The Unsupervised Classification of Land Use Land Cover using Remote Sensing and GIS Techniques for Vadodara district of Gujarat
Article Sidebar
Main Article Content
Abstract
Land Use Land Cover (LULC) transformations caused by urbanization and other factors have significant environmental implications, making their accurate assessment essential for sustainable planning and management. This study examines the LULC dynamics of Vadodara district, Gujarat, using Remote Sensing (RS) and Geographic Information Systems (GIS) techniques. Landsat-8 imagery processed in ArcGIS was analyzed using an unsupervised classification method to identify four LULC categories: water bodies, crop cover, barren land, and built-up areas. Vadodara, as a rapidly expanding economic hub, has experienced notable shifts in land use patterns, including changes in agricultural areas, water bodies, urban land, and barren land. These transformations reflect the district's developmental pressures and their resultant environmental challenges. RS and GIS tools were employed to create thematic maps and analyze LULC changes over time, providing critical insights into spatio-temporal dynamics and facilitating informed decision-making. The results reveal that water bodies cover 5.25% (213 km²), crop cover 33.61% (1362 km²), barren land 35.43% (1032 km²), and built-up areas 25.46% (1446 km²) of the district's total area. The findings underscore significant LULC transitions over recent decades, driven by urbanization and economic activities. RS and GIS technologies proved indispensable in detecting past trends, analyzing current conditions, and forecasting future LULC scenarios. These tools not only enhance the accuracy and efficiency of LULC assessments but also play a crucial role in addressing environmental concerns and supporting sustainable land management practices.
Downloads
Article Details
Section
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
- Attribution: You must credit the original creator, provide a link to the license, and indicate if you made changes. You can do this in any reasonable way, but you can't suggest that the original creator endorses you or your use.
- NonCommercial: You can't use the material for commercial purposes.
- NoDerivatives: If you remix, transform, or build upon the material, you can't distribute the modified material.
- You must own or control the copyright to the work.
- You can't revoke a CC license.
- Anyone who receives the material can rely on the license as long as the material is protected by copyright.
- If you created a work as part of your job, you might not own the copyright.
How to Cite
References
1. Bansal, S. (2017). Understanding the Land Use/ Land Cover (LULC) Change Processes and Predicting Future Scenario in Yamuna Basin, India. Ph. D. (Forestry) Thesis (Unpublished). Forest Research Institute, Dehra Dun, Uttarakhand.
2. Chander, G., Markham, B. L. and Helder, D. L. (2009). Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+ and EO-1 ALI sensors. Remote sensing of environment, 113(5): 893-903.
3. Dodiya, D, Goswami, S., Chauhan, D., Bhuva, M. and Parekh, R. (2019). Land Use & Land Cover Change Detection Using GIS & Remote Sensing. International Research Journal of Engineering and Technology. 6(4): 3001-3005.
4. Herold, M., Latham, J. S., Di Gregorio, A. and Schmullius, C. C. (2006). Evolving standards in land cover characterization. Journal of Land Use Science, 1(2-4): 157-168.
5. Lo, C. P. and J. Choi. (2004). A Hybrid Approach to Urban Land Use/Cover Mapping Using Landsat 7 Enhanced Thematic Mapper Plus (ETM+) Images. International Journal of Remote Sensing, 25: 2687-2700.
6. Mallupattu, P. K. and Reddy, J. R. S. (2013). Analysis of Land Use/Land Cover Changes Using Remote Sensing Data and GIS at an Urban Area, Tirupati, India. Hindawi Publishing Corporation, The Scientific World Journal, Volume 2013, Article ID 268623, 6 pages.
7. Oyekola, M. A. and Adewuyi, G. K. (2018). Unsupervised Classification in Land Cover Types Using Remote Sensing and GIS Techniques. International Journal of Science and Engineering Investigations, 7(72): 11-18.
8. Terry, L. S. and Benjamin M. S. (2012). Role of Remote Sensing for Land-Use and Land-Cover Change Modelling. Remote Sensing and Land Cover: Principles and Applications, 225-239pp.
9. Zalavadiya, U. B., Kapupara, P. J. and Sabhadiya, H. R. (2024). Harvesting Tomorrow: A Closer Look at Information Technology in Agriculture. Farm Chronicle, 03(01): 20-25.