LAND USE/ LAND COVER CHANGE MODELLING: ISSUES AND CHALLENGES
Keywords:Geo-spatial, LULC Change, Modelling, Prediction, Rural Development.
AbstractLand use change modelling are the tools to support timely and effective monitoring of natural resources through spatio-temporal land use/ land cover (LULC) change detection which can help decision makers for optimum resources planning and utilisation for sustainable rural development. Number of models and approaches have been developed in recent past to analyse land use/land cover change considering different type of data/variables at different levels of complexity and resolution. Such different methods/ models have their own limitations, advantages and suitability in a particular condition. There is no agreement among research community about suitability and effectiveness of a particular method. Present study aims to present a comparative study of popularly used LULC change detection models and techniques. Different models and techniques are compared in terms of level of complexity, considered explanatory variables, spatial extent, temporal dynamism,predictability and level of spatial interaction. For each category, a thorough review of models and approaches is presented which helps in understanding the operational concepts and utility of models/ approaches.
How to Cite
Agarwal, Chetan, Glen M. Green, J. Morgan Grove, Tom P. Evans, and Charles M. Schweik.(2002), â€œA review and assessment of land-use change models: dynamics of space, time and human choice,â€ 7559-64.
AkÃ½n, A., Clarke, K. C.andBerberoglu, S. (2014),â€The impact of historical exclusion on the calibration of the SLEUTH urban growth model.â€ International Journal of Applied Earth Observation and Geoinformation, 27, 156-168.
Aspinall, R. (2004),â€Modelling land use change with generalized linear modelsâ€”a multi-model analysis of change between 1860 and 2000 in Gallatin Valley, Montana.â€ Journal of Environmental Management, 72(1), 91-103.
Baker, W. L. (1989),â€A review of models of landscape change, Landscape ecology,â€ 72(2), 111-133.
Batty, M., and Xie, Y. (1994),â€From cells to cities,â€ Environment and Planning B: Planning and Design, 21(7), S31-S48.
Batty, M. (2005),â€Agents, cells, and cities: new representational models for simulating multiscale urban dynamics.â€ Environment and Planning A, 37(8), 1373-1394.
Clarke, K. C. and Gaydos, L. J. (1998), â€œLoose-coupling a cellular automaton model and GIS: long-term urban growth prediction for San Francisco and Washington/Baltimore,â€ International Journal of Geographical Information Science, 12(7), 699-714.
Geist, H. J. and Lambin, E. F. (2002), â€œProximate causes and underlying driving forces of tropical deforestation: Tropical forests are disappearing as the result of many pressures, both local and regional, acting in various combinations in different geographical locations.â€ BioScience, 52(2), 143-150.
Irwin, E. G. and Geoghegan, J. (2001),â€Theory, data, methods: developing spatially explicit economic models of land use change,â€ Agriculture, Ecosystems & Environment, 85(1), 7-24.
Jat, M. K., Garg, P. K. and Khare, D. (2008), â€œMonitoring and modelling of urban sprawl using remote sensing and GIS techniques.â€ International Journal of Applied Earth Observation and Geoinformation, 10(1), 2643.
Jat, M. K., Choudhary, M. and Saxena, A. (2017), â€œUrban growth assessment and prediction using RS, GIS and SLEUTH model for a heterogeneous urban fringe,â€ The Egyptian Journal of Remote Sensing and Space Science, (In press). Vol 10(3), 1-19.
Jetz, W., Wilcove, D. S. and Dobson, A. P. (2007),â€Projected impacts of climate and land-use change on the global diversity of birds,â€ PLoS Biology, 5(6), e157.
Lambin, E. F., Rounsevell, M. D. A. and Geist, H. J. (2000), â€œAre agricultural land-use models able to predict changes in land-use intensity?â€Agriculture, Ecosystems & Environment, 82(1), 321-331.
Matthews, R. B., Gilbert, N. G., Roach, A., Polhill, J. G.and Gotts, N. M. (2007),â€Agent-based land-use models: a review of applications,â€ Landscape Ecology, 22(10), 1447-1459.
Parker, D. C., Manson, S. M., Janssen, M. A., Hoffmann, M. J.and Deadman, P. (2003),â€Multi agent systems for the simulation of land use and land cover change: A review,â€ Annals of the Association of American Geographers, 93(2), 314-337.
Pijanowski, B. C., Tayyebi, A., Doucette, J., Pekin, B. K., Braun, D.andPlourde, J. (2014),â€A big data urban growth simulation at a national scale: configuring the GIS and neural network based land transformation model to run in a high performance computing (HPC) environment,â€ Environmental Modelling & Software, 51, 250-268.
Saxena, A., Jat, M. K. and Choudhary, M. (2016), â€œAnalysis of urban growth using geospatial techniques,â€ International Journal of Earth Sciences and Engineering, 1000(4), 28-50.
Veldkamp, Antonie, and Eric F. Lambin (2001), â€œPredicting land-use change, â€œAgriculture, Ecosystems & Environment, 85(1â€“3), 1-6.
Verburg, P. H., Schot, P. P., Dijst, M. J.and Veldkamp, A. (2004),â€Land use change modelling: current practice and research priorities,â€ GeoJournal, 61(4), 309-324.
Waddell, P. (2002),â€UrbanSim: Modeling urban development for land use, transportation, and environmental planning,â€ Journal of the American Planning Association, 68(3), 297-314.
Xiubin, L. (1996),â€A review of the international researches on land use/land cover change,â€ ACTA GEOGRAPHICA SINICA-CHINESE EDITION-, 51, 558-565.