改进型遥感生态指数及干旱区生态环境评价

张伟; 杜培军; 郭山川; 林聪; 郑鸿瑞; 付萍杰

doi:10.11834/jrs.20221527

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改进型遥感生态指数及干旱区生态环境评价
‍Enhanced remote sensing ecological index and ecological environment evaluation in arid area
2023年27卷第2期页码：299-317
纸质出版日期： 2023-02-07 ，
DOI： 10.11834/jrs.20221527

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张伟，杜培军，郭山川，林聪，郑鸿瑞，付萍杰.2023.改进型遥感生态指数及干旱区生态环境评价.遥感学报，27（2）： 299-317

Zhang W，Du P J，Guo S C，Lin C，Zheng H R and Fu P J. 2023. Enhanced remote sensing ecological index and ecological environment evaluation in arid area. National Remote Sensing Bulletin， 27（2）：299-317
张伟，杜培军，郭山川，林聪，郑鸿瑞，付萍杰.2023.改进型遥感生态指数及干旱区生态环境评价.遥感学报，27（2）： 299-317 DOI： 10.11834/jrs.20221527.

Zhang W，Du P J，Guo S C，Lin C，Zheng H R and Fu P J. 2023. Enhanced remote sensing ecological index and ecological environment evaluation in arid area. National Remote Sensing Bulletin， 27（2）：299-317 DOI： 10.11834/jrs.20221527.

摘要

利用遥感影像对大尺度区域生态环境质量进行长期、定量、动态监测可为区域可持续发展提供有力的决策支撑。基于遥感生态指数（RSEI），针对干旱区生态环境的地域特点和应用需求，从耦合生态系统组成要素的角度出发提出一种改进型遥感生态指数（ERSEI），即在顾及绿度（NDVI）、湿度（Wet）、干度（NDBSI）、热度（LST）因子的同时，引入综合盐度指标（CSI）和水网密度遥感估算模型（EMW），将盐度和水网密度（WND）纳入生态环境质量评价。在Google Earth Engine（GEE）云计算平台上实现ERSEI并将其应用于地处西北干旱区的呼包鄂榆城市群，结果表明，ERSEI可充分体现干旱区地表细节特征，有力突显了水网对周边环境辐射影响的渐变信息。由呼包鄂榆城市群范围内2000年—2020年间ERSEI的空间测度和时序演变分析发现，生态环境质量较好的区域主要分布在河套平原、大青山一线以及靠近吕梁山的一侧，较差的区域主要集中分布在蒙古高原、库布齐沙漠附近以及毛乌素沙地部分地域，且生态环境质量呈现持续性下降，应作为生态风险预警区加强治理。ERSEI为干旱区生态环境质量常态化监测提供了一种快速有效的新方法。

Abstract

Long-term

quantitative and dynamic monitoring of large-scale regional ecological environmental quality using remote sensing images can provide strong decision support for regional sustainable development. Based on the Remote Sensing based Ecological Index (RSEI)

an enhanced Remote Sensing Ecological Index (ERSEI) is proposed from the perspective of the elements of the coupled ecosystem considering regional characteristics and application requirements of the ecological environment in arid areas. The ERSEI considers the factors of greenness (NDVI)

wetness (Wet)

dryness (NDBSI)

and heat (LST) while introducing the Comprehensive Salinity Index (CSI) and estimation model of water network density (EMW). The salinity and Water Network Density (WND) are included in the ecological environment quality assessment. With the help of the Google Earth Engine (GEE) cloud computing platform

ERSEI is applied to the Hohhot-Baotou-Ordos-Yulin urban agglomeration in the arid area of northwestern China. The results show that the ERSEI can fully reflect the detailed characteristics of the surface in arid areas and effectively highligh the gradual information of the radiation impact of the water network on the surrounding environment. According to the spatial measurement and time series evolution analysis of ERSEI in the Hohhot-Baotou-Ordos-Yulin urban agglomeration from 2000 to 2020

it is found that areas with good ecological environment quality are mainly distributed in the Hetao Plain

Daqing Mountain and the side close to Luliang Mountain. Areas with poor ecological environment quality are mainly concentrated in the Mongolian Plateau

near the Hobq Desert and the Mu Us Sandy Land

and the quality of the ecological environment has shown a continuous decline. Therefore

these areas should be treated as ecological risk early warning zones to strengthen governance. The ERSEI provides a fast and effective new method for the normalized monitoring of ecological environment quality in arid areas.

关键词

改进型遥感生态指数生态环境质量评价时序演变分析Google Earth Engine呼包鄂榆城市群生态风险预警区

Keywords

ERSEIspatial measurement of ecological environmenttime series evolution analysisGoogle Earth EngineHohhot-Baotou-Ordos-Yulin urban agglomerationecological risk warning zone

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