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- W1942861091 abstract "Photogrammetric point clouds (PPC) obtained by stereomatching of aerial photographs now have a resolution sufficient to discern individual trees. We have produced such PPCs of a boreal forest and delineated individual tree crowns using a segmentation algorithm applied to the canopy height model derived from the PPC and a lidar terrain model. The crowns were characterized in terms of height and species (spruce, fir, and deciduous). Species classification used the 3D shape of the single crowns and their reflectance properties. The same was performed on a lidar dataset. Results show that the quality of PPC data generally approaches that of airborne lidar. For pixel-based canopy height models, viewing geometry in aerial images, forest structure (dense vs. open canopies), and composition (deciduous vs. conifers) influenced the quality of the 3D reconstruction of PPCs relative to lidar. Nevertheless, when individual tree height distributions were analyzed, PPC-based results were very similar to those extracted from lidar. The random forest classification (RF) of individual trees performed better in the lidar case when only 3D metrics were used (83% accuracy for lidar, 79% for PPC). However, when 3D and intensity or multispectral data were used together, the accuracy of PPCs (89%) surpassed that of lidar (86%)." @default.
- W1942861091 created "2016-06-24" @default.
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- W1942861091 date "2015-10-30" @default.
- W1942861091 modified "2023-10-17" @default.
- W1942861091 title "Characterizing the Height Structure and Composition of a Boreal Forest Using an Individual Tree Crown Approach Applied to Photogrammetric Point Clouds" @default.
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- W1942861091 doi "https://doi.org/10.3390/f6113899" @default.
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