Multi-layered tree crown extraction from LiDAR data using graph-based segmentation
Multi-layered tree crown extraction from LiDAR data using graph-based segmentation
复制标题
使用基于图的分割从 LiDAR 数据中提取多层树冠
DOI：
10.1016/j.compag.2020.105213
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发表时间：
2020-03
影响因子：
8.3
通讯作者：
Fan Jing
中科院分区：
文献类型：
--
作者：
Dong Tianyang;Zhang Xinpeng;Ding Zhanfeng;Fan Jing
随着激光雷达（LiDAR）和无人机（UAV）技术的发展，从LiDAR数据中提取树冠并推断其几何特征变得越来越容易。虽然森林在垂直方向上具有明显的分层现象，但现有的单木检测方法一般都是针对上层树木的检测。结果，林下树木不能被有效地提取。为了有效地从多层森林中检测出单木，提出了一种基于图分割算法的多层森林单木提取方法。首先，利用基于图的分割算法在LiDAR数据生成的冠层高度模型（CHM）上描绘出上层树木的冠层。然后，利用滑动窗口检测方法提取林下树木的LiDAR数据。最后，利用基于图的分割算法提取林下树木信息。为了验证所提出的方法的性能，本研究从两个研究领域中选择了六个实验地块。根据我们的方法的结果，最高的匹配得分和平均得分为上层树木达到91.3和86.3，最高的匹配得分和平均得分为下层树木达到78.1和63.2。与其他方法相比，该方法具有更好的检测效果。实验结果表明，该方法能有效地提取林下和林上树木，从而提高了多层森林中单木提取的准确性。
With the development of Light Detection and Ranging (LiDAR) and Unmanned Aerial Vehicle (UAV) technology, extracting tree crowns from LiDAR data and infering their geometrical features are becoming more available for everyone. Although the forest has a significant stratification phenomenon in the vertical direction, the existing individual tree detection methods generally aimed at solving overstory trees detection. As a result, the understory trees cannot be effectively extracted. To effectively detect individual tree from multi-layer forests, a multi-layered tree extraction method using a graph-based segmentation algorithm was proposed in this study. First, using the graph-based segmentation algorithm delineates the canopy of the overstory tree on the canopy height model (CHM) generated by LiDAR data. Then, using the sliding window detection method extracts the LiDAR data of understory trees. Finally, the information of understory trees is extracted by the graph-based segmentation algorithm. To verify the performance of the proposed method, this study selected six experimental plots from two research areas. According to the result of our method, the highest matching score and average score for overstory trees reached 91.3 and 86.3; the highest matching score and average score for understory trees reached 78.1 and 63.2. Compared with other methods, our method has better detection results. The experimental results show that the proposed method can extract the understory and overstory trees effectively, thus improving the accuracy of individual tree extraction in multi-layered forests.
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DOI：
10.3390/s8063938
发表时间：
2008-06-12
期刊：
Sensors (Basel, Switzerland)
影响因子：
--
作者：
Wang Y;Weinacker H;Koch B
通讯作者：
Koch B
影响因子：
13.5
作者：
M. Wulder;Joanne C. White;R. Nelson;E. Næsset;H. Ørka;N. Coops;T. Hilker;C. Bater;T. Gobakken-
通讯作者：
M. Wulder;Joanne C. White;R. Nelson;E. Næsset;H. Ørka;N. Coops;T. Hilker;C. Bater;T. Gobakken-
DOI：
10.1016/j.isprsjprs.2009.04.002
发表时间：
2009-11
影响因子：
12.7
作者：
J. Reitberger;C. Schnörr;P. Krzystek;Uwe Stilla
通讯作者：
J. Reitberger;C. Schnörr;P. Krzystek;Uwe Stilla
影响因子：
8.2
作者：
A. Harikumar;F. Bovolo;L. Bruzzone
通讯作者：
A. Harikumar;F. Bovolo;L. Bruzzone
影响因子：
5
作者：
Cook, Bruce D.;Corp, Lawrence A.;Montesano, Paul M.
通讯作者：
Montesano, Paul M.