![]() (2010) developed an adaptive clustering method to segment individual tree crowns. (2009) used a normalized-cut segmentation approach that subdivides the tree area into a voxel space and then extracted single trees from identified graphs. Their clustering method depends on seed points extracted from the LiDAR-derived CHM, thus this method is not an approach relying on point clouds only. (2004) applied the k-means clustering algorithm to detect individual trees. Several researchers have developed automated methods for extracting individual trees from the LiDAR point clouds leading to varying degree of success. Crown diameter, also known as crown width, is the span of the crown of a tree. Base height is the length from the base of the crown to the base of the tree. Crown depth is the length along the main axis from the treetop to the base of the crown. Tree height is the length along the main axis from the treetop to the base of a tree. Several fundamental tree parameters such as height, base height, crown depth, and crown diameter can actually be estimated from the LiDAR point clouds, as illustrated in Figure 1. Second, the Gaussian smoothing step for alleviating the rough surface of the canopy may lead to under- or overestimates of tree height. ![]() This will ultimately affect the subsequent estimation of tree metrics. ![]() First, the derivation of CHM introduces errors and uncertainties due to both the interpolation methods and the grid spacing chosen. There are several problems with using these methods. These algorithms are favored in many commercial and business environments mainly because of the speed of processing and the accessibility to software that commonly uses regularly spaced data ( i.e., rasters). A range of methods have been explored to detect single trees, many of which are based on a LiDAR-derived canopy height model (CHM), a raster surface interpolated from LiDAR points hitting on the tree canopy surface (e.g., ). Modern scanning LiDAR technology has allowed for the estimation of inventory elements at the individual tree level due to the great increase in sampling rate. ![]()
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