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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 study evaluated the effects of image pansharpening on Vegetation Indices (VIs) in an agricultural area of Thailand, and found that pansharpening was able to downscale single-date and multi-temporal VI data without introducing significant distortions. The downscaled (15m resolution) VI values can be used for estimating above-ground biomass...