Abstract
Many dendrometric parameters have been estimated by light detection and ranging (LiDAR) technology over the last two decades. Handheld mobile laser scanning (HMLS), in particular, has come into prominence as a cost-effective data collection method for forest inventories. However, most pilot studies were performed in domesticated landscapes, where the environmental settings were far from those presented by (near)natural forest ecosystems. Besides, these studies consisted of numerous data processing steps, which were challenging when employed by manual means. Here we present an automated approach for deriving key inventory data using the HMLS method in natural forest areas. To this end, many algorithms (e.g., cylinder/circle/ellipse fitting) and machine learning models (e.g., random forest classifier) were used in the data processing stage for estimation of the tree diameter at breast height (DBH) and the number of trees. The estimates were then compared against the reference data obtained by field measurements from six forest sample plots. The results showed that correlations between the estimated and reference DBHs were very strong at the plot level (r=0.83–0.99, p<0.05). The average RMSE for tree DBHs was 1.8 cm at the forest landscape level. As for tree detection, 92.5% of 292 trunks were correctly classified on point cloud data. In general, estimation accuracy was sufficient for operational forest inventory needs. However, they could markedly decrease in »hard plots« located at rocky terrains with dense undergrowth and irregular trunks. We concluded that area-based forest inventories might hugely benefit from the HMLS method, particularly in »easy plots«. By improving the algorithmic performances, the accuracy levels can be further increased by future research.
Highlights
Forest management planning requires accurate and updated information for characterizing the current state of forest ecosystems
The aim of this study is to develop a new and automated approach for extracting the number of trees and tree diameter at breast height (DBH) data from 3D point clouds captured by a GeoSLAM ZEB-REVO Handheld mobile laser scanning (HMLS) device
It was seen that there was a good agreement for the individual tree detection results obtained by the present study with those presented by other researchers
Summary
Forest management planning requires accurate and updated information for characterizing the current state of forest ecosystems. Periodical forest inventories are the primary data sources for this information flow (Kangas and Maltamo 2006, Ozkan and Demirel 2018). The number of trees and the diameter at breast height (DBH) are two essential parameters since they form the basis for both forest stand density and timber volume calculations (Wan et al 2019). Unless having these data, neither the sustainable harvest rates nor the revenue of forest enterprises. Conventional data collection methods (i.e., field measurements) are usually expensive, time-consuming, and labor-intensive in forest inventory surveying (Trotter et al 1997). In particular, are more common with the rapid development of light detection and ranging (LiDAR) technology worldwide
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