This paper presents a non-parametric approach for segmenting trees from airborne LiDAR data in deciduous forests. Based on the LiDAR point cloud, the approach collects crown information such as steepness and height on-the-fly to delineate crown boundaries, and most importantly, does not require a priori assumptions of crown shape and size. The approach segments trees iteratively starting from the tallest within a given area to the smallest until all trees have been segmented. To evaluate its performance, the approach was applied to the University of Kentucky Robinson Forest, a deciduous closed-canopy forest with complex terrain and vegetation conditions. The approach identified 94% of dominant and co-dominant trees with a false detection rate of 13%. About 62% of intermediate, overtopped, and dead trees were also detected with a false detection rate of 15%. The overall segmentation accuracy was 77%. Correlations of the segmentation scores of the proposed approach with local terrain and stand metrics was not significant, which is likely an indication of the robustness of the approach as results are not sensitive to the differences in terrain and stand structures.
|Number of pages||10|
|Journal||International Journal of Applied Earth Observation and Geoinformation|
|State||Published - Oct 1 2016|
Bibliographical noteFunding Information:
This work was supported by: (i) the Department of Forestry at the University of Kentucky and the McIntire-Stennis project KY009026 Accession 1001477, (ii) the University of Kentucky Center for Computational Sciences, (iii) the Kentucky Science and Engineering Foundation under the account number KSEF-3405-RDE-018, and (iv) the National Science Foundation under Grant Number CCF-1215985.
© 2016 Elsevier B.V.
- Crown delineation
- Remote forest inventory
- Remote sensing
- Tree detection evaluation
- Tree-level forest data
ASJC Scopus subject areas
- Global and Planetary Change
- Earth-Surface Processes
- Computers in Earth Sciences
- Management, Monitoring, Policy and Law