IJARP SJIF(2018): 4.908

International Journal of Advanced Research and Publications!

Application Of Knowledge-Based Image Classification And Ca - Markov Chain Prediction Model For Landuse / Landcover Change Analysis Of Onitsha And Environs, Anambra State.

Volume 4 - Issue 5, May 2020 Edition
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Ejikeme, J.O; Igbokwe, J.I; Igbokwe, E.C, Paul C.
Landuse/Landcover, Knowledge-based Classification, Cellular Automata, Change Detection.
Knowledge-based classification have proved to be effective for complex object recognition and for image analysis Therefore, this study was aimed on carried out land use/ land cover change detection and prediction using knowledge-based classification and Cellular Automata (CA)_MARKOV model of Onitsha. Landsat images of 2008, 2013 and 2018 covering the study area was acquired and used to carryout LULC classification and change detection. The change detection was carried out using the matrix union overlay in ERDAS 2014. Result shows that build-up area increased from 39.4% to 43% and from 43% to 45.9% between year 2008 to 2013 and 2013 to 2018 respectively. The water body increased in year 2013 from 6.5% to 7.2%, then decreased in 2018 to 6.8%. While the vegetation keeps decreasing all through the year from 42% in year 2008 to 36.1% in year 2018. CA- MARKOV model was then used to predict the landuse/land cover changes in the study area to year 2025. The result shows that built-up area will increase from 45.9% to 48% and also, decreases in vegetation, open space, sand dunes and water body. The study Recommend that Knowledge based classification should be used as it gives a better understanding and classification for land use / land cover types.
[1]. Ahmed, B. & Raquib, A. (2012). Modeling urban landcover growth dynamics using multi-temporal satellite image. A case study of Dhaka Bangladash. ISPRS International journal of Geo-information. ISSN 2220-9964
[2]. Alphan, H. (2003). Landuse hanged and urban urbanization in Adana, land degradation and development 14 (@) 575 – 2721.

[3]. Araya, Y.H (2009). Urban landuse change analysis and modeling: A case study of Setubal and sesimbra. Portugal, unpublished M.Sc Thesis. Institution for geo-informatics. University of Munster.

[4]. Ayodeji, O. Z. (2006). Change detection in landuse and landcover using remote sensing date and GIS: a case study of llorin and it’s environs in Kwara state. Unpublished M.A. thesis, Department of Geography, university of Ibadan 44.

[5]. Batty, M., Xie, Y., & Sun, Z. (2004). Modeling the relationship between landuse and landcover on private lands in the upper Mid – west, WA. Journal of environmental management 59 (2) 247 -263.

[6]. Chatterjee, S. & Price, B. (1991). Regression analysis by examples (2nd. Ed.). New York: John Wiley & Son.

[7]. Chen X, Ma, J. qiao, H, Cheng D, Xu, Y & Zhao, Y (2007). Detecting infestation of take all disease using landsat thematic mapper imagery. International journal of remote sensing, 28 (22), 5183 -5189.

[8]. Chibuike, C., Nnaji, O & Njoku, R. (2016). Spatio temporal analysis of landuse and landcovr change in Owerri municipal and it’s environs, Journal of soil science and Environmental management, 5(2) 33-43

[9]. Dutta, R. (2006) Assessment of tea bush health and yield using Geospatial techniques. Unpublished M.Sc. thesis ITC Enschede, The Netherlands.

[10]. Etok, A., Essien, M.A. & Udosen, C. (2006). Mapping of landcover and landuse changes in the cross river basin in Nigeria using geographic information system approach. Journal of Applied and Theoretical Environmental Sciences, 2(1) 38 -49.

[11]. Faroug, M.N. (2014). Summary of steps in running and interpreting statistical analysis using statiscal package for social sciences (1st ED). Trust issues concept, Kaduna.
[12]. Gajbhiye, S. & Sharma, S.K. (2012). Landuse and landcover detection of Intra river watershed through remote sensing using multi – temporal satellite sate. International Journal of Geometrics and Geosciences, 3 (1), 89 – (96)

[13]. Hadi, B.A. (2005). Mapping of landuse and landcover in developing a catchment management of Perlis.

[14]. Hakan, A. (2005). Perceptions of Coastline change in river deltas: southwest Mediterranean coast of Turkey. International journal of Environment and Pollution 23 (1), 92 -102.

[15]. Halder, J.C. (2013). Landcover and landuse change Detection Mapping in Binpur II Block, Pashcim Medinipur District, West Bengal: A Remote Sensing and GIS Perspective. International journal of Remote sensing 19 (2), 133 -130.

[16]. Hathout, S. (2002). The use of GIS for monitoring and predicting urban growth in East and West St. Paul. Winnipeg, Canada. Journal of Environmental Management 66 (1), 229 – 238.

[17]. Herold, M., Goldstein, N.C., & Clarke, K.C. (2003). The spatiotemporal form of urban growth, measurement, analysis and modeling. Remote sensing of Environment 86 (1), 286 – 302.

[18]. Hang, N.M, Lin, Y.B. & Wang, Y.T. (2000). Monitoring and predicting landuse changes of urbanized paochiao watershed in Taiwan using remote sensing data. Unpublished M.Sc. thesis, University of Taiwan

[19]. Igbokwe, J.I. (1996). Mapping from satellite Remote Sensing. EL’DEMAK Publishers, Enugu.

[20]. Igbokwe, J.I (2005): modeling landcover and landuse patterns of Onitsha and environs using NigeriaSat-1 image Data. Proceedings of the technical session of 40th Annual General Meeting and Conference of Nigerian Institution of Surveyors, Kano, 79-85.

[21]. Igbokwe, J.I & Ezeomedo I. (2013). Mapping and Analysis of landuse and landcover for a sustainable development using high Resolution satellite images and GIS. FIG working week, Abuja. Nigeria.

[22]. Jadab, C. (2013). Landuse and landcover changes detection in Paschim: A remote sensing and GIS perspective. International Journal of Remote sensing 19 (2), 100-112

[23]. Jenson, J.R. (2004). Introductory Digital Image Processing – A Remote sensing Perspective. 3rd Edition 526P; Prentice Hall

[24]. Lei, S., Zhu, J., & Lin, M. (2006). Landscape pattern change prediction of Jinhu coastal area using logistic and cellular automata model. Advances in information sciences and service sciences 4 (11), 200-215.
[25]. Li, X. (2007). Nautral – network based cellular automata for stimulating multiple landuse changes using GIS. Journal of Environmental Management 85 (4), 1063 – 1075

[26]. Marvin, B., (2000). Mapping landcover change with satellite remote sensing in the Twin cities Metropolitan Area in Minnesota.

[27]. Mirbagheri, B. (2006). Urban landuse change simulation using remote sensing and cellular automata model. Unpublished M.Sc. thesis university of Beheshti. Iran.

[28]. Nashat, A. (2002). Assessment of landuse and landcover change analysis using remote sensing data and GIS in Golestan Province. Unpublised M.A Thesis. University of Tarbiat. Modarres.

[29]. Ndkwe, K.N. (1997). Principle of Environmental Remote Sensing and photo Interpretation. New concept publishers, Enugu, Nigeria.

[30]. Njoku, J.D., Ebe. T.E & Edith P. (2010). Detection and mapping of landuse and landcover classes of a Developing city in South eastern Region of Nigeria using Multi-band Digital Remotely – Sensed Data. Journal of environmental science 2(2), 100-150.

[31]. Ojigi, L.M. (2006). Analysis of spatial variations of Abuja landuse and landcover from image classification Algorithms. ISPRS Commission VII Mid-term symposium, Enschede, The Netherlands.

[32]. Olatunde, F.O. (2013). Application of Remote Sensing and GIS in landuse/landcover mapping and Analysis in Auchi, Edo State, Nigeria. Unpublished M.Sc. thesis Nnamdi Azikiwe University, Awka, Nigeria.

[33]. Orisakwe, U. (2008). Geographic information system predictive model for Owerri Urban landuse development. Unpublished PhD Dissertation Nnamdi Azikiwe University, Awka, Nigeria.

[34]. Paul, J.G. (2002). Introductory Remote Sensing: Principles and |Concepts. Macmillan Publication.

[35]. Rimal, B. (2011). Application of Remote sensing and Geographic Information systems in landuse and landcover change in Kathmandu Metropolitan City, Neoal. Journal of Theoretical and Applied Information Technology, Nepal.

[36]. Santosh, H. (2003). Modelling landcover change of Himachal Pradesh, India. Journal of Humanities and social science 8 (1), 5 20-31.

[37]. Sateesh, K. (2011). Landcover and landuse Mapping using Digital classification techniques in Tikamgarh District, Madhya Pradesh. India using Remote sensing. International journal of geomatics and geosciences 2 (2), 40-50.

[38]. Verburg, P.H., Dijst, M.J. & Schot, P. (2004). Determinants of landuse change patterns in the Netherlands. Environment & Planning 31 125 – 150

[39]. Verburg P.H., Schot, P., Dijst, M., & veldkamp, A. (2004): landuse change modeling. Current practice and research priorities. Geo-journal, 61 (4), 309 -324.

[40]. Watson, R.T &Zakri, A.H. (2003): millennium Ecosystem Assessment – Ecosystem & human Wellbeing, A Framework for Assessment. 245., The United Nations Environment Programme and World Resources Institute.

[41]. Wu, F. (2002). Calibration of Stochastic Cellular Automata: The application to rural urban land conversions. International journal of Geographical Information Science, 16 (8), 795 – 818.

[42]. Wu, J. (2008). Landuse changes: Economic, Social and Environmental impacts. Journal of Agricultural & Applied Economics 23 (4).

[43]. Zhou, H., Jiang, H., Zhou, G., Song, X., Yu, S., Chang, J., Liu, S., Jiang, Z & Jiang, B. (2010). Monitoring the change of urban wetland using high spatial resolution remote sensing data. International Journal of Remote Sensing 31 (7), 1717 - 1731