Extraction of Forestry Parameters Based on Multi-Platform LiDAR

Abstract

Quick and accurate acquisition of tree height (TH) and diameter at breast height (DBH) plays a very important role in forestry surveys. These parameters can be collected rapidly and accurately with LiDAR. In this paper, an accurate tree parameters extraction method with combining the use of Unmanned Aerial Vehicle Laser Scanning (UAVLS) to extract TH and Terrestrial Laser Scanning (TLS) to extract DBH was proposed. To verify the applicability of this method, this paper collected LiDAR data in the Laohugou forest area (a natural forest) and Saihanba forest area (an artificial forest), Hebei Province, China. For the extraction of TH, both forest areas had overestimated. The coefficient of determination R2 of TH in Laohugou forest area was 0.9458 and the root mean square error (RMSE) was 0.7 m, while in Saihanba forest area R2 was 0.95 and the RMSE was 0.65 m. A method based on point density analysis was proposed to automatically extract DBH. First, the data by TLS was normalized and made four-centimeter slices at 1.3 m. Then, branches, weeds and outliers were eliminated using an improved K-means algorithm. Finally, point density analysis was performed on all sections, and threshold values were set to automatically complete the extraction of DBH. The automatic DBH extraction by this paper proposed method was consistent with the actual measurements, and the mean intersection over union (MIOU) reached 89%. The R2 of DBH in the Laohugou forest area was 0.9941 and the RMSE was 0.65 cm; the R2 of DBH in the Saihanba forest area was 0.99 and the RMSE was 0.43 cm. These results confirm that the accurate extraction of DBH in two forest areas with different growth conditions and different tree species.

Keywords

• LiDAR
• forestry
• point cloud
• parameter extraction