Spatiotemporal Assessment of Vegetation Greenness and Climatic Variability in Angor District, Uzbekistan (2015–2025), Using Remote Sensing and GIS
Received: 2026-10-11
Published: 2025-11-30
Abstract
Monitoring vegetation greenness in arid agricultural regions is essential for assessing land surface changes and supporting environmental management. This study evaluated spatial changes in the Normalized Difference Vegetation Index (NDVI) in Angor district, southern Uzbekistan, between 2015 and 2025, together with interannual climatic variability over the same period. Landsat-8 Collection 2 Level-2 surface reflectance imagery was processed using Google Earth Engine, while GIS-based raster differencing was applied to identify changes in vegetation greenness. Precipitation and air temperature were characterized using CHIRPS and ERA5-Land datasets, respectively. NDVI values ranged from −0.571 to 0.739 in 2015 and from −0.348 to 0.707 in 2025. The reported five-class change assessment indicated that 63.14% of the classified pixels remained relatively stable, whereas 21.08% exhibited increasing NDVI values and 15.79% showed decreasing values. Climatic analysis revealed that annual precipitation ranged from 108.84 to 263.57 mm, with a mean of approximately 167.27 mm, while mean annual air temperature varied between 18.46 and 20.72 °C. The findings indicate spatially heterogeneous vegetation changes within the district, with a greater proportion of classified pixels showing increasing rather than decreasing NDVI values. However, these changes cannot be attributed directly to climatic variability because irrigation practices, agricultural management, and land-use dynamics may also influence vegetation conditions. The study provides baseline information for vegetation monitoring and environmental assessment in arid agricultural landscapes of southern Uzbekistan.
Keywords
List of references
-
Pettorelli, N., Vik, J. O., Mysterud, A., Gaillard, J. M., Tucker, C. J., & Stenseth, N. C. (2005). Using the satellite-derived NDVI to assess ecological responses to environmental change. Trends in Ecology & Evolution, 20(9), 503–510. https://doi.org/10.1016/j.tree.2005.05.011
-
Fensholt, R., & Proud, S. R. (2012). Evaluation of Earth Observation based global long term vegetation trends—Comparing GIMMS and MODIS global NDVI time series. Remote Sensing of Environment, 119, 131–147. https://doi.org/10.1016/j.rse.2011.12.015
-
Tucker, C. J. (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8(2), 127–150. https://doi.org/10.1016/0034-4257(79)90013-0
-
Funk, C., Peterson, P., Landsfeld, M., Pedreros, D., Verdin, J., Shukla, S., Husak, G., Rowland, J., Harrison, L., Hoell, A., & Michaelsen, J. (2015). The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes. Scientific Data, 2, 150066. https://doi.org/10.1038/sdata.2015.66
-
Muñoz-Sabater, J., Dutra, E., Agustí-Panareda, A., Albergel, C., Arduini, G., Balsamo, G., et al. (2021). ERA5-Land: A state-of-the-art global reanalysis dataset for land applications. Earth System Science Data, 13, 4349–4383. https://doi.org/10.5194/essd-13-4349-2021
-
Zhu, Z., & Woodcock, C. E. (2012). Object-based cloud and cloud shadow detection in Landsat imagery. Remote Sensing of Environment, 118, 83–94. https://doi.org/10.1016/j.rse.2011.10.028
-
U.S. Geological Survey. Landsat Collection 2 Surface Reflectance. USGS rasmiy metodikasi. Unda 0.0000275 koeffitsiyenti va −0.2 offset tasdiqlangan.
About the Authors
License
Copyright (c) 2025 Journal of Hydraulic and Environmental Engineering
This work is licensed under a Creative Commons Attribution 4.0 International License.