Mapping Forest Biomass from Space - Fusion of hyperspectralEO1-Hyperion Data and Tandem-X and WorldView-2 Canopy Heightmodels


Spaceborne sensors allow for wide-scale assessments of forest ecosystems. Combining the products ofmultiple 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 com-bination with hyperspectral predictors (EO1-Hyperion) by using 4 different machine learning algorithmsfor biomass estimation in temperate forest stands near Karlsruhe, Germany. An iterative model selectionprocedure was used to identify the optimal combination of predictors. The most accurate model (RandomForest) reached a r2 of 0.73 with a RMSE of 14.9% (29.4 t/ha). Further results revealed that the predictiveaccuracy depended highly on the statistical model and the area size of the field samples. We concludethat a fusion of canopy height and spectral information allows for accurate estimations of forest biomassfrom space.

International Journal of Applied Earth Observation and Geoinformation