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Tree species classification in an extensive forest area using airborne hyperspectral data under varying light conditions

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Abstract

Although airborne hyperspectral data with detailed spatial and spectral information has demonstrated significant potential for tree species classification, it has not been widely used over large areas. A comprehensive process based on multi-flightline airborne hyperspectral data is lacking over large, forested areas influenced by both the effects of bidirectional reflectance distribution function (BRDF) and cloud shadow contamination. In this study, hyperspectral data were collected over the Mengjiagang Forest Farm in Northeast China in the summer of 2017 using the Chinese Academy of Forestry’s LiDAR, CCD, and hyperspectral systems (CAF-LiCHy). After BRDF correction and cloud shadow detection processing, a tree species classification workflow was developed for sunlit and cloud-shaded forest areas with input features of minimum noise fraction reduced bands, spectral vegetation indices, and texture information. Results indicate that BRDF-corrected sunlit hyperspectral data can provide a stable and high classification accuracy based on representative training data. Cloud-shaded pixels also have good spectral separability for species classification. The red-edge spectral information and ratio-based spectral indices with high importance scores are recommended as input features for species classification under varying light conditions. According to the classification accuracies through field survey data at multiple spatial scales, it was found that species classification within an extensive forest area using airborne hyperspectral data under various illuminations can be successfully carried out using the effective radiometric consistency process and feature selection strategy.

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Acknowledgements

The Mengjiagang Forest Farm provides the forest inventory data and access to their forests. We thank the graduate students (Junling Li, Xiaoyun Xia, Xiaojun Liang, Hao Xiong and Yu Bai) from Chinese Academy of Forestry and Prof. Weiwei Jia from Northeast Forestry University for their help in the fieldwork.

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  1. Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing, 100091, People’s Republic of China

    Wen Jia & Yong Pang

  2. Key Laboratory of Forestry Remote Sensing and Information System, National Forestry and Grassland Administration, Beijing, 100091, People’s Republic of China

    Wen Jia & Yong Pang

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Project Funding: This study was supported by the National Natural Science Foundation of China (Grant No. 42101403), and the National Key Research and Development Program of China (Grant No. 2017YFD0600404).

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Corresponding editor: Tao Xu

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Jia, W., Pang, Y. Tree species classification in an extensive forest area using airborne hyperspectral data under varying light conditions. J. For. Res. 34, 1359–1377 (2023). https://doi.org/10.1007/s11676-022-01593-z

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