Remote Sensing Applications and Decision Support

Remote sensing change detection methods to track deforestation and growth in threatened rainforests in Madre de Dios, Peru

[+] Author Affiliations
Jacob Shermeyer

U.S. Geological Survey, Eastern Geographic Science Center, 12201 Sunrise Valley Drive, MS 521, Reston, Virginia 20190, United States

Barry Haack

George Mason University, Department of Geography and Geoinformation Science, MSN 6C3, Fairfax, Virginia 22030, United States

J. Appl. Remote Sens. 9(1), 096040 (Jun 10, 2015). doi:10.1117/1.JRS.9.096040
History: Received August 27, 2014; Accepted May 11, 2015
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Abstract.  Two forestry-change detection methods are described, compared, and contrasted for estimating deforestation and growth in threatened forests in southern Peru from 2000 to 2010. The methods used in this study rely on freely available data, including atmospherically corrected Landsat 5 Thematic Mapper and Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation continuous fields (VCF). The two methods include a conventional supervised signature extraction method and a unique self-calibrating method called MODIS VCF guided forest/nonforest (FNF) masking. The process chain for each of these methods includes a threshold classification of MODIS VCF, training data or signature extraction, signature evaluation, k-nearest neighbor classification, analyst-guided reclassification, and postclassification image differencing to generate forest change maps. Comparisons of all methods were based on an accuracy assessment using 500 validation pixels. Results of this accuracy assessment indicate that FNF masking had a 5% higher overall accuracy and was superior to conventional supervised classification when estimating forest change. Both methods succeeded in classifying persistently forested and nonforested areas, and both had limitations when classifying forest change.

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© 2015 Society of Photo-Optical Instrumentation Engineers

Citation

Jacob Shermeyer and Barry Haack
"Remote sensing change detection methods to track deforestation and growth in threatened rainforests in Madre de Dios, Peru", J. Appl. Remote Sens. 9(1), 096040 (Jun 10, 2015). ; http://dx.doi.org/10.1117/1.JRS.9.096040


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