Application of Markov Chain Model and ArcGIS in Land Use Projection of Ala River Catchment, Akure, Nigeria
Keywords:
Land cover, Land use change, Markov chain, ArcGIS, Watershed, UrbanizationAbstract
Increase land use change is one of the consequences of rapid population growth of cities in developing countries with its negative consequences on the environment. This study generates previous and present land use of Ala watershed and project the future land use using Markov chain model and ArcGIS software (version 10.2.1). Landsat 7, Enhanced Thematic mapper plus (ETM+) image and Landsat 8 operational land imager (OLI) with path 190 and row 2 used to generate land use (LU) and land cover (LC) images for the years 2000, 2010 and 2019. Six LU/LC classes were considered as follows: developed area (DA), open soil (OS), grass surface (GS), light forest (LF), wetland (WL) and hard rock (HR). Markov chain analysis was used in predicting LU/LC types in the watershed for the years 2029 and 2039. The veracity of the model was tested with Nash Sutcliffe Efficiency index (NSE) and Percent Bias methods. The model results show that the study area is growing rapidly particularly in the recent time. This urban expansion results in significant decrease of WL coverage areas and the significant increase of DA. This implies reduction in the available land for dry season farming and incessant flood occurrence.
References
Adewumi, J.R.; J. K Akomolafe; F.O Ajibade.; and B.B. Fabeku. (2016). Application of GIS and Remote Sensing Technique to Change Detection in Land use/Land cover Mapping of Igbokoda, Ondo State, Nigeria. Journal of Applied Science and Process Engineering, 3(1), 34 - 54
Aitkenhead.; M.J., and I. H. Aalders. (2009). Predicting land cover using GIS, Bayesian and evolutionary algorithm methods. Journal of Environmental Management, 90(1), 236-250.
Aitkenhead, M. J., and I. H. Aalders. (2011). Automating land cover mapping of Scotland using expert system and knowledge integration methods. Remote Sensing of Environment, 115(5), 1285-1295.
Bello, H. O.; O. I. Ojo.; and A. S. Gbadegesin. (2018). Land Use/land cover change analysis using Markov-Based model for Eleyele Reservoir. Journal of Applied Sciences and Environmental Management, 22(12), 1917-1924
Coskun, G. H.; U. Alganci; and G. Usata. (2008). Analysis of land use change and urbanization in the Kucukcekmece water basin (Instabul, Turkey) with temporal satellite data using remote sensing and GIS. Sensors 8, 7213-7223.
Dadhich, P. N.; S. Hanaoka. (2010). Remote sensing, GIS and Markov’s method for land use change detection and prediction of Jaipur district. Journal of Geomatics. 4, 9-15.
Dodiya, D.; S. Goswami.; D. Chauhan; M. Bhuva.; and R. Parekh. (2019). Land use & land cover change detection using GIS & remote sensing. Change, 6(04).
Eludoyin.; A. O., M. O. Akinbode.; and E. Okuku. (2007). Combating Flood Crisis with Geographical Information System: An Example from Akure Southwestern Nigeria. In International Symposium on New Direction in Urban Water Management UNESCO, Paris (pp. 12-14)
Fadairo, G. (2013). An Empirical Evidence to the Urban Storm Water Crisis and the Way out in Akure, Nigeria. European Int J Sci Technol, 2, 53-66.
Federal Office of Statistics. (1992). Federal Government Printer, Lagos, Nigeria. Federal Republic of Nigeria Official Gazette.
Guan, D.; W. Gao.; K. Watari.; and H. Fukahori. (2008). Land use change of Kitakyushu based on landscape ecology and Markov model. Journal of Geographical Sciences, 18(4), 455-468.
Hossen, S.; M. K. Hossain.; and M. F. Uddin. (2019). Land cover and land use change detection by using remote sensing and GIS in Himchari National Park (HNP), Cox’s Bazar. Bangladesh. Journal of Science, Technology and Environment Informatics, 7(02), 544-554.
Ibitoye, M. O.; A. A. Komolafe; A. A. S. Adegboyega.; A. O. Adebola.; and O. D. Oladeji. (2019). Analysis of vulnerable urban properties within river Ala floodplain in Akure, Southwestern Nigeria. Spatial Information Research, 1-15.
Islam, K.; M. F. Rahman.; and M. Jashimuddin. (2018). Modeling land use change using cellular automata and artificial neural network: the case of Chunati Wildlife Sanctuary, Bangladesh. Ecological Indicators, 88, 439-453Islam, M. S.; and R. Ahmed. (2011). Land use change prediction in Dhaka city using GIS aided Markov chain modeling. Journal of Life Earth Science, 6, 81-89.
Jianping, L.; Z. Bai.; and G. Feng. (2005). RS-and-GIS-supported forecast of grassland degradation in southwest Songnen plain by Markov model. Geo-spatial Information Science 8:104–06.
Krause, P.; F. Base.; and D. P. Boyle. (2005). Comparison of different efficiency criteria for hydrological model assessment. Advances in Geoscience, 5, 89-97.
Li Z.; X. Li.; Y. Wang.; A. Ma.; and J. Wang. (2004). Land-use Change Analysis in Yulin Prefecture, Northwestern China using Remote Sensing and GIS. Lanzhou: A paper published in Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Science, Lanzhou, 730000 China.
Lilles, T.M.; and R.W. Kiefer. (2003). Remote sensing and image interpretation (4th Edition). Wiley, New York: John Wiley & Sons Inc.
Logsdon, M. G.; J. E Bell.; V. F. Westerlund. (1996). Probability mapping of land use change: a GIS interface for visualizing transition probability. Comput Environ Urban Syst. 20, 389–398
Mukhopadhyay, A.; A. Mondal.; S. Mukherjee.; D. Khatua.; S. Ghosh.; D. Mitra.; and T. Ghosh. (2014). Forest cover change prediction using hybrid methodology of geoinformatics and Markov chain model: A case study on sub-Himalayan town Gangtok, India. Journal of Earth System Science, 123(6), 1349-1360.
Nash, J.E.; and J.E. Sutcliffe. (1970). River flow forecasting through conceptual models. Part 1: A discussion of principles. Journal of Hydrology 10, 282–290
Nath, B.; Z. Wang.; Y. Ge.; K. P. Islam.; R. Singh.; & Z. Niu. (2020). Land Use and Land Cover Change Modeling and Future Potential Landscape Risk Assessment Using Markov-CA Model and Analytical Hierarchy Process. ISPRS International Journal of Geo-Information, 9(2), 134
Ogunbodede, E. F.; and R. A. Sunmola. (2014). Flooding and traffic management in Akure (Nigeria) metropolitan environment. International Journal of Innovation and Scientific Research, 7(2), 121-130.
Olalekan, B. G.; and A. Fadesola. (2017). Review of Best Practice Approaches in Combatting Vulnerability to Flood Disaster Risk in Ala-River Flooding Akure, Ondo State, Nigeria. Journal of Environmental Studies, 3(1), 5.
Owoeye, J.O.; and O.A. Ibitoye. (2015). Analysis of Akure Urban Land Use Change Detection from Remote Imagery Perspective. Hindawi Publishing Corporation.
Oyinloye, M. A., O. I. Olamiju.; and B. S. Oyetayo. (2013). Combating flood crisis using GIS: Empirical evidences from ala river floodplain, Isikan Area, Akure, Ondo State, Nigeria. Communications in Information Science and Management Engineering, 3(9), 439.
Pôças, I.; M. Cunha.; and L. S. Pereira. (2011). Remote sensing-based indicators of changes in a mountain rural landscape of Northeast Portugal. Applied Geography, 31(3), 871-880.
Rimal, B.; S. Sloan.; H. Keshtkar.; R. Sharma.; S. Rijal.; and U. B. Shrestha. (2020). Patterns of Historical and Future Urban Expansion in Nepal. Remote Sensing, 12(4), 628.
Rimal, B.; L. Zhang.; H. Keshtkar.; N. Wang.; and Y. Lin. (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, 6(9), 288.
Roy, S.; K. Farzana.; M. Papia.; and M. Hasan (2015). Monitoring and prediction of land use/land cover change using the integration of Markov chain model and cellular automation in the Southeastern Tertiary Hilly Area of Bangladesh. International Journal, Science and Basic Applied Research, 24(4), 125-148.
Sathees Kumar.; Nisha Radhakrishnan.; and Samson Mathew. (2014). Land use change modelling using a Markov model and remote sensing. Geomatics, Natural Hazards and Risk, 5(2), 145-156.
Singh, B.; and C. G. Singh (2020). Analysis of Land Use/Land Cover Change Detection Using Remote Sensing and GIS of Fatehgarh Nau Abad Village, Bathinda, Punjab. In Geoecology of Landscape Dynamics Springer, Singapore.
Singh, S. K.; S. Mustak.; P. K. Srivastava.; S. Szabó.; and T. Islam. (2015). Predicting spatial and decadal LULC changes through cellular automata Markov chain models using earth observation datasets and geo-information. Environmental Processes, 2(1), 61-78.
Tendaupenyu, P.; C. H. D. Magadza.; and A. Murwira. (2017). Changes in landuse/landcover patterns and human population growth in the Lake Chivero catchment, Zimbabwe. Geocarto International, 32(7), 797-811.
Thakur, S.; I. Mondal; P. B. Ghosh.; P. Das.; and T. K. De. (2019). A review of the application of multispectral remote sensing in the study of mangrove ecosystems with special emphasis on image processing techniques. Spatial Information Research, 1-13.
Tiwari, M. K.; and A. Saxena. (2011). Change detection of land use/landcover pattern in an around Mandideep and Obedullaganj area, using remote sensing and GIS. International Journal of Technology and Engineering System, 2(3), 398-402.
Varga, O. G.; R. G. Pontius Jr.; S. K. Singh.; and S. Szabó. (2019). Intensity Analysis and the Figure of Merit’s components for assessment of a Cellular Automata–Markov simulation model. Ecological indicators, 101, 933-942
Viana, C. M.; and J. Rocha. (2018). Spatiotemporal analysis and scenario simulation of agricultural land use land cover using GIS and a Markov chain model. In Geospatial Technologies for All: Short Papers, Posters and Poster Abstracts of the 21th AGILE Conference on Geographic Information Science (pp. 12-15). Lund University: Lund, Sweden
Voogt, J.A.; and T. R. Oke. (2003). Thermal remote sensing of urban climates. Remote sensing Environ. 86, 370-384.
Weng, Q. (2002). Land use change analysis in the Zhujiang Delta of China using satellite remote sensing, GIS and stochastic modelling. Journal of environmental management, 64(3), 273-284.
Wu, H.; Z. Li.; K. C. Clarke.; W. Shi.; L. Fang.; A. Lin and J. Zhou (2019). Examining the sensitivity of spatial scale in cellular automata Markov chain simulation of land use change. International Journal of Geographical Information Science, 33(5), 1040-1061
Yi, Y.; Y. Zhao.; G. Ding.; G. Gao.; M. Shi.; and Y. Cao. (2016). Effects of urbanization on landscape patterns in a mountainous area: a case study in the Mentougou district, Beijing, China. Sustainability, 8(11), 1190
Zhang, R.; C. Tang; S. Ma; H. Yuan; L. Gao and W. Fan. (2011). Using Markov chains to analyze changes in wetland trends in arid Yinchuan Plain, China. Mathematical and Computer Modelling, 54(3-4), 924-930.
Additional Files
Published
Issue
Section
License
Copyright (c) 2021 Nigerian Journal of Technological Development
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
In accordance with the Copyright Act of 1976, which became effective January 1, 1978, the following statement signed by each author must accompany the manuscript submitted: "I, the undersigned author, transfer all copyright ownership of the manuscript referenced above to the Nigerian Journal of Technological Development, in the event the work is published. I warrant that the article is original, does not infringe upon any copyright or other proprietary right of any third party, is not under consideration by another journal, and has not been published previously. I have reviewed and approve the submitted version of the manuscript and agree to its publication in the Nigerian Journal of Technological Development." A copyright transfer form should be downloaded from the NJTD Website ( http://njtd.com.ng/index.php/njtd). Author(s) will be consulted, whenever possible, regarding republication of material. All authors must have access to the data presented and the authors and sponsor (if applicable) must agree to share original data with the editor if requested.
