Abstract
The development of new approaches to tree-level parameter extraction for forest resource inventory and management is an important area of ongoing research, which puts forward high requirements for the capabilities of single-tree segmentation and detection methods. Conventional methods implement segmenting routine with same resolution threshold for overstory and understory, ignoring that their lidar point densities are different, which leads to over-segmentation of the understory trees. To improve the segmentation accuracy of understory trees, this paper presents a multi-threshold segmentation approach for tree-level parameter extraction using small-footprint airborne LiDAR (Light Detection And Ranging) data. First, the point clouds are pre-processed and encoded to canopy layers according to the lidar return number, and multi-threshold segmentation using DSM-based (Digital Surface Model) method is implemented for each layer; tree segments are then combined across layers by merging criteria. Finally, individual trees are delineated, and tree parameters are extracted. The novelty of this method lies in its application of multi-resolution threshold segmentation strategy according to the variation of LiDAR point density in different canopy layers. We applied this approach to 271 permanent sample plots of the University of Kentucky’s Robinson Forest, a deciduous canopy-closed forest with complex terrain and vegetation conditions. Experimental results show that a combination of multi-resolution threshold segmentation based on stratification and cross-layer tree segments merging method can provide a significant performance improvement in individual tree-level forest measurement. Compared with DSM-based method, the proposed multi-threshold segmentation approach strongly improved the average detection rate (from 52.3% to 73.4%) and average overall accuracy (from 65.2% to 76.9%) for understory trees. The overall accuracy increased from 75.1% to 82.6% for all trees, with an increase of the coefficient of determination R2 by 20 percentage points. The improvement of tree detection method brings the estimation of structural parameters for single trees up to an accuracy level: For tree height, R2 increased by 5.0 percentage points from 90% to 95%; and for tree location, the mean difference decreased by 23 cm from 105 cm to 82 cm.
Highlights
Over the past decades, airborne LiDAR has been extensively used in the field of forestry to minimize traditional forest inventory practices, which are labor-intensive and cost-consuming
Various methods have been developed to extract individual tree information from high-resolution LiDAR datasets. These techniques generally fall into two categories: canopy height models (CHMs) or digital surface models (DSMs), e.g., [13,14,15,16,17,18] and, more recently, directly using raw LiDAR point clouds methods, e.g., [10,19,20]
This paper proposed a multi-threshold single-tree segmentation approach using small-footprint airborne LiDAR data, which consists of data pre-processing and isolating canopy layers, multiple resolution threshold segmentation for isolated layers, cross-layer combination of tree segments, delineation of individual trees, and tree parameter extraction
Summary
Airborne LiDAR has been extensively used in the field of forestry to minimize traditional forest inventory practices, which are labor-intensive and cost-consuming. Various methods have been developed to extract individual tree information from high-resolution LiDAR datasets These techniques generally fall into two categories: canopy height models (CHMs) or digital surface models (DSMs), e.g., [13,14,15,16,17,18] and, more recently, directly using raw LiDAR point clouds methods, e.g., [10,19,20]. DSM-based methods locate the global maximum elevation amongst lidar surface points, generate vertical profiles, and create a convex hull to delineate tree crown [24]. The approach identified 94% of dominant and co-dominant trees, about 62% of intermediate and overtopped trees, and the overall segmentation accuracy was 77% These approaches have an inherent drawback of missing understory trees due to focusing only on the surface data during individual tree segmentation [11,12]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
