diff --git a/doc/en.rst b/doc/en.rst
index 4f60a10..723ee7d 100644
--- a/doc/en.rst
+++ b/doc/en.rst
@@ -1,34 +1,46 @@
-GuidosToolbox Workbench
-=======================
+GuidosToolbox Workbench (GWB)
+=============================
 
-This document provides usage instructions for the image analysis module **GWB** (`GuidosToolbox Workbench <https://forest.jrc.ec.europa.eu/en/activities/lpa/gwb/>`_), here implemented as a Jupyter dashboard on SEPAL. Citation reference: `GuidosToolbox Workbench: Spatial analysis of raster maps for ecological applications <https://doi.org/10.1111/ecog.05864>`_.
+This article of SEPAL documentation provides usage instructions for the image analysis module **GWB** (`GuidosToolbox Workbench <https://forest.jrc.ec.europa.eu/en/activities/lpa/gwb/>`_) implemented as a Jupyter dashboard on the SEPAL platform.
+
+Citation reference: `GuidosToolbox Workbench: Spatial analysis of raster maps for ecological applications <https://doi.org/10.1111/ecog.05864>`_.
 
 Introduction
 ------------
 
-In 2008, the GuidosToolbox (`GTB <https://forest.jrc.ec.europa.eu/en/activities/lpa/gtb/>`_, `Vogt and Riitters 2017 <https://doi.org/10.1080/22797254.2017.1330650>`_) was developed as a graphical user interface (GUI) to Morphological Spatial Pattern Analysis (`MSPA <https://forest.jrc.ec.europa.eu/en/activities/lpa/mspa/>`_) of raster data (`Soille and Vogt 2009 <https://doi.org/10.1016/j.patrec.2008.10.015>`_). The GTB has since been enhanced with numerous modules for analysis of landscape objects, patterns, and networks, and specialized modules for assessing fragmentation and restoration. The GuidosToolbox Workbench (`GWB <https://forest.jrc.ec.europa.eu/en/activities/lpa/gwb/>`_) provides the most popular GTB modules as a set of command-line applications for 64bit Linux systems. In the following, we describe the implementation of GWB on the SEPAL platform as a Jupyter dashboard based on the `GWB CLI tool <https://docs.sepal.io/en/latest/cli/gwb.html>`_. 
+In 2008, `GuidosToolbox (`GTB <https://forest.jrc.ec.europa.eu/en/activities/lpa/gtb/>`_) (`Vogt and Riitters, 2017 <https://doi.org/10.1080/22797254.2017.1330650>`_) was developed as a graphical user interface (GUI) to the Morphological Spatial Pattern Analysis `(MSPA <https://forest.jrc.ec.europa.eu/en/activities/lpa/mspa/>`_) of raster data (`Soille and Vogt, 2009 <https://doi.org/10.1016/j.patrec.2008.10.015>`_).
+
+GTB has since been enhanced with numerous modules for analysis of landscape objects, patterns, and networks, as well as specialized modules for assessing fragmentation and restoration. 
+
+GWB provides the most popular GTB modules as a set of command-line applications for 64bit Linux systems. 
+
+In this article, we describe the implementation of GWB on the SEPAL platform as a Jupyter dashboard based on the `GWB CLI tool <https://docs.sepal.io/en/latest/cli/gwb.html>`_.
 
 Presentation
 ^^^^^^^^^^^^
 
-To launch the app please follow the `SEPAL registration steps <https://docs.sepal.io/en/latest/setup/register.html>`_ and then move to the SEPAL Apps dashboard (purple wrench icon on the left side panel), search for and click on GWB ANALYSIS.
+To launch the app, `register to SEPAL <https://docs.sepal.io/en/latest/setup/register.html>`_. 
+
+Then, navigate to the SEPAL **Apps** dashboard (purple wrench icon in the left panel). 
+
+Finally, search for and select **GWB ANALYSIS**.
 
 .. thumbnail:: https://raw.githubusercontent.com/12rambau/gwb/master/doc/img/dashboard.png
     :title: SEPAL dashboard 
     :group: gwb-module
     
-The application should launch itself in the About section, allowing to select the tool you want to use. 
+The application should launch itself and display the **About** section. Select the tool you want to use.
 
 .. note::
-    
-    If this is the first time you use the app, you will actually see the following popup:
-    
+
+    If this is the first time you have used the app, you will see the following pop-up window:
+
     .. thumbnail:: https://raw.githubusercontent.com/12rambau/gwb/master/doc/img/licence.png
-        :title: licence
+        :title: Licence
         :group: gwb-module
-        
-    This licence needs to be accepted to use the **GWB** modules. It is also available in the section :code:`Licence` of the app. 
-    If you don't want to accept this Licence, just close the app tab.
+
+    This licence needs to be accepted to use the **GWB** modules. It is also available in the :code:`Licence` section of the app.
+    If you don't want to accept the licence, close the **App** tab.
 
 Usage
 ^^^^^
@@ -36,109 +48,111 @@ Usage
 General structure
 """""""""""""""""
 
-The application is strucured as followed: 
+The application is structured as follows:
 
-On the left side you will find a navigation drawer that you can open and close using :btn:`<fa-solid fa-ellipsis-vertical>` (topleft side of the window).
+On the left side you will find a navigation drawer that you can open and close using the :btn:`<fa-solid fa-ellipsis-vertical>` (upper-left side of the window).
 
-.. tip:: 
+.. tip::
 
-    On small devices such as tablet or phones, the navigation drawer will be hidden by default. Click on :btn:`<fa-solid fa-ellipsis-vertical>` (topleft side of the window) to show the full extent of the app. 
+    On small devices such as tablets or phones, the navigation drawer will be hidden by default. Select the :btn:`<fa-solid fa-ellipsis-vertical>` (upper-left side of the window) to display the full extent of the app.
     
     .. thumbnail:: https://raw.githubusercontent.com/12rambau/gwb/master/doc/img/small_device_without_menu.png
-        :title: small screen without drawer
+        :title: Small screen without drawer
         :width: 49%
         :group: gwb-module
         
     .. thumbnail:: https://raw.githubusercontent.com/12rambau/gwb/master/doc/img/small_device_with_menu.png
-        :title: small screen with drawer
+        :title: Small screen with drawer
         :width: 49%
         :group: gwb-module
-        
-Each name in the list corresponds to one **GWB** module, presented individually in the next sections. By clicking on it you will display the panels relative to the function you want to use. 
+
+Each name in the list corresponds to one **GWB** module, presented separately in the following sections. By selecting a name, the panels relative to the function will be displayed.
 
 .. thumbnail:: https://raw.githubusercontent.com/12rambau/gwb/master/doc/img/landing.png
-    :title: presentation of the structure
+    :title: Presentation of the structure
     :group: gwb-module
 
-.. danger:: 
+.. attention::
 
-    All **GWB** modules require categorical raster input maps in data type unsigned byte (8bit), with discrete integer values within [0, 255] byte. Any other data format will raise an error.
+    All **GWB** modules require categorical raster input maps in data type unsigned bytes (8bit), with discrete integer values within [0, 255] bytes. Any other data format will cause an error.
     
 Launch a module
 """""""""""""""
 
-For all modules, the first step is sanitizing the image provided by the user and changing the band values according to the module requirements.
+For all modules, the first step is sanitizing the image provided by the user and changing the band values according to module requirements.
     
-Then you can select the parameters associated to the selected module and run it by clicking on the final button. 
-In the next section we'll describe every module and their specificities.
+Then, select the parameters associated with the selected module and run it by selecting the final button.
+
+In the next section, each module and its specificities will be described.
 
 .. note::
 
-    The :code:`module_results` folder is not dedicated to save your dada but only produce them. Once created, no binary image using the same name can be produced. If you're running the same analysis with different parameters you can safely reuse the same one, if not please delete/move the previous image before running. A warning message will be displayed in the application.
+    The :code:`module_results` folder is dedicated to producing data, not saving them. Once created, no binary image using the same name can be produced. If you're running the same analysis with different parameters, you can safely reuse the same one; if not, please delete or move the previous image before running. A warning message will be displayed in the application.
 
 Modules
 -------
 
-Each module is presented individually. You can directly jump to the module of interest by clicking on the related link under the section Modules in the right panel of this page and this documentation will guide you through the respective processing steps.
+Each module is presented individually in this article. You can directly jump to the module of interest by selecting the related link under the **Modules** section in the right panel of this page – the documentation will guide you through the respective processing steps.
 
-ACC
-^^^
+Accounting (ACC)
+^^^^^^^^^^^^^^^^
 
-This module will conduct the **Accounting** analysis. Accounting will label and calculate the area of all foreground objects. The result are spatially explicit maps and tabular summary statistics. Details on the methodology and input/output options can be found in the `Accounting <https://ies-ows.jrc.ec.europa.eu/gtb/GTB/psheets/GTB-Objects-Accounting.pdf>`_ product sheet.
+This module will conduct the **Accounting** analysis. Accounting will label and calculate the area of all foreground objects. The results are spatially explicit maps and tabular summary statistics. Details on the methodology and input/output options can be found in the `Accounting product sheet <https://ies-ows.jrc.ec.europa.eu/gtb/GTB/psheets/GTB-Objects-Accounting.pdf>`_.
 
-Setup the input image
-"""""""""""""""""""""
+Set up the input image
+""""""""""""""""""""""
 
 .. tip::
 
-    You can use the default dataset to test the module. Click on the :code:`Download test dataset` button on the top of the second panel. By clicking on this button, the following two files will be added to your :code:`downloads` folder:
+    You can use the default dataset to test the module. Select the :code:`Download test dataset` button on the top of the second panel to add the following files to your :code:`downloads` folder:
     
-    -   :code:`example.tif`: 0 byte - Missing, 1 byte - Background, 2 byte - Foreground
-    -   :code:`clc3class.tif`: 1 byte - Agriculture, 2 byte - Natural, 3 byte - Developed
+    -   :code:`example.tif`: 0 bytes - Missing, 1 byte - Background, 2 bytes - Foreground
+    -   :code:`clc3class.tif`: 1 byte - Agriculture, 2 bytes - Natural, 3 bytes - Developed
     
     .. thumbnail::  https://raw.githubusercontent.com/12rambau/gwb/master/doc/img/test_dataset.png
-        :title: download sample dataset
+        :title: Download sample dataset
         :group: gwb-module
-        
     
-    Once the files are downloaded, follow the normal process using the :code:`downloads/example.tif` file (2 classes).
+    Once the files are downloaded, follow the normal process using the :code:`downloads/example.tif` file (two classes).
     
-The first step requires to reclassify your image. Using the reclassifying panel, select your image in your SEPAL folder.
+The first step requires reclassifying your image. Using the **Reclassifying** panel, select your image in your SEPAL folder.
 
-.. warning:: 
+.. attention:: 
 
-    If the image is not in your SEPAL folders but in your local computer consider reading the `exchange file with SEPAL <https://docs.sepal.io/en/latest/setup/filezilla.html>`_ page of this documentation.
+    If the image is on your local computer and not in your **SEPAL folders**, see `Exchange files with SEPAL <https://docs.sepal.io/en/latest/setup/filezilla.html>`_.
     
-The dropdown menus will list the discrete values of your raster input image. Select each class in your image and place them in one of the following categories: 
+The dropdown menus will list the discrete values of your raster input image. 
+
+Select each class in your image and place them in one of the following categories: 
 
 -   background
 -   foreground
 -   special background 1 (optional)
 -   special background 2 (optional)
 
-Every class that is not set to a reclassifying category will be considered as "missing data" (0 byte).
+Every class that is not set to a reclassifying category will be considered "missing data" (0 byte).
 
 .. thumbnail:: https://raw.githubusercontent.com/12rambau/gwb/master/doc/img/4_classes.png
-    :title: upload 4 classes
+    :title: Upload four classes
     :group: gwb-module
 
 .. tip::
 
-    For forest analysis, set forest as foreground and all the other classes as background. If you specify special background, it will be treated separately in the analysis (e.g. water, buildings).
+    For forest analysis, set **Forest** as foreground and all other classes as background. If you specify a special background, it will be treated separately in the analysis (e.g. water, buildings).
     
 Select the parameters
 """""""""""""""""""""
-You will need to select parameters for your computation: 
+You will need to select parameters for your computation:
 
 .. thumbnail:: https://raw.githubusercontent.com/12rambau/gwb/master/doc/img/acc_params.png
-    :title: acc params
+    :title: ACC parameters
     :group: gwb-module
     
 .. note::
 
     These parameters can be used to perform the default computation:
     
-    -   Foreground connectivity: 8
+    -   foreground connectivity: 8
     -   spatial pixel resolution: 25
     -   area thresholds: 200 2000 20000 100000 200000
     -   option: default
@@ -147,25 +161,25 @@ You will need to select parameters for your computation:
 Foreground connectivity
 #######################
 
-This sets the foreground connectivity of your analysis: 
+This sets the foreground connectivity of your analysis. Specifically: 
 
--   8 neighbors (default) will use every pixel in the vicinity (including diagonals)
--   4 neighbors only use the vertical and horizontal ones
+-   8 neighbours (default) will use every pixel in the vicinity (including diagonals)
+-   4 neighbours will only use the vertical and horizontal ones
 
 .. thumbnail:: https://raw.githubusercontent.com/12rambau/gwb/master/doc/img/connectivity.png
-    :title: connectivity image
+    :title: Connectivity image
     :width: 50%
     :group: gwb-module
     
 Spatial pixel resolution
 ########################
 
-Set the spatial pixel resolution of your image in meters. It is only used for the summary.
+Set the spatial pixel resolution of your image (in metres). It is only used for the summary.
 
 Area thresholds
 ###############
 
-Set up to 5 area thresholds (measured in pixels). 
+Set up to five area thresholds (measured in pixels). 
 
 Options
 #######
@@ -180,29 +194,28 @@ Big3pink
 
 Two options are available: 
 
--   do not highlight the 3 largest objects (False)
--   show the 3 largest objects in pink color (True)
-
+-   do not highlight the three largest objects (False)
+-   show the three largest objects in pink color (True)
 
 Run the analysis
 """"""""""""""""
 
-Once your parameters are all set you can launch the analysis. The blue rectangle will display information about the computation. Upon completion, it will turn to green and display the computation log. 
+Once your parameters are set, launch the analysis. The blue rectangle will display information about the computation. Upon completion, it will turn green and display the computation log.
 
 .. thumbnail:: https://raw.githubusercontent.com/12rambau/gwb/master/doc/img/acc_results.png
-    :title: information logs
+    :title: Information logs
     :group: gwb-module
 
-The resulting files are stored in the folder :code:`module_results/gwb/acc/`, for example:
+The resulting files are stored in the folder :code:`module_results/gwb/acc/`. For example:
 
 -   :code:`<raster_name>_bin_map.tif`
 -   :code:`<raster_name>_bin_map_acc.tif`
 -   :code:`<raster_name>_bin_map_acc.csv`
 -   :code:`<raster_name>_bin_map_acc.txt`
 
-.. danger::
+.. attention::
 
-    If the rectangle turns red, carefully read the information in the log. For example, your current instance may be too small to handle the file you want to analyse. In this case, close the app, open a bigger instance and run your analysis again.
+    If the rectangle turns red, carefully read the information in the log. For example, your current instance may be too small to handle the file you want to analyse. In this case, close the app, open a bigger instance, and run your analysis again.
     
 Here is the result of the computation using the default parameters on the :code:`example.tif` file.
 
@@ -211,57 +224,56 @@ Here is the result of the computation using the default parameters on the :code:
     :align: center
     :group: gwb-module
 
+Euclidean Distance (DIST)
+^^^^^^^^^^^^^^^^^^^^^^^^^
 
-DIST
-^^^^
+This module will conduct the **Euclidean Distance** analysis. Each pixel will show the shortest distance to the foreground boundary. Pixels inside a foreground object have a positive distance value while background pixels have a negative distance value. The results are spatially explicit maps and tabular summary statistics.
 
-This module will conduct the **Euclidean Distance** analysis. Each pixel will show the shortest distance to the foreground boundary. Pixels inside a foreground object have a positive distance value while background pixels have a negative distance value. The result are spatially explicit maps and tabular summary statistics.
-Details on the methodology and input/output options can be found in the `Distance <https://ies-ows.jrc.ec.europa.eu/gtb/GTB/psheets/GTB-Distance-Euclidean.pdf>`_ product sheet.
+Details on the methodology and input/output options can be found in the `Distance product sheet <https://ies-ows.jrc.ec.europa.eu/gtb/GTB/psheets/GTB-Distance-Euclidean.pdf>`_.
 
-Setup the input image
-"""""""""""""""""""""
+Set up the input image
+""""""""""""""""""""""
 
 .. tip::
 
-    You can use the default dataset to test the module. Click on the :code:`Download test dataset` button on the top of the second panel. By clicking on this button, the following two files will be added to your :code:`downloads` folder:
+    You can use the default dataset to test the module. Select the :code:`Download test dataset` button on the top of the second panel to add the following files to your :code:`downloads` folder:
         
-    -   :code:`example.tif`: 0 byte - Missing, 1 byte - Background, 2 byte - Foreground
-    -   :code:`clc3class.tif`: 1 byte - Agriculture, 2 byte - Natural, 3 byte - Developed
+    -   :code:`example.tif`: 0 bytes - Missing, 1 byte - Background, 2 bytes - Foreground
+    -   :code:`clc3class.tif`: 1 byte - Agriculture, 2 bytes - Natural, 3 bytes - Developed
     
     .. thumbnail::  https://raw.githubusercontent.com/12rambau/gwb/master/doc/img/test_dataset.png
-        :title: download sample dataset
+        :title: Download sample dataset
         :group: gwb-module
-        
     
-    Once the files are downloaded, follow the normal process using the :code:`downloads/example.tif` file (2 classes).
+    Once the files are downloaded, follow the normal process using the :code:`downloads/example.tif` file (two classes).
     
-The first step requires to reclassify your image. Using the reclassifying panel, select your image in your SEPAL folder.
+The first step requires reclassifying your image. Using the **Reclassifying** panel, select the image in your **SEPAL folder**.
 
-.. warning:: 
+.. attention:: 
 
-    If the image is not in your SEPAL folders but in your local computer consider reading the `exchange file with SEPAL <https://docs.sepal.io/en/latest/setup/filezilla.html>`_ page of this documentation.
-    
-The dropdown menus will list the discrete values of your raster input image. Select each class in your image and place them in one of the following categories: 
+    If the image is on your local computer and not in your **SEPAL folders**, see `Exchange files with SEPAL <https://docs.sepal.io/en/latest/setup/filezilla.html>`_.
+
+The dropdown menus will list the discrete values of your raster input image. Select each class in your image and place them in one of the following categories:
 
 -   background
 -   foreground
 
-Every class that is not set to a reclassifying category will be considered as "missing data" (0 byte).
+Every class that is not set to a reclassifying category will be considered "missing data" (0 bytes).
 
 .. thumbnail:: https://raw.githubusercontent.com/12rambau/gwb/master/doc/img/2_classes.png
-    :title: upload 2 classes
+    :title: Upload two classes
     :group: gwb-module
 
 .. tip::
 
-    For forest analysis, set forest as foreground and all the other classes as background.
+    For forest analysis, set **Forest** as foreground and all other classes as background.
     
 Select the parameters
 """""""""""""""""""""
-You will need to select parameters for your computation: 
+You will need to select parameters for your computation:
 
 .. thumbnail:: https://raw.githubusercontent.com/12rambau/gwb/master/doc/img/dist_params.png
-    :title: dist params
+    :title: DIST parameters
     :group: gwb-module
     
 .. note::
@@ -274,13 +286,13 @@ You will need to select parameters for your computation:
 Foreground connectivity
 #######################
 
-This set the foreground connectivity of your analysis: 
+This sets the foreground connectivity of your analysis. Specifically,
 
 -   8 neighbors (default) will use every pixel in the vicinity (including diagonals)
--   4 neighbors only use the vertical and horizontal one
+-   4 neighbors will only use the vertical and horizontal one
 
 .. thumbnail:: https://raw.githubusercontent.com/12rambau/gwb/master/doc/img/connectivity.png
-    :title: connectivity image
+    :title: Connectivity image
     :width: 50%
     :group: gwb-module
 
@@ -292,17 +304,16 @@ Two computation options are available:
 -   compute the Euclidian Distance only
 -   compute the Euclidian Distance and the Hysometric Curve
 
-
 Run the analysis
 """"""""""""""""
 
-Once your parameters are all set you can launch the analysis. The blue rectangle will display information about the computation. Upon completion, it will turn to green and display the computation log. 
+Once your parameters are set, launch the analysis. The blue rectangle will display information about the computation. Upon completion, it will turn green and display the **Computation log**.
 
 .. thumbnail:: https://raw.githubusercontent.com/12rambau/gwb/master/doc/img/dist_results.png
-    :title: information logs
+    :title: Information logs
     :group: gwb-module
 
-The resulting files are stored in the folder :code:`module_results/gwb/dist/`, for example:
+The resulting files are stored in the folder :code:`module_results/gwb/dist/`. For example:
 
 -   :code:`<raster_name>_bin_map.tif`
 -   :code:`<raster_name>_bin_map_dist.tif`
@@ -310,10 +321,10 @@ The resulting files are stored in the folder :code:`module_results/gwb/dist/`, f
 -   :code:`<raster_name>_bin_map_dist_hist.png`
 -   :code:`<raster_name>_bin_map_dist_viewport.tif`
 
-.. danger::
+.. attention::
+
+    If the rectangle turns red, carefully read the information in the log. For example, your current instance may be too small to handle the file you want to analyse. In this case, close the app, open a bigger instance, and run your analysis again.
 
-    If the rectangle turns red, carefully read the information in the log. For example, your current instance may be too small to handle the file you want to analyse. In this case, close the app, open a bigger instance and run your analysis again.
-    
 Here is the result of the computation using the default parameters on the :code:`example.tif` file.
     
 .. thumbnail:: https://raw.githubusercontent.com/openforis/sepal-doc/master/docs/source/img/cli/gwb/example_dist_hmc.png
@@ -324,33 +335,38 @@ Here is the result of the computation using the default parameters on the :code:
     :width: 49%
     :group: gwb-module
 
-FAD
-^^^
+Forest area density (FAD)
+^^^^^^^^^^^^^^^^^^^^^^^^^
 
-This module will conduct the **fragmentation** analysis at **five fixed observation scales**. Because forest fragmentation is scale-dependent, fragmentation is reported at five observation scales, which allows different observers to make their own choice about scales and threshold of concern. The change of fragmentation across different observation scales provides additional interesting information. Fragmentation is measured by determining the Forest Area Density (**FAD**) within a shifting, local neighborhood. It can be measured at pixel or patch level. The result are spatially explicit maps and tabular summary statistics. Details on the methodology and input/output options can be found in the `Fragmentation <https://ies-ows.jrc.ec.europa.eu/gtb/GTB/psheets/GTB-Fragmentation-FADFOS.pdf>`_ product sheet.
+This module will conduct the **fragmentation** analysis at **five fixed observation scales**. 
 
-Setup the input image
-"""""""""""""""""""""
+Since forest fragmentation is scale-dependent, fragmentation is reported at five observation scales, allowing different observers to make their own choice about scales and threshold of concern. 
+
+The change of fragmentation across different observation scales provides additional information of interest. 
+
+Fragmentation is measured by determining forest area density (**FAD**) within a shifting, local neighbourhood. It can be measured at pixel or patch level. The results are spatially explicit maps and tabular summary statistics. Details on the methodology and input/output options can be found in the `Fragmentation product sheet <https://ies-ows.jrc.ec.europa.eu/gtb/GTB/psheets/GTB-Fragmentation-FADFOS.pdf>`_.
+
+Set up the input image
+""""""""""""""""""""""
 
 .. tip::
 
-    You can use the default dataset to test the module. Click on the :code:`Download test dataset` button on the top of the second panel. By clicking on this button, the following two files will be added to your :code:`downloads` folder:
-        
-    -   :code:`example.tif`: 0 byte - Missing, 1 byte - Background, 2 byte - Foreground
-    -   :code:`clc3class.tif`: 1 byte - Agriculture, 2 byte - Natural, 3 byte - Developed
-    
+    You can use the default dataset to test the module. Select the :code:`Download test dataset` button on the top of the second panel, which will add the following files to your :code:`downloads` folder:
+
+    -   :code:`example.tif`: 0 bytes - Missing, 1 byte - Background, 2 bytes - Foreground
+    -   :code:`clc3class.tif`: 1 byte - Agriculture, 2 bytes - Natural, 3 bytes - Developed
+
     .. thumbnail::  https://raw.githubusercontent.com/12rambau/gwb/master/doc/img/test_dataset.png
-        :title: download sample dataset
+        :title: Download sample dataset
         :group: gwb-module
-        
     
-    Once the files are downloaded, follow the normal process using the :code:`downloads/example.tif` file (2 classes).
+    Once the files are downloaded, follow the normal process using the :code:`downloads/example.tif` file (two classes).
     
-The first step requires to reclassify your image. Using the reclassifying panel, select your image in your SEPAL folder.
+The first step requires reclassifying your image. Using the **Reclassifying** panel, select the image in your **SEPAL folder**.
 
-.. warning:: 
+.. attention::
 
-    If the image is not in your SEPAL folders but in your local computer consider reading the `exchange file with SEPAL <https://docs.sepal.io/en/latest/setup/filezilla.html>`_ page of this documentation.
+    If the image is on your local computer but not in your **SEPAL folders**, see `Exchange files with SEPAL <https://docs.sepal.io/en/latest/setup/filezilla.html>`_.
     
 The dropdown menus will list the discrete values of your raster input image. Select each class in your image and place them in one of the following categories: 
 
@@ -359,27 +375,26 @@ The dropdown menus will list the discrete values of your raster input image. Sel
 -   special background 1 (optional)
 -   special background 2 (optional)
 
-Every class that is not set to a reclassifying category will be considered as "missing data" (0 byte).
+Every class that is not set to a reclassifying category will be considered "missing data" (0 bytes).
 
 .. thumbnail:: https://raw.githubusercontent.com/12rambau/gwb/master/doc/img/4_classes.png
-    :title: upload 4 classes
+    :title: Upload four classes
     :group: gwb-module
 
 .. tip::
 
-    For forest analysis, set forest as foreground and all the other classes as background. If you specify special background, it will be treated separately in the analysis (e.g. water, buildings).
+    For forest analysis, set **Forest** as foreground and all other classes as background. If you specify a special background, it will be treated separately in the analysis (e.g. water, buildings).
 
-.. warning::
+.. attention::
 
-    The special background 2 is the non-fragmenting background (optional), see the `Fragmentation <https://ies-ows.jrc.ec.europa.eu/gtb/GTB/psheets/GTB-Fragmentation-FADFOS.pdf>`_ product sheet for details.
+    **Special background 2** is the non-fragmenting background (optional). For details, see the `Fragmentation product sheet <https://ies-ows.jrc.ec.europa.eu/gtb/GTB/psheets/GTB-Fragmentation-FADFOS.pdf>`_.
 
-    
 Select the parameters
 """""""""""""""""""""
 You will need to select parameters for your computation: 
 
 .. thumbnail:: https://raw.githubusercontent.com/12rambau/gwb/master/doc/img/fad_params.png
-    :title: acc params
+    :title: ACC parameters
     :group: gwb-module
     
 .. note::
@@ -395,23 +410,23 @@ Foreground connectivity
 
 This sets the foreground connectivity of your analysis: 
 
--   8 neighbors (default) will use every pixel in the vicinity (including diagonals)
--   4 neighbors only use the vertical and horizontal one
+-   8 neighbours (default) will use every pixel in the vicinity (including diagonals)
+-   4 neighbours only will use the vertical and horizontal one
 
 .. thumbnail:: https://raw.githubusercontent.com/12rambau/gwb/master/doc/img/connectivity.png
-    :title: connectivity image
+    :title: Connectivity image
     :width: 50%
     :group: gwb-module
     
 Computation precision
-######################
+#####################
 
-Set the precision used to compute your image. Float precision (default) will give more accurate results compared to rounded byte but will also take more computing resources and disk space.
+Set the precision used to compute your image. **Float precision** (default) will give more accurate results compared to **Rounded byte**, but requires more computing resources and disk space.
 
 Options
 #######
 
-Three computation options are available: 
+Three computation options are available:
 
 -   FAD: per-pixel density, color-coded into 6 fragmentation classes
 -   FAD-APP2: average per-patch density, color-coded into 2 classes
@@ -420,13 +435,13 @@ Three computation options are available:
 Run the analysis
 """"""""""""""""
 
-Once your parameters are all set you can launch the analysis. The blue rectangle will display information about the computation. Upon completion, it will turn to green and display the computation log. 
+Once your parameters are all set you can launch the analysis. The blue rectangle will display information about the computation. Upon completion, it will turn green and display the computation log.
 
 .. thumbnail:: https://raw.githubusercontent.com/12rambau/gwb/master/doc/img/fad_results.png
-    :title: information logs
+    :title: Information logs
     :group: gwb-module
 
-The resulting files are stored in the folder :code:`module_results/gwb/fad/`, for example:
+The resulting files are stored in the folder :code:`module_results/gwb/fad/`. For example:
 
 -   :code:`<raster_name>_bin_map.tif`
 -   :code:`<raster_name>_bin_map_fad_<class_number>.tif`
@@ -436,9 +451,9 @@ The resulting files are stored in the folder :code:`module_results/gwb/fad/`, fo
 -   :code:`<raster_name>_bin_map_fad_mscale.txt`
 -   :code:`<raster_name>_bin_map_fad_mscale.sav`
 
-.. danger::
+.. attention::
 
-    If the rectangle turns red, carefully read the information in the log. For example, your current instance may be too small to handle the file you want to analyse. In this case, close the app, open a bigger instance and run your analysis again.
+    If the rectangle turns red, carefully read the information in the log. For example, your current instance may be too small to handle the file you want to analyse. In this case, close the app, open a bigger instance, and run your analysis again.
     
 Here is the result of the computation using the default parameters on the :code:`example.tif` file.
     
@@ -450,62 +465,61 @@ Here is the result of the computation using the default parameters on the :code:
     :width: 49%
     :group: gwb-module
 
-FRAG
-^^^^
+Fragmentation (FRAG)
+^^^^^^^^^^^^^^^^^^^^
 
-This module will conduct the **fragmentation** analysis at a **user-selected observation scale**. This module and its option are similar to :code:`fad` but allow the user to specify a single (or multiple) specific observation scale. The result are spatially explicit maps and tabular summary statistics. Details on the methodology and input/output options can be found in the `Fragmentation <https://ies-ows.jrc.ec.europa.eu/gtb/GTB/psheets/GTB-Fragmentation-FADFOS.pdf>`_ product sheet.
+This module will conduct the **Fragmentation** analysis at a **user-selected observation scale**. This module and its option are similar to :code:`fad`, but allow the user to specify a single (or multiple) specific observation scale. The results are spatially explicit maps and tabular summary statistics. Details on the methodology and input/output options can be found in the `Fragmentation product sheet <https://ies-ows.jrc.ec.europa.eu/gtb/GTB/psheets/GTB-Fragmentation-FADFOS.pdf>`_.
 
-Setup the input image
-"""""""""""""""""""""
+Set up the input image
+""""""""""""""""""""""
 
 .. tip::
 
-    You can use the default dataset to test the module. Click on the :code:`Download test dataset` button on the top of the second panel. By clicking on this button, the following two files will be added to your :code:`downloads` folder:
+    You can use the default dataset to test the module. Select the :code:`Download test dataset` button on the top of the second panel, which will add the following files to your :code:`downloads` folder:
         
-    -   :code:`example.tif`: 0 byte - Missing, 1 byte - Background, 2 byte - Foreground
-    -   :code:`clc3class.tif`: 1 byte - Agriculture, 2 byte - Natural, 3 byte - Developed
+    -   :code:`example.tif`: 0 bytes - Missing, 1 byte - Background, 2 bytes - Foreground
+    -   :code:`clc3class.tif`: 1 byte - Agriculture, 2 bytes - Natural, 3 bytes - Developed
     
     .. thumbnail::  https://raw.githubusercontent.com/12rambau/gwb/master/doc/img/test_dataset.png
-        :title: download sample dataset
+        :title: Download sample dataset
         :group: gwb-module
         
+    Once the files are downloaded, follow the normal process using the :code:`downloads/example.tif` file (two classes).
     
-    Once the files are downloaded, follow the normal process using the :code:`downloads/example.tif` file (2 classes).
-    
-The first step requires to reclassify your image. Using the reclassifying panel, select your image in your SEPAL folder.
+The first step requires reclassifying your image. Using the **Reclassifying** panel, select the image in your **SEPAL folder**.
 
-.. warning:: 
+.. attention:: 
 
-    If the image is not in your SEPAL folders but in your local computer consider reading the `exchange file with SEPAL <https://docs.sepal.io/en/latest/setup/filezilla.html>`_ page of this documentation.
+    If the image is on your local computer but not in your **SEPAL folders**, see `Exchange files with SEPAL <https://docs.sepal.io/en/latest/setup/filezilla.html>`_.
     
-The dropdown menus will list the discrete values of your raster input image. Select each class in your image and place them in one of the following categories: 
+The dropdown menus will list the discrete values of your raster input image. Select each class in your image and place them in one of the following categories:
 
 -   background
 -   foreground
 -   special background 1 (optional)
 -   special background 2 (optional)
 
-Every class that is not set to a reclassifying category will be considered as "missing data" (0 byte).
+Every class that is not set to a reclassifying category will be considered "missing data" (0 byte).
 
 .. thumbnail:: https://raw.githubusercontent.com/12rambau/gwb/master/doc/img/4_classes.png
-    :title: upload 4 classes
+    :title: Upload four classes
     :group: gwb-module
 
 .. tip::
 
-    For forest analysis, set forest as foreground and all the other classes as background. If you specify special background, it will be treated separately in the analysis (e.g. water, buildings).
+    For forest analysis, set **Forest** as foreground and all other classes as background. If you specify a special background, it will be treated separately in the analysis (e.g. water, buildings).
     
-.. warning::
+.. attention::
+
+    **Special background 2** is the non-fragmenting background (optional). For details, see the `Fragmentation product sheet <https://ies-ows.jrc.ec.europa.eu/gtb/GTB/psheets/GTB-Fragmentation-FADFOS.pdf>`_.
 
-    The special background 2 is the non-fragmenting background (optional), see the `Fragmentation <https://ies-ows.jrc.ec.europa.eu/gtb/GTB/psheets/GTB-Fragmentation-FADFOS.pdf>`_ product sheet for details.
-    
 Select the parameters
 """""""""""""""""""""
 
 You will need to select parameters for your computation: 
 
 .. thumbnail:: https://raw.githubusercontent.com/12rambau/gwb/master/doc/img/frag_params.png
-    :title: acc params
+    :title: ACC parameters
     :group: gwb-module
     
 .. note::
@@ -515,7 +529,7 @@ You will need to select parameters for your computation:
     -   Foreground connectivity: 8
     -   Spatial pixel resolution: 25
     -   Computation precision: float-precision
-    -   Windows size: 23
+    -   Window size: 23
     -   Options: fragmentation at pixel or at patch level with various number of color-coded classes
 
 Foreground connectivity
@@ -523,23 +537,23 @@ Foreground connectivity
 
 This sets the foreground connectivity of your analysis: 
 
--   8 neighbors (default) will use every pixel in the vicinity (including diagonals)
--   4 neighbors only use the vertical and horizontal one
+-   8 neighbours (default) will use every pixel in the vicinity (including diagonals)
+-   4 neighbours will only use the vertical and horizontal one
 
 .. thumbnail:: https://raw.githubusercontent.com/12rambau/gwb/master/doc/img/connectivity.png
-    :title: connectivity image
+    :title: Connectivity image
     :width: 50%
     :group: gwb-module
     
 Spatial pixel resolution
 ########################
 
-Set the spatial pixel resolution of your image in meters. Only use for the summary.
+Set the spatial pixel resolution of your image in metres. This is only used for the summary.
 
 Window size
 ###########
 
-Set up to 10 observation windows sizes (in pixels).
+Set up to 10 observation window sizes (in pixels).
 
 Options
 #######
@@ -554,13 +568,13 @@ Four computation options are available:
 Run the analysis
 """"""""""""""""
 
-Once your parameters are all set you can launch the analysis. The blue rectangle will display information about the computation. Upon completion, it will turn to green and display the computation log. 
+Once your parameters are all set, you can launch the analysis. The blue rectangle will display information about the computation. Upon completion, it will turn green and display the computation log.
 
 .. thumbnail:: https://raw.githubusercontent.com/12rambau/gwb/master/doc/img/frag_results.png
-    :title: information logs
+    :title: Information logs
     :group: gwb-module
 
-The resulting files are stored in the folder :code:`module_results/gwb/frag/`, for example:
+The resulting files are stored in the folder :code:`module_results/gwb/frag/`. For example:
 
 -   :code:`<raster_name>_bin_map.tif`
 -   :code:`<raster_name>_bin_map_frag_fad-<option>_<class>.tif`
@@ -568,54 +582,57 @@ The resulting files are stored in the folder :code:`module_results/gwb/frag/`, f
 -   :code:`<raster_name>_bin_map_frag.txt`
 -   :code:`<raster_name>_bin_map_frag.tif`
 
-.. danger::
+.. attention::
 
-    If the rectangle turns red, carefully read the information in the log. For example, your current instance may be too small to handle the file you want to analyse. In this case, close the app, open a bigger instance and run your analysis again.
+    If the rectangle turns red, carefully read the information in the log. For example, your current instance may be too small to handle the file you want to analyse. In this case, close the app, open a bigger instance, and run your analysis again.
     
-Here is the result of the computation using the FAD-APP2 option on the :code:`example.tif` file.
+Here is the result of the computation using the FAD-APP2 option on the :code:`example.tif` file:
     
 .. thumbnail:: https://raw.githubusercontent.com/openforis/sepal-doc/master/docs/source/img/cli/gwb/example_fad-app2_23.png
     :width: 50%
     :group: gwb-module
 
-LM
-^^
+Landscape mosaic (LM)
+^^^^^^^^^^^^^^^^^^^^^
 
-This module will conduct the **Landscape Mosaic** analysis at a **user-selected observation scale**. The Landscape Mosaic measures land cover heterogeneity, or human influence, in a tri-polar classification of a location accounting for the relative contributions of the three land cover types **Agriculture**, **Natural**, **Developed** in the area surrounding that location. The result are spatially explicit maps and tabular summary statistics. Details on the methodology and input/output options can be found in the `Landscape Mosaic <https://ies-ows.jrc.ec.europa.eu/gtb/GTB/psheets/GTB-Pattern-LM.pdf>`_ product sheet.
+This module will conduct the **Landscape mosaic** analysis at a **user-selected observation scale**.
 
-Setup the input image
-"""""""""""""""""""""
+The **Landscape mosaic** measures land cover heterogeneity, or human influence, in a tri-polar classification of a location accounting for the relative contributions of the three land cover types (**Agriculture**, **Natural**, **Developed**) in the area surrounding that location. 
+
+The results are spatially explicit maps and tabular summary statistics. Details on the methodology and input/output options can be found in the `Landscape mosaic product sheet <https://ies-ows.jrc.ec.europa.eu/gtb/GTB/psheets/GTB-Pattern-LM.pdf>`_.
+
+Set up the input image
+""""""""""""""""""""""
 
 .. tip::
 
-    You can use the default dataset to test the module. Click on the :code:`Download test dataset` button on the top of the second panel. By clicking on this button, the following two files will be added to your :code:`downloads` folder:
+    You can use the default dataset to test the module. Select the :code:`Download test dataset` button on the top of the second panel, which will add the following files to your :code:`downloads` folder:
         
-    -   :code:`example.tif`: 0 byte - Missing, 1 byte - Background, 2 byte - Foreground
-    -   :code:`clc3class.tif`: 1 byte - Agriculture, 2 byte - Natural, 3 byte - Developed
+    -   :code:`example.tif`: 0 bytes - Missing, 1 byte - Background, 2 bytes - Foreground
+    -   :code:`clc3class.tif`: 1 byte - Agriculture, 2 bytes - Natural, 3 bytes - Developed
     
     .. thumbnail::  https://raw.githubusercontent.com/12rambau/gwb/master/doc/img/test_dataset.png
-        :title: download sample dataset
+        :title: Download sample dataset
         :group: gwb-module
-        
-    
-    Once the files are downloaded, follow the normal process using the :code:`downloads/clc3class.tif` file (3 classes).
+
+    Once the files are downloaded, follow the normal process using the :code:`downloads/clc3class.tif` file (three classes).
     
-The first step requires to reclassify your image. Using the reclassifying panel, select your image in your SEPAL folder.
+The first step requires reclassifying your image. Using the **Reclassifying** panel, select the image in your **SEPAL folder**.
 
-.. warning:: 
+.. attention:: 
+
+    If the image is on your local computer and not in your **SEPAL folders**, see `Exchange files with SEPAL <https://docs.sepal.io/en/latest/setup/filezilla.html>`_.
 
-    If the image is not in your SEPAL folders but in your local computer consider reading the `exchange file with SEPAL <https://docs.sepal.io/en/latest/setup/filezilla.html>`_ page of this documentation.
-    
 The dropdown menus will list the discrete values of your raster input image. Select each class in your image and place them in one of the following categories: 
 
 -   dominant land cover 1 (Agriculture)
 -   dominant land cover 2 (Natural)
 -   dominant land cover 3 (Developed)
 
-Every class that is not set to a reclassifying category will be considered as "missing data" (0 byte).
+Every class that is not set to a reclassifying category will be considered "missing data" (0 bytes).
 
 .. thumbnail:: https://raw.githubusercontent.com/12rambau/gwb/master/doc/img/3_classes.png
-    :title: upload 3 classes
+    :title: Upload three classes
     :group: gwb-module
     
 Select the parameters
@@ -624,7 +641,7 @@ Select the parameters
 You will need to select parameters for your computation: 
 
 .. thumbnail:: https://raw.githubusercontent.com/12rambau/gwb/master/doc/img/lm_params.png
-    :title: lm params
+    :title: LM parameters
     :group: gwb-module
     
 .. note::
@@ -636,8 +653,7 @@ You will need to select parameters for your computation:
 Window size
 ###########
 
-Set the square window size (in pixels). It should be an odd number in [3, 5, ...501].
-with :math:`kdim` being the window size, which is related to the observation scale by the following formula: 
+Set the square window size (in pixels). It should be an odd number in [3, 5, ...501], with :math:`kdim` being the window size, which is related to the observation scale by the following formula: 
 
 .. math::
 
@@ -645,20 +661,20 @@ with :math:`kdim` being the window size, which is related to the observation sca
     
 with
 
--   :math:`obs_scale` in hectare
--   :math:`pixres` in meters
+-   :math:`obs_scale` in hectares
+-   :math:`pixres` in metres
 -   :math:`kdim` in pixels
 
 Run the analysis
 """"""""""""""""
 
-Once your parameters are all set you can launch the analysis. The blue rectangle will display information about the computation. Upon completion, it will turn to green and display the computation log. 
+Once your parameters are all set, you can launch the analysis. The blue rectangle will display information about the computation. Upon completion, it will turn green and display the computation log.
 
 .. thumbnail:: https://raw.githubusercontent.com/12rambau/gwb/master/doc/img/lm_results.png
-    :title: information logs
+    :title: Information logs
     :group: gwb-module
 
-The resulting files are stored in the folder :code:`module_results/gwb/lm/`, for example:
+The resulting files are stored in the folder :code:`module_results/gwb/lm/`. For example:
 
 -   :code:`<raster_name>_bin_map.tif`
 -   :code:`<raster_name>_bin_map_lm_23.tif`
@@ -669,11 +685,11 @@ The resulting files are stored in the folder :code:`module_results/gwb/lm/`, for
 -   :code:`heatmap_legend.png`
 -   :code:`lm103class_legend.png`
 
-.. danger::
+.. attention::
 
-    If the rectangle turns red, carefully read the information in the log. For example, your current instance may be too small to handle the file you want to analyse. In this case, close the app, open a bigger instance and run your analysis again.
+    If the rectangle turns red, carefully read the information in the log. For example, your current instance may be too small to handle the file you want to analyse. In this case, close the app, open a bigger instance, and run your analysis again.
     
-Here is the result of the computation using the default parameters on the :code:`clc3classes.tif` file.
+Here is the result of the computation using the default parameters on the :code:`clc3classes.tif` file:
     
 .. thumbnail:: https://raw.githubusercontent.com/openforis/sepal-doc/master/docs/source/img/cli/gwb/lm103class_legend.png
     :width: 49%
@@ -683,59 +699,58 @@ Here is the result of the computation using the default parameters on the :code:
     :width: 49%
     :group: gwb-module
 
-MSPA
-^^^^
+Morphological Spatial Pattern Analysis (MSPA)
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-.. warning:: 
+.. attention:: 
 
-    If you are considering using the MSPA module, keep in mind that the result provides a lot of information (up to 25 classes). The alternative module :code:`GWB_SPA` provides a similar but simplified assessment with up to 6 classes only. Both modules describe morphological features of foreground objects. While MSPA may address certain features of fragmentation, a more comprehensive assessment of fragmentation is obtained with the dedicated fragmentation modules :code:`GWB_FRAG` or :code:`GWB_FAD`.
+    If you are considering using the **MSPA** module, keep in mind that the result provides a lot of information (up to 25 classes). The alternative module :code:`GWB_SPA` provides a similar, yet simplified assessment with up to six classes only. Both modules describe morphological features of foreground objects. While **MSPA** may address certain features of fragmentation, a more comprehensive assessment of fragmentation is obtained with the dedicated fragmentation modules: :code:`GWB_FRAG` or :code:`GWB_FAD`.
 
-This module will conduct the **Morphological Spatial Pattern Analysis**. `MSPA <https://forest.jrc.ec.europa.eu/en/activities/lpa/mspa/>`_ analyses shape and connectivity and conducts a segmentation of foreground patches in up to 25 feature classes. The result are spatially explicit maps and tabular summary statistics. Details on the methodology and input/output options can be found in the `Morphology <https://ies-ows.jrc.ec.europa.eu/gtb/GTB/psheets/GTB-Pattern-Morphology.pdf>`_ product sheet.
+This module will conduct `MSPA <https://forest.jrc.ec.europa.eu/en/activities/lpa/mspa/>`_ analysis shape and connectivity, as well as conduct a segmentation of foreground patches in up to 25 feature classes. The results are spatially explicit maps and tabular summary statistics. Details on the methodology and input/output options can be found in the `Morphology product sheet <https://ies-ows.jrc.ec.europa.eu/gtb/GTB/psheets/GTB-Pattern-Morphology.pdf>`_.
 
-Setup the input image
-"""""""""""""""""""""
+Set up the input image
+""""""""""""""""""""""
 
 .. tip::
 
-    You can use the default dataset to test the module. Click on the :code:`Download test dataset` button on the top of the second panel. By clicking on this button, the following two files will be added to your :code:`downloads` folder:
+    You can use the default dataset to test the module. Select the :code:`Download test dataset` button on the top of the second panel, which will add the following files to your :code:`downloads` folder:
         
-    -   :code:`example.tif`: 0 byte - Missing, 1 byte - Background, 2 byte - Foreground
-    -   :code:`clc3class.tif`: 1 byte - Agriculture, 2 byte - Natural, 3 byte - Developed
+    -   :code:`example.tif`: 0 byte - Missing, 1 byte - Background, 2 bytes - Foreground
+    -   :code:`clc3class.tif`: 1 byte - Agriculture, 2 bytes - Natural, 3 bytes - Developed
     
     .. thumbnail::  https://raw.githubusercontent.com/12rambau/gwb/master/doc/img/test_dataset.png
-        :title: download sample dataset
+        :title: Download sample dataset
         :group: gwb-module
         
+    Once the files are downloaded, follow the normal process using the :code:`downloads/example.tif` file (two classes).
     
-    Once the files are downloaded, follow the normal process using the :code:`downloads/example.tif` file (2 classes).
-    
-The first step requires to reclassify your image. Using the reclassifying panel, select your image in your SEPAL folder.
+The first step requires reclassifying your image. Using the **Reclassifying** panel, select the image in your **SEPAL folder**.
 
-.. warning:: 
+.. attention:: 
 
-    If the image is not in your SEPAL folders but in your local computer consider reading the `exchange file with SEPAL <https://docs.sepal.io/en/latest/setup/filezilla.html>`_ page of this documentation.
+    If the image is on your local computer and not in your SEPAL folders, see `Exchange files with SEPAL <https://docs.sepal.io/en/latest/setup/filezilla.html>`_.
     
 The dropdown menus will list the discrete values of your raster input image. Select each class in your image and place them in one of the following categories: 
 
 -   background
 -   foreground
 
-Every class that is not set to a reclassifying category will be considered as "missing data" (0 byte).
+Every class that is not set to a reclassifying category will be considered "missing data" (0 bytes).
 
 .. thumbnail:: https://raw.githubusercontent.com/12rambau/gwb/master/doc/img/2_classes.png
-    :title: upload 2 classes
+    :title: Upload 2 classes
     :group: gwb-module
 
 .. tip::
 
-    For forest analysis, set forest as foreground and all the other classes as background.
+    For forest analysis, set **Forest** as foreground and all other classes as background.
     
 Select the parameters
 """""""""""""""""""""
 You will need to select parameters for your computation: 
 
 .. thumbnail:: https://raw.githubusercontent.com/12rambau/gwb/master/doc/img/mspa_params.png
-    :title: mspa params
+    :title: MSPA parameters
     :group: gwb-module
     
 .. note::
@@ -754,28 +769,28 @@ Foreground connectivity
 
 This sets the foreground connectivity of your analysis: 
 
--   8 neighbors (default) will use every pixel in the vicinity (including diagonals)
--   4 neighbors only use the vertical and horizontal one
+-   8 neighbours (default) will use every pixel in the vicinity (including diagonals)
+-   4 neighbours will only use the vertical and horizontal one
 
 .. thumbnail:: https://raw.githubusercontent.com/12rambau/gwb/master/doc/img/connectivity.png
-    :title: connectivity image
+    :title: Connectivity image
     :width: 50%
     :group: gwb-module
     
 Edge width
 ##########
 
-Define the width (measured in pixels) of the Core-boundaries (Edges and Perforations).
+Define the width (measured in pixels) of the core-boundaries (Edges and Perforations).
 
 Transition
-###########
+##########
 
 Select if you want to show transition pixels, where connecting pathways go through edges/perforations (transition=1 (true), default) or not (transition=0).
 
 Intext
 ######
 
-Select if you want to distinguish MSPA classes and Holes laying within Core objects (intext=1 (true), default) or not (intext=0).
+Select if you want to distinguish **MSPA** classes and holes laying within core objects (intext=1 (true), default) or not (intext=0).
 
 Disk
 ####
@@ -790,21 +805,21 @@ Select if you want to calculate summary statistics (statistics=0 (false), defaul
 Run the analysis
 """"""""""""""""
 
-Once your parameters are all set you can launch the analysis. The blue rectangle will display information about the computation. Upon completion, it will turn to green and display the computation log. 
+Once your parameters are set, you can launch the analysis. The blue rectangle will display information about the computation. Upon completion, it will turn green and display the computation log.
 
 .. thumbnail:: https://raw.githubusercontent.com/12rambau/gwb/master/doc/img/mspa_results.png
-    :title: information logs
+    :title: Information logs
     :group: gwb-module
 
-The resulting files are stored in the folder :code:`module_results/gwb/mspa/`, for example:
+The resulting files are stored in the folder :code:`module_results/gwb/mspa/`. For example:
 
 -   :code:`<raster_name>_bin_map.tif`
 -   :code:`<raster_name>_bin_map_<4 params>.tif`
 -   :code:`<raster_name>_bin_map_<4 params>.txt`
 
-.. danger::
+.. attention::
 
-    If the rectangle turns red, carefully read the information in the log. For example, your current instance may be too small to handle the file you want to analyse. In this case, close the app, open a bigger instance and run your analysis again.
+    If the rectangle turns red, carefully read the information in the log. For example, your current instance may be too small to handle the file you want to analyse. In this case, close the app, open a bigger instance, and run your analysis again.
     
 Here is the result of the computation using the default parameters on the :code:`example.tif` file.
     
@@ -816,33 +831,36 @@ Here is the result of the computation using the default parameters on the :code:
     :width: 49%
     :group: gwb-module
 
-P223
-^^^^
+Density, Contagion or Adjacency Analysis (P223)
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-This module will conduct the **Density** (P2), **Contagion** (P22) or **Adjacency** (P23) analysis of foreground (**FG**) objects at a user-selected observation scale (`Riitters et al. (2000) <https://www.srs.fs.usda.gov/pubs/ja/ja_riitters006.pdf>`_). The result are spatially explicit maps and tabular summary statistics. The classification is determined by measurements of forest amount (P2) and connectivity (P22) within the neighborhood that is centered on a subject forest pixel. P2 is the probability that a pixel in the neighborhood is forest, and P22 is the probability that a pixel next to a forest pixel is also forest.
+This module will conduct the **Density** (P2), **Contagion** (P22) or **Adjacency** (P23) analysis of foreground (**FG**) objects at a user-selected observation scale (`Riitters et al., 2000 <https://www.srs.fs.usda.gov/pubs/ja/ja_riitters006.pdf>`_). 
 
-Setup the input image
-"""""""""""""""""""""
+The results are spatially explicit maps and tabular summary statistics. 
+
+The classification is determined by measurements of forest amount (P2) and connectivity (P22) within the neighbourhood that is centred on a subject forest pixel. P2 is the probability that a pixel in the neighborhood is forest; P22 is the probability that a pixel next to a forest pixel is also forest.
+
+Set up the input image
+""""""""""""""""""""""
 
 .. tip::
 
-    You can use the default dataset to test the module. Click on the :code:`Download test dataset` button on the top of the second panel. By clicking on this button, the following two files will be added to your :code:`downloads` folder:
+    You can use the default dataset to test the module. Select the :code:`Download test dataset` button on the top of the second panel, which will add the following files to your :code:`downloads` folder:
         
-    -   :code:`example.tif`: 0 byte - Missing, 1 byte - Background, 2 byte - Foreground
-    -   :code:`clc3class.tif`: 1 byte - Agriculture, 2 byte - Natural, 3 byte - Developed
+    -   :code:`example.tif`: 0 byte - Missing, 1 byte - Background, 2 bytes - Foreground
+    -   :code:`clc3class.tif`: 1 byte - Agriculture, 2 bytes - Natural, 3 bytes - Developed
     
     .. thumbnail::  https://raw.githubusercontent.com/12rambau/gwb/master/doc/img/test_dataset.png
-        :title: download sample dataset
+        :title: Download sample dataset
         :group: gwb-module
         
+    Once the files are downloaded, follow the normal process using the :code:`downloads/example.tif` file (two classes).
     
-    Once the files are downloaded, follow the normal process using the :code:`downloads/example.tif` file (2 classes).
-    
-The first step requires to reclassify your image. Using the reclassifying panel, select your image in your SEPAL folder.
+The first step requires reclassifying your image. Using the **Reclassifying** panel, select the image in your **SEPAL folder**.
 
-.. warning:: 
+.. attention:: 
 
-    If the image is not in your SEPAL folders but in your local computer consider reading the `exchange file with SEPAL <https://docs.sepal.io/en/latest/setup/filezilla.html>`_ page of this documentation.
+    If the image is on your local computer but not in your **SEPAL folders**, consider reading `Exchange files with SEPAL <https://docs.sepal.io/en/latest/setup/filezilla.html>`_.
     
 The dropdown menus will list the discrete values of your raster input image. Select each class in your image and place them in one of the following categories: 
 
@@ -850,15 +868,15 @@ The dropdown menus will list the discrete values of your raster input image. Sel
 -   foreground
 -   special background (for P23 only)
 
-Every class that is not set to a reclassifying category will be considered as "missing data" (0 byte).
+Every class that is not set to a reclassifying category will be considered as "missing data" (0 bytes).
 
 .. thumbnail:: https://raw.githubusercontent.com/12rambau/gwb/master/doc/img/p223_classes.png
-    :title: upload 3 classes
+    :title: Upload three classes
     :group: gwb-module
 
 .. tip::
 
-    For forest analysis, set forest as foreground and all the other classes as background. If you specify special background, it will be treated separately in the analysis (e.g. water, buildings)
+    For forest analysis, set **Forest** as foreground and all the other classes as background. If you specify a special background, it will be treated separately in the analysis (e.g. water, buildings).
     
 Select the parameters
 """""""""""""""""""""
@@ -866,7 +884,7 @@ Select the parameters
 You will need to select parameters for your computation: 
 
 .. thumbnail:: https://raw.githubusercontent.com/12rambau/gwb/master/doc/img/p223_params.png
-    :title: p223 params
+    :title: P223 parameters
     :group: gwb-module
     
 .. note::
@@ -880,7 +898,7 @@ You will need to select parameters for your computation:
 Window size
 ###########
 
-Set the square window size (in pixels) It should be an odd number in [3, 5, ...501] with :math:`kdim` being related to the observation scale by the following formula: 
+Set the square window size (in pixels). It should be an odd number in [3, 5, ...501] with :math:`kdim` being related to the observation scale by the following formula: 
 
 .. math::
 
@@ -888,38 +906,39 @@ Set the square window size (in pixels) It should be an odd number in [3, 5, ...5
     
 with 
 
-- :math:`obs_scale` in hectare
-- :math:`pixres` in meters
+- :math:`obs_scale` in hectares
+- :math:`pixres` in metres
 - :math:`kdim` in pixels
 
 Computation precision
-######################
+#####################
 
-Set the precision used to compute your image. Float precision (default) will give more accurate results compared to rounded byte but will also take more computing resources and disk space.
+Set the precision used to compute your image. **Float precision** (default) will give more accurate results compared to **rounded byte**, but will also take more computing resources and disk space.
 
 Algorithm
 #########
 
-The P223 module can run: **FG-Density** (P2), **FG-Contagion** (P22), or **FG-Adjacency** (P23)
+The **P223** module can run: **FG-Density** (P2), **FG-Contagion** (P22), or **FG-Adjacency** (P23)
 
-P223 will provide a color-coded image showing [0,100]% for either **FG-Density**, **FG-Contagion**, or **FG-Adjacency** masked for the Foreground cover. Use the alternative options to obtain the original spatcon output without normalisation, masking, or color-coding.
+**P223** will provide a color-coded image showing [0,100]% for either **FG-Density**, **FG-Contagion**, or **FG-Adjacency** masked for the foreground cover. Use the alternative options to obtain the original spatcon output without normalization, masking, or color-coding.
 
 .. tip::
 
     For original spatcon output **ONLY**:
-    Missing values are coded as 0 (rounded byte), or -0.01 (float precision). For all output types, missing indicates the input window contained only missing pixels.
+    Missing values are coded as 0 (rounded byte), or -0.01 (float precision). For all output types, missing indicates that the input window contained only missing pixels.
 
 .. tip::
 
-    For FG-Contagion and FG-Adjacency output **ONLY**, missing also indicates the input window contained no foreground pixels (there was no information about foreground edge).
+    For FG-Contagion and FG-Adjacency output **ONLY**: 
+    Missing also indicates that the input window contained no foreground pixels (there was no information about foreground edge).
 
 For all output types, :math:`rounded byte = (float precision * 254) + 1`
-    
-You'll find the options displayed with the following names in the dropdown menu:
 
--   FG-Density   (FG-masked and normalised)
--   FG-Contagion (FG-masked and normalised)
--   FG-Adjacency (FG-masked and normalised)
+The options are displayed with the following names in the dropdown menu:
+
+-   FG-Density   (FG-masked and normalized)
+-   FG-Contagion (FG-masked and normalized)
+-   FG-Adjacency (FG-masked and normalized)
 -   FG-Density   (original spatcon output)
 -   FG-Contagion (original spatcon output)
 -   FG-Adjacency (original spatcon output)
@@ -929,21 +948,21 @@ You'll find the options displayed with the following names in the dropdown menu:
 Run the analysis
 """"""""""""""""
 
-Once your parameters are all set you can launch the analysis. The blue rectangle will display information about the computation. Upon completion, it will turn to green and display the computation log. 
+Once your parameters are set, you can launch the analysis. The blue rectangle will display information about the computation. Upon completion, it will turn green and display the computation log.
 
 .. thumbnail:: https://raw.githubusercontent.com/12rambau/gwb/master/doc/img/p223_results.png
-    :title: information logs
+    :title: Information logs
     :group: gwb-module
 
-The resulting files are stored in the folder :code:`module_results/gwb/p223/`, for example:
+The resulting files are stored in the folder :code:`module_results/gwb/p223/`. For example:
 
 -   :code:`<raster_name>_bin_map.tif`
 -   :code:`<raster_name>_bin_map_p<option>_<window>.tif`
 -   :code:`<raster_name>_bin_map_p<option>_<window>.txt`
 
-.. danger::
+.. attention::
 
-    If the rectangle turns red, carefully read the information in the log. For example, your current instance may be too small to handle the file you want to analyse. In this case, close the app, open a bigger instance and run your analysis again.
+    If the rectangle turns red, carefully read the information in the log. For example, your current instance may be too small to handle the file you want to analyse. In this case, close the app, open a bigger instance, and run your analysis again.
 
 Here is the result of the computation using the P2 (Foreground-Density) option on the :code:`example.tif` file.
 
@@ -951,37 +970,37 @@ Here is the result of the computation using the P2 (Foreground-Density) option o
     :width: 50%
     :group: gwb-module
 
-PARC
-^^^^
+Parcellation (PARC)
+^^^^^^^^^^^^^^^^^^^
 
-This module will conduct the **parcellation** analysis. This module provides a statistical summary file (txt/csv- format) with details for each unique class found in the image as well as the full image content: class value, total number of objects, total area, degree of parcellation.
-Details on the methodology and input/output options can be found in the `Parcellation <https://ies-ows.jrc.ec.europa.eu/gtb/GTB/psheets/GTB-Objects-Parcellation.pdf>`_ product sheet.
+This module will conduct the **Parcellation** analysis, providing a statistical summary file (.txt/.csv format) with details for each unique class found in the image, as well as the full image content: class value, total number of objects, total area, and degree of parcellation.
 
-Setup the input image
-"""""""""""""""""""""
+Details on the methodology and input/output options can be found in the `Parcellation product sheet <https://ies-ows.jrc.ec.europa.eu/gtb/GTB/psheets/GTB-Objects-Parcellation.pdf>`_.
+
+Set up the input image
+""""""""""""""""""""""
 
 .. tip::
 
-    You can use the default dataset to test the module. Click on the :code:`Download test dataset` button on the top of the second panel. By clicking on this button, the following two files will be added to your :code:`downloads` folder:
+    You can use the default dataset to test the module. Select the :code:`Download test dataset` button on the top of the second panel, which will add following files to your :code:`downloads` folder:
         
-    -   :code:`example.tif`: 0 byte - Missing, 1 byte - Background, 2 byte - Foreground
-    -   :code:`clc3class.tif`: 1 byte - Agriculture, 2 byte - Natural, 3 byte - Developed
+    -   :code:`example.tif`: 0 bytes - Missing, 1 byte - Background, 2 bytes - Foreground
+    -   :code:`clc3class.tif`: 1 byte - Agriculture, 2 bytes - Natural, 3 bytes - Developed
     
     .. thumbnail::  https://raw.githubusercontent.com/12rambau/gwb/master/doc/img/test_dataset.png
         :title: download sample dataset
         :group: gwb-module
-        
+            
+    Once the files are downloaded, follow the normal process using the :code:`downloads/clc3classes.tif` file (three classes).
     
-    Once the files are downloaded, follow the normal process using the :code:`downloads/clc3classes.tif` file (3 classes).
-    
-The first step requires to select your image in your SEPAL folder. The image must be a categorical tif raster.
+The first step requires selecting your image in your **SEPAL folder**. The image must be a categorical .tif raster.
 
-.. warning:: 
+.. attention:: 
 
-    If the image is not in your SEPAL folders but in your local computer consider reading the `exchange file with SEPAL <https://docs.sepal.io/en/latest/setup/filezilla.html>`_ page of this documentation.
+    If the image is on your local computer and not in your **SEPAL folders**, see `Exchange files with SEPAL <https://docs.sepal.io/en/latest/setup/filezilla.html>`_.
 
 .. thumbnail:: https://raw.githubusercontent.com/12rambau/gwb/master/doc/img/0_classes.png
-    :title: upload 0 classes
+    :title: Upload zero classes
     :group: gwb-module
     
 Select the parameters
@@ -989,7 +1008,7 @@ Select the parameters
 You will need to select parameters for your computation: 
 
 .. thumbnail:: https://raw.githubusercontent.com/12rambau/gwb/master/doc/img/parc_params.png
-    :title: parc params
+    :title: PARC parameters
     :group: gwb-module
     
 .. note::
@@ -1001,37 +1020,36 @@ You will need to select parameters for your computation:
 Foreground connectivity
 #######################
 
-This set the foreground connectivity of your analysis:
+This sets the foreground connectivity of your analysis:
 
--   8 neighbors (default) will use every pixel in the vicinity (including diagonals)
--   4 neighbors only use the vertical and horizontal one
+-   8 neighbours (default) will use every pixel in the vicinity (including diagonals)
+-   4 neighbours will only use the vertical and horizontal one
 
 .. thumbnail:: https://raw.githubusercontent.com/12rambau/gwb/master/doc/img/connectivity.png
-    :title: connectivity image
+    :title: Connectivity image
     :width: 50%
     :group: gwb-module
 
 Run the analysis
 """"""""""""""""
 
-Once your parameters are all set you can launch the analysis. The blue rectangle will display information about the computation. Upon completion, it will turn to green and display the computation log. 
+Once your parameters are all set, you can launch the analysis. The blue rectangle will display information about the computation. Upon completion, it will turn green and display the computation log.
 
 .. thumbnail:: https://raw.githubusercontent.com/12rambau/gwb/master/doc/img/parc_results.png
-    :title: information logs
+    :title: Information logs
     :group: gwb-module
 
-The resulting files are stored in the folder :code:`module_results/gwb/parc/`, for example:
+The resulting files are stored in the folder :code:`module_results/gwb/parc/`. For example:
 
 -   :code:`<raster_name>_bin_map.tif`
 -   :code:`<raster_name>_bin_map_parc.csv`
 -   :code:`<raster_name>_bin_map_parc.txt`
 
+.. attention::
 
-.. danger::
+    If the rectangle turns red, carefully read the information in the log. For example, your current instance may be too small to handle the file you want to analyse. In this case, close the app, open a bigger instance, and run your analysis again.
 
-    If the rectangle turns red, carefully read the information in the log. For example, your current instance may be too small to handle the file you want to analyse. In this case, close the app, open a bigger instance and run your analysis again.
-
-Here is the result of the computation using the default parameters on the :code:`clc3classes.tif` file.
+Here is the result of the computation using the default parameters on the :code:`clc3classes.tif` file:
 
 .. csv-table::
     :header: Class, Value, Count, Area[pixels], APS, AWAPS, AWAPS/data, DIVISION, PARC[%]
@@ -1041,144 +1059,152 @@ Here is the result of the computation using the default parameters on the :code:
     3,3,212,593190.,2798.07,128177.,19008.4,0.783919,11.0897
     8-connected Parcels:, ,421, 4000000,9501.19, ,1310139.4,0.672465,8.07904
 
-RSS
-^^^
+Restoration status summary (RSS)
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-This module will conduct the **Restoration Status Summary analysis**. It will calculate key attributes of the current network status, composed of foreground (forest) patches and it provides the normalized degree of network coherence. The result are tabular summary statistics. Details on the methodology and input/output options can be found in the `Restoration Planner <https://ies-ows.jrc.ec.europa.eu/gtb/GTB/psheets/GTB-RestorationPlanner.pdf>`_ product sheet.
+This module will conduct the **Restoration status summary** analysis, which will calculate key attributes of the current network status, composed of foreground (forest) patches, as well as provide the normalized degree of network coherence.
 
-Setup the input image
-"""""""""""""""""""""
+The results are tabular summary statistics. 
+
+Details on the methodology and input/output options can be found in the `Restoration Planner product sheet <https://ies-ows.jrc.ec.europa.eu/gtb/GTB/psheets/GTB-RestorationPlanner.pdf>`_.
+
+Set up the input image
+""""""""""""""""""""""
 
 .. tip::
 
-    You can use the default dataset to test the module. Click on the :code:`Download test dataset` button on the top of the second panel. By clicking on this button, the following two files will be added to your :code:`downloads` folder:
+    You can use the default dataset to test the module. Select the :code:`Download test dataset` button on the top of the second panel, which will add the following files to your :code:`downloads` folder:
         
-    -   :code:`example.tif`: 0 byte - Missing, 1 byte - Background, 2 byte - Foreground
-    -   :code:`clc3class.tif`: 1 byte - Agriculture, 2 byte - Natural, 3 byte - Developed
+    -   :code:`example.tif`: 0 byte - Missing, 1 byte - Background, 2 bytes - Foreground
+    -   :code:`clc3class.tif`: 1 byte - Agriculture, 2 bytes - Natural, 3 bytes - Developed
     
     .. thumbnail::  https://raw.githubusercontent.com/12rambau/gwb/master/doc/img/test_dataset.png
-        :title: download sample dataset
+        :title: Download sample dataset
         :group: gwb-module
         
-    
-    Once the files are downloaded, follow the normal process using the :code:`downloads/example.tif` file (2 classes).
-    
-The first step requires to reclassify your image. Using the reclassifying panel, select your image in your SEPAL folder.
+    Once the files are downloaded, follow the normal process using the :code:`downloads/example.tif` file (two classes).
+
+The first step requires reclassifying your image. Using the **Reclassifying** panel, select the image in your **SEPAL folder**.
 
-.. warning:: 
+.. attention::
 
-    If the image is not in your SEPAL folders but in your local computer consider reading the `exchange file with SEPAL <https://docs.sepal.io/en/latest/setup/filezilla.html>`_ page of this documentation.
+    If the image is on your local computer and not in your **SEPAL folders**, see `Exchange files with SEPAL <https://docs.sepal.io/en/latest/setup/filezilla.html>`_.
     
-The dropdown menus will list the discrete values of your raster input image. Select each class in your image and place them in one of the following categories: 
+The dropdown menus will list the discrete values of your raster input image. Select each class in your image and place them in one of the following categories:
 
 -   background
 -   foreground
 
-Every class that is not set to a reclassifying category will be considered as "missing data" (0 byte).
+Every class that is not set to a reclassifying category will be considered "missing data" (0 bytes).
 
 .. thumbnail:: https://raw.githubusercontent.com/12rambau/gwb/master/doc/img/2_classes.png
-    :title: upload 2 classes
+    :title: Upload two classes
     :group: gwb-module
 
 .. tip::
 
-    For forest analysis, set forest as foreground and all the other classes as background.
+    For forest analysis, set **Forest** as foreground and all other classes as background.
     
 Select the parameters
 """""""""""""""""""""
-You will need to select parameters for your computation: 
+You will need to select parameters for your computation:
 
 .. thumbnail:: https://raw.githubusercontent.com/12rambau/gwb/master/doc/img/rss_params.png
-    :title: rss params
+    :title: RSS parameters
     :group: gwb-module
     
 .. note::
 
-    This parameters can be used to perform the default computation:
+    These parameters can be used to perform the default computation:
     
     -   Foreground connectivity: 8
 
 Foreground connectivity
 #######################
 
-This set the foreground connectivity of your analysis: 
+This sets the foreground connectivity of your analysis: 
 
--   8 neighbors (default) will use every pixel in the vicinity (including diagonals)
--   4 neighbors only use the vertical and horizontal one
+-   8 neighbours (default) will use every pixel in the vicinity (including diagonals)
+-   4 neighbours will only use the vertical and horizontal one
 
 .. thumbnail:: https://raw.githubusercontent.com/12rambau/gwb/master/doc/img/connectivity.png
-    :title: connectivity image
+    :title: Connectivity image
     :width: 50%
     :group: gwb-module
 
 Run the analysis
 """"""""""""""""
 
-Once your parameters are all set you can launch the analysis. The blue rectangle will display information about the computation. Upon completion, it will turn to green and display the computation log. 
+Once your parameters are set, you can launch the analysis. The blue rectangle will display information about the computation. Upon completion, it will turn green and display the computation log. 
 
 .. thumbnail:: https://raw.githubusercontent.com/12rambau/gwb/master/doc/img/rss_results.png
-    :title: information logs
+    :title: Information logs
     :group: gwb-module
 
-The resulting files are stored in the folder :code:`module_results/gwb/rss/`, for example:
+The resulting files are stored in the folder :code:`module_results/gwb/rss/`. For example:
 
 -   :code:`<raster_name>_bin_map.tif`
 -   :code:`rss<connectivity>.txt`
 -   :code:`rss<connectivity>.csv`
 
-.. danger::
+.. attention::
 
-    If the rectangle turns red, carefully read the information in the log. For example, your current instance may be too small to handle the file you want to analyse. In this case, close the app, open a bigger instance and run your analysis again.
+    If the rectangle turns red, carefully read the information in the log. For example, your current instance may be too small to handle the file you want to analyse. In this case, close the app, open a bigger instance, and run your analysis again.
 
-Here is the result of the computation using the default parameters on the :code:`example.tif` file.
+Here is the result of the computation using the default parameters on the :code:`example.tif` file:
 
 .. csv-table::
     :header: FNAME, AREA, RAC[%], NR_OBJ, LARG_OBJ, APS, CNOA, ECA, COH[%], REST_POT[%]
     
     example_bin_map.tif,428490.00,42.860572,2850,214811,150.34737,311712,221292.76,51.644789,48.355211
 
-SPA
-^^^
+Simplified pattern analysis (SPA)
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-This module will conduct the **Simplified Pattern Analysis**. SPA analyses shape and conducts a segmentation of foreground patches into 2, 3, 5, or 6 feature classes. The result are spatially explicit maps and tabular summary statistics. :code:`GWB_SPA` is a simpler version of :code:`GWB_MSPA`. Details on the methodology and input/output options can be found in the `Morphology <https://ies-ows.jrc.ec.europa.eu/gtb/GTB/psheets/GTB-Pattern-Morphology.pdf>`_ product sheet.
+This module will conduct the **Simplified pattern analysis**, which shapes and conducts a segmentation of foreground patches into two, three, five, or six feature classes. 
 
-Setup the input image
-"""""""""""""""""""""
+The results are spatially explicit maps and tabular summary statistics. 
+
+:code:`GWB_SPA` is a simpler version of :code:`GWB_MSPA`. 
+
+Details on the methodology and input/output options can be found in the `Morphology product sheet <https://ies-ows.jrc.ec.europa.eu/gtb/GTB/psheets/GTB-Pattern-Morphology.pdf>`_.
+
+Set up the input image
+""""""""""""""""""""""
 
 .. tip::
 
-    You can use the default dataset to test the module. Click on the :code:`Download test dataset` button on the top of the second panel. By clicking on this button, the following two files will be added to your :code:`downloads` folder:
+    You can use the default dataset to test the module. Select the :code:`Download test dataset` button on the top of the second panel, which will add the following files to your :code:`downloads` folder:
         
-    -   :code:`example.tif`: 0 byte - Missing, 1 byte - Background, 2 byte - Foreground
-    -   :code:`clc3class.tif`: 1 byte - Agriculture, 2 byte - Natural, 3 byte - Developed
+    -   :code:`example.tif`: 0 bytes - Missing, 1 byte - Background, 2 bytes - Foreground
+    -   :code:`clc3class.tif`: 1 byte - Agriculture, 2 bytes - Natural, 3 bytes - Developed
     
     .. thumbnail::  https://raw.githubusercontent.com/12rambau/gwb/master/doc/img/test_dataset.png
-        :title: download sample dataset
+        :title: Download sample dataset
         :group: gwb-module
-        
     
-    Once the files are downloaded, follow the normal process using the :code:`downloads/example.tif` file (2 classes).
+    Once the files are downloaded, follow the normal process using the :code:`downloads/example.tif` file (two classes).
     
-The first step requires to reclassify your image. Using the reclassifying panel, select your image in your SEPAL folder.
+The first step requires reclassifying your image. Using the **Reclassifying** panel, select the image in your **SEPAL folder**.
 
-.. warning:: 
+.. attention::
 
-    If the image is not in your SEPAL folders but in your local computer consider reading the `exchange file with SEPAL <https://docs.sepal.io/en/latest/setup/filezilla.html>`_ page of this documentation.
+    If the image is on your local computer and not in your **SEPAL folders**, see `Exchange files with SEPAL <https://docs.sepal.io/en/latest/setup/filezilla.html>`_.
     
 The dropdown menus will list the discrete values of your raster input image. Select each class in your image and place them in one of the following categories: 
 
 -   background
 -   foreground
 
-Every class that is not set to a reclassifying category will be considered as "missing data" (0 byte).
+Every class that is not set to a reclassifying category will be considered "missing data" (0 bytes).
 
 .. thumbnail:: https://raw.githubusercontent.com/12rambau/gwb/master/doc/img/2_classes.png
-    :title: upload 2 classes
+    :title: Upload two classes
     :group: gwb-module
 
 .. tip::
 
-    For forest analysis, set forest as foreground and all the other classes as background.
+    For forest analysis, set **Forest** as foreground and all other classes as background.
     
 Select the parameters
 """""""""""""""""""""
@@ -1186,7 +1212,7 @@ Select the parameters
 You will need to select parameters for your computation: 
 
 .. thumbnail:: https://raw.githubusercontent.com/12rambau/gwb/master/doc/img/spa_params.png
-    :title: spa params
+    :title: SPA parameters
     :group: gwb-module
     
 .. note::
@@ -1208,24 +1234,26 @@ Set the number of pattern classes you want to compute:
 Run the analysis
 """"""""""""""""
 
-Once your parameters are all set you can launch the analysis. The blue rectangle will display information about the computation. Upon completion, it will turn to green and display the computation log. 
+Once your parameters are set, you can launch the analysis. The blue rectangle will display information about the computation. Upon completion, it will turn green and display the computation log.
 
 .. thumbnail:: https://raw.githubusercontent.com/12rambau/gwb/master/doc/img/spa_results.png
-    :title: information logs
+    :title: Information logs
     :group: gwb-module
 
-The resulting files are stored in the folder :code:`module_results/gwb/spa/`, for example:
+The resulting files are stored in the folder :code:`module_results/gwb/spa/`. For example:
 
 -   :code:`<raster_name>_bin_map.tif`
 -   :code:`<raster_name>_bin_map_spa<number of classes>.tif`
 -   :code:`<raster_name>_bin_map_spa<number of classes>.txt`
 
-.. danger::
+.. attention::
 
-    If the rectangle turns red, carefully read the information in the log. For example, your current instance may be too small to handle the file you want to analyse. In this case, close the app, open a bigger instance and run your analysis again.
+    If the rectangle turns red, carefully read the information in the log. For example, your current instance may be too small to handle the file you want to analyse. In this case, close the app, open a bigger instance, and run your analysis again.
 
-Here is the result of the computation using SPA2 (2 classes) on the :code:`example.tif` file.
+Here is the result of the computation using SPA2 (two classes) on the :code:`example.tif` file:
 
 .. thumbnail:: https://raw.githubusercontent.com/openforis/sepal-doc/master/docs/source/img/cli/gwb/example_spa2.png
     :width: 50%
     :group: gwb-module
+
+.. custom-edit:: https://raw.githubusercontent.com/sepal-contrib/gwb/release/doc/en.rst