Characterizing the Height Structure and Composition of a Boreal Forest Using an Individual Tree Crown Approach Applied to Photogrammetric Point Clouds

Forests

Benoît St-Onge¹  Félix-Antoine Audet¹  Jean Bégin² 

[1] Department of Geography, University of Quebec at Montreal, Montreal, QC H3C 3P8, Canada; E-Mail:;Faculty of Forestry, Geography and Geomatics, Laval University, Quebec City, QC G1V 0A6, Canada; E-Mail:

关键词: photogrammetry;    point cloud;    image matching;    lidar;    ITC delineation;    species;    view angle;    tree height;    crown area;   

DOI  :  10.3390/f6113899

来源: mdpi

【 摘 要 】

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%).

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.