Satellite Remote Sensing Reveals Global Dam Impacts on Riparian Vegetation Dynamics Under Future Climate Scenarios

The rapid global expansion of hydropower poses questions about the resilience and sustainability of riparian vegetation, especially in the context of ongoing climate change. Satellite remote sensing provides a valuable means for monitoring long-term and spatially continuous changes in vegetation, offering insights into how dams influence RV dynamics worldwide. Here, we integrated satellite-derived environmental indicators, including Normalized Difference Vegetation Index (NDVI), to quantify and compare riparian vegetation trends upstream and downstream of dams globally. By applying paired linear regression analyses to pre- and post-construction NDVI time series, we identified dams associated with significant RV degradation following impoundment. Furthermore, we employed Gradient Boosting Regression Models (GBRM), calibrated using current observational data and driven by CMIP6 climate projections, to forecast global riparian vegetation trends through the year 2100 under various climate scenarios. Our analysis reveals that, although widespread vegetation degradation was not evident up to 2017—and many regions showed slight improvements—future projections under higher-emission pathways (SSP3-7.0 and SSP5-8.5) indicate substantial RV declines after 2040, particularly in high-latitude forests, grasslands, and arid regions. Conversely, tropical and subtropical riparian forests are predicted to maintain stable or increasing NDVI under moderate emission scenarios (SSP1-2.6). These results highlight the potential for adaptive dam development strategies supported by continued satellite-based monitoring to help reduce climate-related risks to riparian vegetation in regions.

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Publication Details

Journal
Remote Sensing
Published
2025-08-30
DOI
https://doi.org/10.3390/rs17173018
Citations
1
Primary Topic
Hydrology and Sediment Transport Processes
Type
article
Field-Weighted Citation Impact
1.72

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article

Satellite Remote Sensing Reveals Global Dam Impacts on Riparian Vegetation Dynamics Under Future Climate Scenarios

Mengxi He, Zhucheng Zhang, Yan Li, Li He et al.
1 citations
Remote Sensing
Hydrology and Sediment Transport Processes
1.72
article

Satellite Remote Sensing Reveals Global Dam Impacts on Riparian Vegetation Dynamics Under Future Climate Scenarios

Mengxi He, Zhucheng Zhang, Yan Li, Li He, Yunlong Liu, Tong Sun
article en
1 citations

Abstract

The rapid global expansion of hydropower poses questions about the resilience and sustainability of riparian vegetation, especially in the context of ongoing climate change. Satellite remote sensing provides a valuable means for monitoring long-term and spatially continuous changes in vegetation, offering insights into how dams influence RV dynamics worldwide. Here, we integrated satellite-derived environmental indicators, including Normalized Difference Vegetation Index (NDVI), to quantify and compare riparian vegetation trends upstream and downstream of dams globally. By applying paired linear regression analyses to pre- and post-construction NDVI time series, we identified dams associated with significant RV degradation following impoundment. Furthermore, we employed Gradient Boosting Regression Models (GBRM), calibrated using current observational data and driven by CMIP6 climate projections, to forecast global riparian vegetation trends through the year 2100 under various climate scenarios. Our analysis reveals that, although widespread vegetation degradation was not evident up to 2017—and many regions showed slight improvements—future projections under higher-emission pathways (SSP3-7.0 and SSP5-8.5) indicate substantial RV declines after 2040, particularly in high-latitude forests, grasslands, and arid regions. Conversely, tropical and subtropical riparian forests are predicted to maintain stable or increasing NDVI under moderate emission scenarios (SSP1-2.6). These results highlight the potential for adaptive dam development strategies supported by continued satellite-based monitoring to help reduce climate-related risks to riparian vegetation in regions.

Remote SensingVol. 17(17)
Tianjin University (CN)
National Natural Science Foundation of China, Chinese Academy of Sciences
Openalex Percentile: Top 14%
Hydrology and Sediment Transport Processes
1.72
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