A Method for Forest Vegetation Height Modeling Based on Aerial Digital Orthophoto Map and Digital Surface Model

• Published in: IEEE transactions on geoscience and remote sensing Vol. 60; pp. 1 - 7
• Main Authors: Deng, Xingsheng, Tang, Guo, Wang, Qingyang, Luo, Lixia, Long, Sichun
• Format: Journal Article
• Language: English
• Published: New York IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Aerial photography; Algorithms; Correlation; Correlation coefficient; Correlation coefficients; Digital mapping; Digital terrain model (DTM); Feature extraction; Forestry; Forests; geometric features; Height; Image color analysis; Indexes; Learning algorithms; Machine learning; Microwave integrated circuits; Modelling; Orthophotography; Photogrammetry; spectral features; Support vector machines; Surface topography; Terrain models; Topographic maps; Topographic surveying; Topographic surveys; Vegetation; vegetation height model (VHM); Vegetation mapping
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/TGRS.2021.3093976

The solutions of vegetation height and the mapping of terrain in forest have always been concerning and are still challenging problems. The purpose of this article is to provide a new technical approach for the digital terrain model (DTM) and forest topographic survey by aerial photogrammetry, in consideration of the forest vegetation height modeling problem. Based on an aerial digital orthophoto map and a digital surface model (DSM), the spectral features and geometric features that are related to forest vegetation height are analyzed and extracted. The nonlinear correlation maximal information coefficient, maximum asymmetry score, and Pearson linear correlation coefficient between feature factors and vegetation height are listed, and the correlations are evaluated as the basis for factors selection. Two kinds of support vector regression algorithms were adopted to establish the machine learning for forest vegetation height model (VHM). Therefore, the DSM can be corrected to DTM. The experimental results show that the accuracy of the forest VHM is better than 1 m. Thus, the proposed method is proved to be feasible and practical. It provides a low-cost and high-efficiency method for the VHM and DTM in forest areas by photogrammetry.