Modelling spatial and temporal changes in land use/cover of Marmara Island (Türkiye) using CA-Markov model
DOI: https://doi.org/10.3846/jeelm.2026.26635Abstract
In this study, Geographical Information Systems and Remote Sensing techniques were used to analyse the temporal spatial dynamics of land use on Marmara Island for 1991, 2001, 2011, and 2023 years representing major socio-economic transitions in Türkiye nd to model future land use for 2055 using the CA–Markov approach. NDVI values derived from multi-temporal Landsat imagery were used to assess vegetation responses to LULC change. Results indicate substantial declines in forests (38.4%), grasslands (28.3%), shrublands (54%), olive groves (15.4%) and agricultural areas (56.4%) between 1991 and 2023, while mining areas increased by 52% and are projected to expand by an additional 46% by 2055. NDVI values (–0.26 to +0.69) show pronounced vegetation loss in northern mining zones and relatively stable agricultural–forest mosaics in the south. Overall, the findings demonstrate that mining driven anthropogenic pressures are reshaping the island’s ecological structure, underscoring the need for sustainable land management.
Keywords:
land use change, CA-Markov model, remote sensing, NDVI, mining areas, landscape planningHow to Cite
Share
License
Copyright (c) 2026 The Author(s). Published by Vilnius Gediminas Technical University.
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
Aksoy, R. (1999). Marmara Adası’nda ilerleyen bölgesel metamorfizma ile tektonik tarihçe arasındaki ilişki [The relationship between progressive regional metamorphism and structural history in the Marmara Island]. Geological Bulletin of Turkey, 42(1), 1–14.
Altıner, F. (2022). Alansal değişime etki eden parametrelerin Ayvalik ılçesi örneğinde ıncelenmesi [The examination of parameters affecting the spatial change in the Ayvalık district example] [Doctoral dissertation, Çanakkale Onsekiz Mart University, School of Graduate Studies]. Canakkale.
Alptekin, G. (2021). Marmara marble and current design examples in the context of sustanability. Journal of Art & Design, 9(4), 1–16.
Amir Siddique, M., Wang, Y., Xu, N., Ullah, N., & Zeng, P. (2021). The spatiotemporal implications of urbanization for urban heat islands in Beijing: A predictive approach based on CA-Markov modeling (2004–2050). Remote Sensing, 13(22), Article 4697. https://doi.org/10.3390/rs13224697
Anwer, H. A., Mohamed, T., & Hassan, A. (2025). Assessing vegetation dynamics in Al Jazirah, Sudan using NDVI-based remote sensing techniques. Journal of the Saudi Society of Agricultural Sciences, 24(4), 1–12. https://doi.org/10.1007/s44447-025-00011-0
Appala, R. K., & Sivakumar, V. L. (2023). Hyderabad city land use/land cover changes multi-spatio temporal comparison using spectral angle mapper with maximum likelihood classification. Journal of Survey in Fisheries Sciences, 10(1S), 1748–1759.
Atik, M., Altan, T., & Artar, M. (2010). Land use changes in relation to coastal tourism developments in Turkish mediterranean. Polish Journal of Environmental Studies, 19(1).
Baqa, M. F., Chen, F., Lu, L., Qureshi, S., Tariq, A., Wang, S., Jing, L., Hamza, S., & Li, Q. (2021). Monitoring and modeling the patterns and trends of urban growth using urban sprawl matrix and CA-Markov model: A case study of Karachi, Pakistan. Land, 10(7), Article 700. https://doi.org/10.3390/land10070700
Bazi, Y., Bashmal, L., Rahhal, M. M. A., Dayil, R. A., & Ajlan, N. A. (2021). Vision transformers for remote sensing image classification. Remote Sensing, 13(3), Article 516. https://doi.org/10.3390/rs13030516
Behera, M., Borate, S. N., Panda, S. N., Behera, P. R., & Roy, P. S. (2012). Modelling and analyzing the watershed dynamics using Cellular Automata (CA)–Markov model – Ageo-information based approach. Journal of Earth System Science, 121(4), 1011–1024. https://doi.org/10.1007/s12040-012-0207-5
Bhatti, M. T., Gilani, H., Ashraf, M., Iqbal, M. S., & Munir, S. (2024). Field validation of NDVI to identify crop phenological signatures. Precision Agriculture, 25(5), 2245–2270. https://doi.org/10.1007/s11119-024-10165-6
Bingöl, F., Altıner, F., & Kelkit, A. (2023). Analyzing the most eligible site selection for biomass energy facilities through weighted overlay analysis: Case of Balikesir (Türkiye) province. Ege Üniversitesi Ziraat Fakültesi Dergisi, 60(1), 19–35. https://doi.org/10.20289/zfdergi.1136651
Boi, M. E., Fois, M., Podda, L., Porceddu, M., & Bacchetta, G. (2023). Using Mediterranean native plants for the phytoremediation of mining sites: An overview of the past and present, and perspectives for the future. Plants, 12(22), Article 3823. https://doi.org/10.3390/plants12223823
Cai, M., Zhang, Y., Guan, X., & Qiu, J. (2025). An adaptive spatiotemporal tensor reconstruction method for GIMMS-3g+ NDVI. Remote Sensing of Environment, 316, Article 114511. https://doi.org/10.1016/j.rse.2024.114511
Cao, M., Tian, Y., Wu, K., Chen, M., Chen, Y., Hu, X., Sun, Z., Zuo, L., Lin, J., Luo, L., Zhu, R., Xu, Z., Bandrova, T., Konecny, M., Yuan, W., Guo, H., Lin, H., & Lü, G. (2023). Future land-use change and its impact on terrestrial ecosystem carbon pool evolution along the Silk Road under SDG scenarios. Science Bulletin, 68(7), 740–749. https://doi.org/10.1016/j.scib.2023.03.012
Cengiz, S., & Yılmaz, B. (2016, October 5–7). Modelling of land use/cover in Malatya, the simulation of 2025-2045 land use/cover. In UZAL-CBS 2016 Symposium (pp. 49–57). Adana.
Chang, H., He, G., Wang, Q., Li, H., Zhai, J., Dong, Y., Zhao, Y., & Zhao, J. (2021). Use of sustainability index and cellular automata-Markov model to determine and predict long-term spatio-temporal variation of drought in China. Journal of Hydrology, 598, Article 126248. https://doi.org/10.1016/j.jhydrol.2021.126248
Chen, L., Yan, G., Wang, T., Ren, H., Calbó, J., Zhao, J., & McKenzie, R. (2012). Estimation of surface shortwave radiation components under all sky conditions: Modeling and sensitivity analysis. Remote Sensing of Environment, 123, 457–469. https://doi.org/10.1016/j.rse.2012.04.006
Cohen, J. (1968). Weighted kappa: Nominal scale agreement with provision for scaled disagreement or partial credit. Psychological Bulletin, 70(4), 213–220. https://doi.org/10.1037/h0026256
Corry, R. C. (2005). Characterizing fine-scale patterns of alternative agricultural landscapes with landscape pattern indices. Landscape Ecology, 20, 591–608. https://doi.org/10.1007/s10980-004-5036-8
Cui, Y., Li, L., Chen, L., Zhang, Y., Cheng, L., Zhou, X., & Yang, X. (2018). Land-use carbon emissions estimation forthe Yangtze River delta urban agglomeration using 1994–2016 Landsat image data. Remote Sensing, 10, Article 1334. https://doi.org/10.3390/rs10091334
Çetinkaya, B., & Toz, G. (2011). Coğrafi veri seçim işlemi sonuçlarının değerlendirilmesinde hata matrisinin kullanımı. İTÜ Dergisi/d, 6(5–6).
Daba, M. H., & You, S. (2022). Quantitatively assessing the future land-use/land-cover changes and their driving factors in the upper stream of the Awash River based on the CA–Markov model and their implications for water resources management. Sustainability, 14(3), Article 1538. https://doi.org/10.3390/su14031538
Dale, V. H., & Kline, K. L. (2013). Issues in using landscape indicators to assess land changes. Ecological Indicators, 28, 91–99. https://doi.org/10.1016/j.ecolind.2012.10.007
Derdemezi, E. T., Tsilimigkas, G., & Kizos, T. (2022). Mining activity and island landscape issues: Evidence from Cyclades islands, Greece. European Planning Studies, 30(2), 384–404. https://doi.org/10.1080/09654313.2021.1958172
DeWitt, J. D., Chirico, P. G., Bergstresser, S. E., & Warner, T. A. (2017). Multi-scale 46-year remote sensing change detectionof diamond mining and land cover in a conflict and post-conflict setting. Remote Sensing Application Society Environment, 8, 126–139. https://doi.org/10.1016/j.rsase.2017.08.002
Dikgwatlhe, P., & Mulenga, F. (2023). Perceptions of local communities regarding the impacts of mining on employment and economic activities in South Africa. Resources Policy, 80, Article 103138. https://doi.org/10.1016/j.resourpol.2022.103138
Ding, Y., Zhao, K., Zheng, X., & Jiang, T. (2014). Temporal dynamics of spatial heterogeneity over cropland quantified by time-series NDVI, near infrared and red reflectance of Landsat 8 OLI imagery. International Journal of Applied Earth Observation and Geoinformation, 30, 139–145. https://doi.org/10.1016/j.jag.2014.01.009
Emir, H. N. S., & Yıldırım, E. (2024). Improving degraded landscapes and evaluating them as a learning environment “The Eden Project”. Peyzaj, 6(1), 15–30. https://doi.org/10.53784/peyzaj.1495581
Esbah, H., Cook, E. A., & Ewan, J. (2009). Effects of increasing urbanization on the ecological integrity of open space preserves. Environmental Management, 43, 846–862. https://doi.org/10.1007/s00267-009-9274-z
Farbo, A., Sarvia, F., De Petris, S., Basile, V., & Borgogno-Mondino, E. (2024). Forecasting corn NDVI through AI-based approaches using sentinel 2 image time series. ISPRS Journal of Photogrammetry and Remote Sensing, 211, 244–261. https://doi.org/10.1016/j.isprsjprs.2024.04.011
Feng, Y., Liu, M., Liu, Y., Tong, X., & Deng, S. (2011). Modeling dynamic urban growth using cellular automata and particle swarm optimization rules. Landscape and Urban Planning, 102(3), Article 188–196. https://doi.org/10.1016/j.landurbplan.2011.04.004
Ferrari, E. (2023). Along the western margin of Park Am Gleisdreieck, an urban hybrid environment. Environment and History, 29(4), 467–475. https://doi.org/10.3197/096734023X16945097374236
Feyzolāhpour, M. (2024). Analysis of changes in the surface of anzali wetland using spectral ındices, random tree classification (RTC), and maximum likelihood classification (MLC) from 1992 to 2022. Geographical Studies of Coastal Areas Journal, 5(3), 18, 17–36.
Foody, G. M. (2022). Global and local assessment of image classification quality on an overall and per-class basis without ground reference data. Remote Sensing, 14(21), Article 5380. https://doi.org/10.3390/rs14215380
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, Article 103760. https://doi.org/10.1016/j.scs.2022.103760
Ghosh, P., Mukhopadhyay, A., Chanda, A., Mondal, P., Akhand, A., Mukherjee, S., Nayak, S. K., Ghosh, S., Mitar, D., Ghosh T., & Hazra, S. (2017). Application of Cellular automata and Markov-chain model in geospatial environmental modeling- A review. Remote Sensing Applications Society Environment, 5, 64–77. https://doi.org/10.1016/j.rsase.2017.01.005
Ghosh, S., Chatterjee, N. D., & Dinda, S. (2021). Urban ecological security assessment and forecasting using integrated DEMATEL-ANP and CA-Markov models: A case study on Kolkata Metropolitan Area, India. Sustainable Cities and Society, 68, Article 102773. https://doi.org/10.1016/j.scs.2021.102773
Giurco, D., & Cooper, C. (2012). Mining and sustainability: Asking the right questions. Minerals Engineering, 29, 3–12. https://doi.org/10.1016/j.mineng.2012.01.006
Gulpinar Sekban, D. U., & Acar, C. (2021). Determining usages in post-mining sites according to landscape design approaches. Land Degradation & Development, 32(8), 2661–2676. https://doi.org/10.1002/ldr.3933
Haddaway, N. R., Smith, A., Taylor, J. J., Andrews, C., Cooke, S. J., Nilsson, A. E., & Lesser, P. (2022). Evidence of the impacts of metal mining and the effectiveness of mining mitigation measures on social–ecological systems in Arctic and boreal regions: A systematic map. Environmental Evidence, 11(1), Article 30. https://doi.org/10.1186/s13750-022-00282-y
Han, J., Hayashi, Y., Gao, X., & Imura, H. (2009). Application of an integrated system dynamics and cellular automata model for urban growth assessment: A case study of Shanghai, China. Landscape and Urban Planning, 91(3), Article 133–141. https://doi.org/10.1016/j.landurbplan.2008.12.002
He, Y., Qiu, H., Song, J., Zhao, Y., Zhang, L., Hu, S., & Hu, Y. (2019). Quantitative contribution of climate change and human activities to runoff changes in the Bahe River watershed of the Qinling Mountains, China. Sustainable Cities and Society, 51, Article 101729. https://doi.org/10.1016/j.scs.2019.101729
Hemmings, S., & Kagel, M. (2010). Memory gardens: Aesthetic education and political emancipation in the “Landschaftspark Duisburg-Nord”. German Studies Review, 33(2), 243–261.
Hou, H., Ding, Z., Zhang, S., Guo, S., Yang, Y., Chen, Z., Mi, J., & Wang, X. (2021). Spatial estimate of ecological and environmental damage in an underground coal mining area on the Loess Plateau: Implications for planning restoration interventions. Journal of Cleaner Production, 287, Article 125061. https://doi.org/10.1016/j.jclepro.2020.125061
Hu, Y., Zheng, Y., & Zheng, X. (2013). Simulation of land-use scenarios for Beijing using CLUE-S and Markov composite models. Chinese Geographical Science, 23(1), 92–100. https://doi.org/10.1007/s11769-013-0594-9
Hussain, S., Mubeen, M., Nasim, W., Mumtaz, F., Abdo, H. G., Mostafazadeh, R., & Fahad, S. (2024). Assessment of future prediction of urban growth and climate change in district Multan, Pakistan using CA-Markov method. Urban Climate, 53, Article 101766. https://doi.org/10.1016/j.uclim.2023.101766
Hyandye, C., & Martz, L. W. (2017). A Markovian and cellular automata land-use change predictive model of the Usangu Catchment. International Journal of Remote Sensing, 38(1), 64–81. https://doi.org/10.1080/01431161.2016.1259675
Istanbul Mineral Exporters Association. (2024). Mineral export index reports. https://imib.org.tr/en/
Kalaycı, M., & Uzun, O. (2017). Recreational purpose evaluation of mine areas after mining. Uluslararası Bilimsel Araştırmalar Dergisi (IBAD), 2(2), 232–244. https://doi.org/10.21733/ibad.2131
Kariuki, R. W., Capitani, C., Munishi, L. K., Shoemaker, A., Courtney Mustaphi, C. J., William, N., Jane, P., & Marchant, R. (2022). Serengeti’s futures: Exploring land use and land cover change scenarios to craft pathways for meeting conservation and development goals. Frontiers in Conservation Science, 3, Article 920143. https://doi.org/10.3389/fcosc.2022.920143
Karlis, D. (2002). An EM type algorithm for maximum likelihood estimation of the normal–inverse Gaussian distribution. Statistics & Probability Letters, 57(1), 43–52. https://doi.org/10.1016/S0167-7152(02)00040-8
Keenan, J., & Holcombe, S. (2021). Mining as a temporary land use: A global stocktake of post-mining transitions and repurposing. The Extractive Industries and Society, 8(3), Article 100924. https://doi.org/10.1016/j.exis.2021.100924
Keshtkar, H., & Voigt, W. (2016). A spatiotemporal analysis of landscape change using an integrated Markov chain and cellular automata models. Modeling Earth Systems and Environment, 2(1), Article 10. https://doi.org/10.1007/s40808-015-0068-4
Khunrattanasiri, W. (2020). Comparative study on CA-Markov model and CLUE-S model for land use changed prediction in National Reserved Forest, Nan province. The Journal of Applied Science, 19, 87–100. https://doi.org/10.14416/j.appsci.2020.02.008
Khwarahm, N. R., Qader, S., Ararat, K., & Fadhil Al-Quraishi, A. M. (2021). Predicting and mapping land cover/land use changes in Erbil/Iraq using CA-Markov synergy model. Earth Science Informatics, 14(1), 393–406. https://doi.org/10.1007/s12145-020-00541-x
Koç, D. E., & Gündüz, Z. (2022). Relationship between geomorphological units with land use in the Marmara Island. Sosyal, Beşerî ve İdari Bilimler Dergisi, 5(9), 1279–1292.
Köse, M., & Kul, A. A. (2020). Investigation of sustainable utilization from wetlands and their surroundings in abandoned mining sites: Istanbul case study. Turkish Journal of Forestry, 21(3), 243–253. https://doi.org/10.18182/tjf.710561
Kriegler, F. J., Malila, W. A., Nalepka, R. F., & Richardson, W. (1969). Preprocessing transformations and their effect on multispectral recognition. Remote Sensing Environment, 1, 97–132.
Kumar, A., & Garg, R. D. (2023). Land cover mapping and change analysis using optimized random forest classifier incorporating fusion of texture and Gabor features. SN Computer Science, 4(5), Article 685. https://doi.org/10.1007/s42979-023-02111-6
Kumar, A., & Sharma, S. K. (2022). Information cryptography using cellular automata and digital image processing. Journal of Discrete Mathematical Sciences and Cryptography, 25(4), 1105–1111. https://doi.org/10.1080/09720529.2022.2072437
Lemenkova, P., & Debeir, O. (2023). Computing vegetation indices from the satellite images using GRASS GIS scripts for monitoring mangrove forests in the coastal landscapes of Niger Delta, Nigeria. Journal of Marine Science and Engineering, 11(4), Article 871. https://doi.org/10.3390/jmse11040871
Li, X., Xu, J., Jia, Y., Liu, S., Jiang, Y., Yuan, Z., Du, H., Han, R., & Ye, Y. (2024). Spatio-temporal dynamics of vegetation over cloudy areas in Southwest China retrieved from four NDVI products. Ecological Informatics, 81, Article 102630. https://doi.org/10.1016/j.ecoinf.2024.102630
Listyono, G. M., & Manurung, P. (2025). Land use and land cover (LULC) change from 2010 to 2015 driven by mining industries: A case study in Obi Island, Indonesia. Journal of Degraded and Mining Lands Management, 12(5), 8677–8691. https://doi.org/10.15243/jdmlm.2025.125.8677
Mansour, S., Al-Belushi, M., & Al-Awadhi, T. (2020). Monitoring land use and land cover changes in the mountainous cities of Oman using GIS and CA-Markov modelling techniques. Land Use Policy, 91, Article 104414. https://doi.org/10.1016/j.landusepol.2019.104414
Markov, A. A. (1906). Extension of the law of large numbers to dependent quantities. Journal Électronique d’Histoire des Probabilités et de la Statistique, 15(1), 135–156. http://eudml.org/doc/128778
McGarigal, K., Compton, B. W., Plunkett, E. B., DeLuca, W. V., Grand, J., Ene, E., & Jackson, S. D. (2018). A landscape index of ecological integrity to inform landscape conservation. Landscape Ecology, 33, 1029–1048. https://doi.org/10.1007/s10980-018-0653-9
Mhlongo, S. E. (2023). Evaluating the post-mining land uses of former mine sites for sustainable purposes in South Africa. Journal of Sustainable Mining, 22(2), Article 3. https://doi.org/10.46873/2300-3960.1381
Mi, J., Yang, Y., Zhang, S., An, S., Hou, H., Hua, Y., & Chen, F. (2019). Tracking the land use/land cover change in an area with underground mining and reforestation via continuous Landsat classification. Remote Sensing, 11(14), Article 1719. https://doi.org/10.3390/rs11141719
Mohan, M., Pathan, S.K., Narendrareddy, K., Kandya, A., & Pandey, S. (2011). Dynamics of urbanization and its impact on land-use/land-cover: A case study of megacity Delhi. Journal of Environmental Protection, 2(9), 1274–1283. https://doi.org/10.4236/jep.2011.29147
Myint, S. W., & Wang, L. (2006). Multicriteria decision approach for land use land cover change using Markov chain analysis and a cellular automata approach. Canadian Journal of Remote Sensing, 32(6), 390–404. https://doi.org/10.5589/m06-032
Ngandam Mfondoum, A. H., Hakdaoui, S., & Batcha, R. (2022). Landsat 8Bands’1 to 7 spectral vectors plus machine learning to improve land use/cover classification using Google Earth Engine. Annals of GIS, 28(3), 401–424. https://doi.org/10.1080/19475683.2022.2026475
Olufayokemi Rasheedat, O., & Ojodale Samuel, U. (2025). Geospatial analysis of vegetation health response to urbanisation and land use/land cover changes in Lokoja, Nigeria using NDVI. Integral Research, 2(4), 49–72.
Punia, A., Joshi, P. K., & Siddaiah, N. S. (2021). Characterizing Khetri copper mine environment using geospatial tools. SN Applied Sciences, 3(2), Article 174. https://doi.org/10.1007/s42452-021-04183-6
Rau, G., & Shih, Y. S. (2021). Evaluation of Cohen’s kappa and other measures of inter-rater agreement for genre analysis and other nominal data. Journal of English for Academic Purposes, 53, Article 101026. https://doi.org/10.1016/j.jeap.2021.101026
Rentier, E. S., & Cammeraat, L. H. (2022). The environmental impacts of river sand mining. Science of the Total Environment, 838, Article 155877. https://doi.org/10.1016/j.scitotenv.2022.155877
Rimal, B., Zhang, L., Keshtkar, H., Haack, B., Rijal, S., & Zhang, P. (2018). Land use/land cover dynamics and modeling of urban land expansion by the integration of cellular automata and Markov chain. ISPRS International Journal of Geo-Information, 7(4), Article 154. https://doi.org/10.3390/ijgi7040154
Rouse, J. W. (1973). Monitoring vegetation systems in the great plains with ERTS. In Third NASA Earth Resources Technology Satellite Symposium (pp. 309–317). https://cir.nii.ac.jp/crid/1571417124296079488#citations_container
Running, S. W. (1990). Estimating terrestrial primary productivity by combining remote sensing and ecosystem simulation. In R. J. Hobbs & H. A. Mooney (Eds.), Remote sensing of biosphere functioning (pp. 65–86). Springer-Verlag. https://doi.org/10.1007/978-1-4612-3302-2_4
Sahu, P., & Kumawat, D. M. (2022). Spatial analysis of Biomass Cover status around an Iron ore mine in the Kalimati forest area of the Joda mining circle of Keonjhar district by using the NDVI application. Journal of Geointerface, 1(2), 44–50.
Sakizadeh, M., Milewski, A., & Taghi Sattari, M. (2023). Analysis of long-term trend of stream flow and interaction effect of land use and landcover on water yield by SWAT model and statistical learning in part of Urmia Lake Basin, Northwest of Iran. Water, 15(4), 1–25. https://doi.org/10.3390/w15040690
Sang, L., Chao, Z., Yang, J., Zhu, D., & Yun, W. (2011). Simulation of land use spatial pattern of towns and villages based on CA–Markov model. Mathematical Computer Modelling, 54(3–4), 938–943. https://doi.org/10.1016/j.mcm.2010.11.019
Sarwar, A., Mehmood, Z., Saba, T., Qazi, K. A., Adnan, A., & Jamal, H. (2019). A novel method for content-based image retrieval to improve the effectiveness of the bag-of-words model using a support vector machine. Journal of Information Science, 45(1), 117–135. https://doi.org/10.1177/0165551518782825
Seto, K. C., Güneralp, B., & Hutyra, L. R. (2012). Global forecasts of urban ex-pansion to 2030 and direct impacts on biodiversity and carbon pools. Proceedings of the National Academy of Sciences, 109(40), 16083–16088. https://doi.org/10.1073/pnas.1211658109
Sharma, L. K., Bu, H., Denton, A., & Franzen, D. W. (2015). Active-optical sensors using red NDVI compared to red edge NDVI for prediction of corn grain yield in North Dakota, USA. Sensors, 15(11), 27832–27853. https://doi.org/10.3390/s151127832
Shivakumar, B. R., & Rajashekararadhya, S. V. (2018). Investigation on land cover mapping capability of maximum likelihood classifier: A case study on North Canara, India. Procedia Computer Science, 143, 579–586. https://doi.org/10.1016/j.procs.2018.10.434
Solaimani, K., & Darvishi, S. (2024). Comparative analysis of land use changes modeling based-on new hybrid models and CA-Markov in the Urmia lake basin. Advances in Space Research, 74(8), 3749–3764. https://doi.org/10.1016/j.asr.2024.06.078
Soltanmohammadi, H., Osanloo, M., & Bazzazi, A. A. (2010). An analytical approach with a reliable logic and a ranking policy for post-mining land-use determination. Land Use Policy, 27, 364–372. https://doi.org/10.1016/j.landusepol.2009.05.001
Subedi, P., Subedi, K., & Thapa, B. (2013). Application of a hybrid cellular automaton–Markov (CA-Markov) model in land-use change prediction: A case study of Saddle Creek Drainage Basin, Florida. Applied Ecology and Environmental Sciences, 1(6), 126–132. https://doi.org/10.12691/aees-1-6-5
Thakur, S., Maity, D., Mondal, I., Basumatary, G., Ghosh, P. B., Das, P., & De, T. K. (2021). Assessment of changes in land use, land cover, and land surface temperature in the mangrove forest of Sundarbans, northeast coast of India. Environment, Development and Sustainability, 23, 1917–1943. https://doi.org/10.1007/s10668-020-00656-7
Theres, B. L., & Selvakumar, R. (2022). Comparison of landuse/landcover classifier for monitoring urban dynamics using spatially enhanced landsat dataset. Environmental Earth Sciences, 81(5), Article 142. https://doi.org/10.1007/s12665-022-10242-x
Tobler, W. R. (1970). A computer movie simulating urban growth in the Detroit region. Economic Geography, 46, 234–240. https://doi.org/10.2307/143141
Ulam, S. (1962). On some mathematical problems connected with patterns of growth of figures. In Proceedings of Symposia in Applied Mathematics (pp. 215–224). American Mathematical Society. https://doi.org/10.1090/psapm/014/9947
Uzun, M. (2023). Analysis of changes caused by marble quarries in Marmara Island with anthropogenic geomorphology approach. Eastern Geographical Review, 28(50), 75–87. https://doi.org/10.5152/EGJ.2023.23071
Uzun, O., & Bollukcu, P. (2009). Assesment of surface mining areas in Bartın central county in terms of biological landscape reclamation. In Bartın Orman Fakültesi Dergisi I. Ulusal Batı Karadeniz Ormancılık Kongresi Bildiriler Kitabı, Özel Sayı (pp. 486–500). https://www.researchgate.net/publication/316879816_BARTIN_MERKEZ_ILCE_SINIRLARI_ICINDE_YER_ALAN_ACIK_OCAK_ISLETMELERININ_PEYZAJ_ONARIMI_-_BIYOLOJIK_ONARIM_ACISINDAN_DEGERLENDIRILMESI
Verburg, P. H., & Overmars, K. P. (2009). Combining top-down and bottom-up dynamics in land use modeling: Exploring the future of abandoned farmlands in Europe with the Dyna-CLUE model. Landscape Ecology, 24(9), 1167–1181. https://doi.org/10.1007/s10980-009-9355-7
Vergni, L., Todisco, F., & Di Lena, B. (2021). Evaluation of the similarity between drought indices by correlation analysis and Cohen’s Kappa test in a Mediterranean area. Natural Hazards, 108(2), 2187–2209. https://doi.org/10.1007/s11069-021-04775-w
Visser, H., & de Nijs, T. (2006). The map comparison kit. Environmental Modelling & Software, 21, 346–358. https://doi.org/10.1016/j.envsoft.2004.11.013
Vohra, R., & Tiwari, K. C. (2023). Analysis of land use and land cover changes and their impact on temperature using landsat satellite imageries. Environment, Development and Sustainability, 25(8), 8623–8650. https://doi.org/10.1007/s10668-022-02416-1
von Neumann, J. (1966). Theory of self-reproducing automata (A. W. Burks, Ed.). University of Illinois Press.
Wegenast, T., & Beck, J. (2020). Mining, rural livelihoods and food security: A disaggregated analysis of sub-Saharan Africa. World Development, 130, Article 104921. https://doi.org/10.1016/j.worlddev.2020.104921
Xu, D., Zhang, K., Cao, L., Guan, X., & Zhang, H. (2022). Driving forces and prediction of urban land use change based on the geodetector and CA-Markov model: A case study of Zhengzhou, China. International Journal of Digital Earth, 15(1), 2246–2267. https://doi.org/10.1080/17538947.2022.2147229
Xue, H., Shen, X., & Pan, W. (2021). Constrained maximum likelihood-based Mendelian randomization robust to both correlated and uncorrelated pleiotropic effects. The American Journal of Human Genetics, 108(7), 1251–1269. https://doi.org/10.1016/j.ajhg.2021.05.014
Yang, X., Zheng, X.-Q., & Lv, L.-N. (2012). A spatiotemporal model of land use change based on ant colony optimization, Markov chain and cellular automata. Ecological Modelling, 233, 11–19. https://doi.org/10.1016/j.ecolmodel.2012.03.011
Zhang, J., Zhang, J., Wu, C., Zhang, Y., Guo, M., Li, H., & Wang, R. (2023). Comprehensive quantitative evaluation of the future mining: A comparison of socio-economic benefits and eco-environmental impacts. Ore Geology Reviews, 162, Article 105727. https://doi.org/10.1016/j.oregeorev.2023.105727
Zheng, F., & Hu, Y. (2018). Assessing temporal-spatial land use simulation effects with CLUE-S and Markov-CA models in Beijing. Environmental Science and Pollution Research, 25(32), 32231–32245. https://doi.org/10.1007/s11356-018-3189-2
Zhou, W., Wang, J., Qian, Y., Pickett, S. T., Li, W., & Han, L. (2018). The rapid but “invisible” changes in urban greenspace: A comparative study of nine Chinese cities. Science of the Total Environment, 627, 1572–1584. https://doi.org/10.1016/j.scitotenv.2018.01.335
View article in other formats
CrossMark check
Published
Issue
Section
Copyright
Copyright (c) 2026 The Author(s). Published by Vilnius Gediminas Technical University.
License
This work is licensed under a Creative Commons Attribution 4.0 International License.