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

• Research Articles • Previous Articles     Next Articles

Forest species diversity mapping based on clustering algorithm

YI Hai-Yan1,2, ZENG Yuan1,2,*(), ZHAO Yu-Jin3, ZHENG Zhao-Ju1, XIONG Jie1,2, ZHAO Dan1   

  1. 1State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
    2University of Chinese Academy of Sciences, Beijing 100049, China
    3State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China
  • Received:2019-12-12 Accepted:2020-02-07 Online:2020-06-20 Published:2020-03-26
  • Contact: ZENG Yuan
  • Supported by:
    National Key R&D Program of China(2016YFC0500201);National Natural Science Foundation of China(41671365)

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 Light Detection and Ranging (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, total carotenoids, equivalent water thickness, 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, Transformed Chlorophyll Ratio Index/Optimization of Soil-adjusted Vegetation Index (TCARI/OSAVI), Carotenoid Reflectance Index (CRI), Water Band Index (WBI), Ratio Vegetation Index (RVI), Photochemical Reflectance Index (PRI) and Canopy Chlorophyll Concentration Index (CCCI). The individual tree isolation showed high accuracy (R 2 = 0.77, RMSE = 16.48). The correlation and stepwise regression analysis showed tree height and skewness were the optimal structural parameters among seven commonly used forest structural parameters (R 2 = 0.32, p < 0.01). The species diversity indices calculated from the self-adaptive FCM clustering algorithm based on the six VIs and two optimal structural parameters correlated well with the field measurements (species richness, R 2 = 0.56, RMSE = 1.81; Shannon-Wiener index, R 2 = 0.83, RMSE = 0.22; Simpson index, R 2 = 0.85, RMSE = 0.09). With the 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 scales.

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