Chin J Plant Ecol ›› 2022, Vol. 46 ›› Issue (10): 1251-1267.DOI: 10.17521/cjpe.2021.0373

• Research Articles • Previous Articles     Next Articles

Study on forest plant diversity monitoring based on Sentinel-2A satellite data in northeast China

ZHOU Kai-Ling1,2, ZHAO Yu-Jin1,*(), BAI Yong-Fei1,3,*()   

  1. 1State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinses Academy of Science, Beijing 100093, China
    2College of Life Science, University of Chinese Academy of Sciences, Beijing 100049, China
    3College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
  • Received:2021-10-15 Accepted:2022-01-14 Online:2022-10-20 Published:2022-05-21
  • Contact: *(BAI Yong-Fei,; ZHAO Yu-Jin,
  • Supported by:
    Strategic Priority Research Program of Chinese Academy of Sciences(XDA23080303)


Aims Plant diversity monitoring is the basis of biodiversity assessment and developing conservation policy. Traditional forest plant diversity monitoring is mainly based on field surveys, which is difficult to quickly obtain the spatial distribution and dynamic change of forest plant diversity. The development of remote sensing technology provides an important tool for assessing forest plant diversity at the regional scale. In this study, we explored two methods of forest plant diversity estimation based on Sentinel-2A satellite images and field data in three selected national nature reserves (Liangshui, Fenglin, and Hunchun).

Methods We used two methods to estimate forest plant diversity: (1) Direct estimation based on spectral diversity at the pixel and cluster scales, respectively; (2) Indirect estimation based on random forest regression. The spectral diversity was calculated based on the coefficient of variation and convex hull area at the pixel scale, respectively. K-means clustering method was used for cluster analysis to calculate the spectral diversity between clusters. For the indirect estimation, we used 10-fold cross validation to select characteristic variables for later diversity calculation.

Important findings Our results showed that: (1) At the pixel scale, the estimation accuracy of Shannon-Wiener diversity index based on convex hull area (R2= 0.74) was better than that of coefficient of variation (R2= 0.60); (2) The pixel-based estimation accuracy of Shannon-Wiener diversity index outperformed clustering basis (R2= 0.59); (3) Based on six feature variables, the Shannon-Wiener diversity index was best estimated using the random forest regression algorithm (R2= 0.79); (4) Both the Simpson diversity index and species richness could not be accurately estimated by the above methods. Our findings indicate the capability of Sentinel-2A satellite images to estimate the Shannon-Wiener diversity index, providing reference and basis for forest plant diversity estimation at a large scale.

Key words: forest plant diversity, Sentinel-2A, spectral diversity, cluster analysis, random forest regression