ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Citation
Articles | Volume V-1-2022
https://doi.org/10.5194/isprs-annals-V-1-2022-25-2022
https://doi.org/10.5194/isprs-annals-V-1-2022-25-2022
17 May 2022
 | 17 May 2022

ESTIMATING TREE CANOPY HEIGHT IN DENSELY FOREST-COVERED MOUNTAINOUS AREAS USING GEDI SPACEBORNE FULL-WAVEFORM DATA

C. Liu and S. Wang

Keywords: Tree canopy height, High vegetation coverage, Mountainous areas, GEDI, Waveform decomposition, Canopy height model

Abstract. Tree canopy height is an important parameter for estimating forest carbon stock, and mountainous areas with dense vegetation cover are the main distribution areas of trees, so it is important to accurately measure the forest canopy height in mountainous areas with high vegetation cover. This paper focuses on the problem of poor inversion accuracy of canopy height estimation in large scale densely forest-covered mountainous areas, uses the complex echoes of GEDI full-waveform spaceborne laser in mountainous forests as the data source, improves the accuracy of forest canopy height estimation from multiple perspectives by improving the detection capability of weak and overlapping waves and constructing a canopy height model considering slope correction and environmental features. The results show that the modified RGD algorithm proposed in this paper can effectively detect the weak and overlapping waves in the echoes and improve the DTM/DSM inversion accuracy significantly (FVC>90%, R2=0.8663/R2=0.8073). In addition, the forest canopy height model is constructed on the basis of the physical geometric model of mountain slope and spatial environment characteristics, and finally the canopy height inversion accuracy of this paper is higher (FVC>90%, R2=0.6729). The experiment proves that the model constructed in this paper is not only applicable to densely forest-covered mountainous areas, but also improves the accuracy of forest canopy height inversion in other environments. This study can provide technical and decision support for forest resource survey and global carbon balance.