Change Detection and Remote Sensing Methodologies to Track Deforestation and Growth in Threatened Global Rainforests
Date
2014-03-09
Authors
Shermeyer, Jacob
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Abstract
This study describes, compares, and contrasts two forestry change detection methodologies for tracking deforestation and growth in three sites from 2000 to 2010. The three study areas include threatened forests in the Democratic Republic of the Congo (DRC), Indonesia, and Peru. The methodologies used in this study rely on freely available data including Landsat 5 and 7 Thematic Mapper (TM) and Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Continuous Fields (VCF). The two methods include conventional supervised signature extraction followed by a maximum likelihood classification and MODIS VCF guided Forest/Non Forest (FNF) Masking utilizing broad spatial resolution data to guide signature extraction. The process chain for each of these methods includes cloud masking of Landsat data, a threshold classification of MODIS VCF, training data or signature extraction, k- nearest-neighbor or maximum likelihood classification, analyst guided thresholding, and post-classification x image differencing to generate forest change maps. In addition to this research, two Forest/Non-Forest maps that are derived from these methods are compared and contrasted against a new global forest cover product called Landsat VCF. Comparisons of all methodologies was based upon an accuracy assessment via 500 validation pixels at each study area. Accuracy is evaluated in terms of both pixel counts and area proportions. Results of this accuracy assessment indicate that FNF Masking had the highest overall accuracy and was the best at labeling change. Conventional Supervised Classification had slightly lower overall accuracy but performed poorly when labeling change areas. Results indicate that Landsat VCF FNF maps had comparable accuracies to the previous two methods; however it was found that Landsat VCF substantially underestimates non-forested land cover and as a result overestimates forested land cover in all study areas.
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Keywords
Forestry, Landsat imagery, Remote sensing, Change detection