The Decisive Influence of the Improved Remote Sensing Ecological Index on the Terrestrial Ecosystem in Typical Arid Areas of China DOI Creative Commons

Guo Long,

Chao Xu, Hongqi Wu

et al.

Land, Journal Year: 2024, Volume and Issue: 13(12), P. 2162 - 2162

Published: Dec. 12, 2024

This study aims to assess the spatiotemporal changes in ecological environment quality (EEQ) arid regions, using Xinjiang as a case study, from 2000 2023, with an improved remote sensing index (IRSEI). Due complex ecology of traditional (RSEI) has limitations capturing dynamics. To address this, we propose enhanced IRSEI model that replaces normalization standardization, improving robustness against outliers. Additionally, kernel normalized difference vegetation (kNDVI) and salinity (NDSI) are integrated saline areas more effectively. The methodology includes time series analysis, spatial distribution statistical evaluations method, coefficient variation, Hurst index. Results show accurately reflects dynamics than RSEI. Temporal analysis reveals stable overall EEQ, some improving. Spatially, is generally better north mountainous regions south plains. Statistical suggest positive trend changes, surpassing degraded ones. contributes monitoring, protection, management region ecosystems, emphasizing need for high-resolution data further analysis.

Language: Английский

The Decisive Influence of the Improved Remote Sensing Ecological Index on the Terrestrial Ecosystem in Typical Arid Areas of China DOI Creative Commons

Guo Long,

Chao Xu, Hongqi Wu

et al.

Land, Journal Year: 2024, Volume and Issue: 13(12), P. 2162 - 2162

Published: Dec. 12, 2024

This study aims to assess the spatiotemporal changes in ecological environment quality (EEQ) arid regions, using Xinjiang as a case study, from 2000 2023, with an improved remote sensing index (IRSEI). Due complex ecology of traditional (RSEI) has limitations capturing dynamics. To address this, we propose enhanced IRSEI model that replaces normalization standardization, improving robustness against outliers. Additionally, kernel normalized difference vegetation (kNDVI) and salinity (NDSI) are integrated saline areas more effectively. The methodology includes time series analysis, spatial distribution statistical evaluations method, coefficient variation, Hurst index. Results show accurately reflects dynamics than RSEI. Temporal analysis reveals stable overall EEQ, some improving. Spatially, is generally better north mountainous regions south plains. Statistical suggest positive trend changes, surpassing degraded ones. contributes monitoring, protection, management region ecosystems, emphasizing need for high-resolution data further analysis.

Language: Английский

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