Long-Term Vegetation Dynamics and Their Climatic and Non-Climatic Drivers in the Indus River Basin During the 1982–2022 Period

Using GIMMS NDVI3g+ data (1982–2022) together with ERA5-Land temperature and precipitation, this study examined long-term vegetation dynamics in the Indus River Basin (IRB) and used a residual trend framework for cautious first-order attribution. Basin-averaged NDVI increased significantly at 0.0061 per decade (p < 0.05), and 65.5% of the basin showed greening, mainly in irrigated croplands and river-adjacent agricultural zones, whereas 12.6% showed degradation concentrated in rapidly urbanizing areas, cryosphere margins, and desert fringes. Partial correlation and residual analyses indicate that climate-related enhancement was most evident in upper-elevation cryosphere transition zones and some lower-basin barren lands, whereas non-climatic residual effects were especially important in intensively managed agricultural landscapes. Because the attribution model includes only temperature and precipitation, the residual component is interpreted here as a non-climatic residual rather than a direct measure of human activity. The study, therefore, provides a spatially explicit basin-wide assessment of vegetation change while highlighting the uncertainty and interpretation limits of residual-based attribution.

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Publication Details

Journal
Land
Published
2026-05-08
DOI
https://doi.org/10.3390/land15050803
Primary Topic
Remote Sensing in Agriculture
Type
article
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article

Long-Term Vegetation Dynamics and Their Climatic and Non-Climatic Drivers in the Indus River Basin During the 1982–2022 Period

Márcio Cataldi, Desalegn Yayeh Ayal, S K Wang, Xinwu Xu et al.
Land
Remote Sensing in Agriculture
article

Long-Term Vegetation Dynamics and Their Climatic and Non-Climatic Drivers in the Indus River Basin During the 1982–2022 Period

Márcio Cataldi, Desalegn Yayeh Ayal, S K Wang, Xinwu Xu, Chunlan Li, Walter Leal, Xinlei Yi, Karamat Ali
article en

Abstract

Using GIMMS NDVI3g+ data (1982–2022) together with ERA5-Land temperature and precipitation, this study examined long-term vegetation dynamics in the Indus River Basin (IRB) and used a residual trend framework for cautious first-order attribution. Basin-averaged NDVI increased significantly at 0.0061 per decade (p < 0.05), and 65.5% of the basin showed greening, mainly in irrigated croplands and river-adjacent agricultural zones, whereas 12.6% showed degradation concentrated in rapidly urbanizing areas, cryosphere margins, and desert fringes. Partial correlation and residual analyses indicate that climate-related enhancement was most evident in upper-elevation cryosphere transition zones and some lower-basin barren lands, whereas non-climatic residual effects were especially important in intensively managed agricultural landscapes. Because the attribution model includes only temperature and precipitation, the residual component is interpreted here as a non-climatic residual rather than a direct measure of human activity. The study, therefore, provides a spatially explicit basin-wide assessment of vegetation change while highlighting the uncertainty and interpretation limits of residual-based attribution.

LandVol. 15(5)
China Meteorological Administration (CN), Universität Hamburg (DE), Universidade Federal Fluminense (BR), Karakoram International University (PK), Northwest Institute of Eco-Environment and Resources (CN), Addis Ababa University (ET), East China Normal University (CN), HAW Hamburg (DE)
Sustainable cities and communities
Openalex Percentile: Top 6%
Remote Sensing in Agriculture
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