kNDVI Spatiotemporal Variations and Climate Lag on Qilian Southern Slope: Sen–Mann–Kendall and Hurst Index Analyses for Ecological Insights DOI Open Access
Qian Zhang,

Guangchao Cao,

Meiliang Zhao

et al.

Forests, Journal Year: 2025, Volume and Issue: 16(2), P. 307 - 307

Published: Feb. 10, 2025

In the context of climate change, southern slope Qilian Mountains stands as a pivotal region for China’s ecological security, holding immense significance sustaining sustainable development. This study aims to precisely monitor and predict dynamic changes in vegetation cover within this region, along with their time-lagged effects on thereby providing scientific basis management. By calculating kNDVI from 2001 2020 Google Earth Engine (GEE) platform, integrating Sen’s trend analysis, Hurst exponent, partial correlation we have conducted an in-depth exploration long-term spatiotemporal variations its delayed responses factors. The primary research findings can be summarized follows: exhibits overall positive trend, notable geographical spatial distribution. proportion areas showing improvement is high 84%, while degraded account only 17%. Furthermore, there average lag response 1.6 months precipitation 0.6 temperature region. speed positively correlates coefficient between Notably, more sensitive area Mountains. not fills gap monitoring but also offers support governance green development initiatives Additionally, it showcases innovative application advanced remote sensing technologies statistical analysis methods research, fresh perspectives future management strategies. These hold profound implications promoting conservation area.

Language: Английский

kNDVI Spatiotemporal Variations and Climate Lag on Qilian Southern Slope: Sen–Mann–Kendall and Hurst Index Analyses for Ecological Insights DOI Open Access
Qian Zhang,

Guangchao Cao,

Meiliang Zhao

et al.

Forests, Journal Year: 2025, Volume and Issue: 16(2), P. 307 - 307

Published: Feb. 10, 2025

In the context of climate change, southern slope Qilian Mountains stands as a pivotal region for China’s ecological security, holding immense significance sustaining sustainable development. This study aims to precisely monitor and predict dynamic changes in vegetation cover within this region, along with their time-lagged effects on thereby providing scientific basis management. By calculating kNDVI from 2001 2020 Google Earth Engine (GEE) platform, integrating Sen’s trend analysis, Hurst exponent, partial correlation we have conducted an in-depth exploration long-term spatiotemporal variations its delayed responses factors. The primary research findings can be summarized follows: exhibits overall positive trend, notable geographical spatial distribution. proportion areas showing improvement is high 84%, while degraded account only 17%. Furthermore, there average lag response 1.6 months precipitation 0.6 temperature region. speed positively correlates coefficient between Notably, more sensitive area Mountains. not fills gap monitoring but also offers support governance green development initiatives Additionally, it showcases innovative application advanced remote sensing technologies statistical analysis methods research, fresh perspectives future management strategies. These hold profound implications promoting conservation area.

Language: Английский

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