1. Mapping forest biomass from space – Fusion of hyperspectral EO1-hyperion data and Tandem-X and WorldView-2 canopy height models
- Author
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Barbara Koch, Joachim Maack, Fabian Faßnacht, Teja Kattenborn, Fabian Enßle, and Jörg Ermert
- Subjects
Canopy ,Global and Planetary Change ,Biomass (ecology) ,Hyperspectral imaging ,Temperate forest ,Statistical model ,Management, Monitoring, Policy and Law ,Random forest ,Geography ,Photogrammetry ,Forest ecology ,Computers in Earth Sciences ,Earth-Surface Processes ,Remote sensing - Abstract
Spaceborne sensors allow for wide-scale assessments of forest ecosystems. Combining the products of multiple sensors is hypothesized to improve the estimation of forest biomass. We applied interferometric (Tandem-X) and photogrammetric (WorldView-2) based predictors, e.g. canopy height models, in combination with hyperspectral predictors (EO1-Hyperion) by using 4 different machine learning algorithms for biomass estimation in temperate forest stands near Karlsruhe, Germany. An iterative model selection procedure was used to identify the optimal combination of predictors. The most accurate model (Random Forest) reached a r 2 of 0.73 with a RMSE of 14.9% (29.4 t/ha). Further results revealed that the predictive accuracy depended highly on the statistical model and the area size of the field samples. We conclude that a fusion of canopy height and spectral information allows for accurate estimations of forest biomass from space.
- Published
- 2015
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