Authors: Hien Phu La Yang Dam Eo Anjin Chang Changjae Kim
Publish Date: 2014/10/03
Volume: 19, Issue: 4, Pages: 1078-1087
Abstract
Tree information such as tree height tree type diameter at breast height and number of trees are critical for effective forest analysis and management In this regard this paper presents an individual tree extraction method that uses airborne LiDAR Light Detection and Ranging and hyperspectral data The Support Vector Machine SVM classifier was first used to extract tree areas from hyperspectral imagery Then Principal Components Analysis PCA was applied to the hyperspectral imagery to derive a PCA image consisting of five bands A segmentation process was performed on the four datasets which are 1 hyperspectral image 2 LiDARbased DCM Digital Canopy Model 3 Fusion of PCA image and LiDARDCM and 4 Fusion of PCA image LiDARbased DCM and NDVI Normalized Difference Vegetation Index Finally individual tree parameters were estimated based on the segmentation results The field data were then compared with the final results The result shows that using fusion data set our method can detect 70 and 92 of reference trees in the forest areas with high and low density respectively Conclusively the tree extraction approach based on the fusion of different data sources provided better results than ones using the single data sources
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