Abstract
Forest aboveground biomass (AGB) estimation over large extents and high temporal resolution is crucial in managing Mediterranean forest ecosystems, which have been predicted to be very sensitive to climate change effects. Although many modeling procedures have been tested to assess forest AGB, most of them cover small areas and attain high accuracy in evaluations that are difficult to update and extrapolate without large uncertainties. In this study, focusing on the Region of Murcia in Spain (11,313 km2), we integrated forest AGB estimations, obtained from high-precision airborne laser scanning (ALS) data calibrated with plot-level ground-based measures and bio-geophysical spectral variables (eight different indices derived from MODIS computed at different temporal resolutions), as well as topographic factors as predictors. We used a quantile regression forest (QRF) to spatially predict biomass and the associated uncertainty. The fitted model produced a satisfactory performance (R2 0.71 and RMSE 9.99 t·ha−1) with the normalized difference vegetation index (NDVI) as the main vegetation index, in combination with topographic variables as environmental drivers. An independent validation carried out over the final predicted biomass map showed a satisfactory statistically-robust model (R2 0.70 and RMSE 10.25 t·ha−1), confirming its applicability at coarser resolutions.
Highlights
The Mediterranean basin represents a hotspot of biological diversity, being a socioecological system very sensitive to climate change effects [1]
The VSURF procedure for variable selection has identified as the best combination of airborne laser scanning (ALS)-derived variables to predict forest aboveground biomass (AGB): mean height of total returns above 2 m (Hmean), 25th percentile of total returns above 2 m (Hp25), 50th percentile of total returns above 2 m (Hp50), tree canopy cover (TCC), and canopy cover of the low stratum (TCC_LS)
The RF model fitted with these variables showed a satisfactory performance in terms of variance explained and root-mean-square error (RMSE)%
Summary
The Mediterranean basin represents a hotspot of biological diversity, being a socioecological system very sensitive to climate change effects [1]. In terrestrial ecosystems, forests play a basic role as carbon sinks containing about 80% of global terrestrial aboveground biomass (AGB). Such information is difficult to produce, and the uncertainties about magnitude and location are often large [2,3]. Much of this uncertainty is due to the lack of detailed information about the spatial distribution of carbon stored in biomass. Forest biomass is an important measure for environmental management to provide more insights into the amount and spatial distribution of carbon storage for supporting future climate change mitigation actions [4]
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