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生态学杂志 ›› 2025, Vol. 44 ›› Issue (4): 1297-1305.doi: 10.13292/j.1000-4890.202504.020

• 研究报告 • 上一篇    下一篇

基于Sentinel-2A的融合多植被指数估算小流域生物多样性

程家琪1,张建军1,2,3,张燕妮1,张晓杰1,张学霞1,2*
  

  1. (1北京林业大学水土保持学院, 北京 100083; 2山西吉县森林生态系统国家野外科学观测研究站, 山西临汾 041000; 3水土保持国家林业和草原局重点实验室, 北京 100083)

  • 出版日期:2025-04-10 发布日期:2025-04-14

Estimation of biodiversity in small watershed based on Sentinel-2A fused multi-vegetation index.

CHENG Jiaqi1, ZHANG Jianjun1,2,3, ZHANG Yanni1, ZHANG Xiaojie1, ZHANG Xuexia1,2*   

  1. (1School of Soil and Water Conservation, Beijing Forestry University, Beijing 100083, China; 2Shanxi Jixian County Forest Ecosystem National Field Scientific Observation and Research Station, Linfen 041000, Shanxi, China; 3State Forestry Administration Key Laboratory of Soil and Water Conservation, Beijing 100083, China).

  • Online:2025-04-10 Published:2025-04-14

摘要: 生物多样性的及时、快速监测对于森林经营管理和多样性保育具有重要意义。传统的样地调查无法满足景观尺度和区域尺度上对生物多样性监测与评价的数据需求。本研究采用2022年27景Sentinel-2A影像数据,利用12种植被指数构建了小流域尺度的生物多样性估算模型,探究了山西吉县蔡家川流域生物多样性的空间分布格局。结果表明:刺槐林分密度不是控制林内生物多样性的唯一因素;Simpson指数(D)、Shannon指数(H)、Pielou指数(J)与红外植被指数百分比(IPVI)和红边叶绿素植被指数(CIre)呈极显著相关性(P<0.01);生物多样性遥感模型中DHJ的估算值与实测值的均方误差(MSE)分别为0.021、0.192、0.016,表明融合多植被指数构建的估算模型可用于研究地区生物多样性的估算与监测,且海拔较高的阴坡生物多样性水平估算最佳。


关键词: 生物多样性, 植被指数, 遥感监测, Sentinel-2A影像

Abstract: Timely and rapid monitoring of biodiversity is of great significance for forest management and diversity conservation. The traditional method of plot survey cannot meet the data needs of biodiversity monitoring and evaluation at landscape and regional scales. In this study, 27 Sentinel-2A image data in 2022 were used to construct a biodiversity estimation model at the small watershed scale using 12 vegetation indices. The spatial distribution pattern of biodiversity Caijiachuan Basin, Jixian County, Shanxi Province was explored with this model. The results showed that stand density of Robinia pseudoacacia was not the only factor controlling biodiversity in the forest. Simpson diversity index (D), Shannon diversity index (H) and Pielou evenness index (J) were significantly correlated with the percentage of infrared percentage vegetation index (IPVI) and red-edge chlorophyll index (CIre) (P<0.01). The mean square errors (MSE) of the estimated and measured values of D, H and J in the remote sensing model of biodiversity were 0.021, 0.192 and 0.016, respectively, indicating that the estimation model constructed by the vegetation indices can be used for biodiversity estimation and monitoring in the study area. The biodiversity level was best estimated on the shady slope areas with high altitude.


Key words: biological diversity, vegetation index, remote sensing monitoring, Sentinel-2A image