Abstract
This paper aims to develop a new methodology for monitoring forest disturbances and regrowth using ALOS PALSAR data in tropical regions. In the study, forest disturbances and regrowth were assessed between 2007 and 2010 in Vietnam, Cambodia and Lao People’s Democratic Republic. The deforestation rate in Vietnam has been among the highest in the tropics in the last few decades, and those in Cambodia and Lao are increasing rapidly. L-band ALOS PALSAR mosaic data were used for the detection of forest disturbances and regrowth, because L-band SAR intensities are sensitive to forest aboveground biomass loss. The methodology used here combines SAR data processing, which is particularly suited for change detection, forest detection and forest disturbances and regrowth detection using expectation maximization, which is closely related to fuzzy logic. A reliable training and testing database has been derived using AVNIR-2 and Google Earth images for calibration and validation. Efforts were made to apply masking areas that are likely to show different SAR backscatter temporal behaviors from the forests considered in the study, including mangroves, inundated forests, post-flooding or irrigated croplands and water bodies, as well as sloping areas and urban areas. The resulting forest disturbances and regrowth map (25-m resolution) indicates disturbance rates of −1.07% in Vietnam, −1.22% in Cambodia and −0.94% in Lao between 2007 and 2010, with corresponding aboveground biomass losses of 60.7 Tg, 59.2 Tg and 83.8 Tg , respectively. It is expected that the method, relying on free of charge data (ALOS and ALOS2 mosaics), can be applied widely in the tropics.
Highlights
Forests act as a carbon source through deforestation and degradation [1] and as a carbon sink through forest regrowth [2]
Synthetic aperture radar (SAR) data processing, which is suited for change detection, forest detection and forest disturbances and regrowth detection using expectation maximization, which is closely related to fuzzy logic
ALOS2 mosaic data for tropical regions where the deforestation rate is still high. This method combines SAR data processing, which is suited for change detection from SAR, masks areas that may induce misdetection or false alarms and expectation maximization (EM)
Summary
Forests act as a carbon source through deforestation and degradation [1] and as a carbon sink through forest regrowth [2]. Major uncertainties exist in the estimates of the carbon emissions that result from forest disturbances and in the uptake of carbon through forest regrowth. Of man-made global emissions [4], though the rate of tropical deforestation has been reported to have decreased from 0.16 Mkm2 ·y−1 in the 1990s to 0.13 Mkm2 ·y−1 in the first decade of the 21st century [5]. Estimates of tropical forest area and change still contain considerable uncertainty, impeding the estimation of the carbon emissions caused by deforestation and forest degradation in the tropics [1]. The call to reduce uncertainties in estimating changes in tropical forest cover is driven by the reporting needs outlined in the Reducing Emissions from Deforestation and forest
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