基于改进的遥感生态指数的黄河三角洲生态环境质量评价及驱动力分析

IF 3.9 3区 环境科学与生态学 Q1 ENGINEERING, CIVIL Stochastic Environmental Research and Risk Assessment Pub Date : 2024-05-13 DOI:10.1007/s00477-024-02740-0
Dongling Ma, Qingji Huang, Qian Zhang, Qian Wang, Hailong Xu, Yingwei Yan
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引用次数: 0

摘要

由于经济发展和人口增长的压力,黄河三角洲的生态环境正在严重退化。要改善和保护生态环境,准确评估和监测生态环境质量至关重要。考虑到该地区陆地盐碱化的特点和长期生态监测的需要,我们首先利用谷歌地球引擎(GEE)构建了改进的遥感生态指数(IRSEI)。IRSEI 基于遥感生态指数 (RSEI),后者由归一化差异植被指数 (NDVI)、WET、地表温度 (LST) 和归一化差异堆积和土壤指数 (NDBSI) 以及净初级生产力 (NPP) 指数组成。采用熵权法构建 IRSEI,用于评估黄河三角洲的生态环境质量。通过图像熵和对比度评估验证了该指数的有效性。然后,我们利用赫斯特指数、森氏斜率估计和变异系数(CV)计算了黄河三角洲 20 年间 IRSEI 的变化范围,分析了生态环境质量的时空演变及其分布格局。此外,我们还结合地理和时间加权回归(GTWR)模型和 Geodetector 进行了综合分析,从时间和空间两个维度了解地形、土壤和气候等驱动因素对 IRSEI 的影响。结果表明(1) 与 RSEI 相比,建议的 IRSEI 在监测黄河三角洲生态环境质量方面表现出更高的可靠性、适应性和灵敏度。(2)从 2000 年到 2020 年,黄河三角洲生态环境质量总体保持稳定,空间分布呈 "Y "型,尤其是利津县及周边地区生态环境质量明显改善。但河口中部和东部生态环境质量呈下降趋势。(3) 驱动因子对黄河三角洲四个下属区域生态环境质量的影响存在差异,显示出空间异质性。FVC、Soil、LST、JS 和 Srad 等因子显著影响并解释了该区域生态环境质量的空间差异。与 RSEI 相比,拟议的 IRSEI 在黄河三角洲具有更好的监测能力,为该地区的土地利用规划和生态保护提供了科学依据。
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Evaluation of eco-environmental quality and analysis of driving forces in the yellow river delta based on improved remote sensing ecological indices

The ecological environment of the Yellow River Delta is undergoing serious degradation due to the pressures of economic development and population growth. To improve and protect the ecological environment, it is crucial to accurately assess and monitor its eco-environmental quality. With consideration of the characteristics of terrestrial salinization in the region and the need for long-term ecological monitoring, we first utilized Google Earth Engine (GEE) to construct the Improved Remote Sensing Ecological Index (IRSEI). The IRSEI is based on the Remote Sensing Ecological Index (RSEI), which consists of the Normalized Difference Vegetation Index (NDVI), WET, Land Surface Temperature (LST), and Normalized Difference Built-Up and Soil Index (NDBSI), as well as the Net Primary Productivity (NPP) index. The entropy weighting method was employed to construct the IRSEI for assessing the eco-environmental quality of the Yellow River Delta. The validity of the index was verified through image entropy and contrast assessment. We then employed the Hurst exponent, Sen's slope estimation, and Coefficient of Variation (CV) to calculate the range of variation of the IRSEI in the Yellow River Delta over a 20-year period to analyze the spatio-temporal evolution of the ecological quality and its distribution pattern. Furthermore, we conducted a comprehensive analysis combining the Geographically and Temporally Weighted Regression (GTWR) model and Geodetector to understand the influence of drivers such as topography, soil, and climate on the IRSEI, considering both the temporal and spatial dimensions. The results indicate that: (1) The proposed IRSEI demonstrates higher reliability, adaptability, and sensitivity compared to RSEI in monitoring the eco-environmental quality of the Yellow River Delta. (2) From 2000 to 2020, the eco-environmental quality of the Yellow River Delta remained generally stable, with a spatial distribution resembling a "Y" shape, showing significant improvement, particularly in Lijin County and its surrounding areas. However, the middle and eastern estuary exhibited a declining trend in eco-environmental quality. (3) The impact of driving factors on the eco-environmental quality varied across the four subordinate regions of the Yellow River Delta, indicating spatial heterogeneity. Factors such as FVC, Soil, LST, JS, and Srad significantly influenced and explained the spatial differentiation of eco-environmental quality in the region. The proposed IRSEI demonstrates better monitoring capabilities in the Yellow River Delta compared to RSEI, providing a scientific basis for land use planning and ecological protection in the area.

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来源期刊
CiteScore
7.10
自引率
9.50%
发文量
189
审稿时长
3.8 months
期刊介绍: Stochastic Environmental Research and Risk Assessment (SERRA) will publish research papers, reviews and technical notes on stochastic and probabilistic approaches to environmental sciences and engineering, including interactions of earth and atmospheric environments with people and ecosystems. The basic idea is to bring together research papers on stochastic modelling in various fields of environmental sciences and to provide an interdisciplinary forum for the exchange of ideas, for communicating on issues that cut across disciplinary barriers, and for the dissemination of stochastic techniques used in different fields to the community of interested researchers. Original contributions will be considered dealing with modelling (theoretical and computational), measurements and instrumentation in one or more of the following topical areas: - Spatiotemporal analysis and mapping of natural processes. - Enviroinformatics. - Environmental risk assessment, reliability analysis and decision making. - Surface and subsurface hydrology and hydraulics. - Multiphase porous media domains and contaminant transport modelling. - Hazardous waste site characterization. - Stochastic turbulence and random hydrodynamic fields. - Chaotic and fractal systems. - Random waves and seafloor morphology. - Stochastic atmospheric and climate processes. - Air pollution and quality assessment research. - Modern geostatistics. - Mechanisms of pollutant formation, emission, exposure and absorption. - Physical, chemical and biological analysis of human exposure from single and multiple media and routes; control and protection. - Bioinformatics. - Probabilistic methods in ecology and population biology. - Epidemiological investigations. - Models using stochastic differential equations stochastic or partial differential equations. - Hazardous waste site characterization.
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