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Abstract
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.
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