Applied Research in Geographical Sciences

Applied Research in Geographical Sciences

Empirical modeling potential transfer of land cover change pa city with neural network algorithms

Authors
fatemeh mohammadyary 1
hamidreza pourkhabbaz 1
hossin aghdar 2
morteza Tavakoly 3
1 Behbahan Khatam Alanbia University of Technology
2 Shahid Chamran University of Ahvaz
3 Associ profe of Geography Tarbiat Modares University of Technology
10.29252/jgs.18.50.219
Abstract
Land-use change is one of the most important challenges of land-use planning that lies with planners, decision-makers and policymakers and has a direct impact on many issues, such as economic growth and the quality of the environment. The present study examines the land use change trends in Behbahan city for 2014 and 2028 using LCM in the GIS environment. Analysis and visibility of user variations, carried out in two periods of Landsat satellite images of 2000 (ETM + sensor) and 2014 (OLI sensors), and land cover maps for each year. The transmission potential modeling was performed by using the multi-layer perceptron artificial neural network algorithm using six independent variables and the distribution of changes in user usage were calculated by Markov chain method. The results of the prediction showed that the most reduction in the changes is the degradation of the rangelands and the greatest increase in the area of agricultural use. According to the horizontal tabulation results of the 2028 map, it can be stated that from the total area of the area 28336.22 hectares of land were unchanged and 33223.78 hectares of land use change. Also Rangeland and forest degradation during this time period can be a danger to urban planners and natural resources.




#s3gt_translate_tooltip_mini { display: none !important; }
Keywords
Trend of change
Markov chain
Neural Network
Land Change Modelar
Brian, W. S, C. Qi and B. Michael (2011) A comparison of classification techniques to support land cover and land use analysis in tropical coastal zones, Applied Geography, 31(2), 525-532.
Caldas, M, Simmons, C, Walker, R, Perz, S. Aldrich, S, Pereira, R, Leite, F, and Arima, E (2010) Settlement Formation and Land Cover and Land Use Change: A Case Study in the Brazilian Amazon. Journal of American Latin Geography, 11 (1), 125-144.
Eastman, J. R (2006) IDRISI Selva. Clark-Labs, Clark University, Worcester, MA.Geneletti, D. 2006. Assessing the impact of alternative land-use zoning policies onfuture ecosystem services. Environ. Impact Assess. Rev. 40, 25–35.
Fan, F, Wang, Q, Wang, Y (2007) land use and land cover change in Guangzhou, Chaina, from 1998 to 2003, based on land sat TM/ETM+ imagery. Sensors, 7 (2), 1323-1342.
FAO (1995) Planning for sustainable use of land resources: towards a new approach. Food and Agriculture Organization of the United Nations, FAO Land and Water Bulletin 2. Rom
Gontier, M, Mörtberg, U, Balfors, B (2009) Applications in Comparing GIS-based habitat models for EIA and SEA. Environmental Impact Assessment Review, 30 (3), 8-18.
Inglis-Smith, C (2006) Satellite imagery based classification mapping for spatially analyzing West Virginia Corridor H urban development. Msc Thesis, The Graduate College of Marshall University.
Kaiser, Edward J, David, R, Godschalk and F. Stuart Chapin, Jr (1995) Urban Land Use Planning. Urbana, IL: University of Illinois Press
Joorabian Shooshtari, SH, Hosseini, S. M, Esmaili-Sari, A, and Gholamalifard, M (2012) Monitoring Land Cover Change, Degradation, and Restoration of the Hyrcanian Forests in Northern Iran (1977–2010), International Journal of Environmental Sciences, 3(3), 1038-1056.
Lu, D, Mausel, P, Brondi´zio, E, Moran, E (2004) Change detection techniques. International Journal of Remote Sensing, 25: 2365-2407.
Maithani, S (2009) A Neural Network based Urban Growth Model of an Indian City, J. Indian Soc. International Journal of Remote Sensing, 37 (2), 363–376.
McConnel, W.J, Sweeney, S.P, Mulley, B (2004) Physical and social access to land: spatio-temporal patterns of agricultural expansion in Madagascar. Agriculture, Ecosystems & Environment, 101 (3), 171-184.
Mozumder, Ch, Tripathi, N. K (2014) Geospatial scenario based modelling of urban and agriculture alintrusions in Ramsar wetland Deepor Beel in Northeast Indiausing a multi-layer perceptron neural network, International Journal of Applied Earth Observation and Geoinformation, 32 (2), 92-104.
Muñoz-Rojas, M, De la Rosa, D, Zavala, L. M, Jordán, A, and Anaya-Romero, M (2011) Changes in Land Cover and Vegetation Carbon Stocks in Andalusia, Southern Spain (1956 - 2007), Science of the Total Environment, 14 (4), 2796-2806.
Nahuelhual, L, Carmona, A, Lara, A, Echeverría, C, González, M. E (2012), Land-cover Change to Forest Plantations: Proximate Causes and Implications for the Landscape in South-central Chile, Landscape and Urban Planning, 1 (2), 12-20.
Pal, M, and P. M, Mather (2005) Support vector machines for classification in remote sensing. International Journal of Remote Sensing 5 (2), 1007-1011.
Seto L. C, Woodcock C. E, Song C, Huang X, Lu J.& Kaufmann R. K (2002) Monitoring land use change in the Pearl river delta using Landsat TM. International journal of remote sensing, 23 (10), 989-1003.
Schulz, J. J, Cayuela, L, Echeverria, C, Salas, J, & Rey Benayas, J. M (2010) Monitoring land cover change of the dryland forest landscape of Central Chile (1975–2008). Applied Geography. 30(3), 436–447.
Singh, V, Dubey, A (2012) Land Use Mapping Using Remote Sensing and GIS Techniques in Naina Gorma Basin, Part of Rewa District, M. P. India. International Journal of Emerging Technology and Advanced Engineering, 11 (3),151-156.
Tayyebi, A, Pijanowski, B. c (2014) Modeling multiple land use changes using ANN, CART and MARS: Comparing tradeoffs in goodness of fit and explanatory power of data mining tools, International Journal of Applied Earth Observation and Geoinformation 28 (2), 116-120.
Thapa, R. B, Murayama, Y (2011) Scenario Based Urban Growth Allocation in Kathmandu Valley, Nepal, Landscape and Urban Planning, 1-2 (3), 140-148.
Václavík, T, Rogan, J (2009) Identifying trends in land Use/Land cover changes in the context of Post Socialist Transformation in Central Europe. GIS Science and Remote Sensing, 49 (3), 1-32.
Wang, Y, Li, Sh (2011) Simulating multiple class urban land-use/cover changes by RBFN-based CA model. Computers and Geosciences, 37 (3), 111–121.
Yuan, H (2002) Development and evaluation of advanced classification systems using remotely sensed data for accurate land-use/land-cover mapping. Phd Thesis, Department of Forestry, North Carolina State University.