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This study provides a computationally-simple method to annually update existing national forest maps and model future forest change. It involves integrating national forest maps and global tree cover maps to monitor annual forest change, and then modeling future change by linear extrapolation of the historical trend.

A new method was developed for mapping forest, agricultural, and urban land cover using remote sensing imagery. It is designed to better detect small land cover objects (e.g. small forest or agricultural patches) and monitor their change (e.g. fine-scale deforestation) using freely-available satellite imagery.

This data set (http://archive.ics.uci.edu/ml/datasets/Forest+type+mapping) contains training and testing data from a remote sensing study which mapped different forest types based on their spectral characteristics at visible-to-near infrared wavelengths, using ASTER satellite imagery. The data set be used to evaluate the performance of different...