Ecoregion-wide, multi-sensor biomass mapping highlights a major underestimation of dry forests carbon stocks

Abstract

Tropical dry forests harbor major carbon stocks but are disappearing rapidly across the globe as agriculture expands into them. Unfortunately, carbon emissions from deforestation in dry forests remain poorly understood as high spatial-temporal and vertical heterogeneity complicate biomass mapping. Here, we use a novel Gradient Boosted Regression framework to test the relative gains of combining optical (MODIS) and radar (Sentinel 1) time series, as well as lidar-based (GEDI) canopy-height information, to map biomass in tropical dry forests. We apply our approach across the entire Dry Chaco ecoregion (about 800,000 km2), using an extensive ground dataset of forest inventory plots for training and validation, to map above-ground biomass (AGB) for the year 2019. Our best AGB model had an r2 of 0.89 (RMSE = 15.1 t/ha) with an estimated AGB in remaining natural vegetation of 4.65 Gt (+/− 0.9 Gt). Seasonal metrics from EVI time-series, combined with seasonal Sentinel 1 metrics, had the highest predictive power, while adding GEDI-based canopy height did not improve models. Our resulting AGB maps had a much higher level of agreement with independent ground-data than global AGB products (agreements between r2 = 0.07?0.41), which all suffer from a huge, up to 14-fold, underestimation of AGB in the Chaco. Most of the remaining AGB stored in Chaco woodlands is found in Argentina (2.4 Gt AGB), followed by Paraguay (1.13 Gt AGB) and Bolivia (1.11 Gt AGB). Our results also highlight that 71% of the remaining AGB is located outside protected areas, and around half of the remaining AGB occurs on land utilized by traditional communities. Together, our analyses reveal substantial risk of continued high carbon emissions should agricultural expansion progress. Considerable co-benefits appear to exist between protecting traditional livelihoods and carbon stocks. Our map, the most accurate and fine-scale AGB map for this global deforestation hotspot, can serve as a basis for land-use and conservation planning aimed at leveraging such co-benefits. More broadly, our analyses reveal the considerable potential of combining time series of optical and radar data for a more reliable mapping of above-ground biomass in tropical dry forests and savannas.