DETECTING URBAN EXPANSION AND ENVIRONMENTAL CHANGE THROUGH REMOTE SENSING AND GIS: CASE OF GULBARGA CITY
Keywords:
Geoinformatics, Land Use/Land Cover (LULC), Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST), Urbanization, Air PollutionAbstract
Geoinformatics based approach is used to detect the long-term spatio-temporal dynamics of land use/land cover (LULC), environmental indices, land surface temperature (LST), and air quality in Gulbarga city. For the assessment of integrated analysis, multi-temporal satellite-derived indices were utilized which was the primary objective. The various factors like, environmental impacts of urban expansion, including NDVI, NDBI, and NDWI, along with thermal and air pollution parameters were evaluated. The overall impact of change was detected by comparing multi-year remote sensing data for vegetation cover, built-up areas, and surface water bodies, which was further correlated with surface temperature and atmospheric conditions.
There was a substantial increase in built-up areas with a consistent decline in vegetation cover and surface water extent. There were prominent changes during the recent decade, and the transformations were strongly associated with rising land surface temperatures. The comparative analysis showcased higher thermal intensity in built-up regions when compared to vegetated and water-covered areas. This created an inverse relationship between vegetation and temperature which was clearly observed. Lastly, particulate matter and gaseous pollutants showed elevated levels in an annual air quality analysis, due to the influence of urban activities such as traffic, industrial emissions, and dust.
In conclusion, there has been noticeable environmental degradation due to increased thermal stress caused by the rapid urbanization which also affected the ecological balance, and air quality. The advancement in multi-temporal geospatial techniques helped in tracking in-depth tangible understanding about urban environmental changes with regards to urban growth. This study reflects the high demand for sustainable planning strategies for urban growth management.
References
I. Anderson J. R. et al. (1976). A Land Use and Land Cover Classification System for Use with Remote Sensor Data. USGS Professional Paper 964.
II. United States Geological Survey (USGS). Landsat Data Users Handbook.
III. Rouse J. W. et al. (1974). Monitoring vegetation systems in the Great Plains with ERTS. NASA SP-351.
IV. Tucker C. J. (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8(2), 127–150.
V. Weng Q. (2004). Estimation of land surface temperature–vegetation abundance relationship for urban heat island studies. Remote Sensing of Environment, 89(4), 467–483.
VI. Voogt J. A. & Oke T. R. (2003). Thermal remote sensing of urban climates. Remote Sensing of Environment, 86(3), 370–384.
VII. Seto K. C. et al. (2012). Global forecasts of urban expansion to 2030 and impacts on biodiversity. PNAS, 109(40), 16083–16088.
VIII. Dewan A. M. & Yamaguchi Y. (2009). Land use and land cover change in Greater Dhaka. Remote Sensing of Environment, 113(3), 625–638.
IX. Carlson T. N. et al. (1994). Satellite estimation of surface energy balance and soil moisture. Journal of Applied Meteorology, 33, 138–146.
X. Gillies R. R. et al. (1997). A method for estimating surface soil moisture using NDVI and LST. Remote Sensing of Environment, 59, 139–154.
XI. European Space Agency (2018). Sentinel-5P TROPOMI User Guide.
XII. Veefkind J. P. et al. (2012). TROPOMI on Sentinel-5 Precursor: A GMES mission for global atmospheric composition monitoring. Remote Sensing of Environment, 120, 70–83.
XIII. van Donkelaar A. et al. (2010). Global estimates of ambient fine particulate matter concentrations from satellite-based aerosol optical depth. Environmental Health Perspectives, 118(6), 847–855.
XIV. Gupta P. & Christopher S. A. (2009). Particulate matter air quality assessment using satellite data. Journal of Geophysical Research.
XV. Jensen J. R. (2005). Introductory Digital Image Processing. Prentice Hall.
XVI. Lillesand T. et al. (2015). Remote Sensing and Image Interpretation. Wiley.
XVII. Central Pollution Control Board (CPCB). National Air Quality Reports.
XVIII. Indian Space Research Organisation. Bhuvan & satellite data resources.
Additional Files
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 International Educational Journal of Science and Engineering
This work is licensed under a Creative Commons Attribution 4.0 International License.
