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

Journal of Applied Remote Sensing
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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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Additional publication details

Publication type Article
Publication Subtype Journal Article
Title Remote sensing change detection methods to track deforestation and growth in threatened rainforests in Madre de Dios, Peru
Series title Journal of Applied Remote Sensing
DOI 10.1117/1.JRS.9.096040
Volume 9
Issue 1
Year Published 2015
Language English
Publisher SPIE
Contributing office(s) Eastern Geographic Science Center
Description e096040: 15 p.
Country Peru
State Madre de Dios
Online Only (Y/N) N
Additional Online Files (Y/N) N