基于Sentinel-2A数据的东北森林植物多样性监测方法研究
收稿日期: 2021-10-15
录用日期: 2022-01-14
网络出版日期: 2022-05-21
基金资助
中国科学院战略性先导科技专项(A类)(XDA23080303)
Study on forest plant diversity monitoring based on Sentinel-2A satellite data in northeast China
Received date: 2021-10-15
Accepted date: 2022-01-14
Online published: 2022-05-21
Supported by
Strategic Priority Research Program of Chinese Academy of Sciences(XDA23080303)
植物多样性监测是开展生物多样性评估, 制定生物多样性保护政策的基础。传统的森林植物多样性监测以实地调查为主, 难以快速获取森林植物多样性的空间分布及其动态变化信息。遥感技术的发展为评估区域尺度森林植物多样性提供了重要工具。该研究选取凉水、丰林和珲春3个国家级自然保护区, 利用Sentinel-2A卫星影像和野外实测数据, 探讨了基于像元和聚类的光谱多样性直接估算方法, 以及基于随机森林回归的森林植物多样性反演方法。研究结果表明: (1)在像元尺度, 基于凸包面积计算的光谱多样性指数对Shannon-Wiener多样性指数的估算精度(R2 = 0.74)优于基于变异系数的方法(R2 = 0.60); (2)基于像元的光谱多样性估算方法对Shannon-Wiener多样性指数的估算精度优于聚类分析方法(R2 = 0.59); (3)基于6个特征变量, 利用随机森林回归算法对Shannon-Wiener多样性指数的估算精度最高(R2 = 0.79); (4)上述方法均不能精确估算Simpson多样性指数和物种丰富度。研究发现基于Sentinel-2A卫星影像能较好地反演Shannon-Wiener多样性指数, 为下一步能在大尺度上进行森林植物多样性估算提供了参考和依据。
关键词： 森林植物多样性; Sentinel-2A; 光谱多样性; 聚类分析; 随机森林回归
周楷玲, 赵玉金, 白永飞 . 基于Sentinel-2A数据的东北森林植物多样性监测方法研究[J]. 植物生态学报, 2022 , 46(10) : 1251 -1267 . DOI: 10.17521/cjpe.2021.0373
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.
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