Tree species identification of combined TLS date and UAV images

ZHONG Hao, WANG Chuhong, LIN Wenshu

JOURNAL OF NANJING FORESTRY UNIVERSITY ›› 2024, Vol. 48 ›› Issue (4) : 104-112.

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南京林业大学学报（自然科学版）
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JOURNAL OF NANJING FORESTRY UNIVERSITY ›› 2024, Vol. 48 ›› Issue (4) : 104-112. DOI: 10.12302/j.issn.1000-2006.202205041

Tree species identification of combined TLS date and UAV images

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Abstract

【Objective】Rapid and accurate identification of tree species is crucial for the research and protection of forest resources. Identification of tree species by remote sensing technology has become an important method of forest investigation. However, there are some problems in tree species identification by remote sensing, such as a lack of information in the upper canopy of terrestrial laser scanning (TLS) data and the lower canopy of unmanned aerial vehicle (UAV) images. Therefore, identifying tree species requires multi-source remote sensing data.【Method】In this study, Pinus sylvestris var. mongolica, P. tabuliformis var. mukdensis, Fraxinus mandshurica and Juglans mandshurica in the urban forestry demonstration base of Northeast Forestry University at Harbin were used as the research objects to identify tree species. TLS point cloud data and UAV image data were acquired. Through the processing of UAV images, the photogrammetric point cloud and orthophoto image were obtained. The UAV image point cloud and TLS point cloud data were registered and fused, then divided into single tree points. Based on the single tree point cloud, shape features, structure features, tree trunk color features, and crown color features were extracted, and tree species identification was performed by a support vector machine classification algorithm. Subsequently, the ability of the method to identify tree species using different characteristics was analyzed by random forest algorithm.【Result】Optimal results were obtained when all the features were used to identify tree species. The total accuracy and Kappa coefficient of tree species identification results were 93.48% and 0.91, respectively, which were improved by 4.35-16.31percentage points and 0.06-0.22, respectively, compared with other comparison schemes.【Conclusion】The tree species recognition method based on the fusion of TLS data and UAV image point cloud data proposed in this study can compensate for the lack of information in the upper canopy of TLS point cloud data and the lower canopy of UAV images to a certain extent, and make full use of the rich information contained in multi-source data for tree species recognition. The method can effectively improve the accuracy of tree species identification.

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tree species identification / multi-source remote sensing / LiDAR / unmanned aerial rehicle (UAV) images

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ZHONG Hao , WANG Chuhong , LIN Wenshu. Tree species identification of combined TLS date and UAV images[J]. JOURNAL OF NANJING FORESTRY UNIVERSITY. 2024, 48(4): 104-112 https://doi.org/10.12302/j.issn.1000-2006.202205041

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