Tree species classification in a typical natural secondary forest using UAV-borne LiDAR and hyperspectral data

ABSTRACT

Recent growth in unmanned aerial vehicle (UAV) technology have promoted the detailed mapping of individual tree species. However, the in-depth mining and comprehending of the significance of features derived from high-resolution UAV data for tree species discrimination remains a difficult task. In this study, a state-of-the-art approach combining UAV-borne light detection and ranging (LiDAR) and hyperspectral was used to classify 11 common tree species in a typical natural secondary forest in Northeast China. First, comprehensive relevant structural and spectral features were extracted. Then, the most valuable feature sets were selected by using a hybrid approach combining correlation-based feature selection with the optimized recursive feature elimination algorithm. The random forest algorithm was used to assess feature importance and perform the classification. Finally, the robustness of features derived from point clouds with different structures and hyperspectral images with different spatial resolutions was tested. Our results showed that the best classification accuracy was obtained by combining LiDAR and hyperspectral data (75.7%) compared to that based on LiDAR (60.0%) and hyperspectral (64.8%) data alone. The mean intensity of single returns and the visible atmospherically resistant index for red-edge band were the most influential LiDAR and hyperspectral derived features, respectively. The selected features were robust in point clouds with a density not lower than 5% (~5 pts/m²) and a resolution not lower than 0.3 m in hyperspectral data. Although canopy surface features were slightly different from original LiDAR features, canopy surface information was also important for tree species classification. This study proved the capabilities of UAV-borne LiDAR and hyperspectral data in natural secondary forest tree species discrimination and the potential for this approach to be transferable to other study areas.

KEYWORDS:

• Unmanned aerial vehicle (UAV)
• light detection and ranging (LiDAR)
• hyperspectral imaging
• tree species classification
• feature selection

Acknowledgements

This research was supported by the National Key R&D Program of China (grant number 2020YFC1511603); Fundamental Research Funds for the Central Universities (grant number 2572020BA07).

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data that support the findings of this study are available from the corresponding author, (M.L), upon reasonable request (https://forestry.nefu.edu.cn/info/1038/1531.htm).

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/15481603.2023.2171706

Additional information

Funding

The work was supported by the Fundamental Research Funds for the Central Universities [2572020BA07]; National Key Research and Development Program of China [2020YFC1511603].