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شبیهسازی حالتهای ممکن کاربری زمین در آبخیز تجن در سال 1420 | ||
| پژوهش های آبخیزداری | ||
| مقاله 8، دوره 34، شماره 2 - شماره پیاپی 131، تیر 1400، صفحه 91-107 اصل مقاله (1.55 M) | ||
| نوع مقاله: پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22092/wmej.2020.341479.1303 | ||
| نویسندگان | ||
| محمد تقی آوند1؛ حمیدرضا مرادی* 2؛ مهدی رمضان زاده لسبویی3 | ||
| 1دانشجوی دکترای علوم و مهندسی آبخیزداری، دانشکده منابع طبیعی و علوم دریایی، دانشگاه تربیت مدرس | ||
| 2دانشیار گروه علوم و مهندسی آبخیزداری، دانشکده منابع طبیعی و علوم دریایی، دانشگاه تربیت مدرس | ||
| 3دانشیار گروه مدیریت جهانگردی، دانشکده علوم انسانی و اجتماعی، دانشگاه مازندران | ||
| چکیده | ||
| این پژوهش با هدف شبیهسازی و پیشبینیکردن تغییر کاربری و پوشش زمین با کاربرد مدل تغییر سرزمین (LCM) بر مبنای دو حالت ممکن مدیریتی تداوم و افزایش کشاورزی در آبخیز تجن استان مازندران به انجام رسید. تصویرهای ماهوارهی لندست و سنجندههای تیام و اوالآی در 1370، 1389 و 1398 تجزیهوتحلیل شد. کاربریها در هر سه مقطع زمانی به پنج طبقهی مسکونی، کشاورزی، جنگل، باغ و مرتع طبقهبندی شد. پیشبینی وضعیت کاربری زمین برای 1398 با نقشهی کاربری سالهای 1370 و 1389 در نرمافزار السیام انجام شد. این نرمافزار شبکهی عصبی پرسپترون چندلایه را برای ساخت زیرمدلهای دگرگونی، تهیهی نقشهی احتمال دگرگونی، و پیشبینی تغییر کاربری زمین بهکار میبرد. متغیرهای مکانی فاصله از جاده، فاصله از رودخانه، شیب، ارتفاع، فاصله از روستا، و فاصله از زمین کشاورزی در جایگاه عاملهای موثر بر تغییر در شبکهی عصبی بهکار برده شد. برای اعتبارسنجی نرمافزار نقشهی کاربری زمین 1398 بهکار برده شد، و ضریب کاپا و ROC بهترتیب 0/84 و 0/89 بهدست آمد. نتیجههای شبیهسازی بر مبنای حالت ممکن تداوم نشاندهندهی 5/74 % کاهش مساحت زمینهای مرتعی، و 9/17 % افزایش در مساحت زمینهای باغی در 20 سال آینده است، در حالیکه در حالت ممکن دوم مساحت زمینهای کشاورزی 8/61% افزایش، و زمینهای مرتعی 5/59 % کاهش نشان میدهد. در نتیجه، زمینهای مرتعی و جنگلی در هر دو حالت ممکن رو به کاهش، و زمینهای کشاورزی و مسکونی رو به افزایش است. | ||
| کلیدواژهها | ||
| آبخیز تجن؛ تصویرهای ماهوارهیی؛ روش LCM؛ شبکهی عصبی مصنوعی؛ کاربری زمین | ||
| عنوان مقاله [English] | ||
| The Consequences of Following Two Different Land Use Change Scenarios in 2040 in the Tajan Watershed | ||
| نویسندگان [English] | ||
| Mohammad Taghi Avand1؛ Hamid Reza Moradi2؛ Mehdi Ramazanzadeh Lasbuie3 | ||
| 1Ph.D. Student Watershed Management Science and Engineering, Faculty of Natural Resources and Marine Sciences, Tarbiat Modares University | ||
| 2Associate Professor of Watershed Management Science and Engineering, Faculty of Natural Resources and Marine Sciences, Tarbiat Modarres University | ||
| 3Associate Professor, Department of Tourism Management, Faculty of Humanities and Social Sciences, Mazandaran University | ||
| چکیده [English] | ||
| The purpose of this research was to model and predict land use/cover changes using the LCM model based on two management scenarios, namely the sustainability scenario and the agricultural increase scenario in the Tajan Watershed, the Province of Mazandaran. Landsat satellite images and the data collected by the TM and OLI sensors were analyzed for 1991, 2010 and 2019. Images from the three-time periods were categorized into five categories: residential, farmland agricultural, forest, orchard, and rangeland. Land use forecasting for 2019 was performed using the 1991 and 2010 land use maps in the LCM model. The LCM model uses the perceptron multilayer neural network to construct transmission sub-models, map the probability of transmission, and ultimately predict the land use changes. Spatial variables such as distance from a road, distance from a river, slope, altitude, distance from a village, and distance from farmland were used as the influencing factors on the neural network changes. The land use map of 2019 was used to validate the model, which resulted in the Kappa and ROC coefficients of 0.84 and 0.89, respectively. The modelling results based on the sustainability scenario show a 5.74 percent decrease in the rangeland area and a 9.17 percent growth in the orchard area over the next 20 years, while the second scenario showed an increase of 8.61% in farmlands and a decrease of 5.59% in rangeland areas. Therefore, both rangeland and forest areas are declining and farmland and residential areas are increasing. A better land use management will require conservation scenarios in different parts of the watershed. | ||
| کلیدواژهها [English] | ||
| Artificial neural network, land use, LCM method, satellite images, Tajan Watershed | ||
| مراجع | ||
|
Abd El-Kawy OR, Rød JK, Ismail HA, Suliman AS. 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. Abdi M, Moradi HR, Sadeghi HR. 2013. Survey of land use changes using remote sensing techniques and geographic information system (Case study: Urmia City Chacha Basin), Second National Conference on Climate Change and its Impact on Agriculture and Environment, August 2013; Iran, Urmia. pp. 939–947. (In Persian). Ahmad A. 2012. Analysis of maximum likelihood classification on multispectral data. Applied Mathematical Sciences, 6(129): 6425– 6436. Armenteras D, Murcia U, González TM, Barón OJ, Arias JE. 2019. Scenarios of land use and land cover change for NW Amazonia: Impact on forest intactness. Global Ecology and Conservation, Volume 17, January 2019. Avand M, Moradi H. 2020. Using machine learning models, remote sensing, and GIS to investigate the effects of changing climates and land uses on flood probability. Journal of Hydrology, Available online 27 October 2020. https://doi.org/10.1016/j.jhydrol.2020.125663 Chen H, Pontius Jr RG. 2010. Diagnostic tools to evaluate a spatial land change projection along a gradient of an explanatory variable. Landscape Ecology, 25(9): 1319–1331. Chim K, Tunnicliffe J, Shamseldin A, Ota T. 2019. Land use change detection and prediction in upper Siem Reap River, Cambodia. Hydrology, 6(3): 64p. https://doi.org/10.3390/hydrology6030064 Falahatkar S, Soffianian AR, Khajeddin SJ, Ziaee HR, Ahmadi Nadoushan M. 2011. Integration of remote sensing data and GIS for prediction of land cover maps. International Journal of Geomatics and Geosciences. 1(4): 847–864. Gholamali Fard M, Jourabian Shoushtari Sh, Hosseini Kahnuj S, Mirzaei M. 2012. Modeling land use change Mazandaran coast province using LCM in the GIS, Journal of Environmental Studies, 38(4): 109–124. (In Persian). Gholamali Fard M, Mirzaei M, Jorabian Shoushtari Sh. 2014. Modeling land use change using of artificial neural network and Markov Chain (Case study: Middle coast of Busher Province). Remote Sensing and Geographical Information System in Natural Resources, 5(1): 61–74. (In Persian). Guan D, Li H, Inohae T, Su W, Nagaie T, Hokao K. 2011. Modeling urban land use change by the integration of cellular automaton and Markov Model. Ecological Modelling. 222 (20–22): 3761–3772. Hashimoto M, Nose T, Muriguchi Y. 2002. Wood products: potential carbon sequestration and impact on net carbon emissions of industrialized countries. Environmental Science & Policy. 5(2): 183–193. Huang Y, Yang B, Wang M, Liu B, Yang X. 2020. Analysis of the future land cover change in Beijing using CA–Markov chain model. Environmental Earth Sciences, 79(2): 60 (2020). https://doi.org/10.1007/s12665-019-8785-z Jorabian Shoushtari Sh, Esmaili Sari A, Hosseini M, Gholamali Fard M. 2018. Application of artificial neural network multilayer perceptron method in land use change modeling of east Mazandaran Province, Geography and Environmental Planning, 72 (4): 125–144. (In Persian). Kim OS. 2010. An assessment of deforestation models for reducing emissions from deforestation and forest degradation (REDD). Transactions in GIS, 14(5): 631–654. Lee Y, Chang H. 2011. The simulation of land use change by using CA-Markov model: A case study of Tainan City, Taiwan. 19th International Conference on Geoinformatics, pp. 24–26. Mas JF, Kolb M, Paegelow M, Camacho Olmedo MT. 2014. Inductive pattern-based land use/cover change models: A comparison of four software packages. Environmental Modelling & Software. 51: 94–111. Mohammadyari F, pourkhabaz HR, Aghdar H, Tavakoli M. 2019. Forcasting land use change trend Behbahan county from 2013 to 2027 using LCM model. Geographical Space Journal, 46(19): 37–56. (In Persian). Musa MI, Lekwot V. 2014. Analysis of forest cover changes in Nimbia Forest Reserve, Kaduna State, Nigeria using geographic information system and remote sensing techniques. Journal of Environment and Earth Science, 4(21): 73–83 Nath B, Wang Z, Ge Y, Islam KP, Singh R, Niu Z. 2020. Land use and land cover change modeling and future potential landscape risk assessment using Markov-CA model and analytical hierarchy process. ISPRS International Journal of Geo-Information, 9(2): 134. https://doi.org/10.3390/ijgi9020134. Norozi M. 2013. Investigation and forecasting land use change using LCM model (Case study: Part of Tajan and Black River Rivers), Master's thesis, Faculty of Agricultural Sciences and Natural Resources, Sari University. 140 p. (In Persian). Samat N, Hasni R, Eltayeb Elhadary YA. 2011. Modelling land use changes at the Peri-Urban areas using geographic information systems and cellular automata model. Journal of Sustainable Development. 4(6): 72–84. Sardari MR, Bazrafshan A, Panagopoulos O, Sardooi ER. 2019. Modeling the impact of climate change and land use change scenarios on soil erosion at the Minab Dam Watershed. Sustainability, 11(12): 1–21. Shahidul Islam Md, Ahmed R. 2011. Land use change prediction Dhaka city using GIS aided Markov Chain modeling. Journal of Life Earth Science. 6: 81–89. Tirupathi C, Shashidhar T. 2020. Investigating the impact of climate and land-use land cover changes on hydrological predictions over the Krishna River Basin under present and future scenarios. Science of the Total Environment, 721p. https://doi.org/10.1016/j.scitotenv.2020.137736 Upadhyay TP, Solberg B, Sankhayan PL. 2006. Use of models to analyses land-use changes, forest/soil degradation and carbon sequestration with special reference to Himalayan region: A review and analysis. Forest Policy and Economics. 9(4): 349–371. Vaclavik T, Rogan J. 2009. 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. GIS Science & Remote Sensing. 46(1): 54–76. Václavík T, Rogan J. 2009. 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. GIS Science & Remote Sensing, 46(1): 54–76. Van Oort PAJ. 2007. Interpreting the change detection error matrix. Remote Sensing of Environment 108 (1): 1–8. Warner A, Blonski S, Gasser G, Royan R, Zanoni V. 2001. An approach to application validation of multispectral sensors using AVIRIS data, 9 p. Zabihi M, Moradi HR, Gholamali Fard m, Khaledi Dervishan AV. 2019. Influence of possible land use / land coverage Modes on landscape components in Hall Watershed. Watershed Research, 32 (1): 99–84. (In Persian). doi: 10.22092/wmej.2018.123624.1156 Zhou D, Lin Z, Liu L. 2012. Regional land salinization assessment and simulation through cellular Automaton-Markov modeling and spatial pattern analysis. Science of the Total Environment. 439: 260–274. https://doi.org/10.1016/j.scitotenv.2012.09.013 | ||
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