Tropical Forest Area and Its Spatio-Temporal Changes during 2007-2017

Abstract:

Tropical forests play an important role in carbon cycle, hydrology, and biodiversity conservation. Because optical imagery from satellite are affected by frequent clouds, cloud shadow, and smokes, identifying and mapping the spatial distribution and temporal changes of tropical forests is a challenging task. A number of satellite-based forest area data products differ substantially in the forest area estimates, spatial distribution, and annual loss and gain rates of forests. In this study, we developed a forest structure- and phenology-based approach and generate 30-m annual maps of forests in the pan-tropical zone during 2007-2017. First, we analyzed the structure (tree density, biomass)-related features from the L-band microwave (25-m ALOS PALSAR and ALOS-2 PALSAR-2) imagery and the canopy phenology (greenness, water content)-related features from optical imagery (e.g., 30-m Landsat). Second, we developed a simple and robust decision tree approach to identify and classify tropical rainforests using the unique features extracted from PALSAR/PALSAR-2 and Landsat imagery. Third, we applied the decision tree algorithms to map and generate annual maps of tropical forests (namely, PALSAR/Landsat Forest data product) at 30-m spatial resolution during 2007-2017 and calculated forest area and spatio-temporal changes. Fourth, we assess the accuracy of the PALSAR/Landsat forest maps using ground reference and very high spatial resolution imagery. The resultant annual maps of tropical forests from the analyses of PALSAR/PALSAR-2/Landsat imagery would provide improved and critical information for the Reducing Emissions from Deforestation and Forest Degradation (REDD+).