Analysis of Ecological Environment in the Shanxi Section of the Yellow River Basin and Coal Mining Area Based on Improved Remote Sensing Ecological Index

Sensors, Journal Year: 2024, Volume and Issue: 24(20), P. 6560 - 6560

Published: Oct. 11, 2024

As a major coal-producing area, the Shanxi section of Yellow River Basin has been significantly affected by coal mining activities in local ecological environment. Therefore, an in-depth study evolution this region holds great scientific significance and practical value. In study, Basin, including its planned was selected as research subject. An improved remotely sensed index model (NRSEI) integrating (RSEI) net primary productivity (NPP) vegetation constructed utilizing Google Earth Engine platform. The NRSEI time series data from 2003 to 2022 were calculated, Sen + Mann-Kendall analysis method employed comprehensively assess environment quality evolutionary trends area. findings paper indicate following data: (1) contribution first principal component is more than 70%, average correlation coefficient higher 0.79. effectively integrates information multiple indicators enhances applicability regional evaluation. (2) Between 2022, showed overall upward trend, with value experiencing phases fluctuation, increase, decline, stabilization. values non-coal areas consistently remained those areas. (3) Over 60% have conditions, especially (4) impact on significant within 6 km radius, while effects gradually diminish 10 range. This not only offers reliable methodology for evaluating large scale over long but also guiding restoration sustainable development

Language: Английский

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Dynamic assessment of eco-environmental quality in Xiong’an new area, China using WB-RSEI new model

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: March 4, 2025

Language: Английский

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Evaluation of Ecological Environment Quality Using an Improved Remote Sensing Ecological Index Model

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(18), P. 3485 - 3485

Published: Sept. 20, 2024

The Remote Sensing Ecological Index (RSEI) model is widely used for large-scale, rapid Environment Quality (EEQ) assessment. However, both the RSEI and its improved models have limitations in explaining EEQ with only two-dimensional (2D) factors, resulting inaccurate evaluation results. Incorporating more comprehensive, three-dimensional (3D) ecological information poses challenges maintaining stability large-scale monitoring, using traditional weighting methods like Principal Component Analysis (PCA). This study introduces an Improved (IRSEI) that integrates 2D (normalized difference vegetation factor, normalized built-up soil heat wetness, factor air quality) 3D (comprehensive factor) factors enhanced monitoring. employs a combined subjective–objective approach, utilizing principal components hierarchical analysis under minimum entropy theory. A comparative of IRSEI Miyun, representative area, reveals strong correlation consistent monitoring trends. By incorporating quality provides accurate detailed assessment, better aligning ground truth observations from Google Earth satellite imagery.

Language: Английский

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Analysis of the evolution and influencing factors of ecological environment quality after the transformation of resource-exhausted cities based on GEE and RSEI: a case study of Xuzhou City, China

Journal of Asian Architecture and Building Engineering, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 19

Published: May 27, 2024

This study used Google Earth Engine (GEE) to get and process Landsat images of Xuzhou from 2008 2020, built remote sensing ecological indices (RSEI). analyzed the spatiotemporal features drivers environment quality after Xuzhou's transformation using landscape pattern index, spatial autocorrelation, geographic detector methods. The results showed that: (1) improved steadily but unevenly 2020; west central urban areas had poor improving quality, while east opposite trend; (2) in different regions did not change consistently (3) distribution significant with high-high clusters low-low center each district county; (4) NDVI is component index that has greatest impact on changes RSEI. Among single factors, land use conditions have strongest influence environmental quality. After interaction dual explanatory power stronger than any factor. research can support planning design, protection, etc. Xuzhou, offer insights for monitoring evaluation resource-exhausted cities.

Language: Английский

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Spatial-temporal variation and influencing factors of ecological environment quality in Jilin Province (China)

Frontiers in Environmental Science, Journal Year: 2024, Volume and Issue: 12

Published: Sept. 13, 2024

Jilin Province is a crucial region of interest for agricultural and forestry development in China. The deterioration its ecological environment could have severe impact on production conservation. A systematic assessment quality essential sustainable development. In this study, we utilized Landsat data from 1990 to 2020 (every 5 years) construct the Remote Sensing Ecological Index (RSEI) Province. We applied Sen’s slope estimator Mann-Kendall trend test examine spatiotemporal changes over 30-year period. Additionally, employed Geo-detector explore socioeconomic natural factors influencing quality. results revealed: 1) From 2020, average RSEI index ranged 0.586 0.699, indicating overall good Spatially, gradually declined east west. 2) exhibited an initial increase, followed by decrease, then another increase trend. This improvement can be attributed implementation government policies, which reversed expansion saline-alkali land. significantly improved western 3) Socioeconomic both influence Among these factors, vegetation coverage has most significant study area, with exerting more than factors. Our research provide relevant support policy-making

Language: Английский

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Landsat series images for evaluating ecological restoration effect from multi-time scale based on an ideal reference

Frontiers in Environmental Science, Journal Year: 2024, Volume and Issue: 12

Published: March 12, 2024

Multi-time scale assessment of ecological restoration effects based on objective and scientific approaches can provide crucial information for implementing environmental protection policies ensuring sustainable regional development. This study evaluated the effect a natural evolution as reference frame, using yearly Landsat time series. Southern Ningxia in China was selected area. The remote sensing index (RSEI) calculated. features were derived from series RSEI reserve areas (NRAs). LandTrendr employed to characterize disturbance–recovery processes. Furthermore, we adopted dynamic time-warping method entire period, along with relative variation ratio (during cycle) capture long-term short-term effects, respectively. following conclusions drawn: First, time-series used successfully monitor Second, majority disturbances (i.e., >60%) occurred between 2000 2005. It is characterized by fewer disturbance times obvious spatial heterogeneity duration. Notably, 2022, improved. Additionally, approximately 40% area portrayed strong similarity NRAs. We conclude that quantifying at multi-time scales practical operational approach policymakers protection. Our presents novel insights assessing quality, capturing processes

Language: Английский

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Long Time-Series and High-Frequency Ecological Evaluation of Henan Section of the Yellow River

Published: Jan. 1, 2024

Language: Английский

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Long time-series and high-frequency ecological evaluation of Henan section of the Yellow River

Global Ecology and Conservation, Journal Year: 2024, Volume and Issue: 57, P. e03351 - e03351

Published: Dec. 9, 2024

Language: Английский

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