Abstract
Since 2018’s presidential declaration of Dodoma to be one among cities in Tanzania, remarkable changes on land use/land cover (LULC) have been experienced over the area as results of population increase and related impacts of urban developments. To achieve the so-called sustainable development accurate information on the LULC changes becomes essential as it brings awareness of the past changes, current trends and expected future urban dynamics. This study took technological advantage of remote sensing and Geographical Information System to analyze LULC changes from 2005 to 2020 and uses it as a major input for the Cellular automata–Markov model to simulate future changes for the years 2025 and 2030. The past LULC changes was analyzed through maximum likelihood classification algorithm and the overall classification accuracy was above 85%, while the simulated model was validated by comparing actual and simulated maps with kappa indices. The results show that from 2005 to 2020, built-up area has increased from 86.17 to 617.02 km2, and water has increased from 5.47 to 10.14 km2. The tremendous decrease is in bare land by 511 km2 (from 1528.51 to 1017.09 km2) and vegetation by 24 km2 (from 1151.11 to 1127.01 km2). Also, the simulated model results show that in the next 10 years from 2020 to 2030, bare land will decrease by 26.97%, while built-up area will increase by 39.96%, also vegetation and water will have substantial increase of 27.57 km2 and 0.22 km2 respectively. Therefore, these observed changes can be used as a future planning benchmark by planning organizations to ensure that sustainable development is attained.
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References
Aarthi, A. D., & Gnanappazham, L. (2018). Urban growth prediction using neural network coupled agents-based cellular automata model for Sriperumbudur Taluk, Tamil Nadu, India. Egyptian Journal of Remote Sensing and Space Science, 21(3), 353–362. https://doi.org/10.1016/j.ejrs.2017.12.004
Abebe, G. A. (2013). Quantifying urban growth pattern in developing countries using remote sensing and spatial metrics, A case study in Kampala Uganda. https://webapps.itc.utwente.nl/librarywww/papers_2013/msc/upm/abebe.pdf
Aburas, M. M., Ho, Y. M., Ramli, M. F., & Ash’aari, Z. H. (2016). The simulation and prediction of spatio-temporal urban growth trends using cellular automata models: A review. International Journal of Applied Earth Observation and Geoinformation, 52, 380–389. https://doi.org/10.1016/j.jag.2016.07.007
Ahmed, B., & Ahmed, R. (2012). Modeling urban land cover growth dynamics using multioral satellite images: A case study of Dhaka, Bangladesh. ISPRS International Journal of Geo-Information, 1(1), 3–31. https://doi.org/10.3390/ijgi1010003
Al-Darwish, Y., Ayad, H., Taha, D., & Saadallah, D. (2018). Predicting the future urban growth and it’s impacts on the surrounding environment using urban simulation models: Case study of Ibb city – Yemen. Alexandria Engineering Journal, 57(4), 2887–2895. https://doi.org/10.1016/j.aej.2017.10.009
Aliani, H., Malmir, M., Sourodi, M., & Kafaky, S. B. (2019). Change detection and prediction of urban land use changes by CA–Markov model (case study: Talesh County). Environmental Earth Sciences, 78(17), 1–12. https://doi.org/10.1007/s12665-019-8557-9
Alsharif, A. A. A., & Pradhan, B. (2014). Urban sprawl analysis of Tripoli metropolitan city (Libya) using remote sensing data and multivariate logistic regression model. Journal of the Indian Society of Remote Sensing, 42(1), 149–163. https://doi.org/10.1007/s12524-013-0299-7
Al-sharif, A. A. A., & Pradhan, B. (2014). Monitoring and predicting land use change in Tripoli metropolitan city using an integrated Markov chain and cellular automata models in GIS. Arabian Journal of Geosciences, 7(10), 4291–4301. https://doi.org/10.1007/s12517-013-1119-7
Behera, M. D., Borate, S. N., Panda, S. N., Behera, P. R., & Roy, P. S. (2012). Modelling and analyzing the watershed dynamics using cellular automata (CA)–Markov model–geo-information based approach. Journal of Earth System Science, 121(4), 1011–1024. https://doi.org/10.1007/s12040-012-0207-5
Bhanjee, S., & Zhang, C. H. (2018). Mapping latest patterns of urban sprawl in Dar es Salaam, Tanzania. Papers in Applied Geography, 4(3), 292–304. https://doi.org/10.1080/23754931.2018.1471413
Bhatta, B. (2011). Analysis of urban growth and sprawl from remote sensing data. In Urban Remote Sensing: Monitoring, Synthesis and Modeling in the Urban Environment. https://doi.org/10.1002/9780470979563
Chotchaiwong, W. (2019). Predicting urban expansion and urban land use changes in Nakhon Ratchasima city using a CA–Markov model under two different scenarios. Land, 8(9), 140. https://doi.org/10.3390/land8090140
Das, S., & Angadi, D. P. (2021). Land use land cover change detection and monitoring of urban growth using remote sensing and GIS techniques: A micro-level study. GeoJournal. https://doi.org/10.1007/s10708-020-10359-1
Dhali, M. K., Chakraborty, M., & Sahana, M. (2019). Assessing spatio-temporal growth of urban sub-centre using Shannon’s entropy model and principle component analysis: A case from North 24 Parganas, lower Ganga River Basin, India. Egyptian Journal of Remote Sensing and Space Science, 22(1), 25–35. https://doi.org/10.1016/j.ejrs.2018.02.002
Eastman, J. R. (2001). Idrisi 32 Release 2 - Guide to GIS and Image Processing. 1(May), 161.
Firman, T. (1997). Land conversion and urban development in the Northern Region of West Java, Indonesia. Urban Studies, 34(7), 1027–1046. https://doi.org/10.1080/0042098975718
Fleiss, J. L., Levin, B., & Paik, M. C. (2003). Wiley: Statistical methods for rates and proportions (3rd ed.). John Wiley & Sons.
Foody, G. (2003). Book review: Classification methods for remotely sensed data. Progress in Physical Geography: Earth and Environment, 27(3), 468–469. https://doi.org/10.1177/030913330302700318
Foody, G. (2010). Assessing the accuracy of remotely sensed data: Principles and practices: Book reviews. The Photogrammetric Record, 25(130), 204–205. https://doi.org/10.1111/j.1477-9730.2010.00574_2.x
Gwaleba, M. J. (2018). Urban growth in Tanzania: Exploring challenges, opportunities and management. International Journal of Social Science Studies, 6(12), 47. https://doi.org/10.11114/ijsss.v6i12.3783
Hackbarth, T. X., & de Vries, W. T. (2021). An Evaluation of massive land interventions for the relocation of capital cities. Urban Science, 5(1), 25. https://doi.org/10.3390/urbansci5010025
Hamad, R., Balzter, H., & Kolo, K. (2018). Predicting land use/land cover changes using a CA–Markov model under two different scenarios. Sustainability, 10(10), 3421. https://doi.org/10.3390/su10103421
Hishe, S., Bewket, W., Nyssen, J., & Lyimo, J. (2020). Analysing past land use land cover change and CA–Markov-based future modelling in the Middle Suluh Valley, Northern Ethiopia. Geocarto International, 35(3), 225–255. https://doi.org/10.1080/10106049.2018.1516241
Hyandye, C., & Martz, L. W. (2017). A Markovian and cellular automata land-use change predictive model of the Usangu catchment. International Journal of Remote Sensing, 38(1), 64–81. https://doi.org/10.1080/01431161.2016.1259675
Kabanda, T. (2019). Land use/cover changes and prediction of Dodoma, Tanzania. African Journal of Science, Technology, Innovation and Development, 11(1), 55–60. https://doi.org/10.1080/20421338.2018.1550925
Kang, J., Fang, L., Li, S., & Wang, X. (2019). Parallel cellular automata markov model for land use change prediction over MapReduce framework. ISPRS International Journal of Geo-Information, 8(10), 454. https://doi.org/10.3390/ijgi8100454
Keshtkar, H., & Voigt, W. (2016b). A spatiotemporal analysis of landscape change using an integrated Markov chain and cellular automata models. Modeling Earth Systems and Environment, 2(1), 1–13. https://doi.org/10.1007/s40808-015-0068-4
Keshtkar, H., & Voigt, W. (2016a). A spatiotemporal analysis of landscape change using an integrated Markov chain and cellular automata models. Modeling Earth Systems and Environment, 2(1), 1–13. https://doi.org/10.1007/s40808-015-0068-4
Kogo, B. K., Kumar, L., & Koech, R. (2019). Analysis of spatio-temporal dynamics of land use and cover changes in Western Kenya. Geocarto International, 6049, 1–16. https://doi.org/10.1080/10106049.2019.1608594
Kukkonen, M. O., Muhammad, M. J., Käyhkö, N., & Luoto, M. (2018). Urban expansion in Zanzibar City, Tanzania: Analyzing quantity, spatial patterns and effects of alternative planning approaches. Land Use Policy, 71(August 2016), 554–565. https://doi.org/10.1016/j.landusepol.2017.11.007
Kumari, M. (2019). Change detection analysis using multi temporal satellite data of Poba reserve forest, Assam and Arunachal Pradesh. Indianjournals.Com, 4(3), 517–525. https://www.researchgate.net/publication/270280879
Lal, K., Kumar, D., & Kumar, A. (2017). Spatio-temporal landscape modeling of urban growth patterns in Dhanbad Urban Agglomeration, India using geoinformatics techniques. Egyptian Journal of Remote Sensing and Space Science, 20(1), 91–102. https://doi.org/10.1016/j.ejrs.2017.01.003
Lu, D., Mausel, P., Brondízio, E., & Moran, E. (2004). Change detection techniques. International Journal of Remote Sensing, 25(12), 2365–2401. https://doi.org/10.1080/0143116031000139863
Mahmoud, H., & Divigalpitiya, P. (2019). Spatiotemporal variation analysis of urban land expansion in the establishment of new communities in Upper Egypt: A case study of New Asyut city. Egyptian Journal of Remote Sensing and Space Science, 22(1), 59–66. https://doi.org/10.1016/j.ejrs.2018.03.006
Mahmoud, M. I., Duker, A., Conrad, C., Thiel, M., & Ahmad, H. S. (2016). Analysis of settlement expansion and urban growth modelling using geoinformation for assessing potential impacts of urbanization on climate in Abuja City, Nigeria. Remote Sensing, 8(3), 220. https://doi.org/10.3390/rs8030220
Mcgranahan, G. (2015). Urbanization. International Encyclopedia of the Social & Behavioral Sciences, 24(1), 958–964. https://doi.org/10.1016/B978-0-08-097086-8.72120-9
Mkalawa, C. C., & Haixiao, P. (2014). Dar es Salaam city temporal growth and its influence on transportation. Urban, Planning and Transport Research, 2(1), 423–446. https://doi.org/10.1080/21650020.2014.978951
Mohamed, A., & Worku, H. (2020). Simulating urban land use and cover dynamics using cellular automata and Markov chain approach in Addis Ababa and the surrounding. Urban Climate, 31, 100545. https://doi.org/10.1016/j.uclim.2019.100545
Msabi, M. M., & Makonyo, M. (2020). Flood susceptibility mapping using GIS and multi-criteria decision analysis: A case of dodoma region, central Tanzania. Remote Sensing Applications: Society and Environment, 100445. https://doi.org/10.1016/j.rsase.2020.100445
Msuya, I., Moshi, I., & Levira, F. (2020). Dodoma : Building a sustainable city to meet neighbourhood needs. March.
Munthali, M. G., Mustak, S., Adeola, A., Botai, J., Singh, S. K., & Davis, N. (2020). Modelling land use and land cover dynamics of Dedza district of Malawi using hybrid cellular automata and Markov model. Remote Sensing Applications: Society and Environment, 17(October 2019), 100276. https://doi.org/10.1016/j.rsase.2019.100276
Dodoma Regional Commissioner Office. (2020). Dodoma Region Profile. http://www.dodoma.go.tz/profile.
Ozturk, D. (2015). Urban growth simulation of Atakum (Samsun, Turkey) using cellular automata-Markov chain and multi-layer perceptron-Markov chain models. Remote Sensing, 7(5), 5918–5950. https://doi.org/10.3390/rs70505918
Pontius, R. G. (2000). Quantification error versus location error in comparison of categorical maps. Photogrammetric Engineering and Remote Sensing, 66(8), 1011–1016.
Quan, B., Bai, Y., Römkens, M. J. M., Chang, K., & tsung, Song, H., Guo, T., & Lei, S. (2015). Urban land expansion in Quanzhou City, China, 1995–2010. Habitat International, 48, 131–139. https://doi.org/10.1016/j.habitatint.2015.03.021
Rawat, J. S., Biswas, V., & Kumar, M. (2013). Changes in land use/cover using geospatial techniques: A case study of Ramnagar town area, district Nainital, Uttarakhand, India. Egyptian Journal of Remote Sensing and Space Science, 16(1), 111–117. https://doi.org/10.1016/j.ejrs.2013.04.002
Republic, T. U., Bureau, N., Ministry, S., & June, F. (2013). Tanzania in Figures 2012. https://www.nbs.go.tz/index.php/en/tanzania-in-figures
Rimal, B., Zhang, L., Keshtkar, H., Wang, N., & Lin, Y. (2017a). Geo-information monitoring and modeling of spatiotemporal urban expansion and land-use/land-cover change using integrated Markov chain cellular automata model. International Journal of Geo-Information. https://doi.org/10.3390/ijgi6090288
Rimal, B., Zhang, L., Keshtkar, H., Wang, N., & Lin, Y. (2017b). Monitoring and modeling of spatiotemporal urban expansion and land-use/land-cover change using integrated Markov chain cellular automata model. ISPRS International Journal of Geo-Information, 6(9), 288. https://doi.org/10.3390/ijgi6090288
Saaty, R. W. (1987). The analytic hierarchy process-what it is and how it is used. Mathematical Modelling, 9(3–5), 161–176. https://doi.org/10.1016/0270-0255(87)90473-8
Sahana, M., Hong, H., & Sajjad, H. (2018). Analyzing urban spatial patterns and trend of urban growth using urban sprawl matrix: A study on Kolkata urban agglomeration, India. Science of the Total Environment, 628–629, 1557–1566. https://doi.org/10.1016/j.scitotenv.2018.02.170
Sang, L., Zhang, C., Yang, J., Zhu, D., & Yun, W. (2011). Simulation of land use spatial pattern of towns and villages based on CA–Markov model. Mathematical and Computer Modelling, 54(3–4), 938–943. https://doi.org/10.1016/j.mcm.2010.11.019
Shaw, R., & Das, A. (2018). Identifying peri-urban growth in small and medium towns using GIS and remote sensing technique: A case study of English Bazar urban agglomeration, West Bengal, India. Egyptian Journal of Remote Sensing and Space Science, 21(2), 159–172. https://doi.org/10.1016/j.ejrs.2017.01.002
Siddiqui, A., Siddiqui, A., Maithani, S., Jha, A. K., Kumar, P., & Srivastav, S. K. (2018). Urban growth dynamics of an Indian metropolitan using CA Markov and logistic regression. Egyptian Journal of Remote Sensing and Space Science, 21(3), 229–236. https://doi.org/10.1016/j.ejrs.2017.11.006
Sumari, N. S., Xu, G., Ujoh, F., Korah, P. I., Ebohon, O. J., & Lyimo, N. N. (2019). A geospatial approach to sustainable urban planning: Lessons for Morogoro Municipal Council, Tanzania. Sustainability (switzerland), 11(22), 6508. https://doi.org/10.3390/su11226508
Tan, K. C., Lim, H. S., MatJafri, M. Z., & Abdullah, K. (2010). Landsat data to evaluate urban expansion and determine land use/land cover changes in Penang Island, Malaysia. Environmental Earth Sciences, 60(7), 1509–1521. https://doi.org/10.1007/s12665-009-0286-z
Tripathy, P., & Bandopadhyay, A. (2018). Urban growth modeling using logistic regression and geo-informatics : A case of Jaipur, India. Society for Environment and Development, (india), 13(1), 47–62.
Twisa, S., & Buchroithner, M. F. (2019). Land-use and land-cover (LULC) change detection in Wami river basin, Tanzania. Land, 8(9), 136. https://doi.org/10.3390/land8090136
Wang, S. Q., Zheng, X. Q., & Zang, X. B. (2012). Accuracy assessments of land use change simulation based on Markov-cellular automata model. Procedia Environmental Sciences, 13(2011), 1238–1245. https://doi.org/10.1016/j.proenv.2012.01.117
Weeks, J. R. (2010). Defining urban areas. In Remote Sensing and Digital Image Processing (Vol. 10). https://doi.org/10.1007/978-1-4020-4385-7_3
Weng, Q., & Quattrochi, D. A. (2018). Urban Remote Sensing. In Urban Remote Sensing (2nd ed.)https://doi.org/10.1201/9781138586642
Weng, Q., & Quattrochi, D. A. (2006). Urban remote sensing. Urban Remote Sensing. https://doi.org/10.1201/b15917
Xiao, J., Shen, Y., Ge, J., Tateishi, R., Tang, C., Liang, Y., & Huang, Z. (2006). Evaluating urban expansion and land use change in Shijiazhuang, China, by using GIS and remote sensing. Landscape and Urban Planning, 75(1–2), 69–80. https://doi.org/10.1016/j.landurbplan.2004.12.005
Yin, G., Mariethoz, G., & McCabe, M. F. (2017). Gap-filling of landsat 7 imagery using the direct sampling method. Remote Sensing, 9(1), 1–20. https://doi.org/10.3390/rs9010012
Yoon, D. (2011). The rationalization of space and time: Dodoma and socialist modernity. Ufahamu: A Journal of African Studies, 36(2).https://doi.org/10.5070/f7362009582
You, H. (2016). Quantifying megacity growth in response to economic transition: A case of Shanghai, China. Habitat International, 53, 115–122. https://doi.org/10.1016/j.habitatint.2015.11.001
Youssef, A. M., Pradhan, B., & Tarabees, E. (2011). Integrated evaluation of urban development suitability based on remote sensing and GIS techniques: Contribution from the analytic hierarchy process. Arabian Journal of Geosciences, 4(3–4), 463–473. https://doi.org/10.1007/s12517-009-0118-1
Zhang, D., Liu, X., Lin, Z., Zhang, X., & Zhang, H. (2020). The delineation of urban growth boundaries in complex ecological environment areas by using cellular automata and a dual-environmental evaluation. Journal of Cleaner Production, 256, 120361. https://doi.org/10.1016/j.jclepro.2020.120361
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We would like to thank Mr. Michael Makonyo for his support in editing and reviewing the paper before submission for publication.
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The study conception and design, material preparation, data collection, data analysis and draft of the manuscript were performed by FCK. Supervision, editing and guiding on the research procedures were done by FL. Both authors read, commented on previous versions of the manuscript and approved the final draft of manuscript.
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Kisamba, F.C., Li, F. Analysis and modelling urban growth of Dodoma urban district in Tanzania using an integrated CA–Markov model. GeoJournal 88, 511–532 (2023). https://doi.org/10.1007/s10708-022-10617-4
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DOI: https://doi.org/10.1007/s10708-022-10617-4