DOI: 10.1016/j.jag.2016.07.006
Scopus记录号: 2-s2.0-84997755034
论文题名: A robust approach for tree segmentation in deciduous forests using small-footprint airborne LiDAR data
作者: Hamraz H ; , Contreras M ; A ; , Zhang J
刊名: International Journal of Applied Earth Observation and Geoinformation
ISSN: 15698432
出版年: 2016
卷: 52 起始页码: 532
结束页码: 541
语种: 英语
英文关键词: Crown delineation
; Remote forest inventory
; Remote sensing
; Tree detection evaluation
; Tree-level forest data
Scopus关键词: canopy architecture
; deciduous forest
; deciduous tree
; forest inventory
; image analysis
; lidar
; remote sensing
; segmentation
英文摘要: 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. © 2016 Elsevier B.V. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/80057
Appears in Collections: 气候变化事实与影响
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作者单位: Department of Computer Science, University of Kentucky, Lexington, KY, United States; Department of Forestry, University of Kentucky, Lexington, KY, United States
Recommended Citation:
Hamraz H,, Contreras M,A,et al. A robust approach for tree segmentation in deciduous forests using small-footprint airborne LiDAR data[J]. International Journal of Applied Earth Observation and Geoinformation,2016-01-01,52