Abstract
To minimize omission and commission errors due to the lack of adequate utilization of forest structure information, this paper presents a tree delineation approach by combining trunk detection with canopy segmentation. First, all potential tree trunk points are detected and detached from leaf-off data by analyzing the points' vertical histogram, and the obtained points are then clustered using the method based on DBSCAN (Density-Based Spatial Clustering of Application with Noise). Meanwhile, the canopy-based segmentation is implemented using leaf-on data within the same plot. The detected trunks and delineated crown segments are then combined using the matching rules. Finally, single trees are isolated from point clouds, and tree-level structure information is estimated. The novelty of this approach lies in that the trunk detection results and the canopy segmentation results serve as mutual references for final individual tree delineation. Experimental results in a canopy-closed deciduous natural forest show that the presented method can identify 84.0% of trees, 90.7% of the identified trees are correct, and the total segmentation accuracy is 87.2%. The determination coefficient R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of tree height is 0.96, and the mean difference of tree position is 76 cm. The results imply that the presented approach has good potential for isolating single trees from airborne LiDAR point clouds and estimating tree-level structural parameters in deciduous forests.
Highlights
As an important part of the terrestrial ecosystem, forest and its change have an important impact on the terrestrial biosphere and the Earth’s surface changes
The following pre-processing routine contains five steps: (1) uniformize lidar point density to eliminate the uneven distribution of points due to the scanning pattern, (2) generate a raster with a resolution value of NPS, (3) search the highest points within each raster unit, and the searched point set is used as lidar surface points to filter the LiDAR data, (4) calculate the heights above ground for all lidar surface points utilizing digital terrain model (DTM), and (5) a Gaussian filter is used to smooth the lidar surface points to weaken the subtle changes of canopy vegetation height
It is worth noting that compared with the overstory trees, the overall segmentation accuracy F of the understory trees is significantly improved by using the method proposed in this paper (4.8 percentage points vs. 2.2 percentage points), which indicates that the proposed method has a more obvious improvement on the low-vegetation segmentation
Summary
As an important part of the terrestrial ecosystem, forest and its change have an important impact on the terrestrial biosphere and the Earth’s surface changes. Accurate and convenient forest inventories are significant for the protection of the ecological environment and the well-being of human beings and wildlife [1]. In forest resource inventory campaigns, tree-level information is usually obtained by visual estimation or simple instrument measurement method, with relatively poor accuracy, which cannot meet the requirements of ‘‘precision forestry’’ and ‘‘digital forestry’’ [2]. Remote-sensing based inventory systems have developed rapidly aiming to reduce the difficulty and cost of forest surveys. Airborne LiDAR-based technology features prominently [3]–[5].
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