CAJ | 학술논문

利用机载激光雷达数据提取天然次生林的树高，旨在探索影响树高提取精度的主要因素。首先，采用高精度曲面建模平差算法（Adjustment Computation of High-accuracy Surface Modeling，HASM-AD）生成研究区不同空间分辨率的数字高程模型（Digital Elevation Model，DEM）、数字地表模型（Digital Surface Model，DSM）和冠层高度模型（Canopy Height Model，CHM）；其次，用树顶点识别算法提取林木树高，设置不同树高识别范围，对比分析不同CHM分辨率和不同树高识别范围对树高提取精度的影响；最后，以天涝池流域30个实测样地数据为样本，对提取精度进行检验。结果显示：提取的样地平均树高与实测值具有明显线性相关关系，线性回归系数为0.694；树高识别范围是影响树高提取精度的重要因素，CHM分辨率对其影响较小。研究表明，采用高采样密度的雷达点云数据、正确选择CHM生成方法和改进树顶点识别算法是提高天然次生林树高提取精度的有效途径。
이용궤재격광뢰체수거제취천연차생림적수고，지재탐색영향수고제취정도적주요인소。수선，채용고정도곡면건모평차산법（Adjustment Computation of High-accuracy Surface Modeling，HASM-AD）생성연구구불동공간분변솔적수자고정모형（Digital Elevation Model，DEM）、수자지표모형（Digital Surface Model，DSM）화관층고도모형（Canopy Height Model，CHM）；기차，용수정점식별산법제취림목수고，설치불동수고식별범위，대비분석불동CHM분변솔화불동수고식별범위대수고제취정도적영향；최후，이천로지류역30개실측양지수거위양본，대제취정도진행검험。결과현시：제취적양지평균수고여실측치구유명현선성상관관계，선성회귀계수위0.694；수고식별범위시영향수고제취정도적중요인소，CHM분변솔대기영향교소。연구표명，채용고채양밀도적뢰체점운수거、정학선택CHM생성방법화개진수정점식별산법시제고천연차생림수고제취정도적유효도경。
The purpose of this study is to evaluate the accuracy of extracting average height of natural secondary forest using airborne LIDAR data and to explore the problems that accompany. The DSMs and DEMs with differ-entspatial resolutions were simulated, by applying HASM-AD algorithm. DSM minus DEM gives CHM, and the tree heights were extracted from CHM. We applied tree vertex recognition algorithm with different recognition scopes. Using 30 measured plot data for verification, we tried to express how CHM spatial revolutionand recog-nition scope could affect tree height extraction accuracy. Firstly, we produced the 0.5 m resolution of CHM and gave 3 trials with setting the recognition scope radius as 0.5 m, 1.0 m and 1.5 m consecutively. The contrast be-tween the results showed that the number of tree vertices extracted was the largest when the recognition scope ra-dius was set as 0.5 m. The algorithm??s ability to recognize tree vertex decreases as recognition scope radius in-creases. Then, we set the recognition scope radius as 0.5 m unchanged and gave 3 trials in which we extracted tree vertex from different CHM with 3 different resolutions (0.1 m, 0.25 m, 0.5 m). The results showed that the number of tree vertices extracted in 3 trials were close. In other words, the recognition scope radius could hardly influence tree vertex extraction. Finally, we compared the average value of the extracted tree heights in each plot to the average of the measured values. The result showed that they were highly correlated with each other, and the regression coefficient between them was 0.694. In conclusion, the recognition scope radius has great influ-ence on tree vertex extraction, while resolution of CHM has little influence on tree vertex extraction. Increasing the sampling density of LIDAR data, choosing an appropriate CHM simulation method and improving the tree vertex recognition algorithm can increase the accuracy of tree height extraction.