Long-Term Dynamic Monitoring and Driving Force Analysis of Eco-Environmental Quality in China
Accurate assessments of the historical and current status of eco-environmental quality (EEQ) are essential for governments to have a comprehensive understanding of regional ecological conditions, formulate scientific policies, and achieve the United Nations Sustainable Development Goals (SDGs). Whil...
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Published in | Remote sensing (Basel, Switzerland) Vol. 16; no. 6; p. 1028 |
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Format | Journal Article |
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Abstract | Accurate assessments of the historical and current status of eco-environmental quality (EEQ) are essential for governments to have a comprehensive understanding of regional ecological conditions, formulate scientific policies, and achieve the United Nations Sustainable Development Goals (SDGs). While various approaches to EEQ monitoring exist, they each have limitations and cannot be used universally. Moreover, previous studies lack detailed examinations of EEQ dynamics and its driving factors at national and local levels. Therefore, this study utilized a remote sensing ecological index (RSEI) to assess the EEQ of China from 2001 to 2021. Additionally, an emerging hot-spot analysis was conducted to study the spatial and temporal dynamics of the EEQ of China. The degree of influence of eight major drivers affecting EEQ was evaluated by a GeoDetector model. The results show that from 2001 to 2021, the mean RSEI values in China showed a fluctuating upward trend; the EEQ varied significantly in different regions of China, with a lower EEQ in the north and west and a higher EEQ in the northeast, east, and south in general. The spatio-temporal patterns of hot/cold spots in China were dominated by intensifying hot spots, persistent cold spots, and diminishing cold spots, with an area coverage of over 90%. The hot spots were concentrated to the east of the Hu Huanyong Line, while the cold spots were concentrated to its west. The oscillating hot/cold spots were located in the ecologically fragile agro-pastoral zone, next to the upper part of the Hu Huanyong Line. Natural forces have become the main driving force for changes in China’s EEQ, and precipitation and soil sand content were key variables affecting the EEQ. The interaction between these factors had a greater impact on the EEQ than individual factors. |
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AbstractList | Accurate assessments of the historical and current status of eco-environmental quality (EEQ) are essential for governments to have a comprehensive understanding of regional ecological conditions, formulate scientific policies, and achieve the United Nations Sustainable Development Goals (SDGs). While various approaches to EEQ monitoring exist, they each have limitations and cannot be used universally. Moreover, previous studies lack detailed examinations of EEQ dynamics and its driving factors at national and local levels. Therefore, this study utilized a remote sensing ecological index (RSEI) to assess the EEQ of China from 2001 to 2021. Additionally, an emerging hot-spot analysis was conducted to study the spatial and temporal dynamics of the EEQ of China. The degree of influence of eight major drivers affecting EEQ was evaluated by a GeoDetector model. The results show that from 2001 to 2021, the mean RSEI values in China showed a fluctuating upward trend; the EEQ varied significantly in different regions of China, with a lower EEQ in the north and west and a higher EEQ in the northeast, east, and south in general. The spatio-temporal patterns of hot/cold spots in China were dominated by intensifying hot spots, persistent cold spots, and diminishing cold spots, with an area coverage of over 90%. The hot spots were concentrated to the east of the Hu Huanyong Line, while the cold spots were concentrated to its west. The oscillating hot/cold spots were located in the ecologically fragile agro-pastoral zone, next to the upper part of the Hu Huanyong Line. Natural forces have become the main driving force for changes in China’s EEQ, and precipitation and soil sand content were key variables affecting the EEQ. The interaction between these factors had a greater impact on the EEQ than individual factors. |
Audience | Academic |
Author | Yang, Zhaohui Liu, Zixi Qin, Kun Chen, Chao Lu, Miao Jia, Peng Zhang, Weiwei Dai, Shaoqing Ji, Jianwan Lu, Huiyuan |
Author_xml | – sequence: 1 givenname: Weiwei surname: Zhang fullname: Zhang, Weiwei – sequence: 2 givenname: Zixi surname: Liu fullname: Liu, Zixi – sequence: 3 givenname: Kun orcidid: 0009-0006-3901-7207 surname: Qin fullname: Qin, Kun – sequence: 4 givenname: Shaoqing orcidid: 0000-0003-0858-4728 surname: Dai fullname: Dai, Shaoqing – sequence: 5 givenname: Huiyuan surname: Lu fullname: Lu, Huiyuan – sequence: 6 givenname: Miao surname: Lu fullname: Lu, Miao – sequence: 7 givenname: Jianwan orcidid: 0000-0002-1996-6281 surname: Ji fullname: Ji, Jianwan – sequence: 8 givenname: Zhaohui surname: Yang fullname: Yang, Zhaohui – sequence: 9 givenname: Chao surname: Chen fullname: Chen, Chao – sequence: 10 givenname: Peng orcidid: 0000-0003-0110-3637 surname: Jia fullname: Jia, Peng |
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CitedBy_id | crossref_primary_10_1016_j_ecoinf_2024_102936 crossref_primary_10_1016_j_ecolind_2024_112485 crossref_primary_10_1038_s41598_024_68639_6 crossref_primary_10_1016_j_ecolind_2024_112109 |
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