Chin J Plant Ecol ›› 2020, Vol. 44 ›› Issue (6): 598-615.DOI: 10.17521/cjpe.2019.0347

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Forest species diversity mapping based on clustering algorithm

Hai-Yan YI1, 2,Yujin Zhao3,朝菊 郑1   

  1. 1. Aerospace Information Research Institute, Chinese Academy of Sciences
    3. Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences
  • Received:2019-12-12 Revised:2020-02-23 Online:2020-06-20 Published:2020-03-26
  • Supported by:
    National Key Research and Development Program of China;National Natural Science Foundation of China (General Program)

Abstract: Abstract Aims Monitoring forest species diversity continuously and efficiently is important to maintain ecosystem services and achieve sustainability and conservation goals. In this paper, we explored the relationship between leaf biochemical and spectral properties and their inner linkage with species diversity, then estimated the forest species diversity based on a clustering algorithm using airborne imaging spectroscopy and LiDAR data in the Gutianshan National Nature Reserve of China. Methods Firstly, we isolated individual tree crowns (ITCs) with the watershed algorithm from the LiDAR data. Then we calculated the optimal vegetation indices (VIs) representing the key biochemical properties from the hyperspectral data and selected optimal structural parameters from commonly used LiDAR-derived structural parameters based on correlation and stepwise regression analysis with the field samples. Finally, a self-adaptive Fuzzy C-Means (FCM) clustering algorithm was applied to map the species diversity (i.e. Richness, Shannon-Wiener index and Simpson index) in the study area for each 20 m × 20 m moving window. Important findings The results indicated that biochemical components (Chlorophyll a & b, carotenoid, leaf water content, specific leaf area, cellulose, lignin, nitrogen, phosphorus and carbon) could be well quantified by leaf spectrum using partial least squares regression (R2 = 0.60 – 0.79, p < 0.01), and represented by hyperspectral VIs, namely, TCARI/OSAVI, CRI, WBI, RVI, PRI and CCCI. The individual tree isolation showed high accuracy (R2 = 0.77, RMSE = 16.48). The correlation and stepwise regression analysis showed tree height and skewness were the optimal structural parameters among 7 commonly used forest structural parameters (R2 = 0.32, p < 0.01). The species diversity indices calculated from the FCM clustering algorithm based on the 6 VIs and 2 optimal structural parameters correlated well with the field measurements (species richness, R2 = 0.56, RMSE = 1.81; Shannon-Wiener index, R2 = 0.83, RMSE = 0.22; Simpson index, R2 = 0.85, RMSE = 0.09). With clustering method combined with crown-by-crown variations in hyperspectral biochemical VIs and LiDAR-derived structural parameters, we created continuous maps of forest species diversity in the examined subtropical forest without the need to identify specific tree species. Our case study in Gutianshan showed the potential of airborne hyperspectral and LiDAR data in mapping species diversity of the subtropical evergreen broad-leaved forest. It could also provide a pathway for monitoring the state and changes of forest biodiversity at regional scale.

Key words: forest species diversity, self-adaptive Fuzzy C-Means clustering algorithm, imaging spectroscopy, LiDAR, individual tree isolation