کاربرد مدل CA-Markovدر حوزه آبخیز مندرجان زاینده رود جهت پیش بینی تغییرات کاربری اراضی

نوع مقاله : پژوهشی

نویسندگان

1 دانشجوی دکتری رشتۀ علوم و مهندسی آبخیز، گروه مهندسی طبیعت، دانشکدۀ منابع طبیعی و علوم زمین، دانشگاه شهرکرد، ایران

2 گروه مهندسی طبیعت، دانشکدۀ منابع طبیعی و علوم زمین، آبخیزداری، دانشگاه شهرکرد، ایران

3 گروه مهندسی طبیعت، دانشکدۀ منابع طبیعی و علوم زمین، آبخیزداری، عضو هیئت علمی دانشگاه شهرکرد، ایران

چکیده

حوضۀ آبخیز مندرجان از تغییرات شدید در نوع کاربری اراضی مصون نمانده است. به ‏طور کلی، هدف اصلی این مطالعه پیش‌بینی تغییرات کاربری اراضی با کاربرد مدل CA-Markov در حوضۀ آبخیز مندرجان استان اصفهان است. برای این‌منظور، از تصاویر ماهواره‌ای لندست مربوط به سال‌های 1991، 2001، 2011، 2021 (در مقاطع زمانی 10ساله) استفاده شد. نقشۀ پوشش اراضی در 4 کلاس کاربری اراضی (شامل اراضی کشاورزی، اراضی کوهستانی، مرتع و اراضی مسکونی) تهیه شد. ضریب کاپای حاصل از نقشۀ کاربری اراضی سال 2011 و پیش‌بینی‌شده برابر با 0/96 و برای سال 2021 برابر با 0/969 بود. نتایج نشان می‏دهد اختلاف‏های طبقه‏های مختلف متفاوت است و بزرگی آن به طور کلی در سال 2011 کمتر از 9 درصد و در سال 2021 کمتر از 15 درصد است. بررسی روند تغییرات کاربری اراضی سال‌های 2031 و 2041 نسبت به سال 2021 نشان داد بیشترین تغییرات افزایشی از لحاظ سطح، اراضی کشاورزی است. در بین تغییرات کاهشی نیز مرتع با 315 و 868 هکتار به‌ترتیب در سال‌های 2031 و 2041 شاهد خواهیم بود. افزایش سطح اراضی کشاورزی و مسکونی باعث کاهش سطح اراضی مرتعی به همین میزان در سال‌های 2031 و 2041 شده است.

کلیدواژه‌ها

موضوعات


  • Abijith, D. & Saravanan, S. (2022). Assessment of land use and land cover change detection and prediction using remote sensing and CA Markov in the northern coastal districts of Tamil Nadu, India. Environ Sci Pollut Res, 29(57):86055–86067. https://doi.org/10.1007/s11356-021-15782-6
  • Ahmad, F., Goparaju, L., & Qayum, A. (2017). LULC analysis of urban spaces using Markov chain predictive model at Ranchi in India. Spatial Information Research, 25(3), 351-359. https://doi.org/10.1007/s41324-017-0102-x
  • Aksoy, H. & Kaptan, S. (2022). Simulation of future forest and land use/cover changes (2019–2039) using the cellular automata-Markov model. Geocarto Int, 37(4):1183–1202. https://doi.org/10.1080/10106049.2020.1778102
  • Aliani, H., Noorollahi, Y. & Babaie Kafaki, S. (2011). Physiographic factors effects on land use change in Talesh region using Remote Sensing and GIS. Renewable natural resources research, 2(3): 9-21. : https://sid.ir/paper/212269/en. (In Persian)
  • Arekhi, S., Niazi, Y. & Shabani, A. (2012). Evaluating Spatial Pattern of Changes Trend of Landuse/landcover with use of Transformation Techniques (Case Study: Dareshar Catchment, Ilam Province), Geographic Space, 12(38), 165. magiran.com/p1061151. (In persian)
  • Asif, M., Kazmi, J. H., Tariq, A., Zhao, N., Guluzade, R., Soufan, W.,... & Aslam, M. (2023). Modelling of land use and land cover changes and prediction using CA-Markov and Random Forest. Geocarto International, 38(1), 2210532., DOI: 1080/10106049.2023.2210532
  • Asori, M., & Adu, P. (2023). Modeling the impact of the future state of land use land cover change patterns on land surface temperatures beyond the frontiers of greater Kumasi: A coupled cellular automaton (CA) and Markov chains approaches. Remote Sensing Applications: Society and Environment, 29, 100908. https://doi.org/10.1016/j.rsase.2022.100908
  • Atef, I., Ahmed, W., & Abdel-Maguid, R. H. (2024). Future land use land cover changes in El-Fayoum governorate: a simulation study using satellite data and CA-Markov model. Stochastic Environmental Research and Risk Assessment, 38(2), 651-66. https://doi.org/10.1007/s00477-023-02592-0
  • Beroho M, Briak H, Cherif EK, Boulahfa I, Ouallali A, Mrabet R, Kebede F, Bernardino A, Aboumaria K Beroho, M., Briak, H., Cherif, E. K., Boulahfa, I., Ouallali, A., Mrabet, R.,... & Aboumaria, K. (2023). Future scenarios of land use/land cover (LULC) based on a CA-markov simulation model: case of a mediterranean watershed in Morocco. Remote Sensing, 15(4), 1162. https://doi.org/10.3390/rs15041162.
  • Buya, S., Tongkumchum, P., Rittiboon, K., & Chaimontree, S. (2022). Logistic regression model of built-up land based on grid-digitized data structure: a case study of krabi, Thailand. Journal of the Indian Society of Remote Sensing, 50(5), 909-922. https://doi.org/10.1007/s12524-022-01503-0
  • Datta, D., Deb, K., Fonseca, C. M., Lobo, F., Condado, P. & Seixas, J. (2007). Multi objective evolutionary algorithm for land-use management problem. International Journal of computational intelligence research, 3(4), 371-384.
  • Deb, K., Mohnn, M. & iishra, S. (2005). Evaluating the -domination based on multi objectives evolutionary algorithm for a quick computation of pareto-optimal solutions. Journal of evolutionary computation, 13(4), 501-525. doi: 10.1162/106365605774666895.
  • Dow, C. L. (2007). Assessing regional land‐use/cover influences on New Jersey Pinelands streamflow through hydrograph analysis. Hydrological Processes: An International Journal, 21(2), 185-197.
  • Eastman, J. R. (2009). IDRISI Taiga guide to GIS and image processing. Clark Labs Clark University, Worcester, MA.
  • El Haj, F. A., Ouadif, L., & Akhssas, A. (2023). Simulating and predicting future land-use/land cover trends using CA-Markov and LCM models. Case Studies in Chemical and Environmental Engineering, 7, 100342.
  • Elhag M, Boteva S (2016) Mediterranean land use and land cover classification assessment using high spatial resolution data. IOP Conf Ser Earth Environ Sci 44(4):42032. https://doi.org/10.1088/1755-1315/44/4/042032
  • Fathizad, H., Karimi, H. , Tazeh, M. and Tavakoli, M. (2014). Prediction of Land Use and Land Cover Changes in Arid and Semi-Arid Regions Using Satellite Images and Markov Chain Models (Case study: Doviraj Basin, Ilam Province). Desert Management, 2(3), 61-76. doi: 10.22034/jdmal.2014.17062.. (In Persian)
  • Fu, F., Deng, S., Wu, D., Liu, W., & Bai, Z. (2022). Research on the spatiotemporal evolution of land use landscape pattern in a county area based on CA-Markov model. Sustainable Cities and Society, 80, 103760. ISSN 2210-6707,
  • Gholizadeh, Z. , Farzadmehr, J. and Rostami Khalaj, M. (2023). Modeling and predicting land use changes using Markov chain Model (Case study: Ghaleh Jogh, Torbat-e-Heydarieh City). Journal of Water and Soil Conservation, 30(2), 75-96. doi: 10.22069/jwsc.2023.21009.3611.. (In Persian)
  • Girma, R., Fürst, C., & Moges, A. (2022). Land use land cover change modeling by integrating artificial neural network with cellular Automata-Markov chain model in Gidabo river basin, main Ethiopian rift. Environmental Challenges, 6, 100419, 100419, ISSN 2667-0100.
  • Hyandye C, Martz LW (2017) A Markovian and cellular automata land-use change predictive model of the Usangu Catchment. Int J Remote Sens 38(1):64–81. https://doi.org/10.1080/01431161.2016.1259675
  • Irani, T. , Abghari, H. and rasooli, A. A. (2024). Analyzing the trend of land use changes in the past and future in Zolachay watershed. Journal of Geography and Environmental Hazards, 13(2), 316-338-. doi: 10.22067/geoeh.2024.88446.1494. (In Persian)
  • Khan, F., Das, B., & Mohammad, P. (2022). Urban growth modeling and prediction of land use land cover change over Nagpur City, India using cellular automata approach. Geospatial technology for landscape and environmental management: sustainable assessment and planning, 261-282. https://doi.org/10.1007/978-981-16-7373-3_13.
  • Khoi D. N. & Suetsugi, T. (2014). Impact of climate and land-use changes on hydrological processes and sediment yield—a case study of the Be River catchment, Vietnam, Hydrological Sciences Journal, 59 (5), 1095-1108.
  • Koko, A. F., Yue, W., Abubakar, G. A., Hamed, R., & Alabsi, A. A. N. (2020). Monitoring and predicting spatio-temporal land use/land cover changes in Zaria City, Nigeria, through an integrated cellular automata and markov chain model (CA-Markov). Sustainability, 12(24), 10452. https://doi.org/10.3390/su122410452
  • Leopold, L. B. (1968). Hydrology for urban land planning: A guidebook on the hydrologic effects of urban land use (Vol. 554). US Geological Survey.
  • Masoomi, H. , Malekian, A. , Salajegheh, A. and Nazari Samani, A. (2020). An Assessment of the Effect of Land Use Change on the Runoff Using the Markov Chain and Cellular Automata in the Bidgol Watershed, the Province of Fars. Watershed Management Research, 33(2), 31-51. doi: 10.22092/wmej.2019.126983.1242.. (In Persian)
  • Memarian H, Balasundram SK, Talib JB, Sung CTB, Sood AM and Abbaspour K (2012) Validation of CA-Markov for simulation of land use and cover change in the Langat Basin, Malaysia
  • Munthali MG, Mustak S, Adeola A, Botai J, Singh SK, Davis N (2020) Modelling land use and land cover dynamics of Dedza district of Malawi using hybrid cellular automata and Markov model. Remote Sens Appl Soc Environ 17:100276. https://doi.org/10.1016/j.rsase.2019.100276
  • Mir Alizadehhfard, S. R. & Alibakhshi, S. M. (2016). Monitoring and forecasting of land use change by applying Markov chain model and land change modeler (Case study: Dehloran Bartash plains, Ilam). RS & GIS for Natural Resources,7(2):33-46. (In Persian)
  • Okwuashi, O., & Ndehedehe, C. E. (2020). Integrating machine learning with Markov chain and cellular automata models for modelling urban land use change. Remote Sens Appl: Soc Environ 21: 100461.https://doi.org/10.1016/j.rsase.2020.100461.
  • Pontius Jr, R. G., & Millones, M. (2011). Death to Kappa: birth of quantity disagreement and allocation disagreement for accuracy assessment. International journal of remote sensing, 32(15), 4407-4429. Int. J. Rem. Sens., 32, 4407-429.
  • Rahimzadeh keivi, M. (2015). Evaluation of the effects of land use change on the amount of runoff in watersheds using combined SWAT hydrological model and remote sensing technique (Case study: Lorestan-Al-Shatter basin). Master's. Thesis of Watershed Science, Faculty of Agriculture and Natural Resources, Malayer University. (In Persian)
  • Sayl, K. N., Sulaiman, S. O., Kamel, A. H., & Al-Ansari, N. (2022). Towards the generation of a spatial hydrological soil group map based on the radial basis network model and spectral reflectance band recognition. International Journal of Design & Nature and Ecodynamics, 17(5), 761-766.. https://doi.org/10.18280/ijdne.170514
  • Sibanda S, Ahmed F (2021) Modelling historic and future land use/land cover changes and their impact on wetland area in Shashe sub-catchment, Zimbabwe. Model Earth Syst Environ 7(1):57–70. https://doi.org/10.1007/s40808-020-00963-y
  • Somvanshi, S. S., Bhalla, O., Kunwar, P., Singh, M., & Singh, P. (2020). Monitoring spatial LULC changes and its growth prediction based on statistical models and earth observation datasets of Gautam Budh Nagar, Uttar Pradesh, India. Environment, Development and Sustainability, 22(2), 1073-109. https://doi.org/10.1007/s10668-018-0234-8
  • Tadese, S., Soromessa, T., & Bekele, T. (2021). Analysis of the current and future prediction of land use/land cover change using remote sensing and the CA‐Markov model in Majang forest biosphere reserves of Gambella, Southwestern Ethiopia. The scientific world journal, 2021(1), 6685045. https://doi.org/10.1155/2021/6685045

مقالات آماده انتشار، اصلاح شده برای چاپ
انتشار آنلاین از تاریخ 01 تیر 1405
  • تاریخ دریافت: 05 فروردین 1405
  • تاریخ بازنگری: 07 اردیبهشت 1405
  • تاریخ پذیرش: 27 خرداد 1405
  • تاریخ اولین انتشار: 27 خرداد 1405
  • تاریخ انتشار: 01 تیر 1405