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Forest species diversity mapping using airborne LiDAR and hyperspectral data

  • role： First author第一作者
  • Affiliation：

    Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China

    College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China

  • Email：dongwx@radi.ac.cn
  • Introduction：董文雪，1992年生，女，硕士研究生，研究方向为植被遥感。E-mail：dongwx@radi.ac.cn
Wenxue DONG12,  
  • role： Corresponding author通信作者
  • Affiliation：

    Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China

  • Email：zengyuan@radi.ac.cn
  • Introduction：曾源，1979年生，女，研究员，研究方向为植被生态遥感。E-mail：zengyuan@radi.ac.cn
Yuan ZENG1*,  
  • Affiliation：

    State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, The Chinese Academy of Sciences, Beijing 100093, China

Yujin ZHAO3,  
  • Affiliation：

    Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China

Dan ZHAO1,  
  • Affiliation：

    Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China

Zhaoju ZHENG1,  
  • Affiliation：

    Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China

    College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China

Haiyan YI12

resumen

Forest species diversity, as a key component of biodiversity, plays an irreplaceable role in maintaining ecological balance, processes, and services. In recent years, forest tree species diversity is facing a serious threat with intensifying human activities and influence of climate change. The status and trends of forest tree species diversity must be dynamically monitored to develop effective forest biodiversity conservation approaches. In this study, an airborne light detection and ranging (LiDAR) (>4 points/m2) and hyperspectral (PHI-3 sensor with spatial resolution of 1 m) data combined with 37 field sample data are used to detect tree species variation in the structural and spectral properties in the Shennongjia Forest Nature Reserve of China. First, we use the morphological crown control method based on a watershed algorithm to isolate individual tree crowns by using LiDAR. We select optimal structural indices from nine commonly used structural indices derived using LiDAR based on the theory of structural and spectral variation hypothesis. Meanwhile, we select optimal vegetation indices from 16 VIs based on the hyperspectral data by conducting a correlation analysis with the field samples. Second, a self-adaptive fuzzy C-means clustering algorithm is applied to map the species diversity (i.e., richness and Shannon-Wiener index) in the study area for each 30 m×30 m window at the individual tree crown scale. Result indicates that the individual tree isolation by using the watershed algorithm can obtain a high accuracy (R2=0.88, RMSE=13.17, P<0.001). The 95th quintile height, canopy cover, and vegetation permeability are the optimal structural indices, and their correlation with the Shannon–Wiener index are from 0.39 to 0.42 (R2=0.39—0.42, P<0.01). The correlation among RI, OSAVI, narrow band NDVI, SR, Vogelmann index 1, PRI, and field inventory Shannon–Wiener index are relatively high based on the airborne hyperspectral data (R2=0.37—0.45, P<0.01). Finally, we use the selected three structural and six vegetation indices to predict the optimal clustering numbers (species richness) and the Shannon–Wiener index by using the self-adaptive fuzzy C-means clustering algorithm. The result shows that the maximum tree species that can be predicted is 20. The prediction accuracy of species richness isR2=0.69, RMSE=3.11, and the Shannon–Wiener index is R2=0.70, RMSE=0.32. This method shows the potential of LiDAR combined with hyperspectral data in mapping species diversity of a subtropical forest. Moreover, it could provide an effective method for analyzing the current situation and its changing trend of forest biodiversity at a regional scale.

palabra clave

forest diversity;species richness;lidar;hyperspectral;individual tree crown isolation;clustering

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