Growing Season Normalized Difference Vegetation Index in the Nepal Himalaya and Adjacent Areas, 2000–2019: Sensitivity to Climate Change and Terrain Factors

Precisely detecting and attributing changes in vegetation greenness is crucial for sustainable ecosystem management. The normalized difference vegetation index (NDVI) is highly responsive to changes in vegetation cover and is essential for assessing vegetation dynamics. This study integrates a digital elevation model (DEM) with climate data (temperature, precipitation, evapotranspiration, and solar radiation) and MODIS-NDVI imagery (2000–2019) to investigate NDVI fluctuations and their correlation with climate change in the central Himalaya. Trend analysis of NDVI time-series data examined vegetation response influenced by elevation, aspect, and slope. The results revealed significant spatial and temporal NDVI variations, with an overall increase of 0.01 per decade (p < 0.05), indicating gradual vegetation improvement, though 26.3% of the area (107,138 km2) showed a decreasing trend. NDVI trends increased with elevation, peaking at 2000–2500 m, and then declined up to 4000 m, where they stabilized. While trends varied across slopes, they were independent of the aspect. Spearman correlation analysis revealed elevation-dependent vegetation responses to climate. Below 1000 masl, the NDVI was negatively correlated with temperature and evapotranspiration and positively with precipitation. At higher elevations (>4000 masl), temperature and evapotranspiration positively correlated with the NDVI, suggesting warming supports growth. These findings highlight complex interactions between vegetation, climate, and topography that are crucial for targeted ecosystem management.

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Publication Details

Journal
Land
Published
2025-04-01
DOI
https://doi.org/10.3390/land14040749
Citations
6
Primary Topic
Remote Sensing in Agriculture
Type
article
Field-Weighted Citation Impact
10.30
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article

Growing Season Normalized Difference Vegetation Index in the Nepal Himalaya and Adjacent Areas, 2000–2019: Sensitivity to Climate Change and Terrain Factors

Maria Bobrowski, Mst Umme Salma Nila, Udo Schickhoff
6 citations
Land
Remote Sensing in Agriculture
10.30
article

Growing Season Normalized Difference Vegetation Index in the Nepal Himalaya and Adjacent Areas, 2000–2019: Sensitivity to Climate Change and Terrain Factors

Maria Bobrowski, Mst Umme Salma Nila, Udo Schickhoff
article en
6 citations

Abstract

Precisely detecting and attributing changes in vegetation greenness is crucial for sustainable ecosystem management. The normalized difference vegetation index (NDVI) is highly responsive to changes in vegetation cover and is essential for assessing vegetation dynamics. This study integrates a digital elevation model (DEM) with climate data (temperature, precipitation, evapotranspiration, and solar radiation) and MODIS-NDVI imagery (2000–2019) to investigate NDVI fluctuations and their correlation with climate change in the central Himalaya. Trend analysis of NDVI time-series data examined vegetation response influenced by elevation, aspect, and slope. The results revealed significant spatial and temporal NDVI variations, with an overall increase of 0.01 per decade (p < 0.05), indicating gradual vegetation improvement, though 26.3% of the area (107,138 km2) showed a decreasing trend. NDVI trends increased with elevation, peaking at 2000–2500 m, and then declined up to 4000 m, where they stabilized. While trends varied across slopes, they were independent of the aspect. Spearman correlation analysis revealed elevation-dependent vegetation responses to climate. Below 1000 masl, the NDVI was negatively correlated with temperature and evapotranspiration and positively with precipitation. At higher elevations (>4000 masl), temperature and evapotranspiration positively correlated with the NDVI, suggesting warming supports growth. These findings highlight complex interactions between vegetation, climate, and topography that are crucial for targeted ecosystem management.

LandVol. 14(4)
Universität Hamburg (DE)
Openalex Percentile: Top 2%
Remote Sensing in Agriculture
10.30
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