An improved estimation of remote sensing-based ecological index for tracking continuous changes in eco-environmental quality
Remote sensing-based ecological index (RSEI) is a widely used index for monitoring eco-environmental quality (EEQ). However, the traditional estimations usually face poor stability in tracking the continuous EEQ changes. To overcome this issue, this study introduces an improved framework for RSEI es...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-12-01
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| Series: | International Journal of Digital Earth |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/17538947.2024.2445625 |
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| _version_ | 1846100610318860288 |
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| author | Mengjiao Xu Wei Zhao Yanqing Yang Yujia Yang Jiujiang Wu Hamza Mukhtar |
| author_facet | Mengjiao Xu Wei Zhao Yanqing Yang Yujia Yang Jiujiang Wu Hamza Mukhtar |
| author_sort | Mengjiao Xu |
| collection | DOAJ |
| description | Remote sensing-based ecological index (RSEI) is a widely used index for monitoring eco-environmental quality (EEQ). However, the traditional estimations usually face poor stability in tracking the continuous EEQ changes. To overcome this issue, this study introduces an improved framework for RSEI estimation to enhance the temporal comparability of EEQ assessments. It incorporates annual mean values and temporal normalization of key ecological indicators to address limitations, including susceptibility to seasonal variability and reliance on single-phase data. Through applying to Liangshan Prefecture, a mountainous region in Southwest China, the improved RSEI captures both long-term trends and short-term ecological dynamics from 2001 to 2020, revealing relatively stable EEQ, with mean values ranging from 0.465–0.470. Regions experiencing significant improvement or degradation accounted for less than 10% of the total area. Environmental improvements were largely attributed to human disturbance reduction and ecological restoration projects in high-altitude areas, while degraded regions were concentrated in the Anning River Valley, driven by population growth and urban expansion, affecting 51% of the area. The assessments demonstrate that the improved method shows greater stability and spatial coherence than the traditional ones, providing a robust metric for tracking EEQ trends for environmental monitoring and sustainable development planning. |
| format | Article |
| id | doaj-art-62d4bd9200d240108d586a994c0322c2 |
| institution | Kabale University |
| issn | 1753-8947 1753-8955 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | International Journal of Digital Earth |
| spelling | doaj-art-62d4bd9200d240108d586a994c0322c22024-12-30T02:05:45ZengTaylor & Francis GroupInternational Journal of Digital Earth1753-89471753-89552024-12-0117110.1080/17538947.2024.2445625An improved estimation of remote sensing-based ecological index for tracking continuous changes in eco-environmental qualityMengjiao Xu0Wei Zhao1Yanqing Yang2Yujia Yang3Jiujiang Wu4Hamza Mukhtar5Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu, People’s Republic of ChinaInstitute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu, People’s Republic of ChinaInstitute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu, People’s Republic of ChinaInstitute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu, People’s Republic of ChinaInstitute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu, People’s Republic of ChinaInstitute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu, People’s Republic of ChinaRemote sensing-based ecological index (RSEI) is a widely used index for monitoring eco-environmental quality (EEQ). However, the traditional estimations usually face poor stability in tracking the continuous EEQ changes. To overcome this issue, this study introduces an improved framework for RSEI estimation to enhance the temporal comparability of EEQ assessments. It incorporates annual mean values and temporal normalization of key ecological indicators to address limitations, including susceptibility to seasonal variability and reliance on single-phase data. Through applying to Liangshan Prefecture, a mountainous region in Southwest China, the improved RSEI captures both long-term trends and short-term ecological dynamics from 2001 to 2020, revealing relatively stable EEQ, with mean values ranging from 0.465–0.470. Regions experiencing significant improvement or degradation accounted for less than 10% of the total area. Environmental improvements were largely attributed to human disturbance reduction and ecological restoration projects in high-altitude areas, while degraded regions were concentrated in the Anning River Valley, driven by population growth and urban expansion, affecting 51% of the area. The assessments demonstrate that the improved method shows greater stability and spatial coherence than the traditional ones, providing a robust metric for tracking EEQ trends for environmental monitoring and sustainable development planning.https://www.tandfonline.com/doi/10.1080/17538947.2024.2445625Remote sensing-based ecological indexeco-environmental qualityland surface temperaturesurface soil moistureNDVI |
| spellingShingle | Mengjiao Xu Wei Zhao Yanqing Yang Yujia Yang Jiujiang Wu Hamza Mukhtar An improved estimation of remote sensing-based ecological index for tracking continuous changes in eco-environmental quality International Journal of Digital Earth Remote sensing-based ecological index eco-environmental quality land surface temperature surface soil moisture NDVI |
| title | An improved estimation of remote sensing-based ecological index for tracking continuous changes in eco-environmental quality |
| title_full | An improved estimation of remote sensing-based ecological index for tracking continuous changes in eco-environmental quality |
| title_fullStr | An improved estimation of remote sensing-based ecological index for tracking continuous changes in eco-environmental quality |
| title_full_unstemmed | An improved estimation of remote sensing-based ecological index for tracking continuous changes in eco-environmental quality |
| title_short | An improved estimation of remote sensing-based ecological index for tracking continuous changes in eco-environmental quality |
| title_sort | improved estimation of remote sensing based ecological index for tracking continuous changes in eco environmental quality |
| topic | Remote sensing-based ecological index eco-environmental quality land surface temperature surface soil moisture NDVI |
| url | https://www.tandfonline.com/doi/10.1080/17538947.2024.2445625 |
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