signature Class Information;
- Color: color field; double click to select a color for the plot;
Band X : X band of the plot;
Band Y : Y band of the plot;
Precision : use custom precision for calculation (precision should be selected according to pixel values):
- 4 = \(10^{-4}\)
- 3 = \(10^{-3}\)
- 2 = \(10^{-2}\)
- 1 = \(10^{-1}\)
- 0 = 1
- -1 = 10
- -2 = \(10^{2}\)
- -3 = \(10^{3}\)
Calculate : calculate the scatter plot for the ROIs checked in the list;
: remove highlighted signatures from this list;
: add a temporary scatter plot to the list (as MC Info = tempScatter) and start the plot calculation of the last temporary ROI (see Working toolbar);
: add a temporary scatter plot to the list (as MC Info = tempScatter) and start the plot calculation of pixels in current display extent;
: add a temporary scatter plot to the list (as MC Info = tempScatter) and start the plot calculation of the entire image;

WARNING: Using a precision value that is too high can result in slow calculation or failure.

Scatter raster¶

This tool allows for the drawing of selection polygons inside the scatter plot; these selection polygons are used for creating a Scatter raster that is a temporary raster classified according to the intersection of scatter plots and drawn polygons.

Pixels of the Input image are classified, according to scatter plot bands, if pixel values are in the range of intersection between scatter plots and selection polygons (polygons should not overlap). The value assigned to the Scatter raster pixels is the sequential number of selection polygon; also the raster color is derived from the selection polygon.

After the creation of a new Scatter raster, old rasters are placed in QGIS Layers inside a layer group named Class_temp_group (custom name can be defined in Temporary group name) and are deleted when the QGIS session is closed.

: activate the cursor for interactively drawing a polygon in the plot; left click on the plot to define the vertices and right click to define the last vertex closing the polygon;
color: select the color of polygon (which is used also in the Scatter raster);
: remove all the selection polygons from the plot;
: calculate the Scatter raster and display it in the map;
: calculate the spectral signature of the Scatter raster (considering all the classified pixels) using the Input image, and save the signature to the ROI Signature list;
Extent : extent of the Scatter raster; available options are:
- Same as display: extent is the same as map display;
- Same as image: extent is the same as the whole image;

Plot¶

Colormap : select a colormap that is applied to highlighted scatter plots in the list when is clicked; if no scatter plot is highlighted then the colormap is applied to all the scatter plots;
: automatically fit the plot to data;
: save the plot image to file (available formats are .jpg, .png, and .pdf);
x y: display x y coordinates of mouse cursor inside the plot;

Example Scatter Plot

Brief Introduction to Remote Sensing¶

Basic Definitions ¶

This chapter provides basic definitions about GIS and remote sensing. For other useful resources see Free and valuable resources about remote sensing and GIS.

GIS definition ¶

There are several definitions of GIS (Geographic Information Systems), which is not simply a program. In general, GIS are systems that allow for the use of geographic information (data have spatial coordinates). In particular, GIS allow for the view, query, calculation and analysis of spatial data, which are mainly distinguished in raster or vector data structures. Vector is made of objects that can be points, lines or polygons, and each object can have one ore more attribute values; a raster is a grid (or image) where each cell has an attribute value (Fisher and Unwin, 2005). Several GIS applications use raster images that are derived from remote sensing.

Remote Sensing definition ¶

A general definition of Remote Sensing is “the science and technology by which the characteristics of objects of interest can be identified, measured or analyzed the characteristics without direct contact” (JARS, 1993).

Usually, remote sensing is the measurement of the energy that is emanated from the Earth’s surface. If the source of the measured energy is the sun, then it is called passive remote sensing, and the result of this measurement can be a digital image (Richards and Jia, 2006). If the measured energy is not emitted by the Sun but from the sensor platform then it is defined as active remote sensing, such as radar sensors which work in the microwave range (Richards and Jia, 2006).

The electromagnetic spectrum is “the system that classifies, according to wavelength, all energy (from short cosmic to long radio) that moves, harmonically, at the constant velocity of light” (NASA, 2013). Passive sensors measure energy from the optical regions of the electromagnetic spectrum: visible, near infrared (i.e. IR), short-wave IR, and thermal IR (see Figure Electromagnetic-Spectrum).

Electromagnetic-Spectrum

by Victor Blacus (SVG version of File:Electromagnetic-Spectrum.png)

[CC-BY-SA-3.0 (http://creativecommons.org/licenses/by-sa/3.0)]

via Wikimedia Commons

http://commons.wikimedia.org/wiki/File%3AElectromagnetic-Spectrum.svg

The interaction between solar energy and materials depends on the wavelength; solar energy goes from the Sun to the Earth and then to the sensor. Along this path, solar energy is (NASA, 2013):

Transmitted - The energy passes through with a change in velocity as determined by the index of refraction for the two media in question.
Absorbed - The energy is given up to the object through electron or molecular reactions.
Reflected - The energy is returned unchanged with the angle of incidence equal to the angle of reflection. Reflectance is the ratio of reflected energy to that incident on a body. The wavelength reflected (not absorbed) determines the color of an object.
Scattered - The direction of energy propagation is randomly changed. Rayleigh and Mie scatter are the two most important types of scatter in the atmosphere.
Emitted - Actually, the energy is first absorbed, then re-emitted, usually at longer wavelengths. The object heats up.

Sensors ¶

Sensors can be on board of airplanes or on board of satellites, measuring the electromagnetic radiation at specific ranges (usually called bands). As a result, the measures are quantized and converted into a digital image, where each picture elements (i.e. pixel) has a discrete value in units of Digital Number (DN) (NASA, 2013). The resulting images have different characteristics (resolutions) depending on the sensor. There are several kinds of resolutions:

Spatial resolution, usually measured in pixel size, “is the resolving power of an instrument needed for the discrimination of features and is based on detector size, focal length, and sensor altitude” (NASA, 2013); spatial resolution is also referred to as geometric resolution or IFOV;
Spectral resolution, is the number and location in the electromagnetic spectrum (defined by two wavelengths) of the spectral bands (NASA, 2013) in multispectral sensors, for each band corresponds an image;
Radiometric resolution, usually measured in bits (binary digits), is the range of available brightness values, which in the image correspond to the maximum range of DNs; for example an image with 8 bit resolution has 256 levels of brightness (Richards and Jia, 2006);
For satellites sensors, there is also the temporal resolution, which is the time required for revisiting the same area of the Earth (NASA, 2013).

Radiance and Reflectance ¶

Sensors measure the radiance, which corresponds to the brightness in a given direction toward the sensor; it useful to define also the reflectance as the ratio of reflected versus total power energy.

Spectral Signature ¶

The spectral signature is the reflectance as a function of wavelength (see Figure Spectral Reflectance Curves of Four Different Targets); each material has a unique signature, therefore it can be used for material classification (NASA, 2013).

Spectral Reflectance Curves of Four Different Targets

(from NASA, 2013)

Landsat Satellite ¶

Landsat is a set of multispectral satellites developed by the NASA (National Aeronautics and Space Administration of USA), since the early 1970’s.

Landsat images are very used for environmental research. The resolutions of Landsat 4 and Landsat 5 sensors are reported in the following table (from http://landsat.usgs.gov/band_designations_landsat_satellites.php); also, Landsat temporal resolution is 16 days (NASA, 2013).

Landsat 4 and Landsat 5 Bands

Landsat 4, Landsat 5 Bands	Wavelength [micrometers]	Resolution [meters]
Band 1 - Blue	0.45 - 0.52	30
Band 2 - Green	0.52 - 0.60	30
Band 3 - Red	0.63 - 0.69	30
Band 4 - Near Infrared (NIR)	0.76 - 0.90	30
Band 5 - SWIR	1.55 - 1.75	30
Band 6 - Thermal Infrared	10.40 - 12.50	120 (resampled to 30)
Band 7 - SWIR	2.08 - 2.35	30

The resolutions of Landsat 7 sensor are reported in the following table (from http://landsat.usgs.gov/band_designations_landsat_satellites.php); also, Landsat temporal resolution is 16 days (NASA, 2013).

Landsat 7 Bands

Landsat 7 Bands	Wavelength [micrometers]	Resolution [meters]
Band 1 - Blue	0.45 - 0.52	30
Band 2 - Green	0.52 - 0.60	30
Band 3 - Red	0.63 - 0.69	30
Band 4 - Near Infrared (NIR)	0.77 - 0.90	30
Band 5 - SWIR	1.57 - 1.75	30
Band 6 - Thermal Infrared	10.40 - 12.50	60 (resampled to 30)
Band 7 - SWIR	2.09 - 2.35	30
Band 8 - Panchromatic	0.52 - 0.90	15

The resolutions of Landsat 8 sensor are reported in the following table (from http://landsat.usgs.gov/band_designations_landsat_satellites.php); also, Landsat temporal resolution is 16 days (NASA, 2013).

Landsat 8 Bands

Landsat 8 Bands	Wavelength [micrometers]	Resolution [meters]
Band 1 - Coastal aerosol	0.43 - 0.45	30
Band 2 - Blue	0.45 - 0.51	30
Band 3 - Green	0.53 - 0.59	30
Band 4 - Red	0.64 - 0.67	30
Band 5 - Near Infrared (NIR)	0.85 - 0.88	30
Band 6 - SWIR 1	1.57 - 1.65	30
Band 7 - SWIR 2	2.11 - 2.29	30
Band 8 - Panchromatic	0.50 - 0.68	15
Band 9 - Cirrus	1.36 - 1.38	30
Band 10 - Thermal Infrared (TIRS) 1	10.60 - 11.19	100 (resampled to 30)
Band 11 - Thermal Infrared (TIRS) 2	11.50 - 12.51	100 (resampled to 30)

A vast archive of images is freely available from the U.S. Geological Survey . For more information about how to freely download Landsat images read this .

Images are identified with the paths and rows of the WRS (Worldwide Reference System for Landsat ).

Sentinel-2 Satellite ¶

Sentinel-2 is a multispectral satellite developed by the European Space Agency (ESA) in the frame of Copernicus land monitoring services. Sentinel-2 acquires 13 spectral bands with the spatial resolution of 10m, 20m and 60m depending on the band, as illustrated in the following table (ESA, 2015).

Sentinel-2 Bands

Sentinel-2 Bands	Central Wavelength [micrometers]	Resolution [meters]
Band 1 - Coastal aerosol	0.443	60
Band 2 - Blue	0.490	10
Band 3 - Green	0.560	10
Band 4 - Red	0.665	10
Band 5 - Vegetation Red Edge	0.705	20
Band 6 - Vegetation Red Edge	0.740	20
Band 7 - Vegetation Red Edge	0.783	20
Band 8 - NIR	0.842	10
Band 8A - Vegetation Red Edge	0.865	20
Band 9 - Water vapour	0.945	60
Band 10 - SWIR - Cirrus	1.375	60
Band 11 - SWIR	1.610	20
Band 12 - SWIR	2.190	20

Sentinel-2 images are freely available from the ESA website https://scihub.esa.int/dhus/ .

ASTER Satellite ¶

The ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) satellite was launched in 1999 by a collaboration between the Japanese Ministry of International Trade and Industry (MITI) and the NASA. ASTER has 14 bands whose spatial resolution varies with wavelength: 15m in the visible and near-infrared, 30m in the short wave infrared, and 90m in the thermal infrared (USGS, 2015). ASTER bands are illustrated in the following table (due to a sensor failure SWIR data acquired since April 1, 2008 is not available ). An additional band 3B (backwardlooking near-infrared) provides stereo coverage.

ASTER Bands

ASTER Bands	Wavelength [micrometers]	Resolution [meters]
Band 1 - Green	0.52 - 0.60	15
Band 2 - Red	0.63 - 0.69	15
Band 3N - Near Infrared (NIR)	0.78 - 0.86	15
Band 4 - SWIR 1	1.60 - 1.70	30
Band 5 - SWIR 2	2.145 - 2.185	30
Band 6 - SWIR 3	2.185 - 2.225	30
Band 7 - SWIR 4	2.235 - 2.285	30
Band 8 - SWIR 5	2.295 - 2.365	30
Band 9 - SWIR 6	2.360 - 2.430	30
Band 10 - TIR 1	8.125 - 8.475	90
Band 11 - TIR 2	8.475 - 8.825	90
Band 12 - TIR 3	8.925 - 9.275	90
Band 13 - TIR 4	10.25 - 10.95	90
Band 14 - TIR 5	10.95 - 11.65	90

MODIS Products ¶

The MODIS (Moderate Resolution Imaging Spectroradiometer) is an instrument operating on the Terra and Aqua satellites launched by NASA in 1999 and 2002 respectively. Its temporal resolutions allows for viewing the entire Earth surface every one to two days, with a swath width of 2,330. Its sensors measure 36 spectral bands at three spatial resolutions: 250m, 500m, and 1,000m (see https://lpdaac.usgs.gov/dataset_discovery/modis).

Several products are available, such as surface reflectance and vegetation indices. In this manual we are considering the surface reflectance bands available at 250m and 500m spatial resolution (Vermote, Roger, & Ray, 2015).

MODIS Bands

MODIS Bands	Wavelength [micrometers]	Resolution [meters]
Band 1 - Red	0.62 - 0.67	250 - 500
Band 2 - Near Infrared (NIR)	0.841 - 0.876	250 - 500
Band 3 - Blue	0.459 - 0.479	500
Band 4 - Green	0.545 - 0.565	500
Band 5 - SWIR 1	1.230 - 1.250	500
Band 6 - SWIR 2	1.628 - 1.652	500
Band 7 - SWIR 3	2.105 - 2.155	500

The following products (Version 6, see https://lpdaac.usgs.gov/dataset_discovery/modis/modis_products_table) are available for download (Vermote, Roger, & Ray, 2015):

MOD09GQ: daily reflectance at 250m spatial resolution from Terra MODIS;
MYD09GQ: daily reflectance at 250m spatial resolution from Aqua MODIS;
MOD09GA: daily reflectance at 500m spatial resolution from Terra MODIS;
MYD09GA: daily reflectance at 500m spatial resolution from Aqua MODIS;
MOD09Q1: reflectance at 250m spatial resolution, which is a composite of MOD09GQ (each pixel contains the best possible observation during an 8-day period);
MYD09Q1: reflectance at 250m spatial resolution, which is a composite of MYD09GQ (each pixel contains the best possible observation during an 8-day period);
MOD09A1: reflectance at 250m spatial resolution, which is a composite of MOD09GA (each pixel contains the best possible observation during an 8-day period);
MYD09A1: reflectance at 250m spatial resolution, which is a composite of MYD09GA (each pixel contains the best possible observation during an 8-day period);

Color Composite ¶

Often, a combination is created of three individual monochrome images, in which each is assigned a given color; this is defined color composite and is useful for photo interpretation (NASA, 2013). Color composites are usually expressed as:

“R G B = Br Bg Bb”

where:

R stands for Red;
G stands for Green;
B stands for Blue;
Br is the band number associated to the Red color;
Bg is the band number associated to the Green color;
Bb is the band number associated to the Blue color.

The following Figure Color composite of a Landsat 8 image shows a color composite “R G B = 4 3 2” of a Landsat 8 image (for Landsat 7 the same color composite is R G B = 3 2 1; for Sentinel-2 is R G B = 4 3 2) and a color composite “R G B = 5 4 3” (for Landsat 7 the same color composite is R G B = 4 3 2; for Sentinel-2 is R G B = 8 4 3). The composite “R G B = 5 4 3” is useful for the interpretation of the image because vegetation pixels appear red (healthy vegetation reflects a large part of the incident light in the near-infrared wavelength, resulting in higher reflectance values for band 5, thus higher values for the associated color red).

Color composite of a Landsat 8 image

Data available from the U.S. Geological Survey

Principal Component Analysis ¶

Principal Component Analysis (PCA) is a method for reducing the dimensions of measured variables (bands) to the principal components (JARS, 1993).

Th principal component transformation provides a new set of bands (principal components) having the following characteristic: principal components are uncorrelated; each component has variance less than the previous component. Therefore, this is an efficient method for extracting information and data compression (Ready and Wintz, 1973).

Given an image with N spectral bands, the principal components are obtained by matrix calculation (Ready and Wintz, 1973; Richards and Jia, 2006):

\[Y = D^t X\]

where:

\(Y\) = vector of principal components
\(D\) = matrix of eigenvectors of the covariance matrix \(C_x\) in X space
\(t\) denotes vector transpose

And \(X\) is calculated as:

\[X = P - M\]

\(P\) = vector of spectral values associated with each pixel
\(M\) = vector of the mean associated with each band

Thus, the mean of \(X\) associated with each band is 0. \(D\) is formed by the eigenvectors (of the covariance matrix \(C_x\)) ordered as the eigenvalues from maximum to minimum, in order to have the maximum variance in the first component. This way, the principal components are uncorrelated and each component has variance less than the previous component(Ready and Wintz, 1973).

Usually the first two components contain more than the 90% of the variance. For example, the first principal components can be displayed in a Color Composite for highlighting Land Cover classes, or used as input for Supervised Classification.

Pan-sharpening ¶

Pan-sharpening is the combination of the spectral information of multispectral bands (MS), which have lower spatial resolution (for Landsat bands, spatial resolution is 30m), with the spatial resolution of a panchromatic band (PAN), which for Landsat 7 and 8 it is 15m. The result is a multispectral image with the spatial resolution of the panchromatic band (e.g. 15m). In SCP, a Brovey Transform is applied, where the pan-sharpened values of each multispectral band are calculated as (Johnson, Tateishi and Hoan, 2012):

\[MSpan = MS * PAN / I\]

where \(I\) is Intensity, which is a function of multispectral bands.

The following weights for I are defined, basing on several tests performed using the SCP. For Landsat 8, Intensity is calculated as:

\[I = (0.42 * Blue + 0.98 * Green + 0.6 * Red ) / 2\]

For Landsat 7, Intensity is calculated as:

\[I = (0.42 * Blue + 0.98 * Green + 0.6 * Red + NIR) / 3\]

Example of pan-sharpening of a Landsat 8 image. Left, original multispectral bands (30m); right, pan-sharpened bands (15m)

Data available from the U.S. Geological Survey

Spectral Indices ¶

Spectral indices are operations between spectral bands that are useful for extracting information such as vegetation cover (JARS, 1993). One of the most popular spectral indices is the Normalized Difference Vegetation Index (NDVI), defined as (JARS, 1993):

\[NDVI = ( NIR - Red ) / ( NIR + Red )\]

NDVI values range from -1 to 1. Dense and healthy vegetation show higher values, while non-vegetated areas show low NDVI values.

Another index is the Enhanced Vegetation Index (EVI) which attempts to account for atmospheric effects such as path radiance calculating the difference between the blue and the red bands (Didan,et al., 2015). EVI is defined as:

\[EVI = G ( NIR - Red ) / ( NIR + C_1 Red - C_2 Blue + L)\]

where: \(G\) is a scaling factor, \(C_1\) and \(C_2\) are coefficients for the atmospheric effects, and \(L\) is a factor for accounting the differential NIR and Red radiant transfer through the canopy. Typical coefficient values are: \(G = 2.5\), \(L = 1\), \(C_1 = 6\), \(C_2 = 7.5\) (Didan,et al., 2015).

Supervised Classification Definitions ¶

This chapter provides basic definitions about supervised classifications.

Land Cover ¶

Land cover is the material at the ground, such as soil, vegetation, water, asphalt, etc. (Fisher and Unwin, 2005). Depending on the sensor resolutions, the number and kind of land cover classes that can be identified in the image can vary significantly.

Supervised Classification ¶

A semi-automatic classification (also supervised classification) is an image processing technique that allows for the identification of materials in an image, according to their spectral signatures. There are several kinds of classification algorithms, but the general purpose is to produce a thematic map of the land cover.

Image processing and GIS spatial analyses require specific software such as the Semi-Automatic Classification Plugin for QGIS.

_images/multispectral_classification.jpg

A multispectral image processed to produce a land cover classification

(Landsat image provided by USGS)

Training Areas ¶

Usually, supervised classifications require the user to select one or more Regions of Interest (ROIs, also Training Areas) for each land cover class identified in the image. ROIs are polygons drawn over homogeneous areas of the image that overlay pixels belonging to the same land cover class.

Region Growing Algorithm¶

The Region Growing Algorithm allows to select pixels similar to a seed one, considering the spectral similarity (i.e. spectral distance) of adjacent pixels. In SCP the Region Growing Algorithm is available for the training area creation. The parameter distance is related to the similarity of pixel values (the lower the value, the more similar are selected pixels) to the seed one (i.e. selected clicking on a pixel). An additional parameter is the maximum width, which is the side length of a square, centred at the seed pixel, which inscribes the training area (if all the pixels had the same value, the training area would be this square). The minimum size is used a constraint (for every single band), selecting at least the pixels that are more similar to the seed one until the number of selected pixels equals the minimum size.

In figure Region growing example the central pixel is used as seed (image a) for the region growing of one band (image b) with the parameter spectral distance = 0.1; similar pixels are selected to create the training area (image c and image d).

Region growing example

Classes and Macroclasses ¶

Land cover classes are identified with an arbitrary ID code (i.e. Identifier). SCP allows for the definition of Macroclass ID (i.e. MC ID) and Class ID (i.e. C ID), which are the identification codes of land cover classes. A Macroclass is a group of ROIs having different Class ID, which is useful when one needs to classify materials that have different spectral signatures in the same land cover class. For instance, one can identify grass (e.g. ID class = 1 and Macroclass ID = 1 ) and trees (e.g. ID class = 2 and Macroclass ID = 1 ) as vegetation class (e.g. Macroclass ID = 1 ). Multiple Class IDs can be assigned to the same Macroclass ID, but the same Class ID cannot be assigned to multiple Macroclass IDs, as shown in the following table.

Example of Macroclasses

Macroclass name	Macroclass ID	Class name	Class ID
Vegetation	1	Grass	1
Vegetation	1	Trees	2
Built-up	2	Buildings	3
Built-up	2	Roads	4

Therefore, Classes are subsets of a Macroclass as illustrated in Figure Macroclass example.

Macroclass example

If the use of Macroclass is not required for the study purpose, then the same Macroclass ID can be defined for all the ROIs (e.g. Macroclass ID = 1) and Macroclass values are ignored in the classification process.

Classification Algorithms ¶

The spectral signatures (spectral characteristics) of reference land cover classes are calculated considering the values of pixels under each ROI having the same Class ID (or Macroclass ID). Therefore, the classification algorithm classifies the whole image by comparing the spectral characteristics of each pixel to the spectral characteristics of reference land cover classes. SCP implements the following classification algorithms.

Minimum Distance¶

Minimum Distance algorithm calculates the Euclidean distance \(d(x, y)\) between spectral signatures of image pixels and training spectral signatures, according to the following equation:

\[d(x, y) = \sqrt{ \sum_{i=1}^{n} (x_i - y_i)^2}\]

where:

\(x\) = spectral signature vector of an image pixel;
\(y\) = spectral signature vector of a training area;
\(n\) = number of image bands.

Therefore, the distance is calculated for every pixel in the image, assigning the class of the spectral signature that is closer, according to the following discriminant function (adapted from Richards and Jia, 2006):

\[x \in C_k \iff d(x, y_k) < d(x, y_j) \forall k \neq j\]

where:

\(C_k\) = land cover class \(k\);
\(y_k\) = spectral signature of class \(k\);
\(y_j\) = spectral signature of class \(j\).

It is possible to define a threshold \(T_i\) in order to exclude pixels below this value from the classification:

\[ \begin{align}\begin{aligned}x \in C_k \iff d(x, y_k) < d(x, y_j) \forall k \neq j\\and\\d(x, y_k) < T_i\end{aligned}\end{align} \]

Maximum Likelihood¶

Maximum Likelihood algorithm calculates the probability distributions for the classes, related to Bayes’ theorem, estimating if a pixel belongs to a land cover class. In particular, the probability distributions for the classes are assumed the of form of multivariate normal models (Richards & Jia, 2006). In order to use this algorithm, a sufficient number of pixels is required for each training area allowing for the calculation of the covariance matrix. The discriminant function, described by Richards and Jia (2006), is calculated for every pixel as:

\[g_k(x) = \ln p(C_k) - \frac{1}{2} \ln | \Sigma_{k} | - \frac{1}{2} (x - y_k)^t \Sigma_{k}^{-1} (x - y_k)\]

where:

\(C_k\) = land cover class \(k\);
\(x\) = spectral signature vector of a image pixel;
\(p(C_k)\) = probability that the correct class is \(C_k\);
\(| \Sigma_{k} |\) = determinant of the covariance matrix of the data in class \(C_k\);
\(\Sigma_{k}^{-1}\) = inverse of the covariance matrix;
\(y_k\) = spectral signature vector of class \(k\).

Therefore:

\[x \in C_k \iff g_k(x) > g_j(x) \forall k \neq j\]

Maximum Likelihood example

In addition, it is possible to define a threshold to the discriminant function in order to exclude pixels below this value from the classification. Considering a threshold \(T_i\) the classification condition becomes:

\[ \begin{align}\begin{aligned}x \in C_k \iff g_k(x) > g_j(x) \forall k \neq j\\and\\g_k(x) > T_i\end{aligned}\end{align} \]

Maximum likelihood is one of the most common supervised classifications, however the classification process can be slower than Minimum Distance.

Spectral Angle Mapping¶

The Spectral Angle Mapping calculates the spectral angle between spectral signatures of image pixels and training spectral signatures. The spectral angle \(\theta\) is defined as (Kruse et al., 1993):

\[\theta(x, y) = \cos^{-1} \left( \frac{ \sum_{i=1}^{n} x_i y_i } { \left( \sum_{i=1}^{n} x_i^2 \right)^\frac{1}{2} * \left( \sum_{i=1}^{n} y_i^2 \right)^\frac{1}{2} } \right)\]

Where:

\(x\) = spectral signature vector of an image pixel;
\(y\) = spectral signature vector of a training area;
\(n\) = number of image bands.

Therefore a pixel belongs to the class having the lowest angle, that is:

\[x \in C_k \iff \theta(x, y_k) < \theta(x, y_j) \forall k \neq j\]

where:

\(C_k\) = land cover class \(k\);
\(y_k\) = spectral signature of class \(k\);
\(y_j\) = spectral signature of class \(j\).

Spectral Angle Mapping example

In order to exclude pixels below this value from the classification it is possible to define a threshold \(T_i\):

\[ \begin{align}\begin{aligned}x \in C_k \iff \theta(x, y_k) < \theta(x, y_j) \forall k \neq j\\and\\\theta(x, y_k) < T_i\end{aligned}\end{align} \]

Spectral Angle Mapping is largely used, especially with hyperspectral data.

Parallelepiped Classification¶

Parallelepiped classification is an algorithm that considers a range of values for each band, forming a multidimensional parallelepiped that defines a land cover class. A pixel is classified if the values thereof are inside a parallelepiped. One of the major drawbacks is that pixels whose signatures lie in the overlapping area of two or more parallelepipeds cannot be classified (Richards and Jia, 2006).

Land Cover Signature Classification¶

Land Cover Signature Classification is available in SCP (see Land Cover Signature Classification). This classification allows for the definition of spectral thresholds for each training input signature (a minimum value and a maximum value for each band). The thresholds of each training input signature define a spectral region belonging to a certain land cover class.

Spectral signatures of image pixels are compared to the training spectral signatures; a pixel belongs to class X if pixel spectral signature is completely contained in the spectral region defined by class X. In case of pixels falling inside overlapping regions or outside any spectral region, it is possible to use additional classification algorithms (i.e. Minimum Distance, Maximum Likelihood, Spectral Angle Mapping) considering the spectral characteristics of the original input signature.

In the following image, a scheme illustrates the Land Cover Signature Classification for a simple case of two spectral bands \(x\) and \(y\). User defined spectral regions define three classes (\(g_a\), \(g_b\), and \(g_c\)). Point \(p_1\) belongs to class \(g_a\) and point \(p_2\) belongs to class \(g_b\). However, point \(p_3\) is inside the spectral regions of both classes \(g_b\) and \(g_c\) (overlapping regions); in this case, point \(p_3\) will be unclassified or classified according to an additional classification algorithm. Point \(p_4\) is outside any spectral region, therefore it will be unclassified or classified according to an additional classification algorithm. Given that point \(p_4\) belongs to class \(g_c\), the spectral region thereof could be extended to include point \(p_4\) .

Land cover signature classification

This is similar to Parallelepiped Classification, with the exception that spectral regions are defined by user, and can be assigned independently for the upper and lower bounds. One can imagine spectral regions as the set of all the spectral signatures of pixels belonging to one class.

In figure Plot of spectral ranges the spectral ranges of three classes (\(g_a\), \(g_b\), and \(g_c\)) are displayed; the colored lines inside the ranges (i.e. semi-transparent area) represent the spectral signatures of pixels that defined the upper and lower bounds of the respective ranges. Pixel \(p_1\) (dotted line) belongs to class \(g_b\) because the spectral signature thereof is completely inside the range of class \(g_b\) (in the upper limit); pixel \(p_2\) (dashed line) is unclassified because the spectral signature does not fall completely inside any range; pixel \(p_3\) (dotted line) belongs to class \(g_a\).

_images/land_cover_signature_classification_plot.jpg

Plot of spectral ranges

It is worth noticing that these spectral thresholds can be applied to any spectral signature, regardless of spectral characteristics thereof; this function can be very useful for separating similar spectral signatures that differ only in one band, defining thresholds that include or exclude specific signatures. In fact, classes are correctly separated if the spectral ranges thereof are not overlapping at least in one band. Of course, even if spectral regions are overlapping, chances are that no pixel will fall inside the overlapping region and be misclassified; which is the upper (or lower) bound of a range do not imply the existence, in the image, of any spectral signature having the maximum (or minimum) range values for all the bands (for instance pixel \(p_1\) of figure Plot of spectral ranges could not exist).

One of the main benefit of the Land Cover Signature Classification is that it is possible to select pixels and and include the signature thereof in a spectral range; therefore, the classification should be the direct representation of the class expected for every spectral signature. This is very suitable for the classification of a single land cover class (defined by specific spectral thresholds), and leave unclassified the rest of the image that is of no interest for the purpose of the classification.

Algorithm raster¶

An algorithm raster represents the “distance” (according to the definition of the classification algorithm) of an image pixel to a specific spectral signature.

In general, an algorithm raster is produced for every spectral signature used as training input. The value of every pixel is the result of the algorithm calculation for a specific spectral signature. Therefore, a pixel belongs to class X if the value of the algorithm raster corresponding to class X is the lowest in case of Minimum Distance or Spectral Angle Mapping (or highest in case of Maximum Likelihood).

Given a classification, a combination of algorithm rasters can be produced, in order to create a raster with the lowest “distances” (i.e. pixels have the value of the algorithm raster corresponding to the class they belong in the classification). Therefore, this raster can be useful to identify pixels that require the collection of more similar spectral signatures (see Classification preview).

Spectral Distance ¶

It is useful to evaluate the spectral distance (or separability) between training signatures or pixels, in order to assess if different classes that are too similar could cause classification errors. The SCP implements the following algorithms for assessing similarity of spectral signatures.

Jeffries-Matusita Distance¶

Jeffries-Matusita Distance calculates the separability of a pair of probability distributions. This can be particularly meaningful for evaluating the results of Maximum Likelihood classifications.

The Jeffries-Matusita Distance \(J_{xy}\) is calculated as (Richards and Jia, 2006):

\[J_{xy} = 2 \left( 1 - e^{-B} \right)\]

where:

\[B = \frac{1}{8} (x - y)^t \left( \frac{\Sigma_{x} + \Sigma_{y}}{2} \right)^{-1} (x - y) + \frac{1}{2} \ln \left( \frac{ | \frac{ \Sigma_{x} + \Sigma_{y}}{2} | }{ | \Sigma_{x} |^{\frac{1}{2}} | \Sigma_{y} |^{\frac{1}{2}} } \right)\]

where:

\(x\) = first spectral signature vector;
\(y\) = second spectral signature vector;
\(\Sigma_{x}\) = covariance matrix of sample \(x\);
\(\Sigma_{y}\) = covariance matrix of sample \(y\);

The Jeffries-Matusita Distance is asymptotic to 2 when signatures are completely different, and tends to 0 when signatures are identical.

Spectral Angle¶

The Spectral Angle is the most appropriate for assessing the Spectral Angle Mapping algorithm. The spectral angle \(\theta\) is defined as (Kruse et al., 1993):

\[\theta(x, y) = \cos^{-1} \left( \frac{ \sum_{i=1}^{n} x_i y_i } { \left( \sum_{i=1}^{n} x_i^2 \right)^\frac{1}{2} * \left( \sum_{i=1}^{n} y_i^2 \right)^\frac{1}{2} } \right)\]

Where:

\(x\) = spectral signature vector of an image pixel;
\(y\) = spectral signature vector of a training area;
\(n\) = number of image bands.

Spectral angle goes from 0 when signatures are identical to 90 when signatures are completely different.

Euclidean Distance¶

The Euclidean Distance is particularly useful for the evaluating the result of Minimum Distance classifications. In fact, the distance is defined as:

\[d(x, y) = \sqrt{ \sum_{i=1}^{n} (x_i - y_i)^2}\]

where:

\(x\) = first spectral signature vector;
\(y\) = second spectral signature vector;
\(n\) = number of image bands.

The Euclidean Distance is 0 when signatures are identical and tends to increase according to the spectral distance of signatures.

Bray-Curtis Similarity¶

The Bray-Curtis Similarity is a statistic used for assessing the relationship between two samples (read this). It is useful in general for assessing the similarity of spectral signatures, and Bray-Curtis Similarity \(S(x, y)\) is calculated as:

\[S(x, y) = 100 - \left( \frac{\sum_{i=1}^{n} | (x_i - y_i) |}{\sum_{i=1}^{n} x_i + \sum_{i=1}^{n} y_i} \right) * 100\]

where:

\(x\) = first spectral signature vector;
\(y\) = second spectral signature vector;
\(n\) = number of image bands.

The Bray-Curtis similarity is calculated as percentage and ranges from 0 when signatures are completely different to 100 when spectral signatures are identical.

Classification Result ¶

The result of the classification process is a raster (see an example of Landsat classification in Figure Landsat classification), where pixel values correspond to class IDs and each color represent a land cover class.

Landsat classification

Data available from the U.S. Geological Survey

A certain amount of errors can occur in the land cover classification (i.e. pixels assigned to a wrong land cover class), due to spectral similarity of classes, or wrong class definition during the ROI collection.

Accuracy Assessment ¶

After the classification process, it is useful to assess the accuracy of land cover classification, in order to identify and measure map errors. Usually, accuracy assessment is performed with the calculation of an error matrix, which is a table that compares map information with reference data (i.e. ground truth data) for a number of sample areas (Congalton and Green, 2009).

The following table is a scheme of error matrix, where k is the number of classes identified in the land cover classification, and n is the total number of collected sample units. The items in the major diagonal (aii) are the number of samples correctly identified, while the other items are classification error.

Scheme of Error Matrix

	Ground truth 1	Ground truth 2	…	Ground truth k	Total
Class 1	\(a_{11}\)	\(a_{12}\)	…	\(a_{1k}\)	\(a_{1+}\)
Class 2	\(a_{21}\)	\(a_{22}\)	…	\(a_{2k}\)	\(a_{2+}\)
…	…	…	…	…	…
Class k	\(a_{k1}\)	\(a_{k2}\)	…	\(a_{kk}\)	\(a_{k+}\)
Total	\(a_{+1}\)	\(a_{+2}\)	…	\(a_{+k}\)	\(n\)

Therefore, it is possible to calculate the overall accuracy as the ratio between the number of samples that are correctly classified (the sum of the major diagonal), and the total number of sample units n (Congalton and Green, 2009).

For further information, the following documentation is freely available: Landsat 7 Science Data User’s Handbook, Remote Sensing Note , or Wikipedia.

Image conversion to reflectance ¶

This chapter provides information about the conversion to reflectance implemented in SCP.

Radiance at the Sensor’s Aperture ¶

Radiance is the “flux of energy (primarily irradiant or incident energy) per solid angle leaving a unit surface area in a given direction”, “Radiance is what is measured at the sensor and is somewhat dependent on reflectance” (NASA, 2011, p. 47).

Images such as Landsat or Sentinel-2 are composed of several bands and a metadata file which contains information required for the conversion to reflectance.

Landsat images are provided in radiance, scaled prior to output. for Landsat images Spectral Radiance at the sensor’s aperture (\(L_{\lambda}\), measured in [watts/(meter squared * ster * \(\mu m\))]) is given by (https://landsat.usgs.gov/Landsat8_Using_Product.php):

\[L_{\lambda} = M_{L} * Q_{cal} + A_{L}\]

where:

\(M_{L}\) = Band-specific multiplicative rescaling factor from Landsat metadata (RADIANCE_MULT_BAND_x, where x is the band number)
\(A_{L}\) = Band-specific additive rescaling factor from Landsat metadata (RADIANCE_ADD_BAND_x, where x is the band number)
\(Q_{cal}\) = Quantized and calibrated standard product pixel values (DN)

Sentinel-2 images (Level-1C) are already provided in Top Of Atmosphere (TOA) Reflectance, scaled prior to output (ESA, 2015).

Top Of Atmosphere (TOA) Reflectance ¶

Images in radiance can be converted to Top Of Atmosphere (TOA) Reflectance (combined surface and atmospheric reflectance) in order to reduce the in between-scene variability through a normalization for solar irradiance. This TOA reflectance (\(\rho_{p}\)), which is the unitless ratio of reflected versus total power energy (NASA, 2011), is calculated by:

\[\rho_{p} = (\pi * L_{\lambda} * d^{2} )/ (ESUN_{\lambda} * cos\theta_{s})\]

where:

\(L_{\lambda}\) = Spectral radiance at the sensor’s aperture (at-satellite radiance)
\(d\) = Earth-Sun distance in astronomical units (provided with Landsat 8 metadata file, and an excel file is available from http://landsathandbook.gsfc.nasa.gov/excel_docs/d.xls)
\(ESUN_{\lambda}\) = Mean solar exo-atmospheric irradiances
\(\theta_{s}\) = Solar zenith angle in degrees, which is equal to \(\theta_{s}\) = 90° - \(\theta_{e}\) where \(\theta_{e}\) is the Sun elevation

It is worth pointing out that Landsat 8 images are provided with band-specific rescaling factors that allow for the direct conversion from DN to TOA reflectance.

Sentinel-2 images are already provided in scaled TOA reflectance, which can be converted to TOA reflectance with a simple calculation using the Quantification Value provided in the metadata (see https://sentinel.esa.int/documents/247904/349490/S2_MSI_Product_Specification.pdf).

Surface Reflectance ¶

The effects of the atmosphere (i.e. a disturbance on the reflectance that varies with the wavelength) should be considered in order to measure the reflectance at the ground.

As described by Moran et al. (1992), the land surface reflectance (\(\rho\)) is:

\[\rho = [\pi * (L_{\lambda} - L_{p}) * d^{2}]/ [T_{v} * ( (ESUN_{\lambda} * cos\theta_{s} * T_{z} ) + E_{down} )]\]

where:

\(L_{p}\) is the path radiance
\(T_{v}\) is the atmospheric transmittance in the viewing direction
\(T_{z}\) is the atmospheric transmittance in the illumination direction
\(E_{down}\) is the downwelling diffuse irradiance

Therefore, we need several atmospheric measurements in order to calculate \(\rho\) (physically-based corrections). Alternatively, it is possible to use image-based techniques for the calculation of these parameters, without in-situ measurements during image acquisition. It is worth mentioning that Landsat Surface Reflectance High Level Data Products for Landsat 8 are available (for more information read http://landsat.usgs.gov/CDR_LSR.php).

DOS1 Correction ¶

The Dark Object Subtraction (DOS) is a family of image-based atmospheric corrections. Chavez (1996) explains that “the basic assumption is that within the image some pixels are in complete shadow and their radiances received at the satellite are due to atmospheric scattering (path radiance). This assumption is combined with the fact that very few targets on the Earth’s surface are absolute black, so an assumed one-percent minimum reflectance is better than zero percent”. It is worth pointing out that the accuracy of image-based techniques is generally lower than physically-based corrections, but they are very useful when no atmospheric measurements are available as they can improve the estimation of land surface reflectance. The path radiance is given by (Sobrino, et al., 2004):

\[L_{p} = L_{min} - L_{DO1\%}\]

where:

\(L_{min}\) = “radiance that corresponds to a digital count value for which the sum of all the pixels with digital counts lower or equal to this value is equal to the 0.01% of all the pixels from the image considered” (Sobrino, et al., 2004, p. 437), therefore the radiance obtained with that digital count value (\(DN_{min}\))
\(L_{DO1\%}\) = radiance of Dark Object, assumed to have a reflectance value of 0.01

In particular for Landsat images:

\[L_{min} = M_{L} * DN_{min} + A_{L}\]

Sentinel-2 images are converted to radiance prior to DOS1 calculation.

The radiance of Dark Object is given by (Sobrino, et al., 2004):

\[L_{DO1\%} = 0.01 * [(ESUN_{\lambda} * cos\theta_{s} * T_{z} ) + E_{down}] * T_{v} / (\pi * d^{2})\]

Therefore the path radiance is:

\[L_{p} = M_{L} * DN_{min} + A_{L} - 0.01* [(ESUN_{\lambda} * cos\theta_{s} * T_{z} ) + E_{down}] * T_{v} / (\pi * d^{2})\]

There are several DOS techniques (e.g. DOS1, DOS2, DOS3, DOS4), based on different assumption about \(T_{v}\), \(T_{z}\) , and \(E_{down}\) . The simplest technique is the DOS1, where the following assumptions are made (Moran et al., 1992):

\(T_{v}\) = 1
\(T_{z}\) = 1
\(E_{down}\) = 0

Therefore the path radiance is:

\[L_{p} = M_{L} * DN_{min} + A_{L} - 0.01 * ESUN_{\lambda} * cos\theta_{s} / (\pi * d^{2})\]

And the resulting land surface reflectance is given by:

\[\rho = [\pi * (L_{\lambda} - L_{p}) * d^{2}]/ (ESUN_{\lambda} * cos\theta_{s})\]

ESUN [W /(m2 * \(\mu m\))] values for Landsat sensors are provided in the following table.

ESUN values for Landsat bands

Band	Landsat 1 MSS*	Landsat 2 MSS*	Landsat 3 MSS*	Landsat 4 TM*	Landsat 5 TM*	Landsat 7 ETM+**
1				1983	1983	1970
2				1795	1796	1842
3				1539	1536	1547
4	1823	1829	1839	1028	1031	1044
5	1559	1539	1555	219.8	220	225.7
6	1276	1268	1291
7	880.1	886.6	887.9	83.49	83.44	82.06
8						1369

* from Chander, Markham, & Helder (2009)

** from http://landsathandbook.gsfc.nasa.gov/data_prod/prog_sect11_3.html

For Landsat 8, \(ESUN\) can be calculated as (from http://grass.osgeo.org/grass65/manuals/i.landsat.toar.html):

\[ESUN = (\pi * d^{2}) * RADIANCE\_MAXIMUM / REFLECTANCE\_MAXIMUM\]

where RADIANCE_MAXIMUM and REFLECTANCE_MAXIMUM are provided by image metadata.

ESUN [W /(m2 * \(\mu m\))] values for Sentinel-2 sensor (provided in image metadata) are illustrated in the following table.

ESUN values for Sentinel-2 bands

Band	Sentinel-2
1	1913.57
2	1941.63
3	1822.61
4	1512.79
5	1425.56
6	1288.32
7	1163.19
8	1036.39
8A	955.19
9	813.04
10	367.15
11	245.59
12	85.25

ESUN [W /(m2 * \(\mu m\))] values for ASTER sensor are illustrated in the following table (from Finn, et al., 2012).

ESUN values for ASTER bands

Band	ASTER
1	1848
2	1549
3	1114
4	225.4
5	86.63
6	81.85
7	74.85
8	66.49
9	59.85

An example of comparison of to TOA reflectance, DOS1 corrected reflectance and the Landsat Surface Reflectance High Level Data Products (ground truth) is provided in Figure Spectral signatures of a built-up pixel.

Spectral signatures of a built-up pixel

Comparison of TOA reflectance, DOS1 corrected reflectance and Landsat Surface Reflectance High Level Data Products

Conversion to Temperature ¶

This chapter provides the basic information about the conversion to At-Satellite Brightness Temperature implemented in SCP and the estimation of Land Surface Temperature.

Conversion to At-Satellite Brightness Temperature ¶

For thermal bands, the conversion of DN to At-Satellite Brightness Temperature is given by (from https://landsat.usgs.gov/Landsat8_Using_Product.php):

\[T_{B} = K_{2} / ln[(K_{1} / L_{\lambda}) + 1]\]

where:

\(K_{1}\) = Band-specific thermal conversion constant (in watts/meter squared * ster * \(\mu m\))
\(K_{2}\) = Band-specific thermal conversion constant (in kelvin)

and \(L_{\lambda}\) is the Spectral Radiance at the sensor’s aperture, measured in watts/(meter squared * ster * \(\mu m\)).

The \(K_{1}\) and \(K_{2}\) constants for Landsat sensors are provided in the following table.

Thermal Conversion Constants for Landsat

Constant	Landsat 4*	Landsat 5*	Landsat 7**
\(K_{1}\)	671.62	607.76	666.09
\(K_{2}\)	1284.30	1260.56	1282.71

* from Chander & Markham (2003)

** from NASA (2011)

For Landsat 8, the \(K_{1}\) and \(K_{2}\) values are provided in the image metadata file.

\(K_{1}\) and \(K_{2}\) are calculated as (Jimenez-Munoz & Sobrino, 2010):

\[K_{1} = c_{1} / \lambda^{5}\]

\[K_{2} = c_{2} / \lambda\]

where (Mohr, Newell, & Taylor, 2015):

\(c_{1}\) = first radiation constant = \(1.191 * 10^{-16} W m^{2} sr^{-1}\)
\(c_{2}\) = second radiation constant = \(1.4388 * 10^{-2} m K\)

Therefore, for ASTER bands \(K_{1}\) and \(K_{2}\) are provided in the following table.

Thermal Conversion Constants for ASTER

Constant	Band 10	Band 11	Band 12	Band 13	Band 14
\(K_{1}\)	\(3.024 * 10^{3}\)	\(2.460 * 10^{3}\)	\(1.909 * 10^{3}\)	\(8.900 * 10^{2}\)	\(6.464 * 10^{2}\)
\(K_{2}\)	\(1.733 * 10^{3}\)	\(1.663 * 10^{3}\)	\(1.581 * 10^{3}\)	\(1.357 * 10^{3}\)	\(1.273 * 10^{3}\)

Estimation of Land Surface Temperature ¶

Several studies have described the estimation of Land Surface Temperature. Land Surface Temperature can be calculated from At-Satellite Brightness Temperature \(T_{B}\) as (Weng, et al. 2004):

\[T = T_{B} / [ 1 + (\lambda * T_{B} / c_{2}) * ln(e) ]\]

where:

\(\lambda\) = wavelength of emitted radiance
\(c_{2} = h * c / s = 1.4388 * 10^{-2}\) m K
\(h\) = Planck’s constant = \(6.626 * 10^{-34}\) J s
\(s\) = Boltzmann constant = \(1.38 * 10^{-23}\) J/K
\(c\) = velocity of light = \(2.998 * 10^{8}\) m/s

The values of \(\lambda\) for the thermal bands of Landsat and ASTER satellites can be calculated from the tables in Landsat Satellite and ASTER Satellite.

Several studies used NDVI for the estimation of land surface emissivity (Sobrino, et al., 2004); other studies used a land cover classification for the definition of the land surface emissivity of each class (Weng, et al. 2004). For instance, the emissivity (\(e\)) values of various land cover types are provided in the following table (from Mallick, et al. 2012).

Emissivity values

Land surface	Emissivity e
Soil	0.928
Grass	0.982
Asphalt	0.942
Concrete	0.937

References ¶

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Basic Tutorials¶

The following are very basic tutorials for land cover classification using the Semi-Automatic Classification Plugin (SCP). It is assumed that you have a basic knowledge of QGIS (you can find a guide to QGIS interface at this page).

Tutorial 1¶

The following is very basic tutorials for land cover classification using the Semi-Automatic Classification Plugin (SCP). It is assumed that you have a basic knowledge of QGIS.

Tutorial 1: Your First Land Cover Classification

Tutorial 1: Your First Land Cover Classification ¶

This is a basic tutorial about the use of SCP for the classification of a multi-spectral image. It is recommended to read the Brief Introduction to Remote Sensing before this tutorial.

The purpose of the classification is to identify the following land cover classes:

Water;
Built-up;
Vegetation;
Bare soil.

Following the video of this tutorial.

http://www.youtube.com/watch?v=GFrDgQ6Nzqs

Data ¶

Download the image from this archive (data available from the U.S. Geological Survey) and unzip the downloaded file.

The downloaded file is actually a Landsat Satellite image (pan-sharpened) including the following bands:

Blue;
Green;
Red;
Near-Infrared;
Short Wavelength Infrared 1;
Short Wavelength Infrared 2.

In this tutorial we pretend this dataset is a generic multi-spectral raster in order to focus on the classification process (in the next tutorial we are going to use an image whose bands are single rasters).

Set the Input Image in SCP ¶

Start QGIS. In the SCP input click the button of the Input image, in order to select the file sample_image.tif. Once selected, sample_image.tif is set as Input image, the image is displayed in the map and bands are loaded in the Band set.

We can display a Color Composite of bands: Near-Infrared, Red, and Green: in the Working toolbar, click the list RGB= and select the item 4-3-2 (corresponding to the band numbers in Band set). You can see that image colors in the map change according to the selected bands, and vegetation is highlighted in red (if the item 3-2-1 was selected, natural colors would be displayed).

Color composite RGB=4-3-2

Create the Training Input File ¶

Now we need to create the Training input in order to collect Training Areas (ROIs) and calculate the Spectral Signature thereof (which are used in classification).

In the SCP dock click the button and define a name (e.g. training.scp ) in order to create the Training input. The path of the file is displayed in Training input. A vector is added to QGIS layers with the same name as the Training input (in order to prevent data loss, you should not edit this layer using QGIS functions).

Definition of Training input in SCP

Create the ROIs ¶

We are going to create ROIs defining the Classes and Macroclasses. Each ROI identifies a land cover class through a Class ID. The Class ID codes used in this tutorial are illustrated in the following table (for now we assign the same code to Class ID and Macroclass ID).

Macroclasses

Class name	Class ID
Water	1
Built-up	2
Vegetation	3
Bare soil	4

ROIs can be created by manually drawing a polygon or with an automatic region growing algorithm.

Zoom in the map over the dark area (it is a lake) in the lower right region of the image. In order to create manually a ROI inside the dark area, click the button in the Working toolbar (you can ignore a message about wavelength unit not provided). Left click on the map to define the ROI vertices and right click to define the last vertex closing the polygon. An orange semi-transparent polygon is displayed over the image, which is a temporary polygon (i.e. it is not saved in the Training input).

TIP : You can draw temporary polygons (the previous one will be overridden) until the shape covers the intended area.

A temporary ROI created manually

If the shape of the temporary polygon is good we can save it to the Training input.

Open the Classification dock to define the Classes and Macroclasses . In the ROI creation set MC ID = 1 and MC Info = Water; also set C ID = 1 and C Info = Lake. Now click to save the ROI in the Training input.

After a few seconds, the ROI is listed in the ROI Signature list and the spectral signature is calculated (because Calculate sig. was checked).

The ROI saved in the Training input

As you can see, the C ID in ROI creation is automatically increased by 1. Saved ROI is displayed as a dark polygon in the map and the temporary ROI is removed. Also, in the ROI Signature list you can notice that the Type is B, meaning that the ROI spectral signature was calculated and saved in the Training input.

Now we are going to create a second ROI for the built-up class using the automatic region growing algorithm. Zoom in the map over the blue area in the upper left region of the image.

In Working toolbar set the Dist value to 0.08 . Click the button in the Working toolbar and click over the blue area of the map. After a while the orange semi-transparent polygon is displayed over the image.

TIP : Dist value should be set according to the range of pixel values; in general, increasing this value creates larger ROIs.

A temporary ROI created with the automatic region growing algorithm

In the ROI creation set MC ID = 2 and MC Info = Built-up ; also set C ID = 2 (it should be already set) and C Info = Buildings.

The ROI saved in the Training input

Again, the C ID in ROI creation is automatically increased by 1.

Create a ROI for the class Vegetation (red pixels in color composite RGB=4-3-2) and a ROI for the class Bare soil (green pixels in color composite RGB=4-3-2) following the same steps described previously. The following images show a few examples of these classes identified in the map.

Vegetation

Bare soil

The following examples display a few RGB color composites of Landsat images.

Built-up ROI: large buildings

Built-up ROI: road

Built-up ROI: buildings and narrow roads

Bare soil ROI: uncultivated land

Vegetation ROI: deciduous trees

Vegetation ROI: crop

Create a Classification Preview ¶

The classification process is based on collected ROIs (and spectral signatures thereof). It is useful to create a Classification preview in order to assess the results (influenced by spectral signatures) before the final classification. In case the results are not good, we can collect more ROIs to better classify land cover.

Before running a classification (or a preview), set the color of land cover classes that will be displayed in the classification raster. In the ROI Signature list, double click the color (in the column Color) of each ROI to choose a representative color of each class.

Definition of class colors

Now we need to select the classification algorithm. In this tutorial we are going to select the Spectral Angle Mapping.

In Classification algorithm select the Spectral Angle Mapping Algorithm. In Classification preview set Size = 500; click the button and then left click a point of the image in the map. The classification process should be rapid, and the result is a classified square centered in clicked point.

Classification preview displayed over the image

Previews are temporary rasters (deleted after QGIS is closed) placed in a group named Class_temp_group in the QGIS panel Layers.

TIP : When loading a previously saved QGIS project, a message could ask to handle missing layers, which are temporary layers that SCP creates during each session and are deleted afterwards; you can click Cancel and ignore these layers.

In general, it is good to perform a classification preview every time a ROI (or a spectral signature) is added to the ROI Signature list. Therefore, the phases Create the ROIs and Create a Classification Preview should be iterative and concurrent processes.

Create the Classification Output ¶

Assuming that the results of classification previews were good (i.e. pixels are assigned to the correct class defined in the ROI Signature list), we can perform the actual land cover classification of the whole image.

In the Classification output click the button and define the path of the classification output, which is a raster file (.tif). If Play sound when finished is checked in Classification process settings, a sound is played when the process is finished.

Result of the land cover classification

Well done! You have just performed your first land cover classification.

Water and vegetation are correctly identified. However, you can see that there are several classification errors (especially soil classified as built-up and vice versa), because the number of ROIs (spectral signatures) is insufficient.

Example of error: Bare soil classified as Built-up

We can improve the classification using some of the tools described in the next tutorial.

Tutorial 2¶

Tutorial 2: Land Cover Classification of Sentinel-2 Images

Tutorial 2: Land Cover Classification of Sentinel-2 Images ¶

This tutorial describes the main phases for the classification of images acquired by Sentinel-2 Satellite. In addition, some of the SCP tools are illustrated.

We are going to classify the following land cover classes:

Water;
Built-up;
Vegetation;
Bare soil.

Following the video of this tutorial.

http://www.youtube.com/watch?v=FcETq8OWM0k

Data Download ¶

We are going to download a Sentinel-2 image provided by the Copernicus Scientific Data Hub. In particular we are going to use the following Sentinel-2 bands (for more information read Sentinel-2 Satellite):

Band 2 - Blue;
Band 3 - Green;
Band 4 - Red;
Band 5 - Vegetation Red Edge;
Band 6 - Vegetation Red Edge;
Band 7 - Vegetation Red Edge;
Band 8 - NIR;
Band 8A - Vegetation Red Edge;
Band 11 - SWIR;
Band 12 - SWIR;

TIP : In case you have slow internet connection you can download a subset of the image (about 50MB) from this link (© Copernicus Sentinel data 2016) which is the result of steps Data Download and Clip the Data.

Start a new QGIS project. Open the tab Download images clicking the button in the SCP Menü, or the SCP Tools, or the SCP dock. Select the tab Sentinel-2 download. We are searching a specific image acquired on May 06, 2016.

In Login Sentinels enter the user name and password for accessing data (free registration is required).

WARNING : The guest/guest account is not available anymore. Free registration is required. See https://scihub.copernicus.eu/news/News00097 .

In Search area enter:

UL X (Lon): 12
UL Y (Lat): 42
LR X (Lon): 13
LR Y (Lat): 41

TIP : In general it is possible to define the area coordinates clicking the button and drawing a rectangle in the map.

In Search set:

Date from: 2016-05-06
to: 2016-05-06

Search Sentinel-2 images

Now click the button Find and after a few seconds the image will be listed in the Image list.

Tip: download this zip file containing the shapefile of Sentinel-2 granules for identifying the zone; load this shapefile in QGIS, select the granules in your search area and open the attribute table to see the zone name.

Sentinel-2 search result

In the result table, click the item T32TQM in the field Zone, which is the Granule S2A_OPER_MSI_L1C_TL_SGS__20160506T153005_A004552_T32TQM, and click the button . A preview will be downloaded and displayed in the map, which is useful for assessing the quality of the image and the cloud cover.

TIP : It is also possible to display the image overview (which is composed of several granules) with the button .

Image preview

Click the tab Download options and uncheck bands 1, 9, and 10. Also, uncheck the options Preprocess images (usually this should be checked, but for the purpose of this tutorial we are going to preprocess images in the step Automatic Conversion to Surface Reflectance) and Load bands in QGIS (because we are going to clip the images).

TIP : The option Only if preview in Layers allows for downloading only images in the result table which are loaded as previews in the map. It is convenient to check this option and remove the images previews in the QGIS layer list, leaving only the previews of images that one wish to download.

Selection of bands for download

In order to start the image download, click the button and select a directory where bands are saved (e.g. Desktop). The download could last a few minutes according to your internet connection speed (band size ranges from 30 to 90MB). The download progress is displayed in a bar.

After the download, all the bands and the metadata files are saved in the output directory.

Download of Sentinel bands

Automatic Conversion to Surface Reflectance ¶

Conversion to reflectance (see Radiance and Reflectance) can be performed automatically. The metadata file (a .xml file whose name contains MTD_SAFL1C) downloaded with the images contains the required information for the conversion. Read Image conversion to reflectance for information about the Top Of Atmosphere (TOA) Reflectance and Surface Reflectance.

In order to convert bands to reflectance, open the tab Preprocessing clicking the button in the SCP Menü, or the SCP Tools, or the SCP dock, and select the tab Sentinel-2.

Click the button Directory containing Sentinel-2 bands and select the directory that should be named S2A_OPER_MSI_L1C_TL_SGS__20160506T153005_A004552_T32TQM. The list of bands is automatically loaded in the table Metadata. Also, the metadata information for each band is loaded (because the metadata file is inside the same directory).

TIP : If a Sentinel-2 image was downloaded directly from the site https://scihub.copernicus.eu and you want to convert images to reflectance using SCP, you should copy the .xml file whose name contains MTD_SAFL1C (included in the granule directory) and paste it inside the same directory of bands (files .jp2).

In order to calculate Surface Reflectance we are going to apply the DOS1 Correction; therefore, enable the option Apply DOS1 atmospheric correction.

TIP : It is recommended to perform the DOS1 atmospheric correction to the entire image (before clipping the image) in order to improve the calculation of parameters based on the image.

Uncheck the option Create Band set and use Band set tools because we are going to define this in the following step Create the Band Set. In order to start the conversion process, click the button and select the directory where converted bands are saved (e.g. Desktop).

Sentinel-2 conversion to reflectance

After a few minutes, converted bands are loaded and displayed (file name starts with RT_). If Play sound when finished is checked in Classification process settings, a sound is played when the process is finished.

Converted Sentinel-2 bands

Clip the Data ¶

Sentinel-2 images have a large extent. In order to reduce the computational time, we are going to clip bands to the same study area as Tutorial 1: Your First Land Cover Classification. Open the tab Preprocessing and select the tab Clip multiple rasters.

Click the button to refresh the layer list, and check all the layers whose name starts with RT_ (the band number is at the end of the layer name).

Click the button and select an area such as the following image, or enter the following values:

UL X: 791810
UL Y: 4643020
LR X: 809750
LR Y: 4626230

Clip area

Click the button and select a directory (e.g. clip) where clipped bands are saved (with the file name prefix defined in Output name prefix). When the process is completed, clipped rasters are loaded and displayed. We can remove the bands whose names start with RT_ from QGIS layers.

Clipped bands

Create the Band Set ¶

Now we need to define the Band set which is the input image for SCP. Open the tab Band set clicking the button in the SCP Menü, or the SCP Tools, or the SCP dock.

Click the button to refresh the layer list, and check all the clipped bands; then click to add selected rasters to the Band set. In the table Band set definition order the band names in ascending order (click to sort bands by name automatically), then highlight band 8A (i.e. single click on band name in the table) and use the buttons or to place this band at number 8. Finally, select Sentinel-2 from the list Quick wavelength settings, in order to set automatically the Center wavelength of each band and the Wavelength unit (required for spectral signature calculation).

Definition of a band set

You can notice that the item << band set >> is selected as Input image in the SCP dock.

Band set

Create the ROIs ¶

In order to collect ROIs we need to Create the Training Input File as described in Tutorial 1: Your First Land Cover Classification (in the SCP dock click the button and define a file name). The Training input stores the ROIs and the Spectral Signature thereof.

Definition of Training input in SCP

We are going to create several ROIs using the Macroclass IDs defined in the following table (see Classes and Macroclasses).

Macroclasses

Macroclass name	Macroclass ID
Water	1
Built-up	2
Vegetation	3
Bare soil	4

In this phase we are creating the database of spectral signatures used to identify land cover classes (the ones defined as macroclasses). However, these macroclasses are composed of several materials having different spectral signatures; in order to achieve good classification results we should separate spectral signatures of different materials, even if belonging to the same macroclass. Thus, we are going to create several ROIs for each macroclass (setting the same MC ID, but assigning a different C ID to every ROI).

In the list RGB= of Working toolbar select 3-2-1 to display a natural color image (see ref:color_composite_definition and Sentinel-2 Satellite). After a few seconds, the Color Composite will be displayed. We can see that urban areas are white and vegetation is green.

TIP : If a Band set is defined, a temporary virtual raster (named band_set.vrt) is created automatically, which allows for the display of Color Composite. In order to speed up the visualization, you can show only the virtual raster and hide all the layers in the QGIS Layers.

Color composite RGB = 3-2-1

Now in the list RGB= of the Working toolbar type 3-7-10 (you can also use the tool RGB list). Using this color composite, urban areas are purple and vegetation is green. You can notice that this color composite RGB = 3-7-10 highlights roads more than natural color (RGB = 3-2-1). Also, you can see that there are clouds in the right part of the image.

Color composite RGB = 3-7-10

Now create the ROIs following the same steps described in Create the ROIs of Tutorial 1: Your First Land Cover Classification. After clicking the button in the Working toolbar you should notice that the cursor in the map displays a value changing over the image. This is the NDVI value of the pixel beneath the cursor (NDVI is displayed because the function Display is checked in ROI creation). The NDVI value can be useful for identifying spectrally pure pixels, in fact vegetation has higher NDVI values than soil.

For instance, move the mouse over a vegetation area and left click to create a ROI when you see a local maximum value. This way, the created ROI and the spectral signature thereof will be particularly representative of healthy vegetation.

NDVI value of vegetation pixel displayed in the map

The color composite RGB = 7-3-2 is also useful for highlighting vegetation.

Create several ROIs (the more is the better). The region growing algorithm can create more homogeneous ROIs (i.e. standard deviation of spectral signature values is low) than manually drawn ones; the manual creation of ROIs can be useful in order to account for the spectral variability of classes (especially when using the algorithm Maximum Likelihood).

In general, you should create one ROI for each color that you can distinguish in the image. Therefore, change the color composite in order to identify the different types of land cover.

TIP : Change frequently the Color Composite in order to clearly identify the materials at the ground; use the mouse wheel on the list RGB= of the Working toolbar for changing the color composite rapidly; also use the buttons and for better displaying the Input image (i.e. image stretching).

A few examples of ROIs are illustrated in the following figures.

Water ROI: lake

Built-up ROI: large buildings

Built-up ROI: road

Built-up ROI: buildings and narrow roads

Vegetation ROI: deciduous trees

Vegetation ROI: crop

Bare soil ROI: uncultivated land

It is worth mentioning that you can show or hide the temporary ROI clicking the button ROI in Working toolbar.

TIP : Install the plugin QuickMapServices in QGIS, and add a map (e.g. OpenStreetMap) in order to facilitate the identification of ROIs using high resolution data.

We can also try to mask clouds in the image, creating ROIs of clouds and assigning the special MC ID = 0 (which is an ID used for labelling intentionally unclassified pixels) and a different C ID. In fact spectral signatures with the MC ID = 0 are normally used in the classification, but every pixel assigned to these spectral signatures is labelled unclassified in the classification result. Therefore, this is a simple way for masking particular spectral signatures such as clouds (of course there are more advanced methods for masking clouds that will be discussed in other tutorials).

Example of ROI for clouds

Create a Classification Preview ¶

As pointed out in Tutorial 1: Your First Land Cover Classification, previews are temporary classifications that are useful for assessing the effects of spectral signatures during the ROI collection.

Set the colors of the spectral signatures in the ROI Signature list; then, in the Classification algorithm select the classification algorithm Maximum Likelihood. In Classification preview set Size = 500; click the button and then left click a point of the image in the map.

The classification preview is displayed in the map.

Example of preview using C IDs

In order to create a classification preview using Macroclass IDs check the option MC ID in the tab Classification algorithm of the SCP dock. In the tab Macroclasses of the SCP dock change the colors of MC ID (in the table Macroclasses double click the color of each macroclass to choose a representative color).

COlors of MC IDs

Now click the button in the Working toolbar to calculate a new preview at the same area of the previous one. In the following figure you can notice that there are fewer classes (only the MC ID) than the previous preview; also, clouds are unclassified (black pixels).

TIP : In the Working toolbar click the button Preview to easily show or hide the classification previews, and the button RGB= to show or hide the Input image.

Example of preview using MC IDs

Assess Spectral Signatures ¶

Spectral signatures are used by Classification Algorithms for labelling image pixels. Different materials may have similar spectral signatures (especially considering multispectral images) such as built-up and soil. If spectral signatures used for classification are too similar, pixels could be misclassified because the algorithm is unable to discriminate correctly those signatures. Thus, it is useful to assess the Spectral Distance of signatures to find similar spectral signatures that must be removed. Of course the concept of distance vary according to the algorithm used for classification.

One can simply assess spectral signature similarity by displaying a signature plot. In order to display the signature plot, in the ROI Signature list highlight two or more spectral signatures (with click in the table), then click the button . The Spectral Signature Plot is displayed in a new window. Move ans zoom inside the Plot to see if signatures are similar (i.e. very close). We can see in the following figure a signature plot of different materials.

Spectral plot

In the plot we can see the line of each signature (with the color defined in the ROI Signature list), and the spectral range (minimum and maximum) of each band (i.e. the semi-transparent area colored like the signature line). The larger is the semi-transparent area of a signature, the higher is the standard deviation, and therefore the heterogeneity of pixels that composed that signature. Spectral signature values are displayed in the Signature details.

Spectral signature values

Additionally, we can calculate the spectral distances of signatures (for more information see Spectral Distance). Highlight two or more spectral signatures with click in the table Plot Signature list, then click the button ; distances will be calculated for each pair of signatures. Now open the tab Spectral distances; we can notice that similarity between signatures vary according to considered algorithm.

Spectral distances

For instance, two signatures can be very similar Spectral Angle Mapping (very low Spectral Angle), but quite distant for the Maximum Likelihood (Jeffries-Matusita Distance value near 2). The similarity of signatures is affected by the similarity of materials (in relation to the number of spectral bands available in the Input image); also, the way we create ROIs influences the signatures.

Create the Classification Output ¶

Repeat iteratively the phases Create the ROIs, Create a Classification Preview, and Assess Spectral Signatures until the classification previews show good results.

To start the classification of the entire image, open the tab Classification output, click the button and define the name of the classification output.

TIP : Set the Available RAM (MB) in RAM settings, in order to reduce the computational time; the recommended value is half of the system RAM.

If Play sound when finished is checked in Classification process settings, a sound is played when the process is finished.

Classification

It is worth mentioning that SCP provides other tools and techniques that can improve the classification results, which are described in Thematic Tutorials.

NASA ARSET Webinar¶

NASA ARSET is a program for fostering the acquisition and use of NASA satellite data for supporting decisions, through online webinars and in-person workshops.

NASA ARSET offered the webinar Land Cover Classification with Satellite Imagery which covered very interesting objectives such as access and download Landsat imagery and learn the basic steps for performing a supervised classifications using the SCP.

NASA ARSET website

This webinar is organized in two sessions:

Introduction to Land Cover Classification and QGIS
Improving a Supervised Land Cover Classification

The entire webinar is very informative, and I recommend watching the recordings. In particular, the Land Cover Signature Classification is illustrated during the exercise of the second session.

Slides of presentations, in English and Spanish, and the recordings of both sessions are freely available at this link https://arset.gsfc.nasa.gov/land/webinars/advanced-land-classification.

Many thanks to NASA ARSET for their effort in teaching remote sensing using open source software.

After these tutorials, please check the Thematic Tutorials .

Thematic Tutorials¶

The following are thematic tutorials. Before these tutorials, it is recommended to read the Basic Tutorials.

Tutorial: Land Cover Signature Classification¶

Create the Band Set
Create the ROIs and Define the Spectral Thresholds
Land Cover Classification
Other Tutorials

This tutorial is about the Land Cover Signature Classification. It is assumed that one has the basic knowledge of SCP and Basic Tutorials.

Following the video of this tutorial.

http://www.youtube.com/watch?v=wUr5ZjpWBo0

First download the sample image from this link (© Copernicus Sentinel data 2016) which is a Sentinel-2 image, and unzip the file.

Create the Band Set ¶

Open the tab Band set, click the button and select the bands of the downloaded Sentinel-2 image. In the table Band set definition order the band names in ascending order (click to sort bands by name automatically), then highlight band 8A (i.e. single click on band name in the table) and use the buttons or to place this band at number 8. Finally, select Sentinel-2 from the list Quick wavelength settings, in order to set automatically the Center wavelength of each band and the Wavelength unit (required for spectral signature calculation).

Band set definition

Create the ROIs and Define the Spectral Thresholds ¶

In the SCP dock click the button and define a file name for the Training input. We are going to create ROIs similarly to Tutorial 2: Land Cover Classification of Sentinel-2 Images.

We are going to use the following Macroclass IDs (see Classes and Macroclasses).

Macroclasses

Macroclass name	Macroclass ID
Water	1
Built-up	2
Vegetation	3
Bare soil	4

In addition, we can mask clouds in the image, creating ROIs of clouds and assigning the special MC ID = 0.

In the list RGB= of Working toolbar define a Color Composite such as RGB = 3-2-1 or RGB = 7-3-2.

Color composite

Now create some ROIs. ROIs are used in Land Cover Signature Classification for defining a spectral region. The Land Cover Signature Classification can use additional classification algorithms for pixels falling inside overlapping regions or outside any spectral region (in this tutorial we are going to use Minimum Distance), therefore it is important that ROIs are homogeneous in order to train correctly the additional algorithm. Following the ROI creation we are going to change the signature thresholds in the LCS threshold.

ROI creation

After the ROI creation, in the ROI Signature list highlight these spectral signatures, then click the button .

Signature plot

Spectral signatures are displayed with the respective colors; also, the semi-transparent area represents the spectral range of each ROI. The minimum and maximum values of these spectral range are displayed in the Plot Signature list. You can manually edit these ranges or use the tools Automatic thresholds. It is worth noticing the same spectral ranges (of spectral signatures in ROI Signature list) are displayed in the Signature threshold.

In Classification algorithm select Use LCS to use the in Land Cover Signature Classification. Now create a classification preview over the lake (see Create a Classification Preview).

Classification preview

You can see that several pixels are unclassified (black) because they are outside any spectral range. In the Plot Signature list highlight a signature of macroclass Water and click the button From pixel . This tool allows you to extend the spectral range to include a pixel signature. Click an unclassified pixel in the map over the lake; you should see that the spectral range of highlighted signature is larger now. Click the button in the Working toolbar.

Classification preview

Now the area classified as water is larger and should include the pixel that was clicked before. Create a temporary ROI over the unclassified area of the lake and click the button From ROI .

Signature plot: the spectral range is extended

This way, the spectral range is extended to include the minimum and maximum value of this ROI for each band.

Signature plot: the spectral range is extended

Creating another classification preview we can see that the classified area is extended according to the temporary ROI.

Classification preview

You can extend the spectral range to classify the whole lake as water.

TIP : During ROI creation, click the button in Working toolbar and right click on the map for displaying the spectral signature of a pixel in the Spectral Signature Plot. This can be useful for assessing unclassified pixels and extend one or more spectral ranges.

Particular attention should be posed on the spectral similarity of classes. For instance soil and built-up can have very similar spectral signatures. Therefore, several ROIs should be collected in the attempt to separate these classes.

Spectral ranges should not overlap in order to avoid unclassified pixels. In the following figure, two signatures have overlapping ranges (it means that potentially there is a signature whose values fall in two classes); these signatures are highlighted in orange in the Plot Signature list (also in the LC Signature threshold) and the combinations MC ID - C ID of overlapping signatures are displayed in the column Color [overlap MC_ID-C_ID].

Overlapping signatures

It is possible to reduce the range with the button From ROI or From pixel if the checkbox – is checked. In this case, the range is reduced to exclude the values of selected pixels or ROIs.

In addition, it is possible to edit the range directly from the plot. In the Plot Signature list highlight a signature, click the button , then click inside the plot to extend or reduce the range. As a general procedure, you should compare spectral signatures and identify one or more values that could separate the overlapping ranges (if spectral ranges are not overlapping at least in one band then classes are correctly separated).

In case two spectral regions belonging to different classes are overlapping, you should consider reducing the ranges, collecting other spectral signatures with reduced ranges, or extending the spectral range of one signature to include the range of the other spectral signature that will be deleted. For instance, it could be convenient to create two spectral ranges (with two spectral signatures) for the same class in order to easily separate a third spectral signature whose values are comprised between the minimum and maximum values of the other two ranges.

TIP : Check the Automatic plot to display automatically the plot of a temporary ROI in the Spectral Signature Plot, and assess the spectral range before saving the ROI.

The plot of a temporary ROI

Now check MC ID in Classification algorithm. When MC ID is checked, the classification is performed using all the spectral signatures (without any modification of original spectral values) but assigning the macroclass code. Moreover, only overlapping signatures belonging to different macroclasses are highlighted in Plot Signature list. This allows spectral signatures sharing the same MC ID to be overlapping.

Overlapping regions belonging to the same MC ID

Also, open the tab LCS threshold for checking the overlap of all the spectral signatures saved in the Training input.

LCS threshold. Overlapping regions are highlighted in orange

Land Cover Classification ¶

After the creation of several ROIs and the definition of spectral ranges, we can perform the classification for the whole image.

Having selected MC ID and LCS in Classification algorithm, click the button in the Classification output and select an output destination. After the processing, the classification will be displayed in QGIS.

LCS classification

Unclassified pixels, displayed in black, are pixels whose spectral signature is not completely contained in any spectral region. Also, pixels contained in more than one spectral region (having different MC ID) are classified as Class Overlap.

LCS classification. Class Overlap

We could create other spectral regions in order to classify all the unclassified pixels. Alternatively, we can use the selected Algorithm for classifying those pixels. Check the Algorithm in Land Cover Signature Classification and select the Minimum Distance in Algorithm; then click the button in the Classification output.

LCS classification. Classification using the additional classification algorithm

Pixels that were unclassified by LCS now are classified using the Minimum Distance, which compares calculates the Euclidean distance between pixels and spectral signatures. Black pixels are clouds classified using the special MC ID = 0.

In addition, we can use the Minimum Distance to classify only pixels that were labelled Class Overlap by LCS, leaving unclassified pixels whose spectral signature is not completely contained in any spectral region. Check only overlap in Land Cover Signature Classification, leaving checked Algorithm; then click the button in the Classification output.

LCS classification. Classification using the additional classification algorithm only for Class Overlap

The Land Cover Signature Classification can be useful for the classification of a single land cover class, defining only the spectral ranges that identify our objective. For instance, if we were interested in built-up classification only, we could collect only ROIs for this class, obtaining a classification such as in the following image.

LCS classification. Classification of the class Built-up

Tutorial: Estimation of Land Surface Temperature with Landsat and ASTER¶

Data Download and Conversion
Clip to Study Area
Land Cover Classification
Reclassification of Land Cover Classification to Emissivity Values
Conversion from At-Satellite Temperature to Land Surface Temperature
Data Download and Conversion of ASTER Image
Clip to Study Area of ASTER image
Land Cover Classification of ASTER Image
Reclassification of Land Cover Classification to Emissivity Values of ASTER Image
Conversion from At Satellite Temperature to Land Surface Temperature of ASTER Image
Other Tutorials

This tutorial is about the estimation land surface temperature using Landsat Satellite and ASTER Satellite images. In this tutorial we are going to use a land cover classification for the definition of surface emissivity, which is required for the calculation of the land surface temperature. It is assumed that one has the basic knowledge of SCP and Basic Tutorials.

Our study area will be Paris (France), an area covered by urban surfaces, vegetation and agricultural fields.

Before downloading data, please watch the following video that illustrates the study area and provides very useful information about thermal infrared images, and their application (footage courtesy of European Space Agency/ESA). Also, a brief description of the area that we are going to classify is available here .

http://www.youtube.com/watch?v=Vjg5REQb-Bc

The thermal infrared band is particularly useful for assessing the temperature difference between the city and the surrounding rural areas, and studying the urban heat island phenomenon. We are going to use Landsat and ASTER images for the estimation of land surface temperature. For more information about the conversion of raster bands please read Conversion to At-Satellite Brightness Temperature. Following the video of this tutorial.

http://www.youtube.com/watch?v=7W4IwlvPLbQ

Data Download and Conversion ¶

We are going to download the Landsat 8 image acquired in 2015 (image ID = LC81990262015270LGN00, data available from the U.S. Geological Survey).

Start a new QGIS project. Open the tab Download images clicking the button in the SCP Menü, or the SCP Tools, or the SCP dock. Select the tab Landsat download.

In Login https://ers.cr.usgs.gov/ you should enter the user name and password for accessing data (free registration at USGS EROS is required) in User and Password. However, in this case login should not be required because this Landsat 8 image is available directly from the Amazon Web Services (AWS) .

In Search area enter:

UL X (Lon): 2
UL Y (Lat): 49
LR X (Lon): 2.5
LR Y (Lat): 48.8

TIP : In general it is possible to define the area coordinates clicking the button and drawing a rectangle in the map.

In Search select L8 OLI/TIRS from the list Satellites and set the acquisition date:

Date from: 2015-09-27
to: 2015-09-27

Now click the button Find and after a few seconds the image will be listed in the Image list.

Landsat search result

In the result table, click the item LC81990262015270LGN00 in the field ImageID, and click the button . A preview will be downloaded and displayed in the map, which is useful for assessing the quality of the image and the cloud cover.

Image preview

Click the tab Download options and leave checked only the following bands:

2 = Blue
3 = Green
4 = Red
5 = Near-Infrared
6 = Short Wavelength Infrared 1
7 = Short Wavelength Infrared 2
10 = Thermal Infrared (TIRS) 1

Bands from 2 to 7 will be used for the land cover classification, and band 10 for the estimation of land surface temperature (see Why using only Landsat 8 band 10 in the estimation of surface temperature?).

Selection of bands for download

The checkbox Preprocess images allows for the automatic conversion of bands after the download, according to the settings defined in Landsat; we are going to apply the DOS1 Correction. Bands from 2 to 7 will be converted to reflectance and band 10 will be converted to At-Satellite Brightness Temperature. Open the tab Landsat, check Apply DOS1 atmospheric correction and uncheck Create Band set and use Band set tools (we are going to create the Band set after the clip of the image to study area).

Conversion settings

In order to start the download and conversion process, open the tab Landsat download, click the button and select the directory where converted bands are saved (e.g. Desktop). After a few minutes, converted bands are loaded and displayed (file name starts with RT_).

Converted Landsat bands

Clip to Study Area ¶

We are going to clip the Landsat images to our study area.

Open the tab Preprocessing clicking the button in the SCP Menü, or the SCP Tools, or the SCP dock. Select the tab Clip multiple rasters and click the button to refresh the layer list and show the loaded rasters. Click the button to select all the rasters to be clipped, and in Clip coordinates type the following values:

UL X: 402705
UL Y: 5461065
LR X: 480824
LR Y: 5381535

Clip area

Now click the button and select the directory where clipped bands are saved (e.g. Desktop). Clipped bands have the prefix clip_ and will be automatically loaded and displayed. We can remove the bands whose names start with RT_ from QGIS layers.

Clipped bands

Land Cover Classification ¶

Now we need to classify land cover, which will be used later for the creation of the emissivity raster. For detailed instructions about the classification process please see Tutorial 2: Land Cover Classification of Sentinel-2 Images.

We are going to use the following Macroclass IDs (see Classes and Macroclasses).

Macroclasses

Macroclass name	Macroclass ID
Water	1
Built-up	2
Vegetation	3
Bare soil	4

Open the tab Band set clicking the button and define the Landsat 8 Band set using clipped bands from 2 to 7 (excluding band 10).

Band set

In the SCP dock click the button , define a file name for the Training input. In the list RGB= of Working toolbar select 4-3-2 to display a false color composite corresponding to the bands: Near-Infrared, Red, and Green (see Color Composite).

Color composite

After the creation of several ROIs for each land cover class, we can perform the classification of the whole image (see Tutorial 2: Land Cover Classification of Sentinel-2 Images). After setting the colors of MC ID (in the tab Macroclasses of the SCP dock), in the tab Classification algorithm check the option MC ID to use Macroclass IDs and select the classification algorithm Maximum Likelihood.

Classification algorithm

Then, open the tab Classification output, click the button and define the name of the classification output.

Land cover classification

Reclassification of Land Cover Classification to Emissivity Values ¶

Now we are going to reclassify the classification raster using the land surface emissivity values. The emissivity (e) values for the land cover classes are provided in the following table (values used in this tutorial are only indicative, because emissivity of every material should be obtained from field survey):

Emissivity values

Land surface	Emissivity e
Water	0.98
Built-up	0.94
Vegetation	0.98
Bare soil	0.93

Open the tab Postprocessing clicking the button in the SCP Menü, or the SCP Tools, or the SCP dock. Select the tab Reclassification and click the button to refresh the layer list and show the loaded rasters. Select the classification raster from the list.

Click the button to add 4 rows to the table Values. In this table, set the old value (the Macroclass ID of the classification) and the new value (the corresponding emissivity e ) for every land cover class. Uncheck the checkbox Use code from Signature list, click the button and define the name of the output raster (e.g. emissivity.tif).

Reclassification

This is the emissivity raster, where each pixel has the emissivity value that we have defined for the respective land cover class.

Emissivity raster

Conversion from At-Satellite Temperature to Land Surface Temperature ¶

Now we are ready to convert the At-Satellite Brightness Temperature to Land Surface Temperature, using the following equation (see Estimation of Land Surface Temperature):

\[T = T_{B} / [ 1 + (\lambda * T_{B} / c_{2}) * ln(e) ]\]

where:

\(\lambda\) = wavelength of emitted radiance
\(c_{2} = h * c / s = 1.4388 * 10^{-2}\) m K = 14388 µm K
\(h\) = Planck’s constant = \(6.626 * 10^{-34}\) J s
\(s\) = Boltzmann constant = \(1.38 * 10^{-23}\) J/K
\(c\) = velocity of light = \(2.998 * 10^{8}\) m/s

The values of \(\lambda\) for Landsat bands are listed in the following table.

Center wavelength of Landsat bands

Satellite	Band	\(\lambda (µm)\)
Landsat 4, 5, and 7	6	11.45
Landsat 8	10	10.8
Landsat 8	11	12

Open the tab Band calc clicking the button in the SCP Menü, or the SCP Tools, or the SCP dock. Click the button to refresh the layer list and show the loaded rasters. We have used band 10 of Landsat 8, therefore in the Expression type the equation for conversion adapted to our rasters:

"clip_RT_LC81990262015270LGN00_B10.tif" / ( 1 + ( 10.8 * "clip_RT_LC81990262015270LGN00_B10.tif" / 14388 ) * ln("emissivity.tif") )

Calculation of surface temperature

Click the button and define the name of the output raster (e.g. surface_temperature.tif). After the calculation, the Land Surface Temperature (in kelvin) will be loaded, and we can change the layer style. In addition, in the tab Band calc we can calculate the temperature in Celsius with the expression:

"surface_temperature.tif" - 273.15

Land Surface Temperature of the Landsat Image

We can notice that the urban area and uncultivated land have the highest temperatures, while vegetation has the lowest temperature. The aim of this tutorial is to describe a methodology for the estimation of surface temperature using open source programs and free images. It is worth highlighting that in order to achieve more accurate results, one should perform field survey for improving the land cover classification and the estimation of surface emissivities.

In addition to Landsat, we are going to use an ASTER image and use the same methodology for the estimation of Land Surface Temperature.

Data Download and Conversion of ASTER Image ¶

Open the tab Download images and select the tab ASTER download.

In Login https://urs.earthdata.nasa.gov enter the user name and password required for accessing data (free registration at EOSDIS Earthdata is required) in User and Password. The ASTER L1T data products are retrieved from the online Data Pool, courtesy of the NASA Land Processes Distributed Active Archive Center (LP DAAC), USGS/Earth Resources Observation and Science (EROS) Center, Sioux Falls, South Dakota, https://lpdaac.usgs.gov/data_access/data_pool.

In Search area enter:

UL X (Lon): 2
UL Y (Lat): 49
LR X (Lon): 2.5
LR Y (Lat): 48.8

In Search set the acquisition date:

Date from: 2000-08-24
to: 2000-08-24

Now click the button Find and after a few seconds the image will be listed in the Image list.

ASTER search result

In the result table, click the item AST_L1T_00308242000111313_20150411071856_3805 in the field ImageID, and click the button . A preview will be downloaded and displayed in the map, which is useful for assessing the quality of the image and the cloud cover.

ASTER image preview

As we did for Landsat, we are going to apply the DOS1 Correction. The checkbox Preprocess images allows for the automatic conversion of bands after the download, according to the settings defined in ASTER. Bands from 1 to 9 will be converted to reflectance and bands from 10 to 14 will be converted to At-Satellite Brightness Temperature.

Open the tab ASTER, check Apply DOS1 atmospheric correction and leave checked Create Band set and use Band set tools (this is useful to automatiacally create a Band set, which is required for the next step).

Conversion settings

In order to start the download and conversion process, open the tab ASTER download, click the button and select the directory where converted bands are saved (e.g. Desktop). After a few minutes, converted bands are loaded and displayed (file name starts with RT_).

Converted ASTER bands

Clip to Study Area of ASTER image ¶

We are going to clip the ASTER images to our study area, because bands are not aligned at the border.

Open the tab Preprocessing clicking the button . Select the tab Clip multiple rasters and click the button to refresh the layer list and show the loaded rasters. Band set`Click the button |select_all| to select all the rasters to be clipped, and check |checkbox| :guilabel:`Use temporary ROI for clipping. Now, we can draw a manual ROI (because a Band set is already defined, see ROI creation) about the same shape of the ASTER image, about 20 pixels within the border thereof (in order to align the border of all the bands).

Clip area

Clipped bands

Land Cover Classification of ASTER Image ¶

Using the same Macroclass IDs used for Landsat, we are going to classify the ASTER image.

Open the tab Band set clicking the button . Click tha button to clear all bands from Band set and define the ASTER Band set using the clipped bands from 1 to 9.

ASTER band set

In the SCP dock click the button , define a file name for the Training input (e.g. training_ASTER). Clear the table ROI Signature list highlighting all the spectral signatures created previously for Landsat and clicking the button .

In the list RGB= of Working toolbar select 3-2-1 to display a false color composite corresponding to the bands: Near-Infrared, Red, and Green (see Color Composite).

ASTER color composite

After the creation of several ROIs for each land cover class, we can perform the classification of the whole image. After setting the colors of MC ID (in the tab Macroclasses of the SCP dock), in the tab Classification algorithm check the option MC ID to use Macroclass IDs and select the classification algorithm Maximum Likelihood. Then, open the tab Classification output, click the button and define the name of the classification output (e.g. classification_aster.tif).

ASTER land cover classification

Reclassification of Land Cover Classification to Emissivity Values of ASTER Image ¶

Now we are going to reclassify the classification raster using the same land surface emissivity values used for Landsat.

Open the tab Postprocessing clicking the button . Select the tab Reclassification and click the button to refresh the layer list and show the loaded rasters. Select the classification raster from the list.

ASTER reclassification

The following figure show the emissivity raster of ASTER image.

ASTER emissivity raster

Conversion from At Satellite Temperature to Land Surface Temperature of ASTER Image ¶

We can convert the At-Satellite Brightness Temperature to Land Surface Temperature, using the same equation used for Landsat (see Estimation of Land Surface Temperature).

The values of \(\lambda\) for ASTER bands are listed in the following table.

Center wavelength of ASTER bands

Satellite	Band	\(\lambda (µm)\)
ASTER	10	8.3
ASTER	11	8.65
ASTER	12	9.1
ASTER	13	10.6
ASTER	14	11.3

We are going to use ASTER band 13 that has a \(\lambda\) value very similar to the Landsat band 10.

Open the tab Band calc clicking the button . Click the button to refresh the layer list and show the loaded rasters, and in the Expression type equation for conversion adapted to our rasters:

"clip_RT_AST_L1T_00308242000111313_20150411071856_3805_13.tif" / ( 1 + ( 10.6 * "clip_RT_AST_L1T_00308242000111313_20150411071856_3805_13.tif" / 14388 ) * ln("emissivity_aster.tif") )

Calculation of surface temperature

Click the button and define the name of the output raster (e.g. surface_temperature_aster.tif). After the calculation, the Land Surface Temperature (in kelvin) will be loaded, and we can change the layer style. In addition, in the tab Band calc we can calculate the temperature in Celsius with the expression:

"surface_temperature_aster.tif" - 273.15

Land Surface Temperature of the ASTER Image

The ASTER image shows temperature values higher than the Landsat image. For instance, we could perform the difference between the two surface temperature rasters (Landsat and ASTER) to assess the variation of temperature. However, we should notice that the two images were acquired in different months (Landsat on 27-09-2015 and ASTER on 24-08-2000).

The large availability of Landsat and ASTER images for the past decades allows for the reliable monitoring of land cover and surface temperature. Nevertheless, cloud cover can limit the number of images that can be effectively used.

This tutorial illustrated a methodology of temperature estimation using these satellite images and open source programs. One should always consider that the estimation accuracy depends on several factors, such as the thematic and spatial accuracy of land cover classifications and the reliability of the emissivity values. Estimation errors can be of 1 K or even more. Other methods have been developed which can provide more accurate results, and the reader can continue the research.

Semi-Automatic OS¶

The Semi-Automatic OS is a lightweight virtual machine for the land cover classification of remote sensing images. It includes the Semi-Automatic Classification Plugin (SCP) for QGIS, already configured along with all the required dependencies, and installed through the official SCP repository ( https://semiautomaticgit.github.io/SemiAutomaticClassificationPlugin/repository.xml ) which provides always the latest version of SCP.

Semi-Automatic OS desktop

The Semi-Automatic OS is based on Debian , and it is designed to require very little hardware resources. It uses LXDE and Openbox as main desktop environment. This virtual machine can be useful for testing the Semi-Automatic Classification Plugin, or when the installation of the required programs in the host system is problematic.
The Semi-Automatic OS is available as a 32 bit and 64 bit virtual machine that can be run in the open source VirtualBox, or any other virtualization program. The following is a guide for the installation of the Semi-Automatic OS in the open source program of virtualization VirtualBox.

Installation in VirtualBox¶

Download VirtualBox open source software (select a proper version depending on your OS) and install it; at the end of the installation restart the system;

Download the Semi-Automatic OS virtual machine (about 800 MB) from here (32 bit or 64 bit);

Extract the virtual machine content in a directory (it requires about 3 GB of disk space); the file is compressed in 7z format (if needed, download the open source extraction software from http://www.7-zip.org/);

Run VirtualBox and create a new Debian virtual machine;

Click the New button;

Type a name for the virtual machine (for instance Semi-Automatic OS); select Linux and Debian (32 or 64 bit) as Type and Version respectively; click Next;

Set the memory size; the more is the better, but this parameter should not exceed a half of the host system RAM (for instance if the host system has 1 GB of RAM, type 512 MB); click Next;

In the Hard drive settings select Use an existing virtual hard drive file and select the downloaded file SemiAutomaticOS.vmdk; click Create;

Start the Semi-Automatic OS by clicking the Start button;

It is recommended to install the virtualbox-guest-utils in the virtual machine, from the Menu > Preferences > Synaptic Package Manager; it allows for a better integration of the Semi-Automatic OS in the host system, such as: the resize of the system window, or the folder sharing.

The Semi-Automatic OS includes a sample dataset of Landsat image (available from the U.S. Geological Survey) and a Sentinel-2 image (© Copernicus Sentinel data 2016) which are the input for the two basic tutorials.

Semi-Automatic OS is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, version 3 of the License. Semi-Automatic OS is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. See http://www.gnu.org/licenses/.

Frequently Asked Questions¶

If you have comments or questions please join the Facebook group or the Google+ Community .

Before asking, please check the official site From GIS to Remote Sensing and the following Frequently Asked Questions.

Plugin installation
- How to install the plugin manually?
- How to install the plugin from the official SCP repository?
Pre processing
Processing
Warnings
- Warning [12]: The following signature will be excluded if using Maximum Likelihood. Why?
Errors
Various

Plugin installation ¶

How to install the plugin manually?¶

The SCP can be installed manually (this can be useful when an internet connection is not available, or the installation is required on multiple computers), following a few steps:

download the SCP zip archive from https://github.com/semiautomaticgit/SemiAutomaticClassificationPlugin/archive/master.zip ;
extract the content of the archive (several files such as COPYING.txt and folders such as ui) in a new folder named SemiAutomaticClassificationPlugin (without -master);
open the QGIS plugins directory (in Windows usually C:\Users\username\.qgis2\python\plugins, in Linux and Mac usually /home/username/.qgis2/python/plugins/) and delete the folder SemiAutomaticClassificationPlugin if present;
copy the folder SemiAutomaticClassificationPlugin inside the QGIS plugins directory;
the plugin should be installed; start QGIS, open the Plugin Manager and be sure that Semi-Automatic Classification Plugin is checked.

How to install the plugin from the official SCP repository?¶

It is possible to install the SCP using the official repository. This repository allows for the installation of the latest version of SCP (master), in some cases also before the availability thereof in the QGIS repository. Therefore, this can be useful if you need a fix or a new function that is still not available in the QGIS repository. Moreover, the master version in the SCP repository can be installed along with the version available in the QGIS repository.

In order to install the SCP repository follow these steps:

Run QGIS 2;
From the main menu, select Plugins > Manage and Install Plugins;

Click Settings then click the button Add;

Inside the Repository details enter:

Name:

SCP

URL:

https://semiautomaticgit.github.io/SemiAutomaticClassificationPlugin/repository.xml

and click OK;

After the repository update, the item Semi-Automatic Classification Plugin - master should be listed with the other plugins;

From the menu All, select the Semi-Automatic Classification Plugin - master and click the button Install plugin; the latest version of SCP should be automatically activated (ignore errors, the restart of QGIS could be necessary to complete the SCP installation); it is possible to deactivate the other SCP installed in the QGIS repository;

Pre processing ¶

Which image bands should I use for a semi-automatic classification?¶

In general, it is preferable to avoid thermal infrared bands. If you are using Landsat 4, 5 or 7 you should select bands: 1, 2, 3, 4, 5, 7 avoiding band 6 that is thermal infrared; for Landsat 8 you should select bands: 2, 3, 4, 5, 6, 7. Landsat 8 band 1 is generally avoided because it is very similar to the blue band and it is mainly used for coastal aerosol study. Landsat thermal infrared band is excluded from classifications because values are mainly related to object temperature.

For Sentinel-2 images you can use bands: 2, 3, 4, 5, 6, 7, 8, 8A, 11, 12.

Which Landsat bands can be converted to reflectance by the SCP?¶

All Landsat 1,2, and 3 MSS and Landsat 4, 5, 7, and 8 images downloaded from http://earthexplorer.usgs.gov/ and processed with the Level 1 Product Generation System (LPGS) can be converted to reflectance automatically by the SCP; products generated by the LPGS have a MTL file included that is required for the conversion. Since version 3.1.1 the SCP can also convert images from the Global Land Cover Facility (images available for free from ftp://ftp.glcf.umd.edu/glcf/Landsat/). In particular, images having an old format of the MTL file (or a .met file) can be processed through the automatic conversion to reflectance and the DOS correction. However, some images do not have the required information and cannot be processed. Also, notice that some images available from the Global Land Cover Facility are already converted to reflectance. For this process, image bands must be renamed in order to remove the final 0 if present (e.g. rename B10 to B1).

Can I apply the conversion to Sentinel-2 images download from the web?¶

Yes, you can convert also images downloaded from the web (actually the conversion is recommended). You should move all the bands (.jp2 files) and if available the .xml file whose name contains MDT_SAFL1C in the same directory. Then select this directory in Sentinel-2 conversion. Images are converted to reflectance.

How are converted Sentinel-2 images that have different resolutions?¶

During the conversion to reflectance, pixels of 20m bands are split in 4 pixels of 10m whose values are the same as the original 20m pixel. The purpose of this operation is to allow for the calculation between all the bands, without changing original values.

Can I apply the Landsat conversion and DOS correction to clipped bands?¶

Yes, you can clip the images before the conversion to reflectance and then copy the MTL file (contained in the Landsat dataset) inside the directory with the clipped bands. If you want to apply the DOS correction (which is an image based technique) you should convert the original Landsat bands (the entire image) and then clip the conversion output (i.e. bands converted to reflectance).

Can I apply the DOS correction to bands with black border (i.e. with NoData value)?¶

If you want to apply the DOS correction to an entire band which has NoData values (the black border with value = 0) then you have to check the checkbox Use NoData value and set the value to 0. This is because DOS is an image based technique, and NoData values must be excluded from the calculation.

How to remove cloud cover from images?¶

DOS1 correction does not remove clouds from the image. However, Landsat 8 images include Band 9 that identifies clouds (see this NASA site). You can use this band for the creation of a mask.

For other Landsat satellites, clouds can be masked using the approach described this paper.

Also, see the following video-tutorial.

How do I create a virtual raster manually in QGIS?¶

In order to create a multi-spectral virtual raster in QGIS:

from the menu Raster select Miscellaneous > Build Virtual Raster (catalog);
click the button Select... and select all the Landsat bands (in numerical order);
select the output file (for instance rgb.vrt); check Separate (bands will be separated) and click OK.

After pan-sharpening of Landsat 8 images, why NIR bands still have 30m resolution?¶

Landsat 8 panchromatic band doesn’t acquire in the Near Infrared (NIR) region (see Landsat Satellite). Therefore, the pan-sharpening process can’t improve the resolution of NIR and SWIR bands (see Pan-sharpening), which appear to have 30m resolution. However, raster all pan-sharpened rasters have 15m resolution to allow raster calculation.

Processing ¶

I get classification errors. How can I improve the accuracy?¶

Several materials have similar spectral signatures (e.g. soil and built-up, or forest and other types of dense low vegetation), which can cause classification errors if ROIs, and spectral signatures thereof, are not acquired correctly. In order to improve the results, you can try to collect more ROIs over these areas, in order to train the algorithm for these very similar areas, also, display the spectral signatures of these areas in Spectral Signature Plot to assess their similarity. You can also use a Signature threshold for these signatures in order to reduce the variability thereof (only pixels very similar to the input signatures will be classified). The Land Cover Signature Classification is also useful for classifying specific materials that can be spectrally similar to other ones.

Is it possible to use the same training input for multiple images?¶

Yes, it is possible if all the images have the same number of bands. However, if images are acquired in different months, land cover changes (especially of vegetation state) will affect the spectral signature (i.e. the same pixel has different spectral signature in different periods). Atmospheric effects could also affect the images differently. That could reduce classification accuracy. Therefore, it is suggested to collect always the ROIs and spectral signatures for every image.

What is the difference between classes and macroclasses?¶

Please see Classes and Macroclasses.

Can I use SCP with images from drones or aerial photographs?¶

Yes, you can use them if they have at least 4 bands. With less than 4 bands, semi-automatic classification algorithms are unable to classify the land cover correctly. Alternative classification methods exist, such as object oriented classification, which is not implemented in SCP.

Why using only Landsat 8 band 10 in the estimation of surface temperature?¶

Several methods were developed for estimating surface temperature. The method described in the tutorial for temperature estimation requires only one band. Moreover, USGS recommends that users refrain from relying on Landsat 8 Band 11 data in quantitative analysis of the Thermal Infrared Sensor data (see Changes to Thermal Infrared Sensor (TIRS) data by USGS).

Warnings ¶

Warning [12]: The following signature will be excluded if using Maximum Likelihood. Why?¶

The ROI is too small (or too homogeneous) for the Maximum Likelihood algorithm because that ROI has a singular covariance matrix. You should create larger ROIs or don’t use the Maximum Likelihood algorithm in the classification process.

Errors ¶

How can I report an error?¶

If you found an error of the Semi-Automatic Classification Plugin please follow these steps in order to collect the required information (log file):

close QGIS if already open;
open QGIS, open the Plugin tab Debug and check the checkbox Records events in a log file ;

Debug

click the button Test dependencies in the tab Debug ;
load the data in QGIS (or open a previously saved QGIS project) and repeat all the steps that cause the error in the Plugin;
- if the issue could be related to the image data, please use this sample dataset ;
if an error message appears (like the one in the following image), copy the whole content of the message in a text file;

Error message

open the tab Debug and uncheck the checkbox Records events in a log file, then click the button and save the log file (which is a text file containing information about the Plugin processes);
open the log file and copy the whole content of the file;
join the Facebook group or the Google+ community , create a new post and copy the error message and the log file (or attach them).

Virtual raster creation issues. Why?¶

The automatic creation of the virtual raster after Landsat conversion to reflectance is not required for the classification. Errors could happen if the output destination path contains special characters (such as accented letters) or spaces; try to rename directories (e.g. rename new directory to new_directory). If you still get the same error you can create a virtual raster manually.

Error [26] ‘The version of Numpy is outdated’. Why?¶

QGIS 32bit could have an older version of Numpy as default; in order to update Numpy:

download this file (which is based on WinPython installer and PyParsing);
extract the file with 7-zip;
copy the content of the extracted directory inside the directory apps\Python27\Lib\site-packages inside the QGIS installation directory (e.g. C:\Program Files (x86)\QGIS Chugiak\apps\Python27\Lib\site-packages) overwriting the files pyparsing, numpy, matplotlib, and scipy.

Alternatively, you should be able to install QGIS and Numpy with the OSGEO4W advanced installer.

Error ‘Plugin is damaged. Python said: ascii’. Why?¶

It could be related to a wrong installation. Please, uninstall QGIS and install it again with administrative rights. Delete also the directory .qgis2 in your user directory. Then run QGIS 2 and try to install the plugin following the Plugin Installation guide.

Also, it could be related to the user name containing special characters. Please try the installation creating a new user without special characters (e.g. user).

Also, if the error message contains something like:: sfnt4 = sfnt4.decode('ascii').lower()

it could be related to a known issue of Matplotlib (a Python library); in order to solve this, you should (as reported at stackoverflow):

open in a text editor the file font_manager.py which is inside the directory C:\PROGRA~1\QGISCH~1\apps\Python27\lib\site-packages\matplotlib\

search for the line

sfnt4 = sfnt4.decode('ascii').lower()

and replace it with the line

sfnt4 = sfnt4.decode('ascii', 'ignore').lower()

Alternatively, try to install QGIS through the OSGEO4W installer, which includes an updated Matplotlib version.

Error [50] ‘Internet error’. Unable to download Sentinel-2 images. Why?¶

The error message usually includes some information about the issue. First, check the user name and password.

Also, there could be an interruption of the service. For Sentinel-2 images please check this website https://scihub.copernicus.eu/news/ for messages about the state of the service.

In case you still get the same error, please follow these steps How can I report an error?.

Error [56] ‘SSL connection error’. Unable to download Sentinel-2 images. Why?¶

First, check the user name and password.

This issue could be related to SSL protocols (TLS v1.1 and TLS v1.2) required for Sentinel-2 download. As described here https://docs.python.org/2/library/ssl.html the protocols TLS v1.1 and TLS v1.2 are available only in Python 2.7.9+ with openssl version 1.0.1+. QGIS could have a previous version of Python where TLS v1.1 and TLS v1.2 are not available. Therefore the Sentinel-2 download process fails.

A temporary solution for Windows OS:

Warning: this could break other QGIS functions, but fortunately you can install multiple versions of QGIS.

Close QGIS if open
Download and install Python for 32bit or for 64bit according to the installed version of QGIS
Copy and replace C:\python27\python.exe to "QGIS installation folder"\bin\ (e.g. C:\Program Files (x86)\QGIS Chugiak\bin\)
Copy and replace C:\python27\pythonw.exe to "QGIS installation folder"\bin\
Copy and replace all the content of C:\python27\ to "QGIS installation folder"\apps\python27\
Now start QGIS and if everything went well you should be able to search and download Sentinel-2 images using SCP

In case you still get the same error, please follow these steps How can I report an error?.

This plugin is broken ‘matplotlib requires pyparsing >= 1.5.6’. Why?¶

It is related to this issue https://hub.qgis.org/issues/14952 which should affect QGIS 32bit only. The installation of QGIS 64bit should work. As a solution you can install a previous version of QGIS 2.8 32bit .

Error installing the plugin, possible missing dependencies. Why?¶

The plugin requires the installation of GDAL, NumPy, SciPy and Matplotlib, which should be installed along with QGIS. If the plugin installation fails, and you get a message about possible missing dependencies, you should try to install or update QGIS and the required dependencies. Notice that in order to avoid this error, python dependencies should not be installed through Anaconda.

Various ¶

What can I do with the SCP?¶

SCP allows for the land cover classification of remote sensing images through Supervised Classification. You can produce a land cover raster using one of the Classification Algorithms available in SCP. These algorithms require spectral signatures or ROIs as input (for definitions please read Brief Introduction to Remote Sensing) that define the land cover classes to be identified in the image.

A multispectral image processed to produce a land cover classification

(Landsat image provided by USGS)

SCP can work with multispectral images acquired by satellites, airplanes, or drones. Also, SCP allows for the direct search and download of free images (see Download images). You cannot use orthophotos with less than 4 bands, SAR data, and LIDAR data with SCP.

Input image in SCP is called Band set, which is used as input for the classification. SCP provides several tools for the Preprocessing of downloaded images, such as the conversion to reflectance and manipulation of bands.

Classification results can be assessed with the tools Accuracy and Classification report. Also, rasters can be manipulated using Postprocessing tools such as Classification to vector, Reclassification, Edit raster directly, Classification sieve, Classification erosion, and Classification dilation.

The Spectral Signature Plot and Scatter Plot allow for the analysis of spectral signatures and ROIs. Also, several Tools are available for easing the ROI creation and editing spectral signatures.

Raster calculation is available through the seamless integration of the tool Band calc with bands in the Band set, calculating mathematical expressions and spectral indices. Also, an output raster can be calculated based on Decision rules.

The tool Batch allows for the automatic execution of several SCP functions using a scripting interface.

See the Basic Tutorials for more information and examples.

How to contribute to SCP ¶

You can contribute to SCP by fixing and adding functionalities (see Where is the source code of SCP?), or translating the user manual (see How can I translate this user manual to another language?).

Also, you can donate to this project at the following link https://fromgistors.blogspot.com/p/donations.html .

Free and valuable resources about remote sensing and GIS ¶

The following links are valuable resources:

The Landsat 8 Data Users Handbook by USGS;

The Landsat 7 Science Data Users Handbook by NASA;

Remote Sensing Note by JARS.

Webinar: Fundamentals of Remote Sensing by NASA.

Webinar: NASA Remote Sensing for Land Management by NASA.

Webinar: Creating and Using Normalized Difference Vegetation Index (NDVI) from Satellite Imagery by NASA.

Webinar: Remote Sensing of Forest Cover and Change Assessment for Carbon Monitoring by NASA.

Webinar: Introduction to Remote Sensing for Conservation Management by NASA.

How can I translate this user manual to another language?¶

It is possible to easily translate the user manual to any language, because it is written in reStructuredText as markup language (using Sphinx). Therefore, your contribution is fundamental for the translation of the manual to your language. The following guide illustrates the main steps for the translation, which can be performed:

using the free online service Transifex;
using the gettext .po files.

Before translating, please read this document from the QGIS translation guide, which helps you understand the reStructuredText.

Method 1. Translation using the free online service Transifex

This is probably the easiest way to translate the manual using an online service.

Join the Semi-automatic Classification Manual project

Go to the page https://www.transifex.com/semi-automatic-classification/semi-automatic-classification-plugin-manual and click the button Help translate. You can sign in using your Google or Facebook account, or with a free registration.
Select your language

Select your language and click the button Join team. If your language is not listed, click the button Request language.
Translation

There are several files to be translated, which refer to the sections of the SCP documentation. To translate the SCP interface you should select the file semiautomaticclassificationplugin.ts .

Method 2. Translation using the gettext .po files

In order to use this method, you should be familiar with GitHub. This translation method allows for the translation of the PO files locally.

Download the translation files

Go to the GitHub project https://github.com/semiautomaticgit/SemiAutomaticClassificationManual_v4/tree/master/locale and download the .po files of your language (you can add your language, if it is not listed), or you can fork the repository. Every file .po is a text file that refers to a section of the User Manual.
Edit the translation files

Now you can edit the .po files. It is convenient to edit those file using one of the following programs: for instance Poedit for Windows and Mac OS X, or Gtranslator for Linux or OmegaT (Java based) for Windows, Linux and Mac OS X. These editors allow for an easy translation of every sentence in the User Manual.

Where is the source code of SCP?¶

You can find the source code of SPC is at the following link https://github.com/semiautomaticgit/SemiAutomaticClassificationPlugin

Semi-Automatik Klassifikation Plugin Dokumentation¶

Introduction¶

Plugin Installation¶

Installation in Windows 32 bit¶

QGIS download and installation¶

Semi-Automatic Classification Plugin installation¶

Configuration of the plugin¶

Installation in Windows 64 bit¶

QGIS download and installation¶

Semi-Automatic Classification Plugin installation¶

Configuration of the plugin¶

Installation in Ubuntu Linux¶

QGIS download and installation¶

Semi-Automatic Classification Plugin installation¶

Configuration of the plugin¶

Installation in Debian Linux¶

QGIS download and installation¶

Semi-Automatic Classification Plugin installation¶

Configuration of the plugin¶

Installation in Mac OS¶

QGIS download and installation¶

Semi-Automatic Classification Plugin installation¶

Configuration of the plugin¶

Die Schnittstelle von SCP¶

SCP Menü¶

SCP Tools¶

Working toolbar¶

Image control¶

Temporary ROI¶

Classification preview¶

SCP dock¶

Classification style¶

Algorithm¶

Land Cover Signature Classification¶

Main Interface Window¶

Login https://ers.cr.usgs.gov/¶

Search area¶

Search¶

Landsat images¶

Download options¶

Download¶

Login Sentinels¶

Search area¶

Search¶

Sentinel images¶

Download options¶

Download¶

Login https://urs.earthdata.nasa.gov¶

Search area¶

Search¶

ASTER images¶

Download¶

Login https://urs.earthdata.nasa.gov¶

Search area¶

Search¶

MODIS images¶

Download¶

Create random points¶

Point coordinates and ROI definition¶

Run¶

Import library file¶

Import shapefile¶

Download USGS Spectral Library¶

Band weight¶

Automatic weight¶

Signature threshold¶

Automatic thresholds¶

LC Signature threshold¶

Automatic thresholds¶

RGB list¶

Automatic RGB¶

Landsat conversion to TOA reflectance and brightness temperature¶

Metadata¶

Run¶

Sentinel-2 conversion¶

Metadata¶

Run¶

ASTER conversion¶

Metadata¶

Run¶

Login https://urs.earthdata.nasa.gov ¶

Login https://urs.earthdata.nasa.gov ¶