Modeling forest biomass using Very-High-Resolution data - Combining textural, spectral and photogrammetric predictors derived from spaceborne stereo images
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We used spectral, textural and photogrammetric information from very-high resolution (VHR) stereo satellite data (Pleiades and WorldView-2) to estimate forest biomass across two test sites located in Chile and Germany. We compared Random Forest model performances of different predictor sets (spectral, textural, and photogrammetric), forest inventory designs and filter sizes (texture information). Best model performances were obtained with photogrammetric combined with either textural or spectral information and smaller, but more field plots. Stereo-VHR images showed a great potential for canopy height model (CHM) generation and could be an adequate alternative to LiDAR and InSAR techniques.
Artículo de publicación ISI
BIOCOMSA project (SA) 208-7321
DOI: DOI: 10.5721/EuJRS20154814
Quote ItemEuropean Journal of Remote Sensing - 2015, 48: 245-261
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