Studying urbanization pattern in Sambalpur City during 1992-2042 using CA-ANN, and Markov-Chain model

Year 2024, Volume: 9 Issue: 3, 356 - 367, 31.10.2024

Avijit Bag , Arabinda Sharma , Sudhakar Pal

https://doi.org/10.26833/ijeg.1452005

Abstract

Models of land use/land cover (LULC) are crucial for assessing changes in LULC, forecasting land use needs, and providing guidance for appropriate land use planning and management, especially in urban areas. Urban sprawl is one of the main causes of the erratic variation in LULC around the globe. In this study, we used the CA-ANN and Markov-Chain models to analyze the LULC simulation based on LULC patterns from previous decades as well as the directional changes of urban expansion in Sambalpur city. We used the random forest (RF) model and Landsat imagery to prepare LULC maps for the years 1992, 2002, 2012, and 2022 for better classification accuracy. The result showed that the overall accuracy and kappa values were 94.24%, 89%, 94%, and 90% and 0.92, 0.80, 0.90, and 0.85, respectively, for the selective years. Based on the transition matrix model (1992–2012), the LULC map of the year 2022 was obtained and validated, and subsequently, LULC maps for the years 2032 and 2042 were predicted with a Kappa value of 94.97% and 93.24%, respectively. The findings indicate that the largest proportion of bare land underwent conversion within the settlement area, with the highest degree of sprawl observed in the northwestern direction and 4-kilometer buffer zones. These findings define current and future patterns in LULC and offer vital information for planning and sustainable land use management in Sambalpur city.

Keywords

Land use/land cover, CA-ANN, Markov- Chain, Urban Sprawl, Random Forest Model

Thanks

The 1st author is thankful to the University Grants Commission, New Delhi for providing with fellowship for carrying out the research work.

References

• 1. Zhang, X. Q. (2016). The trends, promises and challenges of urbanisation in the world. Habitat International, 54(13), 241–252. https://doi.org/10.1016/j.habitatint.2015.11.018
• 2. United Nations, (2018). 68% of the world population projected to live in urban areas by 2050. Retrived June 17, 2023, from https://www.un.org/development/desa/en/news/population/2018-revision-of-world-urbanization-prospects.html
• 3. Sridhar, K. S. (2021). Urbanization and COVID-19 Prevalence in India. Regional Science Association International, 2(July 2020), 493–505. https://doi.org/10.1111/rsp3.12503
• 4. Mishra, V. N., & Rai, P. K. (2016). A remote sensing aided multi-layer perceptron-Markov chain analysis for land use and land cover change prediction in Patna district (Bihar), India. Arabian Journal of Geosciences, 9(4). https://doi.org/10.1007/s12517-015-2138-3
• 5. Uddin, M. S., Mahalder, B., & Mahalder, D. (2023). Assessment of Land Use Land Cover Changes and Future Predictions Using CA-ANN Simulation for Gazipur City Corporation, Bangladesh. Sustainability (Switzerland), 15(16). https://doi.org/10.3390/su151612329
• 6. Baig, M. F., Mustafa, M. R. U., Baig, I., Takaijudin, H. B., & Zeshan, M. T. (2022). Assessment of Land Use Land Cover Changes and Future. Water, 1–17. Retrieved from https://doi.org/ 10.3390/w14030402
• 7. Saputra, M. H., & Lee, H. S. (2019). Prediction of land use and land cover changes for North Sumatra, Indonesia, using an artificial-neural-network-based cellular automaton. Sustainability (Switzerland), 11(11), 1–16. https://doi.org/10.3390/su11113024
• 8. Khwarahm, N. R., Najmaddin, P. M., Ararat, K., & Qader, S. (2021). Past and future prediction of land cover land use change based on earth observation data by the CA–Markov model: a case study from Duhok governorate, Iraq. Arabian Journal of Geosciences, 14(15), 1–14. https://doi.org/10.1007/s12517-021-07984-6
• 9. Jat, M. K., Garg, P. K., & Khare, D. (2008). Monitoring and modelling of urban sprawl using remote sensing and GIS techniques. International Journal of Applied Earth Observation and Geoinformation, 10(1), 26–43. https://doi.org/10.1016/j.jag.2007.04.002
• 10. Forkuor, G., & Cofie, O. (2011). Dynamics of land-use and land-cover change in Freetown, Sierra Leone and its effects on urban and peri-urban agriculture - a remote sensing approach. International Journal of Remote Sensing, 32(4), 1017–1037. https://doi.org/10.1080/01431160903505302
• 11. Araya, Y. H., & Cabral, P. (2010). Analysis and modeling of urban land cover change in Setúbal and Sesimbra, Portugal. Remote Sensing, 2(6), 1549–1563. https://doi.org/10.3390/rs2061549
• 12. Mundia, C., & Murayama, Y. (2010). Modeling spatial processes of urban growth in african cities: A case study of nairobi city. Urban Geography, 31(2), 259–272. https://doi.org/10.2747/0272-3638.31.2.259
• 13. Haregeweyn, N., Fikadu, G., Tsunekawa, A., Tsubo, M., & Meshesha, D. T. (2012). The dynamics of urban expansion and its impacts on land use/land cover change and small-scale farmers living near the urban fringe: A case study of Bahir Dar, Ethiopia. Landscape and Urban Planning, 106(2), 149–157. https://doi.org/10.1016/j.landurbplan.2012.02.016
• 14. Fenta, A. A., Yasuda, H., Haregeweyn, N., Belay, A. S., Hadush, Z., Gebremedhin, M. A., & Mekonnen, G. (2017). The dynamics of urban expansion and land use/land cover changes using remote sensing and spatial metrics: The case of Mekelle city of northern Ethiopia. International Journal of Remote Sensing, 38(14), 4107–4129. https://doi.org/10.1080/01431161.2017.1317936
• 15. Natarajan, K., Latva-Käyrä, P., Zyadin, A., & Pelkonen, P. (2016). New methodological approach for biomass resource assessment in India using GIS application and land use/land cover (LULC) maps. Renewable and Sustainable Energy Reviews, 63, 256–268. https://doi.org/10.1016/j.rser.2016.05.070
• 16. Gibril, M. B. A., Bakar, S. A., Yao, K., Idrees, M. O., & Pradhan, B. (2017). Fusion of RADARSAT-2 and multispectral optical remote sensing data for LULC extraction in a tropical agricultural area. Geocarto International, 32(7), 735–748. https://doi.org/10.1080/10106049.2016.1170893
• 17. Li, H., Wang, C., Zhong, C., Zhang, Z., & Liu, Q. (2017). Mapping typical urban LULC from Landsat imagery without training samples or self-defined parameters. Remote Sensing, 9(7), 1–23. https://doi.org/10.3390/rs9070700
• 18. Prasad, N., & Das, T. (2019). Impact of land use and land cover on aquatic macrophyte community composition in small streams: A case study from cachar district of assam in northeast India. Indian Journal of Ecology, 46(2), 363–370.
• 19. Whyte, A., Ferentinos, K. P., & Petropoulos, G. P. (2018). A new synergistic approach for monitoring wetlands using Sentinels -1 and 2 data with object-based machine learning algorithms. Environmental Modelling and Software, 104, 40–54. https://doi.org/10.1016/j.envsoft.2018.01.023
• 20. Tolentino, F. M., & de Lourdes Bueno Trindade Galo, M. (2021). Selecting features for LULC simultaneous classification of ambiguous classes by artificial neural network. Remote Sensing Applications: Society and Environment, 24(May), 100616. https://doi.org/10.1016/j.rsase.2021.100616
• 21. Thenkabail, P. S., Schull, M., & Turral, H. (2005). Ganges and Indus River basin land use/land cover (LULC) and irrigated area mapping using continuous streams of MODIS data. Remote Sensing of Environment, 95(3), 317–341. https://doi.org/10.1016/j.rse.2004.12.018
• 22. Sobhani, P., Esmaeilzadeh, H., Barghjelveh, S., Sadeghi, S. M. M., & Marcu, M. V. (2022). Habitat integrity in protected areas threatened by lulc changes and fragmentation: A case study in Tehran Province, Iran. Land, 11(1). https://doi.org/10.3390/land11010006
• 23. Martín-Arias, V., Evans, C., Griffin, R., Cherrington, E. A., Lee, C. M., Mishra, D. R., … Rosado, S. (2022). Modeled Impacts of LULC and Climate Change Predictions on the Hydrologic Regime in Belize. Frontiers in Environmental Science, 10(April), 1–16. https://doi.org/10.3389/fenvs.2022.848085
• 24. Kidane, M., Bezie, A., Kesete, N., & Tolessa, T. (2019). The impact of land use and land cover (LULC) dynamics on soil erosion and sediment yield in Ethiopia. Heliyon, 5(12), e02981. https://doi.org/10.1016/j.heliyon.2019.e02981
• 25. kullo, E. D., Forkuo, E. K., Biney, E., Harris, E., & Quaye-Ballard, J. A. (2021). The impact of land use and land cover changes on socioeconomic factors and livelihood in the Atwima Nwabiagya district of the Ashanti region, Ghana. Environmental Challenges, 5(May), 100226. https://doi.org/10.1016/j.envc.2021.100226
• 26. Verburg, P. H., Schot, P. P., Dijst, M. J., Veldkamp, A., Verbürg, P. H., Schot, P. P., … Veldkamp, A. (2016). Land use change modelling: current practice and research priorities Linked references are available on JSTOR for this article: Land use change modelling: current practice and research priorities. Source: GeoJournal GeoJoumal, 61(4), 309–324. Retrieved from http://www.jstor.org/stable/41147951%0Ahttp://www.jstor.org/stable/41147951?seq=1&cid=pdf-reference#references_tab_contents%0Ahttp://about.jstor.org/terms
• 27. Alshari, E. A., & Gawali, B. W. (2022). Modeling Land Use Change in Sana’a City of Yemen with MOLUSCE. Journal of Sensors, 2022. https://doi.org/10.1155/2022/7419031
• 28. Abbas, Z., Yang, G., Zhong, Y., & Zhao, Y. (2021). Spatiotemporal change analysis and future scenario of lulc using the CA-ANN approach: A case study of the greater bay area, China. Land, 10(6). https://doi.org/10.3390/land10060584
• 29. Abd El-Kawy, O. R., Rød, J. K., Ismail, H. A., & Suliman, A. S. (2011). Land use and land cover change detection in the western Nile delta of Egypt using remote sensing data. Applied Geography, 31(2), 483–494. https://doi.org/10.1016/j.apgeog.2010.10.012
• 30. Fallati, L., Savini, A., Sterlacchini, S., & Galli, P. (2017). Land use and land cover (LULC) of the Republic of the Maldives: first national map and LULC change analysis using remote-sensing data. Environmental Monitoring and Assessment, 189(8). https://doi.org/10.1007/s10661-017-6120-2
• 31. Pirotti, F., Sunar, F., & Piragnolo, M. (2016). Benchmark of machine learning methods for classification of a Sentinel-2 image. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, 41(July), 335–340. https://doi.org/10.5194/isprsarchives-XLI-B7-335-2016
• 32. Nguyen, H. T. T., Doan, T. M., Tomppo, E., & McRoberts, R. E. (2020). Land Use / Land Cover Mapping Using Multitemporal Sentinel-2 Imagery and Four Classification. Remote Sens, 12, 1–27. Retrieved from www.mdpi.com/journal/remotesensing
• 33. Haldar, S., Mandal, S., Bhattacharya, S., & Paul, S. (2023). Dynamicity of land use/land cover (LULC): An analysis from peri-urban and rural neighbourhoods of Durgapur Municipal Corporation (DMC) in India. Regional Sustainability, 4(2), 150–172. https://doi.org/10.1016/j.regsus.2023.05.001
• 34. Magidi, J., & Ahmed, F. (2019). Assessing urban sprawl using remote sensing and landscape metrics: A case study of City of Tshwane, South Africa (1984–2015). Egyptian Journal of Remote Sensing and Space Science, 22(3), 335–346. https://doi.org/10.1016/j.ejrs.2018.07.003
• 35. Mahdavifard, M., Ahangar, S. K., Feizizadeh, B., Kamran, K. V., & Karimzadeh, S. (2023). Spatio-Temporal monitoring of Qeshm mangrove forests through machine learning classification of SAR and optical images on Google Earth Engine. International Journal of Engineering and Geosciences, 8(3), 239–250. https://doi.org/10.26833/ijeg.1118542
• 36. Han, H., Yang, C., & Song, J. (2015). Scenario simulation and the prediction of land use and land cover change in Beijing, China. Sustainability (Switzerland), 7(4), 4260–4279. https://doi.org/10.3390/su7044260
• 37. Liu, X., Wei, M., & Zeng, J. (2020). Simulating urban growth scenarios based on ecological security pattern: A case study in quanzhou, china. International Journal of Environmental Research and Public Health, 17(19), 1–19. https://doi.org/10.3390/ijerph17197282
• 38. Theobald, D. M. (2005). Landscape patterns of exurban growth in the USA from 1980 to 2020. Ecology and Society, 10(1). https://doi.org/10.5751/ES-01390-100132
• 39. Hakim, A. M. Y., Baja, S., Rampisela, D. A., & Arif, S. (2019). Spatial dynamic prediction of landuse / landcover change (case study: Tamalanrea sub-district, makassar city). IOP Conference Series: Earth and Environmental Science, 280(1). https://doi.org/10.1088/1755-1315/280/1/012023
• 40. Çubukçu, E. A., Demir, V., & Sevimli, M. F. (2023). Modeling of annual maximum flows with geographic data components and artificial neural networks. International Journal of Engineering and Geosciences, 8(2), 200–211. https://doi.org/10.26833/ijeg.1125412
• 41. Muhammad, R., Zhang, W., Abbas, Z., Guo, F., & Gwiazdzinski, L. (2022). Spatiotemporal Change Analysis and Prediction of Future Land Use and Land Cover Changes Using QGIS MOLUSCE Plugin and Remote Sensing Big Data: A Case Study of Linyi, China. Land, 11(3). https://doi.org/10.3390/land11030419
• 42. Alshari, E. A., & Gawali, B. W. (2021). Development of classification system for LULC using remote sensing and GIS. Global Transitions Proceedings, 2(1), 8–17. https://doi.org/10.1016/j.gltp.2021.01.002
• 43. Mandal, S., Kundu, S., Haldar, S., Bhattacharya, S., & Paul, S. (2020). Monitoring and Measuring the Urban Forms Using Spatial Metrics of Howrah City, India. Remote Sensing of Land, 4(1–2), 19–39. https://doi.org/10.21523/gcj1.20040103
• 44. Verma, R., & Garg, P. K. (2021). Spatio-temporal land use change analysis in directional zones of Lucknow City, India. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, 44(M–3), 181–186. https://doi.org/10.5194/isprs-archives-XLIV-M-3-2021-181-2021
• 45. Hamad, R., Balzter, H., & Kolo, K. (2018). Predicting land use/land cover changes using a CA-Markov model under two different scenarios. Sustainability (Switzerland), 10(10), 1–23. https://doi.org/10.3390/su10103421
• 46. Pal, S., Sharma, A., & Parida, R. (2023). Status of Green and Open Space in Changing Urban Landscape - A Case Study of Sambalpur City. In A. Sharma & P. N. Sinha (Eds.), Geographical Approaches fior Sustainable Society and Environment (1st ed., pp. 236–251). Kunal Books, New Delhi.
• 47. Liu, C., Li, W., Zhu, G., Zhou, H., Yan, H., & Xue, P. (2020). Land use/land cover changes and their driving factors in the northeastern Tibetan plateau based on geographical detectors and Google Earth engine: A case study in Gannan prefecture. Remote Sensing, 12(19). https://doi.org/10.3390/RS12193139
• 48. Ritu, S. M., Sarkar, S. K., & Zonaed, H. (2023). Prediction of Padma River bank shifting and its consequences on LULC changes. Ecological Indicators, 156(October), 111104. https://doi.org/10.1016/j.ecolind.2023.111104
• 49. Khan, A., & Sudheer, M. (2022). Machine learning-based monitoring and modeling for spatio-temporal urban growth of Islamabad. Egyptian Journal of Remote Sensing and Space Science, 25(2), 541–550. https://doi.org/10.1016/j.ejrs.2022.03.012
• 50. Butt, A., Shabbir, R., Ahmad, S. S., & Aziz, N. (2015). Land use change mapping and analysis using Remote Sensing and GIS: A case study of Simly watershed, Islamabad, Pakistan. Egyptian Journal of Remote Sensing and Space Science, 18(2), 251–259. https://doi.org/10.1016/j.ejrs.2015.07.003
• 51. Mosammam, H. M., Nia, J. T., Khani, H., Teymouri, A., & Kazemi, M. (2017). Monitoring land use change and measuring urban sprawl based on its spatial forms: The case of Qom city. Egyptian Journal of Remote Sensing and Space Science, 20(1), 103–116. https://doi.org/10.1016/j.ejrs.2016.08.002
• 52. Mostafa, E., Li, X., Sadek, M., & Dossou, J. F. (2021). Monitoring and forecasting of urban expansion using machine learning-based techniques and remotely sensed data: A case study of Gharbia governorate, Egypt. Remote Sensing, 13(22). https://doi.org/10.3390/rs13224498
• 53. Sidi Almouctar, M. A., Wu, Y., Kumar, A., Zhao, F., Mambu, K. J., & Sadek, M. (2021). Spatiotemporal analysis of vegetation cover changes around surface water based on NDVI: a case study in Korama basin, Southern Zinder, Niger. Applied Water Science, 11(1), 1–14. https://doi.org/10.1007/s13201-020-01332-x
• 54. Kamusoko, C., & Aniya, M. (2008). Hybrid classification of Landsat data and GIS for land use/cover change analysis of the Bindura district, Zimbabwe. International Journal of Remote Sensing, 30(1), 97–115. https://doi.org/10.1080/01431160802244268
• 55. Gwet, K. (2016). EuroHeartCare 2016. European Journal of Cardiovascular Nursing, 15(1_suppl), S1–S115. https://doi.org/10.1177/1474515116634263
• 56. Santhosh, L. G., & Shilpa, D. N. (2023). Assessment of LULC change dynamics and its relationship with LST and spectral indices in a rural area of Bengaluru district, Karnataka India. Remote Sensing Applications: Society and Environment, 29(November 2022), 100886. https://doi.org/10.1016/j.rsase.2022.100886
• 57. El-Tantawi, A. M., Bao, A., Chang, C., & Liu, Y. (2019). Monitoring and predicting land use/cover changes in the Aksu-Tarim River Basin, Xinjiang-China (1990–2030). Environmental Monitoring and Assessment, 191(8), 1–18. https://doi.org/10.1007/s10661-019-7478-0
• 58. Mubako, S., Nnko, H. J., Peter, K. H., & Msongaleli, B. (2022). Evaluating historical and predicted long-term land use/land-cover change in Dodoma Urban District, Tanzania: 1992–2029. Physics and Chemistry of the Earth, 128(April), 103205. https://doi.org/10.1016/j.pce.2022.103205
• 59. Mehriar, M., Masoumi, H., & Mohino, I. (2020). Urban sprawl, socioeconomic features, and travel patterns in Middle East countries: A case study in Iran. Sustainability (Switzerland), 12(22), 1–20. https://doi.org/10.3390/su12229620
• 60. Haldar, S., Mandal, S., Bhattacharya, S., & Paul, S. (2023). Regional Sustainability Dynamicity of Land Use / Land Cover (LULC) An analysis from peri-urban and rural neighbourhoods of Durgapur Municipal Corporation (DMC) in India. Regional Sustainability, 4(2), 150–172. https://doi.org/10.1016/j.regsus.2023.05.001
• 61. Guha, S., & Govil, H. (2022). Seasonal impact on the relationship between land surface temperature and normalized difference vegetation index in an urban landscape. Geocarto International, 37(8), 2252–2272. https://doi.org/10.1080/10106049.2020.1815867
• 62. Wu, R., Li, Z., & Wang, S. (2021). The varying driving forces of urban land expansion in China: Insights from a spatial-temporal analysis. Science of the Total Environment, 766, 142591. https://doi.org/10.1016/j.scitotenv.2020.142591
• 63. Ahmad, S., Paulami, Y., Kansara, B. B., & Kalubarme, M. H. (2020). Monitoring land use changes and its future prospects using cellular automata simulation and artificial neural network for Ahmedabad city, India. GeoJournal, 5. https://doi.org/10.1007/s10708-020-10274-5