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.
Authors
- Márcio Cataldi (ORCID: https://orcid.org/0000-0001-9769-0105)
- Desalegn Yayeh Ayal (ORCID: https://orcid.org/0000-0001-8966-2673)
- S K Wang (ORCID: https://orcid.org/0009-0004-0171-3974)
- Xinwu Xu
- Chunlan Li (ORCID: https://orcid.org/0000-0001-9055-7571)
- Walter Leal
- Xinlei Yi
- Karamat Ali
Institutions
- 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)
Publication Details
- Journal
- Land
- Published
- 2026-05-08
- DOI
- https://doi.org/10.3390/land15050803
- Primary Topic
- Remote Sensing in Agriculture
- Type
- article
- Field-Weighted Citation Impact
- 0.00