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Spatiotemporal ecological vulnerability analysis with statistical correlation based on satellite remote sensing in Samara, Russia

机译:基于Samara,俄罗斯卫星遥感的统计相关性的时空生态脆弱性分析

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摘要

In the present global situation, when everywhere ecology is degraded due to the extreme exhaustion of natural resources. Therefore spatiotemporal ecological vulnerability analysis is necessary for the current situation for sustainable development with protection of fragile eco-environment. Remote sensing is a unique tool to provide complete and continuous land surface information at different scales, which can use for eco-environment analysis. A methodology constructed on the principal component analysis (PCA) to identify satellite remote sensing ecological index (RSEI) for ecological vulnerability analysis and distribution based on four land surface parameters (dryness, greenness, temperature and moisture) by using Landsat TM/ETM+/OLI/TIRS data in the Samara region Russia. The results were verified by the following four methods: location-based, categorization-based, correlation-based and city center to outwards distance-based comparisons. Results indicate that ecological condition was improved from 2010 to 2015 as RSEI increased from 0.79 to 0.98 and from 2015 to 2020 the ecological condition was degraded as RSEI decreased from 0.98 to 0.82 but overall it was improved in this decade. RSEI distribution curve shows moderate to good and excellent ecological conditions and degraded ecological condition was basically characterized by high human interference and socioeconomic activities in the study area. Such a technique is a baseline for highly accurate ecological conditions mapping, monitoring and can use for decision making, management and sustainable development.
机译:在目前的全球局势中,由于自然资源极端耗尽,生态变得退化。因此,利用时空生态脆弱性分析是对可持续发展的现状,保护脆弱的生态环境是必要的。遥感是一种独特的工具,可以在不同的尺度上提供完整和连续的土地表面信息,可用于生态环境分析。在主要成分分析(PCA)上构建的方法,以识别卫星遥感生态指数(RSEI),用于使用Landsat TM / ETM + / OLI基于四个地表参数(干燥,绿色,温度和水分)的生态脆弱性分析和分布/ TIRS数据在萨马拉地区俄罗斯。结果通过以下四种方法验证:基于位置的,基于分类的,基于相关的和城市中心,以外的基于距离的比较。结果表明,从2010年至2015年改善了生态条件,因为RSEI从0.79增加到0.98岁,从2015年到2020年,生态状况降低,因为Rsei从0.98减少到0.82,但总体而言,这十年来改善了这十年。 RSEI分布曲线显示中等至良好,优异的生态条件,生态状况下降基本上是在研究区内的高人为干扰和社会经济活动的特征。这种技术是高度准确的生态条件映射,监测和可用于决策,管理和可持续发展的基线。

著录项

  • 来源
    《Journal of Environmental Management》 |2021年第1期|112138.1-112138.13|共13页
  • 作者单位

    Scientific Research Laboratory of Automated Systems of Scientific Research (SRL-35) Samara National Research University Samara Russia;

    Scientific Research Laboratory of Automated Systems of Scientific Research (SRL-35) Samara National Research University Samara Russia Department of Land Surveying and Geo- Informatics Smart Cities Research Institute The Hong Kong Polytechnic University Kowloon Hong Kong China;

    Scientific Research Laboratory of Automated Systems of Scientific Research (SRL-35) Samara National Research University Samara Russia Image Processing Systems Institute of the RAS-Branch of the FSRC 'Crystallography and Photonics' Samara Russia;

    Scientific Research Laboratory of Automated Systems of Scientific Research (SRL-35) Samara National Research University Samara Russia Image Processing Systems Institute of the RAS-Branch of the FSRC 'Crystallography and Photonics' Samara Russia;

  • 收录信息 美国《科学引文索引》(SCI);美国《化学文摘》(CA);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Ecological vulnerability; Satellite remote sensing; Principal components analysis; Land surface parameters;

    机译:生态脆弱性;卫星遥感;主要成分分析;陆地面参数;

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