BioVeL ESW DIFF - ENM Statistical Workflow with raster difference computation

Created: 2013-07-08 15:06:00 Last updated: 2016-06-22 14:45:16

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The difference between each corresponding raster cell value is computed and stored in the difference file, regardless of the input files’ geographical extent and origin. If the files have a different geographical extent and/or origin the raster-diff workflow automatically crops them to the same extent and resamples the values using the “nearest neighbour” method, resulting in a perfect cell match between the two rasters. The resulting value in the difference file ranges from the negative and positive maximum values of the input files to represent the maximum possible change in both directions.

The diff result values are classified into six positive and negative classes. The predicted differences are presented for each species as a heat map, where cells with colors from green to red indicate an increase and from green to blue a decrease of predicted potential for a species. The workflow enables the computation of overall coverage, overall intensity and the difference in intensity or coverage between two raster layers. Overall coverage is computed as the percentage of raster cells with values >0, and overall intensity is computed as the sum of all valued cells divided by the number of raster cells. Summary statistics for the raster layers are also provided: number of raster cells, mean intensity, median, CV, SD, Min value, Max value and number of cells outside the mask.

This workflow store the computed difference image to the BioVeL Geoserver and create PNG overwiews contains the input and output files and the distribution points. The difference result image could be shown in the BioSTIF web visualisation client

The workflow contains the computation methods:

- Computation of the difference layer:

difference = inputfile_B - inputfile_A

- Computation of the overall coverage and intensity overall_coverage = ((ncell(x)-cellStats(x, 'countNA')-count(x,0))/ncell(x))*100 overall_intensity = cellStats(x, sum)/(ncell(x)-cellStats(x, 'countNA'))

- Used standard R functions:

cellStats: https://r-forge.r-project.org/scm/viewvc.php/pkg/raster/R/cellStats.R?view=markup&root=raster

The PNG overview will be created with these values (corresponding to the Geoserver style) ERDASImagine file:

breakpoints = c(0,1,25,50,75,100) GeoTIFF file breakpoints = c(0,2,63,127,190,254) colors = c(rgb(1.0,1.0,1.0), rgb(0.996,0.898,0.851), rgb(0.988,0.682,0.569), rgb(0.984,0.416,0.290), rgb(0.871,0.176,0.149), rgb(0.647,0.059,0.082))

Expected as input files are those raster images as a URL reference e.g. output files from openModeller created by an ENM workflow. *A file upload into the workflow is not possible, only a reference to the input file as URL*

Supported formats are:

- ERDASImagine (.img) files with a value range 0 - 100, noData value 101 - GeoTIFF (.tif) files with a value range 0- 254, noData Value 255

Input files with a different extent will be intersected to a common extent and the current layer (inputfile 1) will be resampled to the prediction layer (inputfile 2) using the "nearest neighbor" sampling method.

The difference layer will be computed as inputfile_2 - inputfile_1

For working with the ESW in a own localhost an R instance should by installed on a own machine (http://cran.r-project.org/). For the computation the followed extensions must be installed: rgdal, raster and rserve. Rserve must be started before the computation.

--------------------

This workflow has been created by the Biodiversity Virtual e-Laboratory (BioVeL http://www.biovel.eu/) project. BioVeL is funded by the EU’s Seventh Framework Program, grant no. 283359.

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Workflow Components

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Descriptions (1)

The ENM Statistical Difference Workflow (ESW DIFF) allows the computation of the extent and intensity of change in species potential distribution through computation of the differences between two raster layers using the R statistical environment (R Core Team 2013). The difference file is computed from two input files (in this case present projection and 2050 projection) coming from the Ecological Niche Modelling (ENM) Workflow (http://www.myexperiment.org/workflows/3355). The difference between each corresponding raster cell value is computed and stored in the difference file, regardless of the input files’ geographical extent and origin. If the files have a different geographical extent and/or origin the raster-diff workflow automatically crops them to the same extent and resamples the values using the “nearest neighbour” method, resulting in a perfect cell match between the two rasters. The resulting value in the difference file ranges from the negative and positive maximum values of the input files to represent the maximum possible change in both directions. The diff result values are classified into six positive and negative classes. The predicted differences are presented for each species as a heat map, where cells with colors from green to red indicate an increase and from green to blue a decrease of predicted potential for a species. The workflow enables the computation of overall coverage, overall intensity and the difference in intensity or coverage between two raster layers. Overall coverage is computed as the percentage of raster cells with values >0, and overall intensity is computed as the sum of all valued cells divided by the number of raster cells. Summary statistics for the raster layers are also provided: number of raster cells, mean intensity, median, CV, SD, Min value, Max value and number of cells outside the mask. This workflow store the computed difference image to the BioVeL Geoserver and create PNG overwiews contains the input and output files and the distribution points. The difference result image could be shown in the BioSTIF web visualisation client The workflow contains the computation methods: - Computation of the difference layer: difference = inputfile_B - inputfile_A - Computation of the overall coverage and intensity overall_coverage = ((ncell(x)-cellStats(x, 'countNA')-count(x,0))/ncell(x))*100 overall_intensity = cellStats(x, sum)/(ncell(x)-cellStats(x, 'countNA')) - Used standard R functions: cellStats: https://r-forge.r-project.org/scm/viewvc.php/pkg/raster/R/cellStats.R?view=markup&root=raster The PNG overview will be created with these values (corresponding to the Geoserver style) ERDASImagine file: breakpoints = c(0,1,25,50,75,100) GeoTIFF file breakpoints = c(0,2,63,127,190,254) colors = c(rgb(1.0,1.0,1.0), rgb(0.996,0.898,0.851), rgb(0.988,0.682,0.569), rgb(0.984,0.416,0.290), rgb(0.871,0.176,0.149), rgb(0.647,0.059,0.082)) Expected as input files are those raster images as a URL reference e.g. output files from openModeller created by an ENM workflow. *A file upload into the workflow is not possible, only a reference to the input file as URL* Supported formats are: - ERDASImagine (.img) files with a value range 0 - 100, noData value 101 - GeoTIFF (.tif) files with a value range 0- 254, noData Value 255 Input files with a different extent will be intersected to a common extent and the current layer (inputfile 1) will be resampled to the prediction layer (inputfile 2) using the "nearest neighbor" sampling method. The difference layer will be computed as inputfile_2 - inputfile_1 For working with the ESW in a own localhost an R instance should by installed on a own machine (http://cran.r-project.org/). For the computation the followed extensions must be installed: rgdal, raster and rserve. Rserve must be started before the computation. -------------------- This workflow has been created by the Biodiversity Virtual e-Laboratory (BioVeL http://www.biovel.eu/) project. BioVeL is funded by the EU’s Seventh Framework Program, grant no. 283359.

Dependencies (0)

Inputs (6)

Name	Description
input1	Input of the current layer. The difference will be computed as predictionLayer - currentLayer Expected as input files are raster images as a URL from openModeller created by an ENM workflow. A file upload is not possible! Supported formats are: - ERDASImagine (.img) files with a value range 0 - 100, noData value 101 - GeoTIFF (.tif) files with a value range 0 - 254, noData Value 255 Input files with a different extent will be intersected to a common extent and the current layer (inputfile 1) will be resampled to the prediction layer (inputfile 2) using the "nearest neighbour" sampling method
input2	Input of the prediction layer. The difference will be computed as predictionLayer - currentLayer Expected as input files are raster images as a URL from openModeller created by an ENM workflow. A file upload is not possible! Supported formats are: - ERDASImagine (.img) files with a value range 0 - 100, noData value 101 - GeoTIFF (.tif) files with a value range 0 - 254, noData Value 255 Input files with a different extent will be intersected to a common extent and the current layer (inputfile 1) will be resampled to the prediction layer (inputfile 2) using the "nearest neighbor" sampling method
label_species	Enter your favorite name for the species used in the "changes" output table only
label_difference_file	Enter your favorite name for the created difference file, this will be stored on Geoserver NOTE: Please use only alphanumeric characters (space and hyphen), all other characters will be replaced. During the storage a timestamp will be added to the filename
PNG_output_size	Size of the output PNG's in pixels.
occurrence_csv	URL to the CSV File with occurrence points coming from ENM. If you don't want to display any points, type "none" at the begin, else paste a CSV URL or the content from a CSV or a CSV file as input Example CSV URL: http://biovel.iais.fraunhofer.de/biostif/data/dendrolimus_pini.csv

Processors (12)

Name	Type	Description
Raster_SHIM_service	workflow
rasterFormat	stringconstant	Value ArcGrid
Merge_String_List_to_a_String	localworker	Script String seperatorString = "\n"; if (seperator != void) { seperatorString = seperator; } StringBuffer sb = new StringBuffer(); for (Iterator i = stringlist.iterator(); i.hasNext();) { String item = (String) i.next(); sb.append(item); if (i.hasNext()) { sb.append(seperatorString); } } String csv = ""; if(csv_url.startsWith("http")){ csv = "&url="+csv_url+"&contenttype=csv&label=species_points"; } concatenated = "http://biovel.iais.fraunhofer.de/biostif/main.jsp?debug=true&layers="+sb.toString()+csv;
comma	stringconstant	Value ,
replace_special_chars	beanshell	Script if(stringIn.length()>0){ stringOut = stringIn.replaceAll(" ", "_").replaceAll("[^a-zA-Z0-9-_]+",""); } else { throw new RuntimeException("No Raster layer name supplied"); }
R_Raster_Diff_with_s	workflow
check_style	beanshell	Script if(output_format.length()>0){ if(output_format.equals("HFA")){ geoserver_style = "diff_img_color"; } else if(output_format.equals("GTiff")){ geoserver_style = "diff_tiff_color"; } } else { throw new RuntimeException("no output format supplied"); }
parse_csv	beanshell	Script import java.io.StringReader; import java.io.BufferedReader; BufferedReader reader = new BufferedReader(new StringReader(csv_content)); String temp = null; StringBuffer text = new StringBuffer(); while ((temp = reader.readLine()) != null) { text.append(temp + "\n"); }; String csv_string = text.toString().substring(0,text.toString().length()-1);
upload_csv_data	workflow	This part is responsible for uploading the species occurrence points to the BioSTIF service.
check_csv	beanshell	Script import java.util.ArrayList; ArrayList flag = new ArrayList(); if(csv_content.length() > 0){ if(csv_content.startsWith("none") \|\| csv_content.startsWith("http")){ csv_url = csv_content; } else { csv_string = csv_content; csv_url = ""; flag.add(1); } }
Merge_String_List_to_a_String_2	localworker	Script String seperatorString = "\n"; if (seperator != void) { seperatorString = seperator; } StringBuffer sb = new StringBuffer(); for (Iterator i = stringlist.iterator(); i.hasNext();) { String item = (String) i.next(); sb.append(item); if (i.hasNext()) { sb.append(seperatorString); } } concatenated = sb.toString();
Beanshell	beanshell	Script out1 = in1+in2;

Beanshells (10)

Name	Inputs	Outputs
replace_special_chars	stringIn	stringOut
check_style	output_format	geoserver_style
parse_csv	csv_content	csv_string
check_csv	csv_content	csv_string csv_url flag
Beanshell	in1 in2	out1
parse_json_raster_shim_results	json	layername wmsurl pngurl error serverurl formats resolution boundingbox srs nativeFormat
check_service_ok	status	status_failed status_ok
flatten_outputs	stif_layerdescription png_url raster_layername	out_stif_layerdescription out_pngurl out_layername
trimRESTurlResult	url	resultUrl
checkDataUpload	status	dataUpload_ok dataUpload_failed uploadStatus

Outputs (11)

Name	Description
changes	This file contains the changes computation in the submitted files: Overall coverage in the input raster files and their difference overall_coverage = ((ncell(x)-cellStats(x, 'countNA')-count(x,0))/(ncell(x)-cellStats(x, 'countNA')))*100 difference = coverage_prediction - coverage_current Overall intensity absolute and in percent and their difference overall_intensity = (cellStats(x, sum)/(ncell(x)-cellStats(x, 'countNA'))) difference = intensity_prediction - intensity_current
currentLayerPng	PNG overview of the input current layer with a background country borders based on the TDWG level4 layer Used color ramp: ERDASImagine file: breakpoints = (0,1,25,50,75,100) GeoTIFF file breakpoints = c(0,2,63,127,190,254) colors = c(rgb(1.0,1.0,1.0),rgb(0.996,0.898,0.851),rgb(0.988,0.682,0.569),rgb(0.984,0.416,0.290),rgb(0.871,0.176,0.149),rgb(0.647,0.059,0.082))
diffImg	Image file of the difference layer, the format is the same as the input files: ERDASImagine or GeoTIFF
diffLayerPng	PNG overview of the output difference layer with a background country borders based on the TDWG level4 layer Used color ramp: ERDASImagine file: breakpoints = c(-100,-70,-50,-25,-1,1,25,50,70,100) GeoTIFF file breakpoints = c(-254,-190,-127,-63,-2,2,63,127,190,254) colors = c(rgb(0.396,0.310,0.635),rgb(0.196,0.533,0.741),rgb(0.4,0.761,0.647),rgb(0.671,0.867,0.643),rgb(1.0,1.0,1.0),rgb(0.992,0.682,0.380),rgb(0.957,0.427,0.263),rgb(0.835,0.243,0.310),rgb(0.620,0.004,0.259))
matrix_diff	Matrix table of the output raster file for the difference layer. The file is in ArcGRID format
matrix_x	Matrix table of the input raster file for the current layer, The file is in ArcGRID format
matrix_y	Matrix table of the input raster file for the prediction layer,. The file is in ArcGRID format
predictionLayerPng	PNG overview of the input prediction layer with a background country borders based on the TDWG level4 layer Used color ramp: ERDASImagine file: breakpoints = c(0,1,25,50,75,100) GeoTIFF file breakpoints = c(0,2,63,127,190,254) colors = c(rgb(1.0,1.0,1.0),rgb(0.996,0.898,0.851),rgb(0.988,0.682,0.569),rgb(0.984,0.416,0.290),rgb(0.871,0.176,0.149),rgb(0.647,0.059,0.082))
summary	This file contains the summary for the current, prediction and the difference layer: - Number of cells (ncell), - Mean and median value (mean,median) - Coefficient of variation (cv) - Standard deviation (sd) - current minimal and maximal values (min,max) - Number of noData values in file (countNA)
BioSTIF_URL	URL to BioSTIF with the difference layer
logfile	This file contains a overview about the used files and the computation

Datalinks (32)

Source	Sink
rasterFormat:value	Raster_SHIM_service:raster_format
replace_special_chars:stringOut	Raster_SHIM_service:layerLabel
R_Raster_Diff_with_s:diff_matrix	Raster_SHIM_service:inputBody
check_style:geoserver_style	Raster_SHIM_service:raster_style
comma:value	Merge_String_List_to_a_String:seperator
Raster_SHIM_service:STIF_layerdescription	Merge_String_List_to_a_String:stringlist
Beanshell:out1	Merge_String_List_to_a_String:csv_url
label_difference_file	replace_special_chars:stringIn
input2	R_Raster_Diff_with_s:input2
label_species	R_Raster_Diff_with_s:label
input1	R_Raster_Diff_with_s:input1
PNG_output_size	R_Raster_Diff_with_s:png_size
parse_csv:csv_string	R_Raster_Diff_with_s:occurrence_csv
R_Raster_Diff_with_s:output_format	check_style:output_format
occurrence_csv	parse_csv:csv_content
check_csv:flag	upload_csv_data:flag
check_csv:csv_string	upload_csv_data:csvDataContent
parse_csv:csv_string	check_csv:csv_content
upload_csv_data:csvDataURI	Merge_String_List_to_a_String_2:stringlist
Merge_String_List_to_a_String_2:concatenated	Beanshell:in1
check_csv:csv_url	Beanshell:in2
R_Raster_Diff_with_s:changes	changes
R_Raster_Diff_with_s:currentLayerPng	currentLayerPng
R_Raster_Diff_with_s:diffImg	diffImg
R_Raster_Diff_with_s:diffLayerPng	diffLayerPng
R_Raster_Diff_with_s:diff_matrix	matrix_diff
R_Raster_Diff_with_s:x_matrix	matrix_x
R_Raster_Diff_with_s:y_matrix	matrix_y
R_Raster_Diff_with_s:predictionLayerPng	predictionLayerPng
R_Raster_Diff_with_s:summary	summary
Merge_String_List_to_a_String:concatenated	BioSTIF_URL
R_Raster_Diff_with_s:logfile	logfile