مدل‌سازی تغییرات کاربری اراضی با استفاده از مدل ساز تغییر سرزمین (LCM ) مطالعه موردی: شهرستان نکا
محورهای موضوعی :
کاربری اراضی
سیده معصومه فتح الهی رودباری
1
(دانشجوی محیط زیست, دانشکده صومعه سرا, دانشگاه گیلان)
کامران نصیراحمدی
2
(سازمان حفاظت محیط زیست استان مازندران)
مهرداد خانمحمدی
3
(استاد گروه محیط زیست, دانشکده صومعه سرا, دانشگاه گیلان)
تاریخ دریافت : 1396/11/28
تاریخ پذیرش : 1396/12/12
تاریخ انتشار : 1397/03/01
کلید واژه:
شبکه عصبی مصنوعی,
مدل‌سازی تغییرات کاربری اراضی,
LCM,
شهرستان نکا,
چکیده مقاله :
تغییرات کاربری به خصوص توسعه شهرها باعث تخریب زیستگاه های طبیعی و کاهش تنوع زیستی می شود. به طور معمول برنامه ریزان شهری جهت کنترل کردن تغییرات کاربری اراضی از روش مدل سازی استفاده می کنند. در این مطالعه، هدف مدل سازی تغییرات کاربری اراضی شهرستان نکا، با استفاده از LCM(Land Change Modeler) بوده است. جهت به دست آوردن نقشه کاربری اراضی منطقه از تصاویر ماهواره Landsat سنجنده های TM و ETM+ و TIRS_OLI متعلق به سال های 1988 و 2002 و 2016 استفاده گردید. همچنین جهت مدل سازی پتانسیل انتقال با استفاده از شبکه عصبی مصنوعی انجام گرفت. در این مطالعه 5 زیر مدل به همراه 9 متغیر استفاده گردید. سپس جهت پیش بینی تغییرات کاربری سال 2016 از دوره واسنجی 1988تا 2002 و زنجیره مارکف و مدل پیش بینی سخت استفاده شد. در نهایت نیز از نقشه سال های 2002 تا 2016 برای پیش بینی تغییرات کاربری اراضی متعلق به سال 2030 استفاده گردید. صحت مدل‌سازی با استفاده از ماتریس خطا ارزیابی شد. نتایج نشان داد که در طی سال های 1988 تا 2016 اراضی جنگلی 2297 هکتار کاهش داشته و بیشترین تغییرات مربوط به تبدیل اراضی جنگلی به کشاورزی بوده است. نتایج مدل سازی با استفاده از شبکه عصبی مصنوعی نیز صحت بالایی بالای (69 درصد) داشته است. نتیجه مدل سازی برای سال 2030 نیز نشان داد که مساحت جنگل کاهش میابد و اراضی کشاورزی و مناطق شهری افزایش پیدا می کنند.
چکیده انگلیسی:
Land use/cover changes, especially human urbanization Cause destruction of natural habitats and threaten biodiversity. Regularly, Land use/cover models are one the most important methods for evaluating this trend. The objective of this study is the investigation of land use/cover change and modeling in the Neka city using Land Change Modeler (LCM). Landsat TM (١٩٨8), ETM+ (٢٠٠2), and OLI (2016) data was used for land use/cover classification and change. In addition, transition potential modeling was conducted using an artificial neural network. In this method, 5 sub-models and 9 variables were used. Then calibration period (1988-2002) was used by Markov chain and hard prediction for extrapolating the 2016 land use/cover changes. Finally, land use/cover maps for 2002 and 2016 were used for land use/cover map extending prediction to the year 2030. The accuracy assessment of model was conducted by Error Matrix. The results of this study showed the annual rate of decline in the forest was 2297 Hectare during the period 1988-2016. The biggest changes were in the conversion of forest lands to agriculture. Modeling results using artificial neural network also showed acceptable accuracy (69%). The results of modeling for 2030 also showed that the area of the forest is decreasing, Agricultural lands and urban areas are increasing.
منابع و مأخذ:
References:
Talebi Amiri, Sh., Azari Dehkordi, F, Sadeghi, S.H.,Soof BAF, S.R. (1388). Analysis of the degradation of the Nema waters of the watersheds using the ecological metrics of the Territory. Environmental science 6 (3), 133-144. (in Persian)
Gholami, M.H., Mokhir B., Greginia, A., Hossein Zadeh Sahafi, H. (1388). Investigation on the percentage and severity of parasitic infection of river whitefish (Leuciscus cephalus) and Blackfish (Capoeta capoeta gracilis) of Neka river. Marine Science and Technology Researches 4 (3), 66-59. (in Persian)
Makhdoom, M., Dervish, Sefat, AA, Jafarzadeh, H., Makhdoom, AS. (1383). Evaluating and Planning the Environment with Geographic Information Systems (GIS). second edition. Tehran University Press, 309 pages. (in Persian)
Abd El-Kawy, O.R., Rød, J.K., Ismail, H.A., Suliman, A.S. (2011). Land use and land cover change detection in the western Nile delta of Egypt using remote sensing data. Applied Geography 31(2)483-494 .
Bakr, N., Weindorf, D.C., Bahnassy, M.H., Marei, S.M., El-Badawi, M.M. (2010). Monitoring land cover changes in a newly reclaimed area of Egypt using multi-temporal Landsat data. Applied Geography 30(4),592-605 .
Chen, H., Pontius Jr, R.G (2010). Diagnostic tools to evaluate a spatial land change projection along a gradient of an explanatory variable. Landscape Ecology 25(9),1319-1331.
Díaz, G.I., Nahuelhual, L., Echeverría, C., Marín, S. (2011). Drivers of land abandonment in Southern Chile and implications for landscape planning. Landscape and Urban Planning 99(3-4),207-217.
Eastman, J.R. (2006). IDRISI Andes. Guide to GIS and Image Processing. Clark Labs, Clark University, Worcester, MA.
Eastman, J.R., Van Fossen, M.E., Solarzano, L.A. (2005). Transition potential modeling for land cover change. In: Maguire, D., Goodchild, M., Batty, M. (Eds.), GIS, Spatial Analysis and Modeling. ESRI Press, Redlands, California.
Haibo, Y., Longjiang, D., Hengliang, G., Jie, Z. (2011). Tai'an land use Analysis and Prediction Based on RS and Markov Model. Procedia Environmental Sciences 10,2625-2610.
Kennedy, R.E., Cohen, W.B., Schroeder, T.A(2009). Trajectory-based change detection for automated characterization of forest disturbance dynamics. Remote Sensing of Environment 110 (3),370-386.
Khoi, D.D., Murayama, Y. (2010). Forecasting Areas Vulnerable to Forest Conversion in the Tam Dao National Park Region, Vietnam. Remote Sensing 2(5) ,1249-1272.
Kim, O.S. (2010). An Assessment of Deforestation Models for Reducing Emissions from Deforestation and Forest Degradation (REDD). Transactions in GIS 14(5), 631-654.
Naghdi, R., Bonyad, A., Maskani, H. (2008). Processes of forest products and production costs in Guilan forests, Iran. Caspian Journal of Environmental Sciences 6 (2), 167-173.
Oñate-Valdivieso, F., Sendra, J.B. (2010). Application of GIS and remote sensing techniques in generation of land use scenarios for hydrological modeling. Journal of Hydrology 395 (3-4),256-263.
Pérez-Vega, A., Mas, J., Ligmann-Zielinska, A. (2012). Comparing two approaches to land use/cover change modeling and their implications for the assessment of biodiversity loss in a deciduous tropical forest. Environmental Modelling & Software 29 (1),11-23.
Pijanowski, B.C., Brown, D.G., Shellito, B.A., Manik, G.A. (2002). Using neural networks and GIS to forecast land use changes: a Land Transformation Model. Computers, Environment and Urban Systems 26 (6)553-575.
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.
Václavík, T., Rogan, J. (2010). Identifying trends in land use/land cover changes in the context of post-socialist transformation in Central Europe: A case study of the greater Olomouc region, Czech Republic. GIScience & Remote Sensing 46(1),54-76.
Van Oort, P.A.J. (2007). Interpreting the change detection error matrix. Remote Sensing of Environment 108 (1),1-8.
Verburg, P.H., Soepboer, W., Veldkamp, A., Limpiada, R., Espaldon, V., Mastura, S. (2004). Modeling the Spatial Dynamics of Regional Land Use: The CLUE-S Model. Environmental Management 30 (3),391-405.
ian, G., Crane, M., Su, J. (2007). An analysis of urban development and its environmental impact on the Tampa Bay watershed. Journal of Environmental Management 85 (4),965-976.
Yuan, F., Sawaya, K.E., Loeffelholz, B.C., Bauer, M.E. (2005). Land cover classification and change analysis of the Twin Cities (Minnesota) Metropolitan Area by multitemporal Landsat remote sensing. Remote Sensing of Environment 98,317-328.
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References:
Talebi Amiri, Sh., Azari Dehkordi, F, Sadeghi, S.H.,Soof BAF, S.R. (1388). Analysis of the degradation of the Nema waters of the watersheds using the ecological metrics of the Territory. Environmental science 6 (3), 133-144. (in Persian)
Gholami, M.H., Mokhir B., Greginia, A., Hossein Zadeh Sahafi, H. (1388). Investigation on the percentage and severity of parasitic infection of river whitefish (Leuciscus cephalus) and Blackfish (Capoeta capoeta gracilis) of Neka river. Marine Science and Technology Researches 4 (3), 66-59. (in Persian)
Makhdoom, M., Dervish, Sefat, AA, Jafarzadeh, H., Makhdoom, AS. (1383). Evaluating and Planning the Environment with Geographic Information Systems (GIS). second edition. Tehran University Press, 309 pages. (in Persian)
Abd El-Kawy, O.R., Rød, J.K., Ismail, H.A., Suliman, A.S. (2011). Land use and land cover change detection in the western Nile delta of Egypt using remote sensing data. Applied Geography 31(2)483-494 .
Bakr, N., Weindorf, D.C., Bahnassy, M.H., Marei, S.M., El-Badawi, M.M. (2010). Monitoring land cover changes in a newly reclaimed area of Egypt using multi-temporal Landsat data. Applied Geography 30(4),592-605 .
Chen, H., Pontius Jr, R.G (2010). Diagnostic tools to evaluate a spatial land change projection along a gradient of an explanatory variable. Landscape Ecology 25(9),1319-1331.
Díaz, G.I., Nahuelhual, L., Echeverría, C., Marín, S. (2011). Drivers of land abandonment in Southern Chile and implications for landscape planning. Landscape and Urban Planning 99(3-4),207-217.
Eastman, J.R. (2006). IDRISI Andes. Guide to GIS and Image Processing. Clark Labs, Clark University, Worcester, MA.
Eastman, J.R., Van Fossen, M.E., Solarzano, L.A. (2005). Transition potential modeling for land cover change. In: Maguire, D., Goodchild, M., Batty, M. (Eds.), GIS, Spatial Analysis and Modeling. ESRI Press, Redlands, California.
Haibo, Y., Longjiang, D., Hengliang, G., Jie, Z. (2011). Tai'an land use Analysis and Prediction Based on RS and Markov Model. Procedia Environmental Sciences 10,2625-2610.
Kennedy, R.E., Cohen, W.B., Schroeder, T.A(2009). Trajectory-based change detection for automated characterization of forest disturbance dynamics. Remote Sensing of Environment 110 (3),370-386.
Khoi, D.D., Murayama, Y. (2010). Forecasting Areas Vulnerable to Forest Conversion in the Tam Dao National Park Region, Vietnam. Remote Sensing 2(5) ,1249-1272.
Kim, O.S. (2010). An Assessment of Deforestation Models for Reducing Emissions from Deforestation and Forest Degradation (REDD). Transactions in GIS 14(5), 631-654.
Naghdi, R., Bonyad, A., Maskani, H. (2008). Processes of forest products and production costs in Guilan forests, Iran. Caspian Journal of Environmental Sciences 6 (2), 167-173.
Oñate-Valdivieso, F., Sendra, J.B. (2010). Application of GIS and remote sensing techniques in generation of land use scenarios for hydrological modeling. Journal of Hydrology 395 (3-4),256-263.
Pérez-Vega, A., Mas, J., Ligmann-Zielinska, A. (2012). Comparing two approaches to land use/cover change modeling and their implications for the assessment of biodiversity loss in a deciduous tropical forest. Environmental Modelling & Software 29 (1),11-23.
Pijanowski, B.C., Brown, D.G., Shellito, B.A., Manik, G.A. (2002). Using neural networks and GIS to forecast land use changes: a Land Transformation Model. Computers, Environment and Urban Systems 26 (6)553-575.
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.
Václavík, T., Rogan, J. (2010). Identifying trends in land use/land cover changes in the context of post-socialist transformation in Central Europe: A case study of the greater Olomouc region, Czech Republic. GIScience & Remote Sensing 46(1),54-76.
Van Oort, P.A.J. (2007). Interpreting the change detection error matrix. Remote Sensing of Environment 108 (1),1-8.
Verburg, P.H., Soepboer, W., Veldkamp, A., Limpiada, R., Espaldon, V., Mastura, S. (2004). Modeling the Spatial Dynamics of Regional Land Use: The CLUE-S Model. Environmental Management 30 (3),391-405.
ian, G., Crane, M., Su, J. (2007). An analysis of urban development and its environmental impact on the Tampa Bay watershed. Journal of Environmental Management 85 (4),965-976.
Yuan, F., Sawaya, K.E., Loeffelholz, B.C., Bauer, M.E. (2005). Land cover classification and change analysis of the Twin Cities (Minnesota) Metropolitan Area by multitemporal Landsat remote sensing. Remote Sensing of Environment 98,317-328.
