植物生态学报 ›› 2020, Vol. 44 ›› Issue (6): 598-615.DOI: 10.17521/cjpe.2019.0347

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利用聚类算法监测森林乔木物种多样性

衣海燕1,曾源1,赵玉金2,郑朝菊1   

  1. 1. 中国科学院空天信息创新研究院
    2. 中国科学院植物研究所植被与环境变化国家重点实验室
  • 收稿日期:2019-12-12 修回日期:2020-02-23 出版日期:2020-06-20 发布日期:2020-03-26
  • 通讯作者: 曾源
  • 基金资助:
    国家重点研发计划项目;国家自然科学基金面上项目

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
    2.
    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)

摘要: 摘 要 本研究基于机载LiDAR和高光谱数据, 从森林物种叶片的生理化学源头探寻生化特征与光谱特征的内在关联, 探讨生化多样性、光谱多样性与物种多样性之间的响应机制, 选择最优植被指数并结合最优结构参数, 通过聚类方法构建森林物种多样性遥感估算模型, 在古田山自然保护区开展森林乔木物种多样性监测。研究结果表明: (1)从16种叶片生化组分中, 筛选出叶绿素a、叶绿素b、类胡萝卜素、叶片含水量、比叶面积、纤维素、木质素、氮、磷和碳可通过偏最小二乘法用叶片光谱有效模拟(R2 = 0.60 – 0.79, p < 0.01), 并选择有效的植被指数TCARI/OSAVI、CRI、WBI、RVI、PRI和CCCI表征相应的最优生化组分; (2)基于机载LiDAR数据利用结合形态学冠层控制的分水岭算法可获得高精度单木分离结果(R2 = 0.77, RMSE = 16.48), 同时采用逐步回归方法从常用的森林结构参数中选取了树高和偏度作为最优结构参数(R2 = 0.32, p < 0.01); (3)基于6个最优植被指数和2个最优结构参数, 以20 m × 20 m为窗口通过自适应模糊C均值方法进行聚类, 实现了研究区森林乔木物种丰富度(Richness, R2 = 0.56, RMSE = 1.81)和多样性指数Shannon-Wiener (R2 = 0.83, RMSE = 0.22)与Simpson (R2 = 0.85, RMSE = 0.09)的成图。本研究在冠层尺度上获取了与物种多样性相关的生化、光谱和结构参数, 将单木个体作为最小单元, 利用聚类算法直接估算物种类别差异, 无需判定具体的树种属性, 是利用遥感数据进行区域尺度森林物种多样性监测与成图的实践, 可为亚热带地区常绿阔叶林的森林物种多样性监测提供借鉴。

关键词: 森林物种多样性, 自适应模糊C均值聚类, 高光谱, LiDAR, 单木分离

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