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摘要:
Individual tree detection (ITD) and the area-based approach (ABA) are combined to generate tree-lists using airborne LiDAR data. ITD based on the Canopy Height Model (CHM) was applied for overstory trees, while ABA based on nearest neighbor (NN) imputation was applied for understory trees. Our approach is intended to compensate for the weakness of LiDAR data and ITD in estimating understory trees, keeping the strength of ITD in estimating overstory trees in tree-level. We investigated the effects of three parameters on the performance of our proposed approach: smoothing of CHM, resolution of CHM, and height cutoff (a specific height that classifies trees into overstory and understory). There was no single combination of those parameters that produced the best performance for estimating stems per ha, mean tree height, basal area, diameter distribution and height distribution. The trees in the lowest LiDAR height class yielded the largest relative bias and relative root mean squared error. Although ITD and ABA showed limited explanatory powers to estimate stems per hectare and basal area, there could be improvements from methods such as using LiDAR data with higher density, applying better algorithms for ITD and decreasing distortion of the structure of LiDAR data. Automating the procedure of finding optimal combinations of those parameters is essential to expedite forest management decisions across forest landscapes using remote sensing data.
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篇名 Generating Tree-Lists by Fusing Individual Tree Detection and Nearest Neighbor Imputation Using Airborne LiDAR Data
来源期刊 林学期刊(英文) 学科 医学
关键词 Tree-List Generation Individual TREE DETECTION Nearest NEIGHBOR IMPUTATION Parameter Sensitivity AIRBORNE LiDAR
年,卷(期) 2018,(4) 所属期刊栏目
研究方向 页码范围 500-531
页数 32页 分类号 R73
字数 语种
DOI
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研究主题发展历程
节点文献
Tree-List
Generation
Individual
TREE
DETECTION
Nearest
NEIGHBOR
IMPUTATION
Parameter
Sensitivity
AIRBORNE
LiDAR
研究起点
研究来源
研究分支
研究去脉
引文网络交叉学科
相关学者/机构
期刊影响力
林学期刊(英文)
季刊
2163-0429
武汉市江夏区汤逊湖北路38号光谷总部空间
出版文献量(篇)
314
总下载数(次)
0
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