# historical-imagery
Challenges and opportunities in synthesizing historical geospatial data using statistical models. (2016) Ecological Informatics. Article, Supplements, and Appendices.

If you use this code, please cite the paper:
M.V. Eitzel, Maggi Kelly, Iryna Dronova, Yana Valachovic, Lenya Quinn-Davidson, Jon Solera, Perry de Valpine, Challenges and opportunities in synthesizing historical geospatial data using statistical models, Ecological Informatics, Volume 31, January 2016, Pages 100-111, http://doi.org/10.1016/j.ecoinf.2015.11.011.

This repository includes the Article, Appendices, and Supplements.

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Article:
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Eitzel_et_al-Ecological-Informatics2016.pdf

Appendices:
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AppendixA.pdf
AppendixB.pdf
AppendixC.pdf
AppendixD.pdf
AppendixE.pdf
AppendixF.pdf
AppendixG.pdf

Supplements:
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Orthorectification-Georegistration_Guide.pdf

SampleHistoricalRuleset_BM.dcp        eCognition ruleset for Blake Mountain in 1948
SampleHistoricalRuleset_WC.dcp        eCognition ruleset for Willow Creek in 1948
SampleNAIPruleset_BM.dcp              eCognition ruleset for Blake Mountain in 2009
SampleNAIPruleset_WC.dcp              eCognition ruleset for Willow Creek in 2009

AccuracyAssessment.R                  R code to conduct accuracy assessment
CreateTextureVariables.R              R code to calculate texture variables
FitGAMsForAllScalesOneSite.R          R code to fit models

CoverChangeGISanalysisFunctions.py    Python script containing functions to create datasets at each cell size
CreateAccuracyAssessmentPoints.py     Python script to generate points for accuracy assessment
CreateDataMultipleScalesOneSite.py    Python script to create datasets at each cell size