From 582a7567f34ef0ea0e4551130364717d9967f00f Mon Sep 17 00:00:00 2001 From: vogtpet <68806384+vogtpet@users.noreply.github.com> Date: Wed, 26 Apr 2023 13:07:26 +0200 Subject: [PATCH 1/3] Update en.rst - removed modules FAD and P223 which no longer exist in GWB version 1.9.1 - note that all images/screenshots on this page do not show up, they are listed to be in https://raw.githubusercontent.com/12rambau/gwb/master/doc/img/ - Jupyter interfaces to the new modules GSC, SC, and SPLITLUMP may be too complex but can be run by using the respective CLI module --- doc/en.rst | 263 +---------------------------------------------------- 1 file changed, 1 insertion(+), 262 deletions(-) diff --git a/doc/en.rst b/doc/en.rst index 4f60a10..9025044 100644 --- a/doc/en.rst +++ b/doc/en.rst @@ -324,136 +324,10 @@ Here is the result of the computation using the default parameters on the :code: :width: 49% :group: gwb-module -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 `_ product sheet. - -Setup 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 - - .. 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/example.tif` file (2 classes). - -The first step requires to reclassify your image. Using the reclassifying panel, select your image in your SEPAL folder. - -.. warning:: - - If the image is not in your SEPAL folders but in your local computer consider reading the `exchange file with SEPAL `_ 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: - -- 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). - -.. thumbnail:: https://raw.githubusercontent.com/12rambau/gwb/master/doc/img/4_classes.png - :title: upload 4 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). - -.. warning:: - - The special background 2 is the non-fragmenting background (optional), see the `Fragmentation `_ 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/fad_params.png - :title: acc params - :group: gwb-module - -.. note:: - - These parameters can be used to perform the default computation: - - - Foreground connectivity: 8 - - Computation precision: float-precision - - Options: per-pixel density, color-coded into 6 fragmentation classes (FAD) - -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 - -.. thumbnail:: https://raw.githubusercontent.com/12rambau/gwb/master/doc/img/connectivity.png - :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. - -Options -####### - -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 -- FAD-APP5: average per-patch density, color-coded into 5 classes - -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. - -.. thumbnail:: https://raw.githubusercontent.com/12rambau/gwb/master/doc/img/fad_results.png - :title: information logs - :group: gwb-module - -The resulting files are stored in the folder :code:`module_results/gwb/fad/`, for example: - -- :code:`_bin_map.tif` -- :code:`_bin_map_fad_.tif` -- :code:`_bin_map_fad_barplot.png` -- :code:`_bin_map_fad_mscale.csv` -- :code:`_bin_map_fad_mscale.tif` -- :code:`_bin_map_fad_mscale.txt` -- :code:`_bin_map_fad_mscale.sav` - -.. 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. - -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_fad_barplot.png - :width: 49% - :group: gwb-module - -.. thumbnail:: https://raw.githubusercontent.com/openforis/sepal-doc/master/docs/source/img/cli/gwb/example_fad_mscale.png - :width: 49% - :group: gwb-module - 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 `_ product sheet. +This module will conduct the **fragmentation** analysis at a **user-selected 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 `_ product sheet. Setup the input image """"""""""""""""""""" @@ -816,141 +690,6 @@ Here is the result of the computation using the default parameters on the :code: :width: 49% :group: gwb-module -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) `_). 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. - -Setup 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 - - .. 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/example.tif` file (2 classes). - -The first step requires to reclassify your image. Using the reclassifying panel, select your image in your SEPAL folder. - -.. warning:: - - If the image is not in your SEPAL folders but in your local computer consider reading the `exchange file with SEPAL `_ 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: - -- background -- foreground -- special background (for P23 only) - -Every class that is not set to a reclassifying category will be considered as "missing data" (0 byte). - -.. thumbnail:: https://raw.githubusercontent.com/12rambau/gwb/master/doc/img/p223_classes.png - :title: upload 3 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) - -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 - :group: gwb-module - -.. note:: - - These parameters can be used to perform the default computation: - - - Window size: 27 - - Computation precision: Float (default) - - Algorithm: FG-Density - -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: - -.. math:: - - obs_scale = (pixres * kdim)^2 / 10000 - -with - -- :math:`obs_scale` in hectare -- :math:`pixres` in meters -- :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. - -Algorithm -######### - -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. - -.. 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. - -.. 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 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) -- FG-Density (original spatcon output) -- FG-Contagion (original spatcon output) -- FG-Adjacency (original spatcon output) -- FG-Shannon (original spatcon output) -- FG-SumD (original spatcon output) - -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. - -.. thumbnail:: https://raw.githubusercontent.com/12rambau/gwb/master/doc/img/p223_results.png - :title: information logs - :group: gwb-module - -The resulting files are stored in the folder :code:`module_results/gwb/p223/`, for example: - -- :code:`_bin_map.tif` -- :code:`_bin_map_p