Landscape Spatiotemporal Heterogeneity Decreased the Resistance of Alpine Grassland to Soil Droughts

Alpine grasslands face increasing threats from soil droughts due to climate change. While extensive research has focused on the direct impacts of drought on vegetation, the role of landscape fragmentation and spatiotemporal heterogeneity in shaping the response of these ecosystems to drought remains inadequately explored. This study aims to fill this gap by examining the Gannan alpine grassland in the northeastern Qinghai-Tibet Plateau. Using remote sensing data, indicators of spatial and temporal heterogeneity were derived, including spatial variance (SCV), spatial autocorrelation (SAC), and temporal autocorrelation (TAC). Two soil drought thresholds (Tr: threshold of rapid resistance loss and Tc: threshold of complete resistance loss) representing percentile-based drought intensities were identified to assess NDVI decline under drought conditions. Our findings indicate that the grassland has low resistance to soil droughts, with mean Tr and Tc of 8.93th and 7.36th percentile, respectively. Both increasing and decreasing spatiotemporal heterogeneity reduced vegetation resistance, with increasing SCV having a more pronounced effect. Specifically, increasing SCV increased Tr and Tc 1.4 times faster and 2.6 time slower than decreasing SCV, respectively. These results underscore the critical role of landscape heterogeneity in modulating grassland responses to drought, suggesting that managing vegetation patches could enhance ecosystem resilience.

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Publication Details

Journal
Remote Sensing
Published
2025-04-04
DOI
https://doi.org/10.3390/rs17071293
Primary Topic
Ecosystem dynamics and resilience
Type
article
Field-Weighted Citation Impact
0.00

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article

Landscape Spatiotemporal Heterogeneity Decreased the Resistance of Alpine Grassland to Soil Droughts

Zhibin He, Jiachang Jiang, Yuxin Wang, Hu Liu et al.
Remote Sensing
Ecosystem dynamics and resilience
article

Landscape Spatiotemporal Heterogeneity Decreased the Resistance of Alpine Grassland to Soil Droughts

Zhibin He, Jiachang Jiang, Yuxin Wang, Hu Liu, Wenzhi Zhao
article en

Abstract

Alpine grasslands face increasing threats from soil droughts due to climate change. While extensive research has focused on the direct impacts of drought on vegetation, the role of landscape fragmentation and spatiotemporal heterogeneity in shaping the response of these ecosystems to drought remains inadequately explored. This study aims to fill this gap by examining the Gannan alpine grassland in the northeastern Qinghai-Tibet Plateau. Using remote sensing data, indicators of spatial and temporal heterogeneity were derived, including spatial variance (SCV), spatial autocorrelation (SAC), and temporal autocorrelation (TAC). Two soil drought thresholds (Tr: threshold of rapid resistance loss and Tc: threshold of complete resistance loss) representing percentile-based drought intensities were identified to assess NDVI decline under drought conditions. Our findings indicate that the grassland has low resistance to soil droughts, with mean Tr and Tc of 8.93th and 7.36th percentile, respectively. Both increasing and decreasing spatiotemporal heterogeneity reduced vegetation resistance, with increasing SCV having a more pronounced effect. Specifically, increasing SCV increased Tr and Tc 1.4 times faster and 2.6 time slower than decreasing SCV, respectively. These results underscore the critical role of landscape heterogeneity in modulating grassland responses to drought, suggesting that managing vegetation patches could enhance ecosystem resilience.

Remote SensingVol. 17(7)
Chinese Academy of Sciences (CN), Northwest Institute of Eco-Environment and Resources (CN), State Forestry and Grassland Administration (CN), University of Chinese Academy of Sciences (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 92%
Ecosystem dynamics and resilience
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Landscape Spatiotemporal Heterogeneity Decreased the Resistance of Alpine Grassland to Soil Droughts — Zhibin He, Jiachang Jiang, et al. · Remote Sensing (2025) | TGRS Research Map | TGRS