[01] Addo Koranteng, Institute of Research Innovation and Development (IRID), Kumasi Polytechnic, Kumasi, Ghana. [02] Tomasz Zawila-Niedzwiecki, Faculty of Forestry, Warsaw University of Life Sciences, Warsaw, Poland. [03] Isaac Adu-Poku, Rudan Engineering Limited, Accra, Ghana.

Increasing population and other anthropogenic activities have profound effect on large areas of forested land and other land use/cover forms throughout the world. There is a certain cause and effect relationship between changing practice for development and land use change, thus necessitating an assessment of land use dynamics and the projection trend. A combination of geospatial and remote techniques were utilized to evaluate the present and future landuse/ landcover scenario of southern part of the Western Region of Ghana. Multi-temporal satellite imageries of the Landsat series and DMC were used to map the changes in land use from 1990 to 2010. Four major land use classes (Forest, Agriculture, Built-up and water) were considered as the most dynamic land cover/use (LULC) practice. Markov modelling was applied for prediction of probable land use/ land cover change scenario for the years 2020, 2030 and 2040. The study showed that in years 2020 to 2040 in the predictable future, there will be a gradual increase in built up areas, while a stability in agricultural land use is envisaged. Agricultural land use would still remain the dominant land use type. Forests would be drastically reduced from close to 87% in 1990 to just fewer than 20% in 2040. This precarious situation would demand that prudent land use decisions to be made to keep Ghana’s REDD+ program on track and to mitigate the effects of the climate change phenomenon.

Land Use Land Cover, Cellular-Automata-Markov, Land Use/Cover Modelling, Remote Sensing

[01] Abay, T. (2014). Factors Affecting Forest User’s Participation in Participatory Forest Management; Evidence from Alamata Community Forest, Tigray; Ethiopia. [02] Avelar, S., & Tokarczyk, P. (2014). Analysis of land use and land cover change in a coastal area of Rio de Janeiro using high-resolution remotely sensed data. Journal of Applied Remote Sensing, 8 (1), 083631. doi: 10.1117/1.JRS.8.083631. [03] Cobbinah, P. B., Erdiaw-Kwasie, M. O., & Amoateng, P. (2015). Africa’s urbanisation: Implications for sustainable development. Cities, 47, 62–72. doi: 10.1016/j.cities.2015.03.013. [04] Congalton, R. G. (1991). A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment, 37 (1), 35–46. doi: 10.1016/0034-4257(91)90048-B. [05] Desta, S. B. (2014). Deforestation and a Strategy for Rehabilitation in Beles Sub Basin, Ethiopia. Journal of Economics and Sustainable Development. [06] Eastman J. R. (2006) Eastman: Guide to GIS and Image Process. Clark Labs, Clark University. [07] Farwig, N., Lung, T., Schaab, G., & Böhning-Gaese, K. (2014). Linking Land-Use Scenarios, Remote Sensing and Monitoring to Project Impact of Management Decisions. Biotropica, 46 (3), 357–366. doi: 10.1111/btp.12105. [08] Feddema, J. J.; Oleson, K. W.; Bonan, G. B.; Mearns, L. O.; Buja, L. E.; Meehl, G. A.; Washington, W. M. (2005). Atmospheric science: The importance of land-cover change in simulating future climates. Science, 310, 1674–1678. [09] Foley, J. A.; Defries, R.; Asner, G. P.; Barford, C.; Bonan, G.; Carpenter, S. R.; Chapin, F. S.; Coe, M. T.; Daily, G. C. & Gibbs, H. K.; (2005). Global consequences of land use. Science, 309, 570–574. [10] Foody, G. M. (2002). Status of land cover classification accuracy assessment. Remote Sensing of Environment, 80 (1), 185–201. doi: 10.1016/S0034-4257(01)00295-4. [11] Giri, C., Zhu, Z., & Reed, B. (2005). Comparative analyses of the Global land Cover 2000 and MODIS land cover data sets, Remote Sensing of Environment, 94, pp 123-132. [12] Guan D; Li H; Inohaec T; Su W; Nagaiec T; Hokao K. (2011). Modeling urban land use change by the integration of cellular automaton and Markov model, Ecological Modelling 222 (2011) 3761– 3772, doi: 10.1016/j.ecolmodel.2011.09.009. [13] Hathi, P., Haque, S., Pant, L., Coffey, D., & Spears, D. (2014). Place and Child Health: The Interaction of Population Density and Sanitation in Developing Countries. [14] Kalema, V. N., Witkowski, E. T. F., Erasmus, B. F. N., & Mwavu, E. N. (2014). The Impacts Of Changes In Land Use On Woodlands In An Equatorial African Savanna. Land Degradation & Development, n/a–n/a. doi: 10.1002/ldr.2279. [15] Kissinger, G., & Herold, V. D. S. (2014). Drivers of Deforestation and Forest Degradation: A Synthesis Report for REDD+ Policymakers. [16] Koranteng, A., & Zawila-Niedzwiecki, T. (2015). Modelling forest loss and other land use change dynamics in Ashanti Region of Ghana. Folia Forestalia Polonica, 57 (2), 96–111. doi: 10.1515/ffp-2015-0010. [17] Lambin, E. F., Geist, H. J., & Lepers, E. (2003). Dynamics Of Land -Use And Land -Cover Change In Tropical Regions. Annual Review of Environment and Resources, 28 (1), 205–241. doi: 10.1146/annurev.energy.28.050302.105459. [18] Lillesand, T., Kiefer, R. W., & Chipman, J. (2014). Remote Sensing and Image Interpretation. John Wiley & Sons.. [19] Medrilzam, M., Dargusch, P., Herbohn, J., & Smith, C. (2013). The socio-ecological drivers of forest degradation in part of the tropical peatlands of Central Kalimantan, Indonesia. Forestry, 87 (2), 335–345. doi: 10.1093/forestry/cpt033. [20] Miller, J. H. (1998). Active nonlinear tests (ANTs) of complex simulation models. Management Science 44 (6): 820-830. [21] Michetti, M., & Zampieri, M. (2014). Climate–Human–Land Interactions: A Review of Major Modelling Approaches. Land, 3 (3), 793–833. doi: 10.3390/land3030793. [22] Mishra, V. N., Rai, P. K., & Mohan, K. (2014). Prediction of land use changes based on land change modeler (LCM) using remote sensing: A case study of Muzaffarpur (Bihar), India. Journal of the Geographical Institute Jovan Cvijic, SASA, 64 (1), 111–127. [23] Moghadam H S; Helbich M. (2013). Spatiotemporal urbanization processes in the megacity of Mumbai, India: A Markov chains-cellular automata urban growth model, Applied Geography 40 (2013) 140e149, [24] Monserud, R. A. and Leamans, R., (1992) Comparing global vegetation maps with the kappa statistic. Ecological Modelling. Vol. 62, pp. 275-293. [25] Monson, R. K. (Ed.). (2013). Ecology and the Environment. New York, NY: Springer New York. doi: 10.1007/978-1-4614-7612-2. [26] Mousivand, A. J., Alimohammadi Sarab, A., Shayan, S., (2007). A new approach of predicting land use and land cover changes by satellite imagery and Markov chain model (Case study: Tehran). MSc Thesis. Tarbiat Modares University, Tehran, Iran. [27] Nigatu Wondrade, Dick, Ø. B., & Tveite, H. (2014). GIS based mapping of land cover changes utilizing multi-temporal remotely sensed image data in Lake Hawassa Watershed, Ethiopia. Environmental Monitoring and Assessment, 186 (3), 1765–80. doi: 10.1007/s10661-013-3491-x. [28] Obeng-Odoom, F. (2014). Sustainable Urban Development in Africa? The Case of Urban Transport in Sekondi-Takoradi, Ghana. American Behavioral Scientist, 59 (3), 424–437. doi: 10.1177/0002764214550305. [29] Overmars, K. P., & Verburg, P. H. (2006). Multilevel modelling of land use from field to village level in the Philippines. Agricultural Systems, 89 (2-3), 435–456. doi: 10.1016/j.agsy.2005.10.006. [30] Potter, C., Genovese, V., Gross, P., Boriah, S., Steinbach, M., & Kumar, V. (2007). Revealing land cover change in California with satellite data. EOS, Transactions, American Geophysical Union, 88 (26), 269. [31] Razavi, B. S. (2014). Predicting the Trend of Land Use Changes Using Artificial Neural Network and Markov Chain Model (Case Study: Kermanshah City), 6 (4), 215–226. [32] Riebsame, W. E., Meyer, W. B., and Turner, B. L. (1994). Modeling Land-use and Cover as Part of Global Environmental Change. Climate Change. Vol. 28. p. 45. [33] Robinove, C. J., (1986). Spatial diversity index mapping of classes in grid cell maps, Photogrammetric Engineering 6. Remote Sensing, 52: 1171-1173. [34] Singh, A. (1989). Digital Change Detection Techniques Using Remotely Sensed Data. International Journal of Remote Sensing. Vol. 10, No. 6, pp. 989-1003. [35] Smits P. C., Dellepiane S. G., Schowengerdt R. A. (1999). Quality assessment of image classification algorithms for land-cover mapping: a review and a proposal for a cost-based approach. International Journal of Remote Sensing, 20, 1461-1486. [36] Snedecor, G, W. & Cochran, W. G. (1989), Statistical Methods, Eighth Edition, Iowa State University Press. [37] Srivastava, P. K., Han, D., Rico-Ramirez, M. A., Bray, M., & Islam, T. (2012). Selection of classification techniques for land use/land cover change investigation. Advances in Space Research, 50 (9), 1250–1265. doi: 10.1016/j.asr.2012.06.032. [38] Tang. J., Wang, L., & Yao, Z. (2007). Spatio‐temporal urban landscape change analysis using the Markov chain model and a modified genetic algorithm, International Journal of Remote Sensing, 28: 15, 3255-3271, DOI: 10.1080/01431160600962749. [39] Voogt J. A. and Oke T. R. (2003). Thermal remote sensing of urban climates. Remote Sensing and Environment 86, 370-384. [40] Wang, Q., Shi, W., & Atkinson, P. M. (2014). Sub-pixel mapping of remote sensing images based on radial basis function interpolation. ISPRS Journal of Photogrammetry and Remote Sensing, 92, 1–15. doi: 10.1016/j.isprsjprs.2014.02.012. [41] Wang, Y., Wang, S., Yang, S., Zhang, L., Zeng, H., & Zheng, D. (2014). Using a Remote Sensing Driven Model to Analyze Effect of Land Use on Soil Moisture in the Weihe River Basin, China. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7 (9), 3892–3902. doi: 10.1109/JSTARS.2014.2345743. [42] Willis, K. S. (2015). Remote sensing change detection for ecological monitoring in United States protected areas. Biological Conservation, 182, 233–242. doi: 10.1016/j.biocon.2014.12.006. [43] www.ghana.gov.gh [44] Zamyatin A., Markov N. (2005). Approach to land cov- er change modelling using the cellular automata // Proceedings of 8 Conference on Geographic Information Science, Estoril, Portugal, AGILE, 587–592. [45] Zhang, X., Yan, G., Li, Q., Li Z-L., Wan H., Guo Z. (2006). Evaluating the fraction of vegetation cover based on NDVI spatial scale correction model. International Journal of Remote Sensing, 27, 5359-5372.