Improving Aboveground Forest Biomass Maps: From High-Resolution to National Scale
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Author/sDurante Hernández, Pilar; Martín Alcón, Santiago; Gil Tena, Assu; Algeet, Nur; Tomé, José Luis; [et al.]
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 0.7...
quantile regression forest