Detecting Long-Term Series Eco-Environmental Quality Changes and Driving Factors Using the Remote Sensing Ecological Index with Salinity Adaptability (RSEISI): A Case Study in the Tarim River Basin, China

被引:6
|
作者
Chen, Wen [1 ,2 ,3 ]
Wang, Jinjie [1 ,2 ,3 ]
Ding, Jianli [1 ,2 ,3 ]
Ge, Xiangyu [1 ,2 ,3 ]
Han, Lijing [1 ,2 ,3 ]
Qin, Shaofeng [1 ,2 ,3 ]
机构
[1] Xinjiang Univ, Coll Geog & Remote Sci, Urumqi 830017, Peoples R China
[2] Xinjiang Univ, Xinjiang Key Lab Oasis Ecol, Urumqi 830017, Peoples R China
[3] Xinjiang Univ, Higher Educ Inst, Key Lab Smart City & Environm Modelling, Urumqi 830017, Peoples R China
关键词
Tarim River Basin; eco-environmental quality; modified remote-sensing ecological index; temporal information entropy; spatial-temporal change mechanisms; CENTRAL-ASIA; WATER-USE; SUSTAINABILITY; MANAGEMENT;
D O I
10.3390/land12071309
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Ecological challenges resulting from soil salinization in the Tarim River Basin (TRB), exacerbated by climate change and human activities, have emphasized the need for a quick and accurate assessment of regional ecological environmental quality (EEQ) and driving mechanisms. To address this issue, this study has developed a remote-sensing ecological index with salinity adaptability (RSEISI) for EEQ assessment in the Tarim River Basin by integrating the comprehensive salinity index (CSI) into the remote-sensing ecological index (RSEI). The RSEISI enhances the sensitivity of soil salinity and characterizes the surface features of arid regions, thus expanding the applicability. Then, we used time-series analysis methods and a geodetector to quantify the spatial temporal trends and driving factors of EEQ in the TRB from 2000 to 2022. The results show that the RSEISI with salinity adaptation effectively monitors the EEQ of the TRB. The EEQ of the TRB displayed the situation of oasis expansion, desert deterioration, and glacier melting, and the multiyear average EEQ grades were dominated by medium and poor grades in desert and saline areas, while medium, good, and excellent grades were concentrated in oasis and mountainous areas. Looking at the trend of change in conjunction with land-use types, the EEQ of the TRB showed a mild degradation trend mainly in unused land, followed by a mild improvement trend in cropland and grassland. The Hurst index indicated that the EEQ of most areas of the TRB will improve in the future. Soil type, land use, precipitation, and temperature were considered to be key factors affecting the EEQ across the TRB, and changes in the EEQ were found to be the interaction of multiple factors. This study may provide innovative concepts and methodologies, scientific and technological support for ecological management, and green development models in the northwest arid zone.
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页数:23
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共 12 条
  • [1] Eco-Environmental Quality Assessment Using the Remote Sensing Ecological Index in Suzhou City, China
    Fang, Gang
    Pablo II, Renato Dan A.
    Zhang, Yin
    [J]. SUSTAINABILITY, 2023, 15 (17)
  • [2] Study on Driving Factors and Spatiotemporal Differentiation of Eco-Environmental Quality in Jianghuai River Basin of China
    Cai, Hong
    Ma, Xueqing
    Chen, Pengyu
    Guo, Yanlong
    [J]. SUSTAINABILITY, 2024, 16 (11)
  • [3] Exploring the Driving Factors of Remote Sensing Ecological Index Changes from the Perspective of Geospatial Differentiation: A Case Study of the Weihe River Basin, China
    Zhang, Kaili
    Feng, Rongrong
    Zhang, Zhicheng
    Deng, Chun
    Zhang, Hongjuan
    Liu, Kang
    [J]. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH, 2022, 19 (17)
  • [4] Long-Term Ecological and Environmental Quality Assessment Using an Improved Remote-Sensing Ecological Index (IRSEI): A Case Study of Hangzhou City, China
    Cai, Cheng
    Li, Jingye
    Wang, Zhanqi
    [J]. LAND, 2024, 13 (08)
  • [5] Spatial and Temporal Heterogeneity of Eco-Environmental Quality in Yanhe Watershed (China) Using the Remote-Sensing-Based Ecological Index (RSEI)
    Zhang, Lingda
    Hou, Quanhua
    Duan, Yaqiong
    Ma, Sanbao
    [J]. LAND, 2024, 13 (06)
  • [6] Analyses of driving factors on the spatial variations in regional eco-environmental quality using two types of species distribution models: A case study of Minjiang River Basin, China
    Li, Yudong
    Li, Zhijian
    Wang, Junjian
    Zeng, Hui
    [J]. ECOLOGICAL INDICATORS, 2022, 139
  • [7] Assessment of eco-environmental quality changes and spatial heterogeneity in the Yellow River Delta based on the remote sensing ecological index and geo-detector model
    Cai, Zongcai
    Zhang, Zhen
    Zhao, Fei
    Guo, Xiaohui
    Zhao, Jinbiao
    Xu, Yangyang
    Liu, Xiaopeng
    [J]. ECOLOGICAL INFORMATICS, 2023, 77
  • [8] Eco-environmental vulnerability evaluation in mountainous region using remote sensing and GIS - A case study in the upper reaches of Minjiang River, China
    Li, AN
    Wang, AS
    Liang, SL
    Zhou, WC
    [J]. ECOLOGICAL MODELLING, 2006, 192 (1-2) : 175 - 187
  • [9] Analysis of the temporal and spatial changes of ecological environment quality using the optimization remote sensing ecological index in the middle Yellow River Basin, China
    Li, Guanwen
    Zhang, Naichang
    Cao, Yongxiang
    Xia, Zhaohui
    Bao, Chenfang
    Fan, Liangxin
    Xue, Sha
    [J]. EARTH SCIENCE INFORMATICS, 2024,
  • [10] Assessment of Eco-environmental Quality on Land Use and Land Cover Changes Using Remote Sensing and GIS: A case study of Miyun county
    Wang, Xiaofeng
    Gong, Wenfeng
    Huang, Xinfeng
    Liu, Tao
    Zhou, Ying
    Li, Heng
    [J]. Nature Environment and Pollution Technology, 2018, 17 (03): : 739 - 746