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Improving Aboveground Forest Biomass Maps: From High-Resolution to National Scale

1
Agresta Sociedad Cooperativa, 28012 Madrid, Spain
2
Departamento de Agronomía, Universidad de Almería, 04120 Almería, Spain
3
Departamento de Sistemas y Recursos Naturales, ETSIMFMN, Universidad Politécnica de Madrid, 28040 Madrid, Spain
4
Centro Andaluz para la Evaluación y Seguimiento del Cambio Global (CAESCG), 04120 Almería, Spain
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(7), 795; https://doi.org/10.3390/rs11070795
Received: 26 January 2019 / Revised: 20 March 2019 / Accepted: 31 March 2019 / Published: 3 April 2019
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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. View Full-Text
Keywords: mediterranean forest; climate change; ALS; MODIS; quantile regression forest; uncertainty mediterranean forest; climate change; ALS; MODIS; quantile regression forest; uncertainty
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Durante, P.; Martín-Alcón, S.; Gil-Tena, A.; Algeet, N.; Tomé, J.L.; Recuero, L.; Palacios-Orueta, A.; Oyonarte, C. Improving Aboveground Forest Biomass Maps: From High-Resolution to National Scale. Remote Sens. 2019, 11, 795.

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