Improving Forest Canopy Height Mapping in Wuyishan National Park Through Calibration of ZiYuan-3 Stereo Imagery Using Limited Unmanned Aerial Vehicle LiDAR Data

被引:1
作者
Jian, Kai [1 ,2 ]
Lu, Dengsheng [1 ,2 ]
Lu, Yagang [3 ]
Li, Guiying [1 ,2 ]
机构
[1] Fujian Normal Univ, Key Lab Humid Subtrop Ecogeog Proc, Minist Educ, Fuzhou 350117, Peoples R China
[2] Fujian Normal Univ, Inst Geog, Fuzhou 350117, Peoples R China
[3] Natl Forestry & Grassland Adm, Inst East China Inventory & Planning, Hangzhou 310019, Peoples R China
来源
FORESTS | 2025年 / 16卷 / 01期
基金
中国国家自然科学基金;
关键词
forest canopy height; ZiYuan-3; images; machine learning; causal inference; Wuyishan National Park; ELEVATION MODEL DEM; AIRBORNE LIDAR; QUALITY ASSESSMENT; VERTICAL ACCURACY; TREE HEIGHT; WORLDVIEW-2; GENERATION; EXTRACTION; EQUATION; SAMPLES;
D O I
10.3390/f16010125
中图分类号
S7 [林业];
学科分类号
0829 ; 0907 ;
摘要
Forest canopy height (FCH) is a critical parameter for forest management and ecosystem modeling, but there is a lack of accurate FCH distribution in large areas. To address this issue, this study selected Wuyishan National Park in China as a case study to explore the calibration method for mapping FCH in a complex subtropical mountainous region based on ZiYuan-3 (ZY3) stereo imagery and limited Unmanned Aerial Vehicle (UAV) LiDAR data. Pearson's correlation analysis, Categorical Boosting (CatBoost) feature importance analysis, and causal effect analysis were used to examine major factors causing extraction errors of digital surface model (DSM) data from ZY3 stereo imagery. Different machine learning algorithms were compared and used to calibrate the DSM and FCH results. The results indicate that the DSM extraction accuracy based on ZY3 stereo imagery is primarily influenced by slope aspect, elevation, and vegetation characteristics. These influences were particularly notable in areas with a complex topography and dense vegetation coverage. A Bayesian-optimized CatBoost model with directly calibrating the original FCH (the difference between the DSM from ZY3 and high-precision digital elevation model (DEM) data) demonstrated the best prediction performance. This model produced the FCH map at a 4 m spatial resolution, the root mean square error (RMSE) was reduced from 6.47 m based on initial stereo imagery to 3.99 m after calibration, and the relative RMSE (rRMSE) was reduced from 36.52% to 22.53%. The study demonstrates the feasibility of using ZY3 imagery for regional forest canopy height mapping and confirms the superior performance of using the CatBoost algorithm in enhancing FCH calibration accuracy. These findings provide valuable insights into the multidimensional impacts of key environmental factors on FCH extraction, supporting precise forest monitoring and carbon stock assessment in complex terrains in subtropical regions.
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页数:21
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