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主办单位:煤炭科学研究总院有限公司、中国煤炭学会学术期刊工作委员会
耦合遥感生态指数模型的山西省及规划矿区生态环境评价
  • Title

    Ecological environment assessment of Shanxi Province and planned mining area based on coupling Remote Sensing Ecological Index (RSEI) model

  • 作者

    季翔林阎跃观郭伟滕永佳赵传武

  • Author

    JI Xianglin;YAN Yueguan;GUO Wei;TENG Yongjia;ZHAO Chuanwu

  • 单位

    中国矿业大学(北京)地球科学与测绘工程学院北京师范大学地理科学学部遥感科学与工程研究院

  • Organization
    School of Geoscience and Surveying Engineering, China University of Mining and Technology (Beijing)
    Institute of Remote Sensing Science and Engineering, Department of Geographic Science, Beijing Normal University
  • 摘要
    生态环境质量是评价宜居水平的重要指标,定量评价区域生态环境质量的变化规律可为地区生态环境保护和治理提供科学依据。以我国山西省及其规划矿区为研究区域,基于谷歌地球引擎(Google Earth Engine,GEE)平台和生态系统服务和权衡的综合评估(Integrated Valuation of Ecosystem Services and Tradeoffs,InVEST)模型,在原有的遥感生态指数(Remote Sensing Ecology Index,RSEI)加入植被净初级生产力(Net Primary Productivity,NPP)和生境质量(Habitat Quality,HQ),分别用来表征碳汇水平和生境适宜度水平。选取2000—2018年有代表性的3个年份进行分析,通过构建基于耦合NPP、HQ和RSEI的新型遥感生态指数模型(NH-RSEI),评价山西省生态环境质量的时空异质性,并结合缓冲区分析法、驱动力分析法,对规划矿区的环境变化进行分析。研究表明:2000—2018年,山西省的整体生态水平处于中等偏下但有所改善,改善程度要小于矿区;晋东部的矿区生态环境处于中等偏上且较为稳定,晋西部的矿区生态环境较差但明显改善;矿区对半径6 km以内缓冲区的生态环境质量有较大影响,对于缓冲重叠区的影响具有叠加效应,这种效应随着时间的推移正逐渐减小。NH-RSEI均值与高程、坡度具有很强的相关性,且具有明显的阶段性变化特征,二者是影响NH-RSEI分布的关键因素。本研究为评价大尺度、长序列的生态环境质量提供了一种可靠的途径,对山西省及其规划矿区的生态恢复以及可持续发展管理具有重要意义。
  • Abstract

    Ecological environment quality is an important indicator to evaluate the livability, and quantitative assessment on the change rule of regional ecological environment quality could provide scientific basis for the regional ecological environment protection and governance. Taking Shanxi Province and its planned mining area as the research area, the original Remote Sensing Ecology Index (RSEI) is added with Net Primary Productivity (NPP) and Habitat Quality (HQ) to characterize the carbon sink level and habitat suitability respectively based on Google Earth Engine (GEE) platform. Herein, three representative years from 2000 to 2018 were selected for analysis. Specifically, the temporal and spatial heterogeneity of ecological environment quality in Shanxi Province was evaluated by building a new remote sensing ecological index model (NH-RSEI) based on coupling NPP, HQ and RSEI. Meanwhile, the environmental change in the planned mining area was analyzed by combining the buffer analysis method and driving force analysis method. The research shows that the overall ecological level of Shanxi Province from 2000 to 2018 was at the lower middle level with less improvement than the mining area. The ecology of the mining area in the east of Shanxi Province is above the middle level and relatively stable, while that in the west of Shanxi Province is poor with obvious improvement. In addition, the mining area has great impact on the ecological environment quality of the buffer zone within the radius of about 6 km, and the impact is superimposed on the overlapped buffer zone, which is gradually decreasing over time. The mean value of NH-RSEI has a strong correlation with elevation and slope, and has obvious characteristics of phased changes, which are the key factors affecting the distribution of NH-RSEI. Generally, this study provides a reliable way to assess the ecological environment quality in large scale and long sequence. Therefore, it is of great significance to the ecological restoration and sustainable development management of Shanxi Province and its planned mining areas.

  • 关键词

    规划矿区InVEST模型遥感生态指数生境质量生态环境评价

  • KeyWords

    planned mining area;InVEST model;Remote Sensing Ecological Index (RSEI);habitat quality;ecological environment assessment

  • 基金项目(Foundation)
    国家自然科学基金重点项目(41930650);宁夏回族自治区重点研发项目(2022BEG03064);地理信息工程国家重点实验室、自然资源部测绘科学与地球空间信息技术重点实验室联合资助基金项目(2021-03-04);
  • DOI
  • 引用格式
    季翔林,阎跃观,郭伟,滕永佳,赵传武.耦合遥感生态指数模型的山西省及规划矿区生态环境评价[J].煤田地质与勘探,2023,51(03):103-112.
  • Citation
    JI Xianglin,YAN Yueguan,GUO Wei,et al. Ecological environment assessment of Shanxi Province and planned mining area based on coupling Remote Sensing Ecological Index (RSEI) model[J]. Coal Geology & Exploration,2023,51(3):103−112
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