GuidosToolbox Workbench – GWB

The GuidosToolbox Workbench (GWB, homepage) is a subset of the desktop software package GuidosToolbox (GTB) designed as a cmd-line application for Linux 64bit servers.

This document provides usage instructions for the cmd-line implementation of GWB. Documentation on the GWB SEPAL browser-based application is available here.

Initial setup

As regular user, please first copy the GWB setup into your $HOME account using the command:

$ cp -fr /opt/GWB/*put ~/

You will now find the new directories input and output in your $HOME account.

  • input: This directory contains module-specific parameter files, two sample geotif images and a README file.

  • output: This directory is empty.

All GWB modules require categorical raster input maps in data type unsigned byte (8bit), with discrete integer values within [0, 255] byte. The two sample images in the directory input are:

  • example.tif: 0 byte - Missing, 1 byte - Background, 2 byte - Foreground

  • clc3class.tif: 1 byte - Agriculture, 2 byte - Natural, 3 byte - Developed

GWB is designed to apply the module-specific settings of the respective parameter file to all tif-images placed in the directory input. The module-specific results will be written into the directory output.

Note

  • Please also run the above cp-command to update your GWB-setup files with potentially modified files provided by a newer version of GWB.

  • The directory input has a subdirectory backup having backup copies of all parameter files. This subdirectory may also be used to temporarily store images that should be excluded from processing.

Example of the GWB setup in the user account /home/prambaud.

$ pwd
/home/prambaud

$ ls output/
$ ls input/
acc-parameters.txt   clc3class.tif        example.tif
frag-parameters.txt  mspa-parameters.txt  parc-parameters.txt
rec-parameters.txt   spa-parameters.txt   backup
dist-parameters.txt  fad-parameters.txt   lm-parameters.txt
p223-parameters.txt  readme.txt           rss-parameters.txt

$ less input/readme.txt
Images:
- GWB will process all images from the folder 'input' having the suffix: .tif

Parameter files: *-parameter.txt
- please do not delete these files
- modify only the settings at the end of the file enclosed by *****

Directory backup: not needed for processing
- a set of backup parameter files is included here
- temporarily store images here that you want to exclude from processing

Usage Instructions Overview

To get an overview of all GWB modules enter the command: GWB

$ GWB
===============================================================================
GWB: GuidosToolbox-Workbench:
===============================================================================
cmd-line image analysis modules from GuidosToolbox
(https://forest.jrc.ec.europa.eu/en/activities/lpa/gtb/):
Usage of GWB implies compliance with the conditions in the EULA_GWB.pdf
(https://ies-ows.jrc.ec.europa.eu/gtb/GWB/EULA_GWB.pdf)

GWB_ACC: Accounting of image objects and area classes
    Requirements: 1b-BG, 2b-FG, optional: 0b-missing,
    optional: 3b-special background 1, 4b-special background 2
    Parameter file: input/acc-parameters.txt

GWB_DIST: Euclidean Distance and Hypsometric Curve
    Requirements: 1b-BG, 2b-FG, optional: 0b-missing
    Parameter file: input/dist-parameters.txt

GWB_FAD: Multiscale fragmentation analysis
    Requirements: 1b-BG, 2b-FG, optional: 0b-missing,
    optional: 3b-special BG, 4b-non-fragmenting BG
    Parameter file: input/fad-parameters.txt

GWB_FRAG: user-selected custom scale fragmentation analysis
    Requirements: 1b-BG, 2b-FG, optional: 0b-missing,
    optional: 3b-special BG, 4b-non-fragmenting BG
    Parameter file: input/frag-parameters.txt

GWB_LM: Landscape Mosaic
    Requirements: 1b-Agriculture, 2b-Natural, 3b-Developed
    optional: 0b-missing
    Parameter file: input/lm-parameters.txt

GWB_MSPA: Morphological Spatial Pattern Analysis (up to 25 classes)
    Requirements: 1b-BG, 2b-FG, optional: 0b-missing
    Parameter file: input/mspa-parameters.txt

GWB_P223: Foreground Density [%], Contagion [%], or Adjacency [%]
    Spatcon: P2, P22, P23, Shannon, Sumd
    Requirements: 1b-BG, 2b-FG, 3b-specific BG (for Adjacency), optional: 0b-missing
    Parameter file: input/p223-parameters.txt

GWB_PARC: Landscape Parcellation index
    Requirements: [1b, 255b]-land cover classes, optional: 0b-missing
    Parameter file: input/parc-parameters.txt

GWB_REC: Recode class values
    Requirements: categorical map with up to 256 classes [0b, 255b]
    Parameter file: input/rec-parameters.txt

GWB_RSS: Restoration Status summary
    Requirements: 1b-BG, 2b-FG, optional: 0b-missing
    Parameter file: input/rss-parameters.txt

GWB_SPA: Spatial Pattern Analysis (2, 3, 5, or 6 classes)
    Requirements: 1b-BG, 2b-FG, optional: 0b-missing
    Parameter file: input/spa-parameters.txt

More details in the module-specific parameter files, or run: GWB_XXX --help

Usage:
    a) standalone mode (within the directory GWB):
        ./GWB_ACC  OR add a custom full path to your input and output directory i.e.:
            ./GWB_ACC -i=<your dir_input> -o=<your dir_output>

    b) system mode (GWB installed in /opt/):
        add the full path to your input and output directory i.e.:
            GWB_ACC -i=<your dir_input> -o=<your dir_output>

To get started in system mode, copy the input/output directories to
your home folder using the command:
cp -fr /opt/GWB/*put ~/
===============================================================================

It is also possible to use the “help” option: GWB_ACC --help

$ GWB_ACC --help
----------------------------------------------------------------------------------
usage: /usr/bin/GWB_ACC -i=dir_input -o=dir_output
-i=<full path to directory 'input'>
(with your input images and parameter files);
Standalone mode: GWB/input
-o=<full path to directory 'output'>
(location for results, must exist and must be empty);
Standalone mode: GWB/output
--help: show options

Standalone mode: ./GWB_ACC
System mode/use custom directories: GWB_ACC -i=<your dir_input> -o=<your dir_output>
----------------------------------------------------------------------------------

Tip

When used for the first time, please accept the EULA terms. This step is only needed once.

Additional, general remarks:

  • The directory output must be empty before running a new analysis. Please watch out for hidden files/folders in this directory, which may be the result of an interrupted execution. The safest way to empty the directory is to delete it and recreate a new directory output.

  • GWB will automatically process all suitable geotiff images (single band and of datatype byte) from the directory input. Images of different format or that are not compatible with the selected analysis module requirements will be skipped. Details on each image processing result can be found in the log-file in the directory output.

  • GWB is written in the the IDL language. It includes all required IDL libraries and the source code of each module, stored in the folder: /opt/GWB/tools/source/.

  • To list your current version of GWB, or to check for potential new GWB versions, please run the command:

    $ /opt/GWB/check4updates
    
  • Any distance or area measures are calculated in pixels. It is therefore crucial to use images in equal area projection. Conversion to meters/hectares require to know the pixel resolution.

Available Commands

Danger

Please enter your own settings by amending the module-specific parameters within the section marked with ******* in the respective input/<module>-parameters.txt file. Don’t change anything else in the parameter file, don’t delete or add lines or the module execution will crash. If in doubt, consult the respective input/backup/<module>-parameters.txt file.

GWB_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 product sheet.

Requirements

Single band geotiff in data format byte:

  • 0 byte: missing (optional)

  • 1 byte: background

  • 2 byte: foreground (forest)

  • 3 byte: special background 1 (optional)

  • 4 byte: special background 2 (optional)

Processing parameter options are stored in the file input/acc-parameters.txt.

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; GTB_ACCOUNTING parameter file:
;;    ***  do NOT delete header lines starting with ";;" ***
;;
;; ACC: Accounting of image objects and patch area size classes
;; Input image requirements: 1b-background, 2b-foreground, optional: 0b-missing
;; optional: 3b-special background 1, 4b-special background 2
;; Please specify entries at lines 23-26 ONLY using the following options:
;;
;; line 23: Foreground connectivity: 8 (default) or 4
;; line 24: spatial pixel resolution in meters:
;; line 25: up to 5 area thresholds [unit: pixels] in increasing order
;;          and separated by a single space.
;; line 26: output option:   default (stats + image of viewport) OR
;;          detailed (stats + images of ID, area, viewport)
;;
;; an example parameter file with default output would look like this:
;; 8
;; 25
;; 200 2000 20000 100000 200000
;; default
****************************************************************************
8
25
200 2000 20000 100000 200000
default
****************************************************************************

Example

The results are stored in the directory output, one directory for each input image accompanied by a log-file providing details on computation time and processing success of each input image.

GWB_ACC Command and listing of results in the directory output:

$ GWB_ACC -i=/home/prambaud/input -o=/home/prambaud/output
IDL 8.8.0 (linux x86_64 m64).
(c) 2020, Harris Geospatial Solutions, Inc.

GWB_ACC using:
dir_input= /home/prambaud/input
dir_output= /home/prambaud/output
% Loaded DLM: TIFF.
Done with: clc3class.tif
Done with: example.tif
Accounting finished sucessfully

$ ls -R output/
output/:
acc.log  clc3class_acc  example_acc

output/clc3class_acc:
clc3class_acc.csv  clc3class_acc.tif  clc3class_acc.txt

output/example_acc:
example_acc.csv  example_acc.tif  example_acc.txt

example statistics and graphical result of input image example.tif:

Accounting size classes result using:
example
Base settings: 8-connectivity, pixel resolution: 25 [m]
Conversion factor: pixel_to_hectare: 0.0625000, pixel_to_acres: 0.154441
---------------------------------------------------------------------------------------------
Size class 1: [1, 200] pixels; color: black
        # Objects      Area[pixels]     % of all objects  % of total FGarea
            2789             31190           97.8596         7.2790497
---------------------------------------------------------------------------------------------
Size class 2: [201, 2000] pixels; color: red
        # Objects      Area[pixels]     % of all objects  % of total FGarea
                44             23643           1.54386         5.5177484
---------------------------------------------------------------------------------------------
Size class 3: [2001, 20000] pixels; color: yellow
        # Objects      Area[pixels]     % of all objects  % of total FGarea
                14             98972          0.491228         23.097855
---------------------------------------------------------------------------------------------
Size class 4: [20001, 100000] pixels; color: orange
        # Objects      Area[pixels]     % of all objects  % of total FGarea
                2             59874         0.0701754         13.973255
---------------------------------------------------------------------------------------------
Size class 5: [100001, 200000] pixels; color: brown
        # Objects      Area[pixels]     % of all objects  % of total FGarea
                0                 0           0.00000         0.0000000
---------------------------------------------------------------------------------------------
Size class 6: [200001 -> ] pixels; color: green
        # Objects      Area[pixels]     % of all objects  % of total FGarea
                1            214811         0.0350877         50.132092
---------------------------------------------------------------------------------------------
---------------------------------------------------------------------------------------------
Sum of all classes:
        # Objects      Area[pixels]     % of all objects  % of total FGarea
            2850            428490           100.000         100.00000

Median Patch Size:                5
Average Patch Size:          150.347
Standard Deviation:          4143.11

Three largest object IDs and area[pixels]; color: pink
These 3 objects overlay objects listed above
1)                  1            214811
2)                901             33508
3)               1662             26366
../_images/example_acc.tif

Accounting has been used to map and summarize forest patch size classes in the FAO SOFO2020 report and the Forest Europe State of Europe’s Forest 2020 report with additional technical details in the respective JRC Technical Reports for FAO and FE.

GWB_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 result are spatially explicit maps and tabular summary statistics. Details on the methodology and input/output options can be found in the Distance product sheet.

Requirements

Single band geotiff in data format byte:

  • 0 byte: missing (optional)

  • 1 byte: background

  • 2 byte: foreground (forest)

Processing parameter options are stored in the file input/dist-parameters.txt.

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; GTB_DIST parameter file:
;;    ***  do NOT delete header lines starting with ";;" ***
;;
;; DIST: Euclidean Distance + Hypsometric Curve
;; Input image requirements: 1b-background, 2b-foreground, optional: 0b-missing
;;
;; Please specify entries at lines 17-18 ONLY using the following options:
;;
;; line 17: Foreground connectivity: 8 (default) or 4
;; line 18: 1-Eucl.Distance only   or  2- Eucl.Distance + Hysometric Curve
;;
;; an example parameter file with default settings would look like this:
;; 8
;; 2
****************************************************************************
8
2
****************************************************************************

Example

The results are stored in the directory output, one directory for each input image accompanied by a log-file providing details on computation time and processing success of each input image.

GWB_DIST command and listing of results in the directory output:

$ GWB_DIST -i=/home/prambaud/input -o=/home/prambaud/output
IDL 8.8.0 (linux x86_64 m64).
(c) 2020, Harris Geospatial Solutions, Inc.

GWB_DIST using:
dir_input= /home/prambaud/input
dir_output= /home/prambaud/output
% Loaded DLM: TIFF.
Done with: clc3class.tif
% Loaded DLM: LAPACK.
% Loaded DLM: PNG.
Done with: example.tif
DIST finished sucessfully

$ ls -R output/
output/:
dist.log  example_dist

output/example_dist:
example_dist_hist.png      example_dist_hmc.csv  example_dist_hmc.png
example_dist_hmc.txt       example_dist.tif      example_dist.txt
example_dist_viewport.tif

Example statistics (hypsometric curve) and spatial result of input image example.tif:

../_images/example_dist_hmc.png ../_images/example_dist.tif

Remarks

  • The result provides additional statistics in txt and csv format.

  • Spatially explicit distance per-pixel values are shown in a pseudo-elevation color map. Positive values are associated with land (forest: yellow, orange, red, green), negative values with sea (non-forest: cyan to dark blue) and a value of zero corresponds to the coast line (forest– non-forest boundary).

  • Actual per-pixel distance values are provided in a dedicated image (not shown here)

  • Per-pixel distance values can be summarized with the Hypsometric curve (see above).

Euclidean Distance maps of forest patches have been used to map and summarize forest fragmentation, see for example Kozak et al.

GWB_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.

Requirement

Single band geotiff in data format byte:

  • 0 byte: missing (optional)

  • 1 byte: background

  • 2 byte: foreground (forest)

  • 3 byte: specific background (optional)

  • 4 byte: non-fragmenting background (optional)

Processing parameter options are stored in the file input/fad-parameters.txt.

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; GTB_FAD parameter file:
;;    ***  do NOT delete header lines starting with ";;" ***
;;
;; FAD = multi-scale fragmentation analysis at fixed observation scales of
;; [7x7, 13x13, 27x27, 81x81, 243x243] pixels
;;
;; 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
;;
;; Input image requirements: 1b-background, 2b-foreground, optional:
;;    0b-missing, 3b-special background, 4b-non-fragmenting background
;;
;; FAD will provide 5+1 images and summary statistics.
;;
;; Please specify entries at lines 28-30 ONLY using the following options:
;; line 28: FAD  or  FAD-APP2  or  FAD-APP5
;; line 29: Foreground connectivity: 8 (default) or 4
;; line 30: high-precision: 1 (default) or 0
;;         (1-float precision, 0-rounded byte)
;;
;; an example parameter file doing FAD-APP5 and using 8-connected foreground:
;; FAD-APP5
;; 8
;; 1
****************************************************************************
FAD
8
1
****************************************************************************

Example

The results are stored in the directory output, one directory for each input image accompanied by a log-file providing details on computation time and processing success of each input image.

GWB_FAD command and listing of results in the directory output:

$ GWB_FAD -i=/home/prambaud/input -o=/home/prambaud/output
IDL 8.8.0 (linux x86_64 m64).
(c) 2020, Harris Geospatial Solutions, Inc.

GWB_FAD using:
dir_input= /home/prambaud/input
dir_output= /home/prambaud/output
% Loaded DLM: TIFF.
% Loaded DLM: LAPACK.
% Loaded DLM: PNG.
Done with: clc3class.tif
Done with: example.tif
FAD finished sucessfully

$ ls -R output/
output/:
clc3class_fad  example_fad  fad.log

output/clc3class_fad:
clc3class_fad_13.tif      clc3class_fad_27.tif       clc3class_fad_81.tif
clc3class_fad_mscale.csv  clc3class_fad_mscale.tif   clc3class_fad_243.tif
clc3class_fad_7.tif       clc3class_fad_barplot.png  clc3class_fad_mscale.sav
clc3class_fad_mscale.txt

output/example_fad:
example_fad_13.tif      example_fad_27.tif       example_fad_81.tif
example_fad_mscale.csv  example_fad_mscale.tif   example_fad_243.tif
example_fad_7.tif       example_fad_barplot.png  example_fad_mscale.sav
example_fad_mscale.txt

Example statistics and spatial result of a multi-scale per-pixel analysis of the input image example.tif:

../_images/example_fad_barplot.png ../_images/example_fad_mscale.tif

Remarks

  • The result provides additional statistics in txt and csv format.

  • The IDL-specific sav-file contains all information to conduct fragmentation change analysis in GTB.

  • In addition to the above multi-scale image, the result provides fragmentation images at each of the 5 fixed observation scales.

  • Options to report at pixel- or patch-level and to select the number of fragmentation classes (6, 5, 2).

Fragmentation has been used to map and summarize the degree of forest fragmentation by Riitters et al. (2002, 2012) as well as the US Forest Inventory and Analysis (FIA) reports since 2003.

GWB_FRAG

This module will conduct the fragmentation analysis at a user-selected observation scale. This module and its option are similar to GWB_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.

Requirements

Single band geotiff in data format byte:

  • 0 byte: missing (optional)

  • 1 byte: background

  • 2 byte: foreground (forest)

  • 3 byte: specific background (optional)

  • 4 byte: non-fragmenting background (optional)

Processing parameter options are stored in the file input/frag-parameters.txt.

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; GTB_FRAG parameter file:
;;    ***  do NOT delete header lines starting with ";;" ***
;;
;; FAD = fragmentation analysis at up to 10 user-selected observation scales
;;
;; 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
;;
;; Input image requirements: 1b-background, 2b-foreground, optional:
;;    0b-missing, 3b-special background, 4b-non-fragmenting background
;;
;; FAD will provide an image per observation scale and summary statistics.
;;
;; Please specify entries at lines 32-36 ONLY using the following options:
;; line 32: FAD  or  FAD-APP2  or  FAD-APP5
;; line 33: Foreground connectivity: 8 (default) or 4
;; line 34: pixel resolution [meters]
;; line 35: up to 10 window sizes [unit: pixels] in increasing order
;;          and separated by a single space.
;; line 36: high-precision: 1 (default) or 0
;;          (1-float precision, 0-rounded byte)
;;
;; an example parameter file doing FAD-APP5 and using 8-connected foreground:
;; FAD-APP5
;; 8
;; 100
;; 27
;; 1
****************************************************************************
FAD-APP2
8
100
23
1
****************************************************************************

Example

The results are stored in the directory output, one directory for each input image accompanied by a log-file providing details on computation time and processing success of each input image.

GWB_FRAG command and listing of results in the directory output:

$ GWB_FRAG -i=/home/prambaud/input -o=/home/prambaud/output
IDL 8.8.0 (linux x86_64 m64).
(c) 2020, Harris Geospatial Solutions, Inc.

GWB_FRAG using:
dir_input= /home/prambaud/input
dir_output= /home/prambaud/output
% Loaded DLM: TIFF.
Done with: clc3class.tif
Done with: example.tif
Frag finished sucessfully

$ ls -R output/
output/:
clc3class_frag  example_frag  frag.log

output/clc3class_frag:
clc3class_fad-app2_23.tif  clc3class_frag.csv  clc3class_frag.sav
clc3class_frag.txt

output/example_frag:
example_fad-app2_23.tif  example_frag.csv  example_frag.sav
example_frag.txt

Example statistics and spatial result of custom-scale per patch analysis of the input image example.tif, here FAD-APP2 showing Continuous forest patches in light green and Separated forest patches in dark green.

FAD-APP: Foreground Area Density summary analysis for image:
example.tif
================================================================================
8-conn FG: area, # patches, aps [pixels]: 428490, 2850, 150.34737
Pixel resolution: 100[m], pix2ha: 1.00000, pix2acr: 2.47105
Observation scale:   1
Neighborhood area:   23x23
    [hectare]:     529.00
    [acres]:    1307.19
================================================================================
FAD-APP 5-class:
        Rare:      1.2089
    Patchy:      7.1572
Transitional:      4.2668
    Dominant:     87.3670
    Interior:      0.0000
FAD-APP 2-class:
Separated:      8.3661
Continuous:     91.6339
================================================================================
    FAD_av:     75.2900
../_images/example_fad-app2_23.tif

Remarks

  • The result provides additional statistics in txt and csv format.

  • The IDL-specific sav-file contains all information to conduct fragmentation change analysis in GTB.

  • The result provides one fragmentation image for each custom observation scale. In the example above, the user selected 1 observation scale with local neighborhood of 23x23 pixels.

  • Options to report at pixel- or patch-level and to select the number of fragmentation classes (6, 5, 2).

Fragmentation has been used to map and summarize the degree of forest fragmentation in the FAO SOFO2020 report and the Forest Europe State of Europe’s Forest 2020 report with additional technical details in the respective JRC Technical Reports for FAO and FE.

GWB_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 product sheet.

Requirements

Single band geotiff in data format byte:

  • 0 byte: missing (optional)

  • 1 byte: Agriculture

  • 2 byte: Natural

  • 3 byte: Developed

Warning

Input image values > 3 byte will be considered as missing data

Processing parameter options are stored in the file input/lm-parameters.txt.

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; GTB_LM parameter file:
;;    ***  do NOT delete header lines starting with ";;" ***
;;
;; LM will provide an image and summary statistics.
;; Please specify entries at line 14 ONLY using the following options:
;; line 14: kdim: square window size [pixels], uneven in [3, 5, ...501]
;;          obs_scale [hectare] = (pixres[m] * kdim)^2 / 10000
;;
;; example parameter file
;; (assuming a pixel resolution of 30m, a 11x11 window ~ 10.9 ha):
;; 11
****************************************************************************
23
****************************************************************************

Example

The results are stored in the directory output, one directory for each input image accompanied by a log-file providing details on computation time and processing success of each input image.

GWB_LM command and listing of results in the directory output:

$ GWB_LM -i=/home/prambaud/input -o=/home/prambaud/output
IDL 8.8.0 (linux x86_64 m64).
(c) 2020, Harris Geospatial Solutions, Inc.

GWB_LM using:
dir_input= /home/prambaud/input
dir_output= /home/prambaud/output
% Loaded DLM: TIFF.
% Loaded DLM: PNG.
Done with: clc3class.tif
Done with: example.tif
LM finished sucessfully

$ ls -R output/
output/:
clc3class_lm_23  example_lm_23  lm23.log

output/clc3class_lm_23:
clc3class_lm_23_103class.tif  clc3class_lm_23_heatmap.png   clc3class_lm_23.tif
lm103class_legend.png         clc3class_lm_23_heatmap.csv   clc3class_lm_23_heatmap.sav
heatmap_legend.png

output/example_lm_23:
example_lm_23_103class.tif  example_lm_23_heatmap.png   example_lm_23.tif
lm103class_legend.png       example_lm_23_heatmap.csv   example_lm_23_heatmap.sav
heatmap_legend.png

Example statistics (heatmap) and spatial result of custom-scale analysis of the input image clc3class.tif, showing degree of predominance of land cover types Agriculture, Natural, Developed.

../_images/lm103class_legend.png ../_images/clc3class_lm_23.tif

Remarks

  • The IDL-specific sav-file contains all information to conduct LM change analysis in GTB.

  • LM is not restricted to Ag, Nat, Dev but can be applied to any 3 types of dominant land cover.

  • The result provides the LM analysis for a single custom observation scale. In the example above, and assuming a pixel resolution of 100 meter, an observation scale of 23x23 pixels corresponds to a local neighborhood (analysis scale) of 2300x2300 meters ~ 50 hectare.

  • The heatmap facilitates assessments of temporal changes and/or comparison between different sites.

The Landscape Mosaic has been used to map and summarize the degree of landscape heterogeneity in many occasions (see references in the Landscape Mosaic product sheet), including the RPA, Embrapa, and MAES reports.

GWB_MSPA

Warning

If your are considering using the MSPA tool, keep in mind that the process is relatively complex and provide a lot of information (up to 25 classes). If you are only interested in fragmentation and/or less than 6 classes, please consider using GWB_FRAG or GWB_SPA.

This module will conduct the Morphological Spatial Pattern Analysis. MSPA analyses shape and connectivity and conducts a segmentation of foreground (i.e. forest) 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 product sheet.

Requirements

Single band geotiff in data format byte:

  • 0 byte: missing (optional)

  • 1 byte: background

  • 2 byte: foreground (forest)

Processing parameter options are stored in the file input/mspa-parameters.txt.

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; GTB_MSPA parameter file:
;;    ***  do NOT delete header lines starting with ";;" ***
;;
;; MSPA: Morphological Spatial Pattern Analysis (up to 25 classes)
;; Input image requirements: 1b-background, 2b-foreground, optional: 0b-missing
;;
;; MSPA will provide an image and summary statistics.
;; (see tools/docs/MSPA_Guide.pdf for details)
;; Please specify entries at lines 23-26 ONLY using the following options:
;;
;; line 23: MSPA parameter 1: Foreground connectivity: 8 (default) or 4
;; line 24: MSPA parameter 2: EdgeWidth: 1 (default) or larger integer values
;; line 25: MSPA parameter 3: Transition: 1 (default) or 0
;; line 26: MSPA parameter 4: IntExt: 1 (default) or 0
;;
;; a parameter file with the default settings would look like this:
;; 8
;; 1
;; 1
;; 1
****************************************************************************
8
1
1
1
****************************************************************************

Example

The results are stored in the directory output, one directory for each input image accompanied by a log-file providing details on computation time and processing success of each input image.

GWB_MSPA command and listing of results in the directory output:

$ GWB_MSPA -i=/home/prambaud/input -o=/home/prambaud/output
IDL 8.8.0 (linux x86_64 m64).
(c) 2020, Harris Geospatial Solutions, Inc.

GWB_MSPA using:
dir_input= /home/prambaud/input
dir_output= /home/prambaud/output
% Loaded DLM: TIFF.
Done with: clc3class.tif
Done with: example.tif
MSPA processing finished sucessfully

$ ls -R output/
output/:
example_mspa  mspa.log

output/example_mspa:
example_8_1_1_1.tif  example_8_1_1_1.txt

Example statistics of the input image example.tif and explanatory sketch of the basic MSPA feature classes:

MSPA results using:
example (MSPA: 8_1_1_1, FG_area: 428490, iFG_area: 485606)

MSPA-class [color]:  FG/data pixels [%]  #/BGarea
============================================================
    CORE(s) [green]:            --/--     0
    CORE(m) [green]:      75.09/32.19     1196
    CORE(l) [green]:            --/--     0
        ISLET [brown]:       3.26/ 1.40     2429
PERFORATION [blue]:       2.17/ 0.93     423
        EDGE [black]:      13.54/ 5.80     890
        LOOP [yellow]:       0.60/ 0.26     541
        BRIDGE [red]:       1.42/ 0.61     765
    BRANCH [orange]:       3.93/ 1.68     4685
    Background [grey]:         --/57.14     2319/571240
    Missing [white]:            0.03      51/270
    Opening [grey]:  88.24 Integrity     2291/57116
Core-Opening [darkgrey]:       --/ 0.59     717/5927
Border-Opening [grey]:         --/ 5.12     1574/51189
../_images/mspalegend.gif ../_images/example_8_1_1_1.tif

Remarks

  • MSPA is very versatile and can be applied to any binary map, scale and thematic layer. Please consult the MSPA Guide, the Morphology product sheet and/or the MSPA website for further information.

  • The simplified version, GWB_SPA provides fewer classes. GWB_SPA may be useful to get started and may be sufficient to address many assessments.

MSPA is a purely geometric analysis scheme, which can be applied to any type of raster image. It has been used in more than 100 peer-reviewed publications to map and summarize the spatial pattern, fragmentation and connectivity of forest and other land cover patches, including the detection of structural and functional connecting pathways, analyzing urban greenspace, landscape restoration up to classifying zooplankton species.

GWB_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.

Requirement

Single band geotiff in data format byte:

  • 0 byte: missing (optional)

  • 1 byte: background

  • 2 byte: foreground (forest)

  • 3 byte: specific background (for P23 only)

Processing parameter options are stored in the file input/p223-parameters.txt.

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; GTB_P223 parameter file:
;;    ***  do NOT delete header lines starting with ";;" ***
;;
;; FG-Density (P2), FG-Contagion (P22), or FG-Adjacency (P23)
;; Input image requirements: 1b-background, 2b-foreground,
;; 3b-specific background (for P23), optional: 0b-missing
;;
;; 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 11, 12, 13 to obtain the original spatcon
;; output without normalisation, masking, or color-coding.
;;
;; 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.
;; 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, rounded byte = (float precision * 254) + 1
;;
;; Please specify entries at lines 41-43 ONLY using the following options:
;; line 41:  1 FG-Density   (FG-masked and normalised), or
;;           2 FG-Contagion (FG-masked and normalised), or
;;           3 FG-Adjacency (FG-masked and normalised), or
;;          11 FG-Density   (original spatcon output), or
;;          12 FG-Contagion (original spatcon output), or
;;          13 FG-Adjacency (original spatcon output), or
;;          14 FG-Shannon   (original spatcon output), or
;;          15 FG-SumD      (original spatcon output)
;; line 42: kdim: square window size [pixels], uneven in [3, 5, ..., 501]
;;          obs_scale [hectare] = (pixres * kdim)^2 / 10000
;; line 43: high-precision: 1 (default, float precision) or 0 (rounded byte)
;;
;; an example parameter file for FG-Density and using a 27x27 window:
;; 1
;; 27
;; 1
****************************************************************************
1
27
1
****************************************************************************

Example

The results are stored in the directory output, one directory for each input image accompanied by a log-file providing details on computation time and processing success of each input image.

GWB_P223 command and listing of results in the directory output:

$ GWB_P223 -i=/home/prambaud/input -o=/home/prambaud/output
IDL 8.8.0 (linux x86_64 m64).
(c) 2020, Harris Geospatial Solutions, Inc.

GWB_P223 using:
dir_input= /home/prambaud/input
dir_output= /home/prambaud/output
% Loaded DLM: TIFF.
Done with: clc3class.tif
Done with: example.tif
P2 finished sucessfully

$ ls -R output/
output/:
example_p2_27  p2_27.log

output/example_p2_27:
example_p2_27.tif  example_p2_27.txt

Example statistics and spatial result of the input image example.tif for P2, showing degree of forest density:

P2-summary at Observation Scale: 27
Total Foreground Area [pixels]: 428490
Average P2: 73.7660
../_images/example_p2_27.tif

Remarks

  • Density, Contagion or Adjacency are scale-dependent (specified by the size of the moving window).

  • This moving window approach (originally called Pf/Pff) forms the base for other derived analysis schemes, such as GWB_LM/GWB_FAD/GWB_FRAG.

Both, Density and Contagion add a first spatial information content on top of the primary information of forest, forest amount. Information on forest Density and Contagion is an integral part of many national forest inventories and forest resource assessments. However, the derived products Fragmentation and Landscape Mosaic may be easier to communicate.

GWB_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 product sheet.

Requirements

Single band geotiff in data format byte:

  • 0 byte: missing (optional)

  • at least two different landcover classes

Processing parameter options are stored in the file input/parc-parameters.txt.

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; GTB_PARC parameter file:
;;    ***  do NOT delete header lines starting with ";;" ***
;;
;; PARC: Landscape Parcellation index
;; Input image requirements: [1b, 255b]-land cover classes,
;;    optional: 0b-missing
;;
;; PARC will provide summary statistics only.
;;
;; Please specify entries at lines 17 ONLY using the following options:
;; line 17: Foreground connectivity: 8 (default) or 4
;;
;; an example parameter file using 8-connected foreground:
;; 8
****************************************************************************
8
****************************************************************************

Example

The results are stored in the directory output, one directory for each input image accompanied by a log-file providing details on computation time and processing success of each input image.

GWB_PARC command and listing of results in the directory output:

$ GWB_PARC -i=/home/prambaud/input -o=/home/prambaud/output
IDL 8.8.0 (linux x86_64 m64).
(c) 2020, Harris Geospatial Solutions, Inc.

GWB_PARC using:
dir_input= /home/prambaud/input
dir_output= /home/prambaud/output
% Loaded DLM: TIFF.
Done with: clc3class.tif
Done with: example.tif
PARC finished sucessfully

$ ls -R output/
output/:
clc3class_parc  example_parc  parc.log

output/clc3class_parc:
clc3class_parc.csv  clc3class_parc.txt

output/example_parc:
example_parc.csv  example_parc.txt

Example statistics of the input image clc3class.tif showing statistics and degree of parcellation for each land cover class as well as for the entire image area:

Class   Value      Count     Area[pixels]     APS          AWAPS       AWAPS/data     DIVISION      PARC[%]
    1       1          45       2448931    54420.7000  2076600.0000  1271360.0000        0.1520        1.1937
    2       2         164        957879     5840.7300    82557.6000    19770.0000        0.9138       17.7426
    3       3         212        593190     2798.0700   128177.0000    19008.4000        0.7839       11.0897
================================================================================================================
8-conn. Parcels:      421       4000000     9501.1875                1310139.4429        0.6725        8.0790

Remarks

  • Parcellation is a normalized summary index in [0, 100] %.

  • GWB_PARC provides a tabular summary only.

Parcellation, or the degree of dissection, may be useful to provide a quick tabular summary for each land cover class and the entire image. Together with the degree of division, it may be used to make a statement on the dissection of a particular land cover class. Because Parcellation is a normalized index, measuring Parcellation can be used to quantify temporal changes over a given site as well as directly compare the degree of parcellation of different sites. Being able to quantify changes in percent may also be useful to investigate if a given landscape planning measure had in fact a tangible influence on a specific land cover type or not.

GWB_REC

This module will conduct recoding of categorical land cover classes.

Danger

Please ensure to strictly follow the instructions outlined in the file input/rec-parameters.txt. In particular:

  • Do not delete or insert any new lines.

  • Modify the first column only in this file.

  • Insert the new recoded class value as an integer number for each of the 256 classes.

  • Class values that are not encountered in the image will be skipped.

Requirements

Single band geotiff in data format Byte.

Processing parameter options are stored in the file input/rec-parameters.txt.

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; GTB_REC parameter file:
;;    ***  do NOT delete header lines starting with ";;" ***
;;
;; REC: Recode image classes
;; Input image requirements: [0b, 255b] - classes
;; Output: the same image coverage but with recoded class values
;;
;; Please specify 256 lines (line 20 - 275) having two entries per line:
;; new_recoded_value [0, 255]   old_original_value[0, 255]
;;
;; The first column: must have 256 entries showing the recoded values
;; The second column: MUST be in sequential order from 0 to 255, DO NOT EDIT
;; Class values not found in the image will be skipped.
;; i.e., to recode the class 55 to 3, line 75 would read: 3 55
;;
;; Recode lookup table:
;; new_recoded_value[0, 255]  old_original_value[0, 255]
****************************************************************************
0    0
1    1
2    2
3    3
4    4
5    5
6    6
7    7
8    8
9    9
10   10
11   11
12   12
13   13
14   14
15   15
16   16
17   17
18   18
19   19
20   20
21   21
22   22
23   23
24   24
25   25
26   26
27   27
28   28
29   29
30   30
31   31
32   32
33   33
34   34
35   35
36   36
37   37
38   38
39   39
40   40
41   41
42   42
43   43
44   44
45   45
46   46
47   47
48   48
49   49
50   50
51   51
52   52
53   53
54   54
55   55
56   56
57   57
58   58
59   59
60   60
61   61
62   62
63   63
64   64
65   65
66   66
67   67
68   68
69   69
70   70
71   71
72   72
73   73
74   74
75   75
76   76
77   77
78   78
79   79
80   80
81   81
82   82
83   83
84   84
85   85
86   86
87   87
88   88
89   89
90   90
91   91
92   92
93   93
94   94
95   95
96   96
97   97
98   98
99   99
100  100
101  101
102  102
103  103
104  104
105  105
106  106
107  107
108  108
109  109
110  110
111  111
112  112
113  113
114  114
115  115
116  116
117  117
118  118
119  119
120  120
121  121
122  122
123  123
124  124
125  125
126  126
127  127
128  128
129  129
130  130
131  131
132  132
133  133
134  134
135  135
136  136
137  137
138  138
139  139
140  140
141  141
142  142
143  143
144  144
145  145
146  146
147  147
148  148
149  149
150  150
151  151
152  152
153  153
154  154
155  155
156  156
157  157
158  158
159  159
160  160
161  161
162  162
163  163
164  164
165  165
166  166
167  167
168  168
169  169
170  170
171  171
172  172
173  173
174  174
175  175
176  176
177  177
178  178
179  179
180  180
181  181
182  182
183  183
184  184
185  185
186  186
187  187
188  188
189  189
190  190
191  191
192  192
193  193
194  194
195  195
196  196
197  197
198  198
199  199
200  200
201  201
202  202
203  203
204  204
205  205
206  206
207  207
208  208
209  209
210  210
211  211
212  212
213  213
214  214
215  215
216  216
217  217
218  218
219  219
220  220
221  221
222  222
223  223
224  224
225  225
226  226
227  227
228  228
229  229
230  230
231  231
232  232
233  233
234  234
235  235
236  236
237  237
238  238
239  239
240  240
241  241
242  242
243  243
244  244
245  245
246  246
247  247
248  248
249  249
250  250
251  251
252  252
253  253
254  254
255  255
****************************************************************************

Example

The results are stored in the directory output, one directory for each input image accompanied by a log-file providing details on computation time and processing success of each input image.

GWB_REC command and listing of results in the directory output:

$ GWB_REC -i=/home/prambaud/input -o=/home/prambaud/output
IDL 8.8.0 (linux x86_64 m64).
(c) 2020, Harris Geospatial Solutions, Inc.

GWB_REC using:
dir_input= /home/prambaud/input
dir_output= /home/prambaud/output
% Loaded DLM: TIFF.
Done with: clc3class.tif
Done with: example.tif
Recode finished sucessfully

$ ls -R output/
output/:
clc3class_rec  example_rec  rec.log

output/clc3class_rec:
clc3class_rec.tif

output/example_rec:
example_rec.tif

Remarks

  • The recoded images have the suffix _rec.tif to distinguish them from the original images.

  • To verify the recoding run the command:

    $ gdalinfo -hist <path2image>
    

Recoding may be useful to quickly setup a forest mask from a land cover map by reassigning specific land cover classes to forest. Please note that most GWB modules require a (pseudo) binary forest mask of data type Byte with the assignment:

  • 0 byte: missing data (optional)

  • 1 byte: Background

  • 2 byte: Foreground (i.e., forest)

GWB_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 product sheet.

Requirements

Single band geotiff in data format Byte:

  • 0 byte: missing (optional)

  • 1 byte: background

  • 2 byte: foreground (forest)

Warning

Any other values are considered as missing data

Processing parameter options are stored in the file input/rss-parameters.txt.

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; GTB_RESTORATION-STATUS parameter file:
;;    ***  do NOT delete header lines starting with ";;" ***
;;
;; RSS: Restoration Status = network coherenceof image objetcs
;; Input image requirements: 1b-background, 2b-foreground, optional: 0b-missing
;;
;; Please specify entry at lines 14 ONLY using the following options:
;; line 14: Foreground connectivity: 8 default) or 4
;;
;; an example parameter file with default output would look like this:
;; 8
****************************************************************************
8
****************************************************************************

Example

The result is stored in a single csv-file in the directory output, listing the statistics for each input image in one line, accompanied by a log-file providing details on computation time and processing success of each input image.

GWB_RSS command and listing of results in the directory output:

$ GWB_RSS -i=/home/prambaud/input -o=/home/prambaud/output
IDL 8.8.0 (linux x86_64 m64).
(c) 2020, Harris Geospatial Solutions, Inc.

GWB_RSS using:
dir_input= /home/prambaud/input
dir_output= /home/prambaud/output
% Loaded DLM: TIFF.
Done with: clc3class.tif
Done with: example.tif
RSS finished sucessfully

$ ls -R output/
output/:
rss8.csv  rss8.log

Summary statistics for each input image showing the normalized degree of network coherence and additional key network parameters:

FNAME

AREA

RAC[%]

NR_OBJ

LARG_OBJ

APS

CNOA

ECA

COH[%]

clc3class.tif

957879.00

23.946975

164

176747

5840.7256

180689

281211.93

29.357771

example.tif

428490.00

42.860572

2850

214811

150.34737

311712

221292.76

51.644789

Remarks

  • GWB_RSS provides a succinct summary of key network status attributes including area, extent, patch summary statistics, equivalent connected area and degree of network coherence.

  • As a normalized index, Coherence can be used to directly compare the integrity of different networks or to quantitatively assess changes in network integrity over time.

  • The provision of key network status attributes is essential for any restoration planning.

  • The desktop application GuidosToolbox provides additional, interactive tools for restoration planning.

With the provision of a normalized degree of network coherence, GWB_RSS provides a powerful tool to measure and rank the integrity of forest networks for different regions of interest. This feature may be useful to set priorities for restoration planning or to measure implementation progress and overall success of policy regulations.

GWB_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. GWB_SPA is a simpler version of GWB_MSPA. Details on the methodology and input/output options can be found in the Morphology product sheet.

Requirements

Single band geotiff in data format byte:

  • 0 byte: missing (optional)

  • 1 byte: background

  • 2 byte: foreground (forest)

Processing parameter options are stored in the file input/spa-parameters.txt.

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; GTB_SPA parameter file:
;;    ***  do NOT delete header lines starting with ";;" ***
;;
;; SPA: Spatial Pattern Analysis (2, 3, 5, or 6 classes)
;; Input image requirements: 1b-background, 2b-foreground, optional: 0b-missing
;;
;; SPAx will provide an image and summary statistics using 8-connectivity.
;; Line 18: enter a single number, representing the number of pattern classes:
;; 2: SLF, Coherent
;; 3: Core, Core-Openings, Margin
;; 5: Core, Core-Openings, Edge, Perforation, Margin
;; 6: Core, Core-Openings, Edge, Perforation, Islet, Margin
;;
;; an example parameter file would look like this:
;; 5
****************************************************************************
2
****************************************************************************

Example

The results are stored in the directory output, one directory for each input image accompanied by a log-file providing details on computation time and processing success of each input image.

GWB_SPA command and listing of results in the directory output:

$ GWB_SPA -i=/home/prambaud/input -o=/home/prambaud/output
IDL 8.8.0 (linux x86_64 m64).
(c) 2020, Harris Geospatial Solutions, Inc.

GWB_SPA using:
dir_input= /home/prambaud/input
dir_output= /home/prambaud/output
% Loaded DLM: TIFF.
Done with: clc3class.tif
Done with: example.tif
SPA2 finished sucessfully

$ ls -R output/
output/:
example_spa2  spa2.log

output/example_spa2:
example_spa2.tif  example_spa2.txt

Statistics and spatial result of the input image example.tif showing a 2-class segmentation (SPA2): Coherent and Small & Linear Features (SLF):

SPA2: 8-connected Foreground, summary analysis for image:
/home/prambaud/input/example.tif

Image Dimension X/Y: 1000/1000
Image Area =               Data Area                    + No Data (Missing) Area
        = [ Foreground (FG) +   Background (BG)  ]     +          Missing
        = [        FG       + {Core-Opening + other BG} ] +       Missing

================================================================================
        Category              Area [pixels]:
================================================================================
        Coherent:                 388899
+              SLF:                  39591
--------------------------------------------------------------------------------
= Foreground Total:                 428490
+ Background Total:                 571240
--------------------------------------------------------------------------------
=  Data Area Total:                 999730

        Data Area:                 999730
+          Missing:                    270
--------------------------------------------------------------------------------
= Image Area Total:                1000000


================================================================================
        Category    Proportion [%]:
================================================================================
    Coherent/Data:     38.9004
+         SLF/Data:      3.9602
--------------------------------------------------------------------------------
        FG/Data:     42.8606
--------------------------------------------------------------------------------
    Coherent/FG:     90.7603
+           SLF/FG:      9.2397
================================================================================


================================================================================
        Category          Count [#]:
================================================================================
        Coherent:             847
        FG Objects:            2850
            SLF:            6792
================================================================================
../_images/example_spa2.tif

Remarks

  • The full version, GWB_MSPA provides many more features and classes.

  • Please use GWB_MSPA if you need an edge width > 1 pixel and/or to detect connecting pathways.

GWB_SPA is a purely geometric analysis scheme, which can be applied to any type of raster image. It is ideal to describe the morphology of foreground (forest) patches for basic mapping and statistics, which may be sufficient in many application fields. Advanced analysis, including the detection of connecting pathways require using the full version GWB_MSPA.