Land Use Change Forecasting in Kakinada Using Open-Source Remote Sensing and GIS Tools
DOI:
https://doi.org/10.64882/ijrt.v13.i4.438Keywords:
Land Use Land Cover (LULC), Remote Sensing, GIS, Urban ExpansionAbstract
Understanding land use and land cover (LULC) dynamics is essential for effective environmental management and urban planning. This study investigates LULC changes in Kakinada over multiple time periods using QGIS and ArcGIS. Satellite imagery underwent preprocessing, including cloud masking, image enhancement, and georeferencing, to ensure spatial accuracy. QGIS’s raster-based tools enabled classification into five LULC categories—built-up areas, agricultural land, forest cover, water bodies, and barren land—while ArcGIS supported advanced spatial analysis and visualization. Results indicate a significant increase in built-up areas from 12.5% in 2014 to a projected 36.4% by 2040, a ~191% relative increase, driven by population growth and urbanization. Conversely, agricultural land decreased from 48.6% to 34.8% (~28.4% reduction), and forest cover declined from 28.2% to 25.6%. Future LULC changes were simulated using the MOLUSCE plugin in QGIS, employing an Artificial Neural Network (ANN) with Multilayer Perceptron (MLP) architecture and Cellular Automata (CA), achieving a prediction accuracy above 86% and a Kappa coefficient of 0.942. These findings underscore the rapid urban expansion in Kakinada and the urgent need for sustainable land management strategies to balance development with environmental conservation.
References
Anderson, J. R., Hardy, E. E., Roach, J. T., & Witmer, R. E. (1976). A land use and land cover classification system for use with remote sensor data. USGS Professional Paper 964. https://doi.org/10.3133/pp964
BBBike. (n.d.). Road network data extract. Retrieved from https://extract.bbbike.org/
Campbell, J. B., & Wynne, R. H. (2011). Introduction to remote sensing (5th ed.). Guilford Press.
Chen, J., Chen, J., Liao, A., Cao, X., Chen, L., Chen, X., ... & Mills, J. (2015). Global land cover mapping at 30m resolution: A POK-based operational approach. ISPRS Journal of Photogrammetry and Remote Sensing, 103, 7–27. https://doi.org/10.1016/j.isprsjprs.2014.09.002
Congalton, R. G., & Green, K. (2009). Assessing the accuracy of remotely sensed data: Principles and practices (2nd ed.). CRC Press.
DIVA-GIS. (n.d.). Spatial data for study area boundaries. Retrieved from https://diva-gis.org/data.html
Eastman, J. R., Van Fossen, M., & Solorzano, L. A. (2005). Transition potential modeling for land-cover change. In GIS, Spatial Analysis, and Modeling (pp. 357–385). ESRI Press.
Foley, J. A., DeFries, R., Asner, G. P., Barford, C., Bonan, G., Carpenter, S. R., ... & Snyder, P. K. (2005). Global consequences of land use. Science, 309(5734), 570–574. https://doi.org/10.1126/science.1111772
Government of Andhra Pradesh. (2020). Kakinada Smart City Plan. Retrieved from https://smartcities.gov.in
Hansen, M. C., Potapov, P. V., Moore, R., Hancher, M., Turubanova, S. A., Tyukavina, A., ... & Townshend, J. R. G. (2013). High-resolution global maps of 21st-century forest cover change. Science, 342(6160), 850–853. https://doi.org/10.1126/science.1244693
Huang, C., Goward, S. N., Masek, J. G., Thomas, N., Zhu, Z., & Vogelmann, J. E. (2010). An automated approach for reconstructing recent forest disturbance history using Landsat. Remote Sensing of Environment, 114(8), 1741–1753. https://doi.org/10.1016/j.rse.2010.03.008
HydroSHEDS. (n.d.). HydroRivers dataset. Retrieved from https://www.hydrosheds.org/products/hydrorivers
Jensen, J. R. (2005). Introductory digital image processing: A remote sensing perspective (3rd ed.). Prentice Hall.
Kumar, P., Sajjad, H., Joshi, P. K., & Rehman, S. (2018). Urban growth and environmental issues in India. In Urban development challenges, risks and resilience in Asian mega cities (pp. 123–141). Springer. https://doi.org/10.1007/978-981-10-6043-4_7
Lambin, E. F., & Meyfroidt, P. (2011). Global land use change, economic globalization, and the looming land scarcity. Proceedings of the National Academy of Sciences, 108(9), 3465–3472. https://doi.org/10.1073/pnas.1100480108
Lehner, B., & Döll, P. (2004). Development and validation of a global database of rivers and lakes: HydroSHEDS. Journal of Hydrology, 296(1–4), 1–22. https://doi.org/10.1016/j.jhydrol.2004.03.028
Lu, D., & Weng, Q. (2007). A survey of image classification methods and techniques for improving classification performance. International Journal of Remote Sensing, 28(5), 823–870. https://doi.org/10.1080/01431160600746456
Millennium Ecosystem Assessment. (2005). Ecosystems and human well-being: Synthesis. Island Press.
Mishra, V. N., Rai, P. K., & Mohan, K. (2014). Land use/land cover mapping of Chittar basin, Tamil Nadu, India using remote sensing and GIS. Journal of the Indian Society of Remote Sensing, 42(4), 759–771. https://doi.org/10.1007/s12524-013-0357-7
Rawat, J. S., & Kumar, M. (2015). Monitoring land use/cover change using remote sensing and GIS techniques: A case study of Hawalbagh block, district Almora, Uttarakhand, India. The Egyptian Journal of Remote Sensing and Space Science, 18(1), 77–84. https://doi.org/10.1016/j.ejrs.2015.02.002
Seto, K. C., Güneralp, B., & Hutyra, L. R. (2012). Global forecasts of urban expansion to 2030 and direct impacts on biodiversity and carbon pools. Proceedings of the National Academy of Sciences, 109(40), 16083–16088. https://doi.org/10.1073/pnas.1211658109
Trisurat, Y., Eawpanich, P., & Kalliola, R. (2021). Land use and land cover prediction with QGIS-based machine learning algorithms: A case study of Tha Chin River Basin, Thailand. Environmental Modelling & Software, 136, 104940. https://doi.org/10.1016/j.envsoft.2020.104940
Turner, B. L., Lambin, E. F., & Reenberg, A. (2007). The emergence of land change science for global environmental change and sustainability. Proceedings of the National Academy of Sciences, 104(52), 20666–20671. https://doi.org/10.1073/pnas.0704119104
USGS EarthExplorer. (n.d.). Landsat 8 and 9 data. Retrieved from https://earthexplorer.usgs.gov/
Wulder, M. A., Loveland, T. R., Roy, D. P., Crawford, C. J., Masek, J. G., Woodcock, C. E., ... & Zhu, Z. (2018). Current status of Landsat program, science, and applications. Remote Sensing of Environment, 225, 127–147. https://doi.org/10.1016/j.rse.2019.02.015
Zhaxylyk, N., Koshim, A., & Baimaganbetov, S. (2020). Analysis and prediction of land use/land cover changes in Korgalzhyn District, Kazakhstan. Journal of Environmental Management, 270, 110885. https://doi.org/10.1016/j.jenvman.2020.110885
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