Geospatial Assessment of State Lands in the Cape Coast Urban Area
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 32807
Geospatial Assessment of State Lands in the Cape Coast Urban Area

Authors: E. B. Quarcoo, I. Yakubu, K. J. Appau

Abstract:

Current land use and land cover (LULC) dynamics in Ghana have revealed considerable changes in settlement spaces. As a result, this study is intended to merge the cellular automata and Markov chain models using remotely sensed data and Geographical Information System (GIS) approaches to monitor, map, and detect the spatio-temporal LULC change in state lands within Cape Coast Metropolis. Multi-temporal satellite images from 1986-2020 were pre-processed, geo-referenced, and then mapped using supervised maximum likelihood classification to investigate the state’s land cover history (1986-2020) with an overall mapping accuracy of approximately 85%. The study further observed the rate of change for the area to have favored the built-up area 9.8 (12.58 km2) to the detriment of vegetation 5.14 (12.68 km2), but on average, 0.37 km2 (91.43 acres, or 37.00 ha.) of the landscape was transformed yearly. Subsequently, the CA-Markov model was used to anticipate the potential LULC for the study area for 2030. According to the anticipated 2030 LULC map, the patterns of vegetation transitioning into built-up regions will continue over the following ten years as a result of urban growth.

Keywords: LULC, cellular automata, Markov Chain, state lands, urbanisation, public lands, cape coast metropolis.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 47

References:


[1] Uttara, S., Bhuvandas, N. and Aggarwal, V. (2012), “Impacts of Urbanization on Environment”, International Journal of Research in Engineering and Applied Sciences, Vol. (2), pp. 1637-1645.
[2] GSS (Ghana Statistical Survey), 2010 Population and Housing Census, 2012.
[3] Nsiah-Gyabaah K. (2010), “Urbanization Processes-Environmental and Health effects in Africa”, Panel Contribution to the PERN Cyber seminar on Urban Spatial Expansion. Principal, Sunyani Polytechnic, Ghana.
[4] Forkuor, D. (2010), “Land allocation and its effects on the spatial planning and development of Kumasi Metropolis”, Doctor of Philosophy (PhD) Thesis, Kwame Nkrumah University of Science and Technology, Faculty of Social Sciences, Kumasi.
[5] Goswami, M., Ravishankar, C., Nautiyal, S. and Schaldach, R. (2019), “Integrated Landscape Modelling in India: Evaluating the Scope for Micro-Level Spatial Analysis over Temporal Scale”, In Tropical Ecosystems: Structure, Functions and Challenges in the Face of Global Change, pp. 289–315. Springer, Singapore.
[6] Kleemann, J., Baysal, G., Bulley, H.N. and Fürst, C. (2017), “Assessing Driving Forces of Land Use and Land Cover Change by a Mixed-Method Approach in North-Eastern Ghana”, West Africa, Journal of Environmental Management, Vol. (196), pp. 411–442.
[7] Acheampong, M., Yu.Q., Enomah, L.D., Anchang, J. and Eduful, M. (2018), “Land Use/Cover Change in Ghana’s Oil City: Assessing the Impact of Neoliberal Economic Policies and Implications for Sustainable Development Goal Number One–A Remote Sensing and GIS Approach”, Land Use Policy, Vol. (73), pp. 373–384.
[8] Pervez, W., Uddin, V., Khan, S.A. and Khan, J.A. (2016), “Satellite-Based Land Use Mapping: Comparative Analysis of Landsat-8, Advanced Land Imager and Big Data Hyperion Imagery”, Journal of Applied Remote Sensing, Vol. (10).
[9] Singh, P., Kikon, N. and Verma, P. (2017), “Impact of Land Use Change and Urbanization on Urban Heat Island in Lucknow City, Central India, A Remote Sensing-Based Estimate”, Sustainable Cities and Society, Vol. (32), pp. 100–114.
[10] Panwar, S. And Malik, D.S. (2017), “Evaluating Land Use/ Land Cover Change Dynamics in Bhimtal Lake Catchment Area, Using Remote Sensing and GIS Techniques”, Journal of Remote Sensing And GIS, Vol. (6), No. (199), 2 pp.
[11] Liping, C., Yujun, S. and Saeed, S. (2018), “Monitoring and Predicting Land Use and Land Cover Changes Using Remote Sensing and GIS Techniques”, A Case Study of a Hilly Area, Jiangle, China, Plos ONE, Vol. (13), No. (7), pp. 200-493.
[12] Michetti, M. And Zampieri, M. (2014), “Climate–Human– Land Interactions: A Review of Major Modelling Approaches”, Land, Vol. (3) No. (3), pp. 793–833. DOI:10.3390/ Land3030793.
[13] Shamsi, S.R.F. (2010), “Integrating Linear Programming and Analytical Hierarchical Processing in Raster GIS to Optimize Land Use Pattern at Watershed Level”, Journal of Applied Sciences and Environmental Management, Vol. (14), No. (2), pp. 81–84.
[14] Hyandye, C. (2015), “A Markovian and Cellular Automata Land-Use Change Predictive Model of the Usangu Catchment”, International Journal of Remote Sensing, pp. 64–81.
[15] Koranteng, A., Adu-Poku, I., Donkor, E. and Zawiła-Niedźwiecki, T. (2020), “Geospatial Assessment of Land Use and Land Cover Dynamics in the Mid-Zone of Ghana”, Folia Forestalia Polonica, Vol. (62) No. (4), pp.288–305. doi:https://doi.org/10.2478/ffp-2020-0028.
[16] Ralha, C.G., Abreu, C.G., Coelho, C.G.C., Zaghetto, A., Macchiavello, B. and Machado, R.B. (2013), “A Multiagent Model System for Land-Use Change Simulation”, Remote Sensing of Environment, Vol. (42), pp. 30–46.
[17] Stefanov, W.L., Ramsey, M.S. and Christensen, P.R. (2001), “Monitoring Urban Land Cover Change: An Expert System Approach to Land Cover Classification of Semiarid to Arid Urban Centers”, Remote Sensing of Environment, Vol. (77), No. (2), pp. 173–185.
[18] Singh, S.K., Mustak, S., Srivastava, P.K., Szabó, S. and Islam, T. (2015), “Predicting Spatial and Decadal LULC Changes Through Cellular Automata Markov Chain Models Using Earth Observation Datasets and Geo-Information”, Environmental Processes, Vol. (2), No. (1), pp. 61–78.
[19] Subedi, P., Subedi, K. and Thapa, B. (2013), “Application of A Hybrid Cellular Automaton–Markov (CA-Markov) Model in Land-Use Change Prediction: A Case Study of Saddle Creek Drainage Basin, Florida”, Science and Education, Vol. (1), No. (6), pp. 126–132.
[20] Sohl, T.L. and Claggett, P.R. (2013), “Clarity Versus Complexity: Land-Use Modelling as A Practical Tool for Decision-Makers”, Journal of Environmental Management, Vol. (129), pp. 235–243.
[21] Zhao, L. and Peng, Z.-R. (2012), “Land System: An Agent-Based Cellular Automata Model of Land Use Change Developed for Transportation Analysis”, Journal of Transport Geography, Vol. (25), pp. 35–49.
[22] Stevens, D. and Dragićević, S. (2007), “A GIS-Based Irregular Cellular Automata Model of Land-Use Change”, Environment and Planning B: Urban Analytics and City Science, Vol. (34), No. (4), pp. 708–724.
[23] He, J., Li, X., Yao, Y., Hong, Y. and Jinbao, Z. (2018), “Mining Transition Rules of Cellular Automata for Simulating Urban Expansion by Using the Deep Learning Techniques”, International Journal of Geographical Information Science, Vol. (32), No. (10), pp. 2076–2097.
[24] Hyandye, C. and Martz, L.W. (2017), “A Markovian and Cellular Automata Land-Use Change Predictive Model of the Usangu Catchment”, International Journal of Remote Sensing, Vol. (38), No. (1), pp. 64–81.
[25] Memarian, H., Balasundram, S.K., Talib, J.B., Sung, C.T.B., Sood, A.M. and Abbaspour, K. (2012), “Validation Of CA-Markov for Simulation of Land Use and Cover Change in the Langat Basin, Malaysia”, Journal of Geographic Information System, Vol. (4) No. (6), pp. 542–554.
[26] Etemadi, H., Smoak, J.M. and Karami, J. (2018), “Land Use Change Assessment in Coastal Mangrove Forests of Iran Utilizing Satellite Imagery and CA–Markov Algorithms to Monitor and Predict Future Change”, Environmental Earth Sciences, Vol. (77) No. (5), 208pp.
[27] Rimal, B., Zhang, L., Keshtkar, H., Wang, N. and Lin, Y. (2017), “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, Vol. (6), No. (9), 288pp.
[28] Mansour, S., Al-Belushi, M. and Al-Awadhi, T. (2020), “Monitoring Land Use and Land Cover Changes in The Mountainous Cities of Oman Using GIS and Camarkov Modelling Techniques”, “Land Use Policy, 91”, DOI: 10.1016/J.Landusepol.2019.104414
[29] Parsa, V.A., Yavari, A. and Nejadi, A. (2016), “Spatio-Temporal Analysis of Land Use/Land Cover Pattern Changes in Arasbaran Biosphere Reserve: Iran”, Modeling Earth Systems and Environment, Vol. (2), No. (4), pp.1–13.
[30] Chatterjee, S., Ghosh, Dey, N. (2016), “Forest type classification: A Hybrid NN-GA model-based approach”, In Information Systems Design and Intelligent Applications, pp. 227–236.
[31] Lilles and., T. M. and R. W. (2000), “Remote Sensing and Image interpretation”, John Wiley and Sons, New York.
[32] Hamad, R. Balzter, H. and Kolo, K. (2018), “Predicting land use/land cover changes using a CA-Markov model under two different scenarios, Sustainability, Vol. (10), No. (10), pp. 1-23.
[33] Abdulrahman, A. I. and Ameen, S.A. (2020), “Predicting Land Use and Land Cover Spatiotemporal Changes Utilizing CA-Markov Model in Duhok District between 1999 and 2033”, Academic Journal of Nawroz University, vol. 9, no. 4, pp.71. doi: https://doi.org/10.25007/ajnu.v9n4a892.
[34] Omar, N. Q., Ahamad, M. S. S., Wan Hussin, W. M. A., Samat, N. and Binti Ahmad, S. Z. (2013), “Markov CA, Multi Regression, and Multiple Decision Making for Modelling Historical Changes in Kirkuk City, Iraq”, Journal of the Indian Society of Remote Sensing, Vol. (42), No. (1), 2013, pp.165–178.doi: https://doi.org/10.1007/s12524-013-0311-2.