Large-scale Forest Height Mapping with Quad-Pol TanDEM-X and GEDI Data over Sparse Forest in Danling, China

The intent of this paper is to make an attempt at a large-scale forest height estimation technique framework construction via multi-data fusion of Quad-Pol TanDEM-X and GEDI Data. Faced with complex forest scenes, such as diverse tree species, sparse forest distribution and complex terrain, the authors design a strategy by fusing the traditional Random Volume over Ground (RVoG) model with ancillary forest height information from GEDI observations using BP neural network trained by sparse GEDI samples. To guarantee the accuracy for canopy height retrievals, the vertical wavenumber, as the critical parameter for RVoG construction, are refined by GEDI samples. This a large-scale forest height estimation technique framework is implemented over sparse forest in Danling county of China using quad-Pol TanDEM-X and GEDI Data. The results show that the proposed technique framework can meet the requirement of a large-scale forest height mapping in a resolution of 10m, with an average accuracy of 3.80 m, which is better than that of the traditional three-stage inversion method based on RVoG model.