Multi-station LiDAR scanning-based hierarchical features for generation of an allometric stem volume model

Abstract

Terrestrial laser scanning technology has developed rapidly, and substantial data have been accumulated in dynamic forest monitoring. Point cloud data of standing trees not only provide tree parameters but also show three-dimensional tree structure. Selection of the key parameters from the point cloud data is a prerequisite for volume estimation of standing trees. Our study collected three phases of data over 5 years from Liriodendron chinense plantation forest. A series of the height-related characteristic parameters were extracted from the scanned points of each tree stems, including a proposed new parameter and the height cumulative percentage (H-z%). The upper diameter accuracy obtained by multi-station scanning is high, and the correlation coefficient with manually measured data is 0.9864. The shape of the upper tree trunk extracted by the point cloud is equivalent to that of the sample trees (height of 10 to 20 m) with points at H-25% and H-50% of the height. These two parameters also show a high correlation with volume. Results show that H-25% can better associated with tree volume, with R-2 at 0.951, 0.957, and 0.901 at three stages, respectively. The volume dynamic change calculated by model 2 is linearly correlated with the rate in point cloud restoration, the intercept is -0.081, and the slope is 1.14. Compared with previous researches, the volume model established based on point cloud hierarchical parameters in this study could be used for monitoring the dynamic volume changes in Liriodendron forest. The H-z value extracted from multi-station scanning point cloud data could be used to represent the dynamic forest structure. The results of this study contribute to further development of terrestrial laser scanning-based modeling and estimation methods for individual tree and forest growth, thereby improving the accuracy of forest inventories estimation and providing better tools for decision-making processes. (C) The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License.