Simplified Pattern Analysis (GWB_SPA)#

This module conducts 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#

A single band (Geo)TIFF image 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.

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; GWB_SPA parameter file:
;; NOTE: do NOT delete or add any lines in this parameter file!
;;
;; SPA: Spatial Pattern Analysis (2, 3, 5, or 6 classes)
;; see https://ies-ows.jrc.ec.europa.eu/gtb/GTB/MSPA_Guide.pdf
;; Input image requirements: 1b-background, 2b-foreground, optional: 0b-missing
;;
;; SPAx will provide one (1) image and summary statistics using 8-connectivity.
;; line 21: enter a single number, representing the number of pattern classes:
;;    2: SLF, Contiguous
;;    3: Core, Core-Openings, Margin
;;    5: Core, Core-Openings, Edge, Perforation, Margin
;;    6: Core, Core-Openings, Edge, Perforation, Islet, Margin
;; line 22: statistics: 0 (default) or 1 (add summary statistics)
;;
;; an example parameter file would look like this:
;; 6
;; 0
****************************************************************************
2
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_SPA command and listing of results in the directory output:

$ GWB_SPA -i=$HOME/input -o=$HOME/output
IDL 9.1.0 (linux x86_64 m64).
(c) 2024, NV5 Geospatial Solutions, Inc.

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

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

output/example_spa6:
example_spa2.tif  example_spa2.txt

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

SPA2: 8-connected Foreground, summary analysis for image:
~/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]:
================================================================================
        Contiguous:                 388899
+              SLF:                  39591
--------------------------------------------------------------------------------
= Foreground Total:                 428490
+ Background Total:                 571240
--------------------------------------------------------------------------------
=  Data Area Total:                 999730

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


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


================================================================================
        Category          Count [#]:
================================================================================
        Contiguous:             847
        FG Objects:            2850
               SLF:            6792
================================================================================

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.