18 October 2016 Mapping growing stock at 1-km spatial resolution for Spanish forest areas from ground forest inventory data and GLAS canopy height
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National forest inventories provide measurements of forest variables (e.g. growing stock) that can be used for the estimation of above ground biomass (AGB). Mapping growing stock brings knowledge about spatial distribution and temporal dynamics of ABG, which is necessary for carbon cycle analysis. Several studies have been conducted on the integration of ground and optical remote sensing data to map forest biomass over Europe. Nevertheless, more direct information on forest biomass could be obtained by LiDAR techniques, which directly assess vertical forest structure by measuring the distance between the sensor and the scattering elements located inside the canopy volume. Thus, global 1-km maps of forest canopy height have been recently obtained from the Geoscience Laser Altimeter System (GLAS). The current study aims to produce a forest growing stock map in Spain. Five different forest type areas were identified in three provinces along a North – South gradient accounting for different ecosystems and climatic conditions. Growing stock ground data from the Third Spanish National Forest Inventory were assigned to each forest type and aggregated to 1-km spatial resolution. GLAS-derived canopy height was extracted for the locations of selected ground data. A relationship between inventory growing stock and satellite canopy height was found for each class. The obtained relationships were then extended all over Spain. The accuracy of the resulting growing stock map was assessed at province level against the Third Spanish National Forest Inventory growing stock estimations (R = 0.85, RMSE = 21 m3 ha-1).
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S. Sánchez-Ruiz, S. Sánchez-Ruiz, M. Chiesi, M. Chiesi, F. Maselli, F. Maselli, M. A. Gilabert, M. A. Gilabert, } "Mapping growing stock at 1-km spatial resolution for Spanish forest areas from ground forest inventory data and GLAS canopy height", Proc. SPIE 10005, Earth Resources and Environmental Remote Sensing/GIS Applications VII, 100051I (18 October 2016); doi: 10.1117/12.2241166; https://doi.org/10.1117/12.2241166

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