Abstract
Three-dimensional light detection and ranging (LiDAR) point clouds acquired from unmanned aerial vehicles (UAVs) represent a relatively new type of remotely sensed data. Point cloud density of thousands of points per square meter with survey-grade accuracy makes the UAV laser scanning (ULS) a very suitable tool for detailed mapping of forest environment. We used RIEGL VUX-SYS to scan forest stands of Norway spruce and Scots pine, the two most important economic species of central European forests, and evaluated the suitability of point clouds for individual tree stem detection and stem diameter estimation in a fully automated workflow. We segmented tree stems based on point densities in voxels in subcanopy space and applied three methods of robust circle fitting to fit cross-sections along the stems: (1) Hough transform; (2) random sample consensus (RANSAC); and (3) robust least trimmed squares (RLTS). We detected correctly 99% and 100% of all trees in research plots for spruce and pine, respectively, and were able to estimate diameters for 99% of spruces and 98% of pines with mean bias error of −0.1 cm (−1%) and RMSE of 6.0 cm (19%), using the best performing method, RTLS. Hough transform was not able to fit perimeters in unfiltered and often incomplete point representations of cross-sections. In general, RLTS performed slightly better than RANSAC, having both higher stem detection success rate and lower error in diameter estimation. Better performance of RLTS was more pronounced in complicated situations, such as incomplete and noisy point structures, while for high-quality point representations, RANSAC provided slightly better results.
Highlights
Recent sustainable forestry standards require careful planning based on highly accurate inventory data of forest stands and properties [1]
Due to the low flying altitude, varying around 100 m above ground level, and arbitrarily low speed of multicopter-type unmanned aerial vehicles (UAVs) carriers, the density of resulting point clouds can reach the level of thousands of points per square meter
The points were evenly distributed in 3D space, as illustrated by Figure 3, which shows point density and ratio of resulting point cloud size related to original point counts for different levels of thinning
Summary
Recent sustainable forestry standards require careful planning based on highly accurate inventory data of forest stands and properties [1]. Increasing demands on inventory data quality together with increasing costs of human labor in advanced countries force the forest owners to increase the efficiency of data collection and to simplify assessment of required parameters of forest trees and stands in means of automation. Special attention has been paid to noncontact data collection methods providing accurate three-dimensional data that allow reconstructing forest stands and effectively estimating their parameters. The novel methods, made possible by advances in technology and computer vision algorithms, are mostly represented by two technologies: laser scanning and multiview photogrammetry. Laser scanning methods utilize light detection and ranging (LiDAR) technology for precise range measurement of objects in surroundings. Laser scanners provide 3D positions of up to 1 million points per second with a millimeter level precision
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