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Remote Sensing for Natural Resources    2025, Vol. 37 Issue (6) : 107-117     DOI: 10.6046/zrzyyg.2024288
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Mapping mountain vegetation using realistic 3D models integrating optical images and light detection and ranging data
ZHANG Jinhua1,2,3(), HU Zhongwen1,2,3(), ZHANG Yinghui1,2,3, ZHANG Qian1,2,3, WANG Jingzhe1,4, WU Guofeng1,2,3
1. MNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area, Shenzhen University, Shenzhen 518060, China
2. Guangdong Key Laboratory of Urban Informatics, Shenzhen University, Shenzhen 518060, China
3. School of Architecture and Urban Planning, Shenzhen University, Shenzhen 518060, China
4. School of Artificial Intelligence, Shenzhen Polytechnic University, Shenzhen 518060, China
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Abstract  

Vegetation distribution serves as a crucial foundation for natural resource conservation and ecosystem health assessment. In mountainous regions, substantial terrain undulations and complex vegetation types complicate the mapping process. Moreover, the traditional remote sensing-based vegetation classification, whose mapping relies on 2D imagery, fails to depict the vertical structure and 3D spatial distribution of vegetation. To investigate the potential of realistic 3D models in fine-scale vegetation classification and mapping, this study proposed a realistic 3D model-based mapping approach for mountain vegetation by integrating optical images and light detection and ranging (LiDAR) data. Focusing on Neilingding Island in Guangdong, this study constructed a multi-source dataset using realistic 3D models, multispectral images, and LiDAR point clouds acquired by unmanned aerial vehicle (UAV)-based measurements, followed by data registration and feature extraction. Subsequently, the LightGBM algorithm was employed to achieve fine-scale vegetation classification and to assess the classification performance of multi-source data features. Finally, semantic 3D mesh models of vegetation were generated by projecting the 2D vegetation maps onto the 3D models. The results indicate that realistic 3D models can effectively distinguish vegetation types. Their combination with multispectral and LiDAR data provides a more comprehensive description of the topography and vegetation structures in mountainous areas. Compared to using a single data source, this approach achieves an increase in the overall accuracy (OA) of 2D classification by 4.28% to 11.29%. Concurrently, the OA of the 3D mapping based on realistic 3D models reached 92.06%, with a Kappa coefficient of 0.89. This approach can reflect the accurate, visualized, 3D distribution patterns of mountain vegetation and improve the accuracy of fine-scale vegetation information extraction. This study demonstrates the significant potential of 3D model-multisource data integration for natural resource monitoring and provides novel ideas and methods for fine-scale and 3D information extraction of regional vegetation.
Keywords realistic 3D model      3D mapping      fine-scale classification of vegetation      mountain vegetation      light detection and ranging (LiDAR)      multispectral      multi-source data fusion     
ZTFLH:  TP79  
Issue Date: 31 December 2025
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Jinhua ZHANG
Zhongwen HU
Yinghui ZHANG
Qian ZHANG
Jingzhe WANG
Guofeng WU
Cite this article:   
Jinhua ZHANG,Zhongwen HU,Yinghui ZHANG, et al. Mapping mountain vegetation using realistic 3D models integrating optical images and light detection and ranging data[J]. Remote Sensing for Natural Resources, 2025, 37(6): 107-117.
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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2024288     OR     https://www.gtzyyg.com/EN/Y2025/V37/I6/107
Fig.1  Location of study area
Fig.2  Schematic illustrations of multi-source data registration
Fig.3  Technical flowchart
数据源 特征类型 特征参数
实景三维模型 几何特征 线性度、平面度、球形度、全方差、各向异性、曲率变化、特征熵、特征值总和、垂直度、粗糙度、体积密度、表面密度
纹理特征 均值、方差、能量、同质性、对比度、不相似性、相关性、熵
多光谱 光谱波段 蓝、绿、红、红边、近红外、近红外-940
植被指数 ARI1,MCARI,NDVI,NLI,RGRI
LiDAR 地形特征 DSM,DEM,CHM,Slope,Aspect
高度特征 累积高度百分位数(1%,10%,20%,30%,40%,50%,60%,70%,80%,90%,99%)、平均绝对偏差、冠层起伏率、变异系数、峰度、二次幂平均、偏斜度、方差
多源数据融合 以上所有特征的组合
Tab.1  Description of the feature set
Fig.4  Illustration of 3D semantic mapping
数据 特征 F1/% OA/%
相思林 荔枝林 混合林 灌草丛 建筑 裸地 水体
实景三维模型 几何 75.29 87.68 71.95 86.08 90.37 86.8 98.59 85.28
纹理 86.18 80.33 76.53 81.76 88.33 86.61 98.43 85.46
几何+纹理 92.19 90.80 89.25 91.97 93.51 93.39 99.65 92.97
多光谱 波段 88.23 78.25 71.29 88.99 90.73 91.71 96.42 86.60
指数 74.80 72.57 64.47 83.83 81.98 87.84 94.3 80.07
波段+指数 89.40 80.11 73.94 90.80 93.31 93.86 97.39 88.46
LiDAR 地形 86.78 96.38 82.86 92.70 93.29 91.39 97.24 91.53
高度 90.36 92.98 81.10 88.77 90.17 88.02 89.59 88.81
地形+高度 94.15 97.90 90.60 96.09 95.83 95.28 98.47 95.47
多源数据融合 所有特征 99.51 99.90 99.3 99.85 99.84 99.86 99.99 99.75
Tab.2  Classification accuracy evaluation from different features
Fig.5  Fine vegetation classification maps using different data
Fig.6  Schematic illustrations of classification accuracy and feature importance
Fig.7  3D map of the sample meshes
类别 相思林/m2 荔枝林/m2 混合林/m2 灌草丛/m2 建筑/m2 裸地/m2 水体/m2 PA/%
相思林 7 589.17 3.22 887.19 0.67 7.37 5.96 0.00 89.35
荔枝林 0.57 2 695.00 116.16 3.09 96.51 0.00 0.00 92.57
混合林 273.48 18.97 10 865.33 134.58 110.48 42.96 0.00 94.93
灌草丛 3.79 3.29 187.89 1 504.19 27.87 11.43 0.00 86.52
建筑 0.72 1.90 8.00 0.83 947.26 19.21 0.00 96.86
裸地 72.38 5.40 74.17 19.10 36.73 1 514.38 4.81 87.69
水体 0.00 0.00 0.00 0.00 0.00 19.14 359.28 94.94
UA/% 95.58 98.8 89.51 90.48 77.25 93.88 98.68 —
OA/% 92.06
Kappa 0.89
Tab.3  Confusion matrix of the 3D mapping
Fig.8  Textured 3D mesh and semantic 3D mesh of the whole scene
植被类型 平均海拔/m 平均坡度/(°) 二维面积/m2 二维面积占比/% 三维面积/m2 三维面积占比/% 三维面积/二维面积
相思林 51.34 25.26 84 658.3 19.04 349 930.5 28.79 4.13
荔枝林 7.68 15.32 12 452.8 2.80 17 616.1 1.45 1.41
混合林 42.55 25.73 320 953.0 72.18 803 781.6 66.14 2.50
灌草丛 7.97 20.10 26 605.3 5.98 43 982.0 3.62 1.65
Tab.4  Spatial distribution parameters of different vegetation
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