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

Monitoring and assessing spatiotemporal changes and driving factors of ecological environment quality in the middle Yellow River Basin (MYRB) is significant for ecological environment protection, management, and high-quality development. We reconstructed data from 1986‒2023 Landsat series images using the harmonic analysis of time series (HANTS) algorithm on the Google Earth Engine platform to optimize the remote-sensing ecological index (RSEI) calculation process, and analyzed the trends and sustainability of ecological environment quality changes. The HANTS algorithm reduced dispersion and anomalies, filled in missing images, and enhanced the Landsat series image quality. The RSEI accurately reflected the ecological environment quality from 1986‒2023 in the MYRB, reducing the "pseudo-variation" conclusion of multi-year evaluations, and enhancing the stability of regional ecological environment quality assessments. Ecological environment quality in the MYRB generally showed an improving trend from 1986‒2023, with significant improvement covering 71.6% of the area; however, the change in ecological environment quality showed weak sustainability. The results reflected the positive effects of ecological restoration and the negative impact of urban construction. The optimized RSEI effectively reflected the ecological environment quality of the MYRB, improved the long-term RSEI stability, and satisfied the requirements of large-scale and long-term ecological environment quality monitoring.

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Publication Details

Journal
Earth Science Informatics
Published
2024-08-13
DOI
https://doi.org/10.1007/s12145-024-01441-0
Citations
5
Primary Topic
Land Use and Ecosystem Services
Type
article
Field-Weighted Citation Impact
1.41
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article

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

G. Y. Li, Naichang Zhang, Sha Xue, Liangxin Fan et al.
5 citations
Earth Science Informatics
Land Use and Ecosystem Services
1.41
article

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

G. Y. Li, Naichang Zhang, Sha Xue, Liangxin Fan, Zhaohui Xia, Chenfang Bao, Yongxiang Cao
article en
5 citations

Abstract

Monitoring and assessing spatiotemporal changes and driving factors of ecological environment quality in the middle Yellow River Basin (MYRB) is significant for ecological environment protection, management, and high-quality development. We reconstructed data from 1986‒2023 Landsat series images using the harmonic analysis of time series (HANTS) algorithm on the Google Earth Engine platform to optimize the remote-sensing ecological index (RSEI) calculation process, and analyzed the trends and sustainability of ecological environment quality changes. The HANTS algorithm reduced dispersion and anomalies, filled in missing images, and enhanced the Landsat series image quality. The RSEI accurately reflected the ecological environment quality from 1986‒2023 in the MYRB, reducing the "pseudo-variation" conclusion of multi-year evaluations, and enhancing the stability of regional ecological environment quality assessments. Ecological environment quality in the MYRB generally showed an improving trend from 1986‒2023, with significant improvement covering 71.6% of the area; however, the change in ecological environment quality showed weak sustainability. The results reflected the positive effects of ecological restoration and the negative impact of urban construction. The optimized RSEI effectively reflected the ecological environment quality of the MYRB, improved the long-term RSEI stability, and satisfied the requirements of large-scale and long-term ecological environment quality monitoring.

Earth Science InformaticsVol. 17(6)
Northwest University (CN), Institute of Soil and Water Conservation (CN), PowerChina (China) (CN), China Power Engineering Consulting Group (China) (CN), Henan Polytechnic University (CN), State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau (CN), Northwest A&F University (CN)
Life in Land
Openalex Percentile: Top 20%
Land Use and Ecosystem Services
1.41
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