An improved estimation of remote sensing-based ecological index for tracking continuous changes in eco-environmental quality DOI Creative Commons

Mengjiao Xu,

Wei Zhao, Yanqing Yang

et al.

International Journal of Digital Earth, Journal Year: 2024, Volume and Issue: 17(1)

Published: Dec. 29, 2024

Remote sensing-based ecological index (RSEI) is a widely used for monitoring eco-environmental quality (EEQ). However, the traditional estimations usually face poor stability in tracking continuous EEQ changes. To overcome this issue, study introduces an improved framework RSEI estimation to enhance temporal comparability of assessments. It incorporates annual mean values and normalization key indicators address limitations, including susceptibility seasonal variability reliance on single-phase data. Through applying Liangshan Prefecture, mountainous region Southwest China, captures both long-term trends short-term dynamics from 2001 2020, revealing relatively stable EEQ, with ranging 0.465–0.470. Regions experiencing significant improvement or degradation accounted less than 10% total area. Environmental improvements were largely attributed human disturbance reduction restoration projects high-altitude areas, while degraded regions concentrated Anning River Valley, driven by population growth urban expansion, affecting 51% The assessments demonstrate that method shows greater spatial coherence ones, providing robust metric environmental sustainable development planning.

Language: Английский

Immediate assessment of forest fire using a novel vegetation index and machine learning based on multi-platform, high temporal resolution remote sensing images DOI Creative Commons
Hanqiu Xu, Jiahui Chen,

Guojin He

et al.

International Journal of Applied Earth Observation and Geoinformation, Journal Year: 2024, Volume and Issue: 134, P. 104210 - 104210

Published: Oct. 11, 2024

Language: Английский

Citations

6
Constructing urban thermal environment networks using remote sensing data: A perspective based on LCZ and source-sink theory DOI Creative Commons
Yue Shi, Qiang Fan, Xiaonan Song

et al.

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Journal Year: 2025, Volume and Issue: 18, P. 6382 - 6392

Published: Jan. 1, 2025

Language: Английский

Citations

0
Dual effects on vegetation caused by urban expansion in global drylands: Insights from the enhanced vegetation disturbance index DOI
Tao Qi, Chunyang He, Qiang Ren

et al.

Resources Conservation and Recycling, Journal Year: 2025, Volume and Issue: 222, P. 108438 - 108438

Published: June 5, 2025

Language: Английский

Citations

0
An improved estimation of remote sensing-based ecological index for tracking continuous changes in eco-environmental quality DOI Creative Commons

Mengjiao Xu,

Wei Zhao, Yanqing Yang

et al.

International Journal of Digital Earth, Journal Year: 2024, Volume and Issue: 17(1)

Published: Dec. 29, 2024

Remote sensing-based ecological index (RSEI) is a widely used for monitoring eco-environmental quality (EEQ). However, the traditional estimations usually face poor stability in tracking continuous EEQ changes. To overcome this issue, study introduces an improved framework RSEI estimation to enhance temporal comparability of assessments. It incorporates annual mean values and normalization key indicators address limitations, including susceptibility seasonal variability reliance on single-phase data. Through applying Liangshan Prefecture, mountainous region Southwest China, captures both long-term trends short-term dynamics from 2001 2020, revealing relatively stable EEQ, with ranging 0.465–0.470. Regions experiencing significant improvement or degradation accounted less than 10% total area. Environmental improvements were largely attributed human disturbance reduction restoration projects high-altitude areas, while degraded regions concentrated Anning River Valley, driven by population growth urban expansion, affecting 51% The assessments demonstrate that method shows greater spatial coherence ones, providing robust metric environmental sustainable development planning.

Language: Английский

Citations

1