Above-ground biomass mapping in West African dryland forest using Sentinel-1 and 2 datasets - A case study

Abstract

The Sudanian Savanna (SS) of West Africa is characterized by tropical savannas and woodlands. Accurate estimation of AGB and carbon stocks in this biome is important for addressing sustainable development goals as the information can aid natural resource management at varied spatial scales. Previous AGB mapping efforts focused on humid forests, with little attention on savannas. This study explored the use of annual monthly time-series of Senitinel-1 (S-1) and Sentinel-2 (S-2) data to map AGB in the SS. Backscatter, spectral reflectance, and derivatives (vegetation indices and biophysical parameters) were combined with field inventory data in a Random Forest regression to map AGB. Eight experiments were conducted with different data configurations to determine: (1) the potential of S-1 and S-2 for AGB mapping, (2) optimal image acquisition period for AGB mapping, and (3) contribution of image derivatives to improving the accuracy of AGB mapping. The predicted map was validated with 40% of the inventory data. Uncertainty in the AGB was assessed using mean absolute error, root mean squared error, coefficient of determination and symmetrical mean absolute percentage error. Results show that about 90% of the study area have low AGB stocks of less than 90 Mg/ha. Compared to S-1 (RMSE: 78.6; MAE: 25.6), S-2 achieved better prediction accuracy (RMSE: 60.6; MAE: 19.2), although combination of the two according to seasonality produced the best results (RMSE: 45.4; MAE: 16.3). Images acquired in the dry season were found to be more useful for predicting AGB than those of rainy season. Also, stress-related vegetation indices and a red-edge dependent normalized difference vegetation index not tested in previous AGB studies using Sentinels were found to be significant contributors to the superior performance of S-2. Since biomass is a finite resource, our results can provide valuable information on the sustainable use of biomass and energy security including studies on carbon cycling and ecosystem functions in the region. The demonstrated possibility of using open access earth observation data to map and monitor AGB in data scarce regions is useful and beneficial to attaining SDG indicators 15.2.1 (sustainable forest management) and 15.3.1 (proportion of land that is degraded over total land area). Further work on developing species-specific wood densities and allometric equations is required to improve AGB and carbon stock estimation in the SS.