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
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.
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2025, Vol. 37 
), 胡忠文1,2,3(
