CA-Markov Yöntemiyle 2018'den 2042'ye Arazi Kullanımı ve Arazi Örtüsü Değişimlerinin Tahmini: Türkiye'den Bir Vaka Çalışması

Year 2025, Volume: 25 Issue: 1, 34 - 52, 26.03.2025

Alkan Günlü , Fatih Sivrikaya , Hasan Emre Ünal

https://doi.org/10.17475/kastorman.1660540

Abstract

Çalışmanın amacı: Akyazı Orman İşletme Müdürlüğü’nde 2030 ve 2042 yıllarında arazi kullanım sınıflarında meydana gelebilecek potansiyel değişiklikleri belirlemektir.
Çalışma alanı: Çalışma alanı olarak Akyazı Orman İşletme Müdürlüğü seçilmiştir.
Materyal ve yöntem: Bu çalışmada, 2006, 2012 ve 2018 yıllarına ait CORINE arazi örtüsü veri setleri kullanılmıştır. Markov modeli, 2006 ve 2012 yılları için CORINE'den türetilen arazi kullanım sınıfları haritalarına dayanarak 2018 yılı için geçiş alanı ve geçiş olasılığı matrislerini türetilmiştir. Bu matrisler kullanılarak, 2018 yılındaki arazi kullanım sınıfları tahmini, CA-Markov modülü kullanılarak 10 yıllık bir simülasyon yoluyla gerçekleştirilmiştir.
Temel sonuçlar: Tahmin edilen arazi kullanım sınıfları haritası ile 2018 CORINE verilerinden türetilen arazi kullanım sınıfları haritası arasında bir karşılaştırma yapılmış ve %91.1'lik bir benzerlik oranı bulunmuştur. 2018'den 2042'ye kadar olan 24 yıl için toplam orman alanının %3.8 veya 581.5 hektar genişleyeceği tahmin edilmektedir.
Araştırma vurguları: Gelecek için elde edilen öngörülen çıktılar, özellikle orman ekosistemlerinin uzun vadeli sürdürülebilirliğinin sağlanmasında, revize edilmiş orman yönetim stratejilerinin geliştirilmesine yardımcı olabilir.

Keywords

Orman Amenajmanı, Sürdürülebilirlik, Simülasyon, Değişim Tespiti, Türkiye

Prediction of Land Use and Land Cover Changes from 2018 to 2042 Using CA-Markov: A Case Study from Türkiye

Abstract

Aim of study: To determine the potential changes that may occur in land use classes in Akyazı Forest Enterprise for 2030 and 2042.
Area of study: Akyazı Forest Enterprise was selected as the study area.
Material and method: In this study, the Coordination of Information on the Environment (CORINE) land use land cover (LULC) datasets for the years 2006, 2012 and 2018 were used. The Markov model derived transition area and transition probability matrices (TPM) for 2018 based on the LULC maps derived from CORINE for 2006 and 2012. These matrices were used to predict LULC classes in 2018 through a 10-year simulation using the CA-Markov module.
Main results: A comparison was made between the projected LULC classes map and the land use class map derived from the 2018 CORINE data, and a similarity rate of 91.1% was found. For the 24 years from 2018 to 2042, the total forest area is predicted to increase by 3.8% or 581.5 ha.
Research highlights: The forecasted outcomes acquired for the future can aid in developing revised forest management strategies, particularly in ensuring the long-term viability of forest ecosystems.

Keywords

Forest Management, Sustainability, Simulation, Change Detection, Türkiye

References

• Abdelkarim, A. (2023). Monitoring and forecasting of land use/ land cover (LULC) in Al-Hassa Oasis, Saudi Arabia based on the integration of the Cellular Automata (CA) and the Cellular Automata-Markov Model (CA-Markov). Geology, Ecology, and Landscapes, https://doi.org/10.1080/24749508.2022.2163741
• Agudelo-Hz, W. J., Castillo-Barrera, N. C. & Uriel, M. G. (2023). Scenarios of land use and land cover change in the Colombian Amazon to evaluate alternative post-conflict pathways. Scientific Reports, 13(1), 2152.
• 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.
• Aksoy, H. & Kaptan, S. (2021). Monitoring of land use/land cover changes using GIS and CA-Markov modeling techniques: A study in Northern Turkey. Environmental monitoring and assessment, 193(8), 507.
• Aksoy, H. & Kaptan, S. (2022). Simulation of future forest and land use/cover changes (2019–2039) using the cellular automata-Markov model. Geocarto International, 37(4), 1183-1202.
• Aksoy, H., Kaptan, S., Varol, T., Cetin, M. & Ozel, H. B. (2022). Exploring land use/land cover change by using density analysis method in yenice. International Journal of Environmental Science and Technology, 19(10), 10257-10274.
• Alan, İ., Demirörs, Z., Bayar, R. & Karabacak, K. (2020). Markov Chains based land cover estimation model development: The case of Ankara Province. lnternational Journal of Geography and Geography Education, (42), 650-667.
• Alkan, S. (2014). Kırsal nüfus değişiminin, ormanlar ve ormancılık üzerine etkileri (Trabzon ili örneği). Kastamonu University Journal of Forestry Faculty, 14(1), 69-78.
• Amini Parsa, V., Yavari, A. & Nejadi, A. (2016). Spatio-temporal analysis of land use/land cover pattern changes in Arasbaran Biosphere Reserve: Iran. Modeling earth systems and environment, (2), 1-13.
• Aneesha Satya, B., Shashi, M. & Deva, P. (2020). Future land use land cover scenario simulation using open-source GIS for the city of Warangal, Telangana, India. Applied Geomatics, (12), 281-290.
• Anonymous. (2015). Sakarya Orman Bölge Müdürlüğü, Akyazı Orman İşletme Müdürlüğü, Merkez Orman İşletme Şeflliği, fonksiyonel orman amenajman planı, Orman Genel Müdürlüğü, Orman İdaresi ve Planlama Dairesi, Ankara.
• Asif, M., Kazmi, J. H., Tariq, A., Zhao, N., Guluzade, R., Soufan, W., Almutairi, K. F., Sabagh, A. E. & Aslam, M. (2023). Modelling of land use and land cover changes and prediction using CA-Markov and Random Forest. Geocarto International, 38 (1), 2210532, https://doi.org/10.1080/10106049.2023.2210532.
• Atef, I., Ahmed, W. & Abdel-Maguid, R. H. (2023). 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, 1-14. https://doi.org/10.1007/s00477-023-02.
• Ayazli, I. E. (2024). Investigating the interactions between spatiotemporal land use/land cover dynamics and private land ownership. Land Use Policy, 141, 107165.
• Aydın, T. K. & Durdruran, S. S. (2024). Determining future scenarios of urban areas with cellular automata/Markov Chain Model method; example of Ereğli District Konya‑Türkiye (2030–2040). Earth Science Informatics, https://doi.org/10.1007/s12145-024-01283-w
• Badshah, M. T., Hussain, K., Rehman, A. U., Mehmood, K., Muhammad, B. & et al. (2024). The role of random forest and Markov chain models in understanding metropolitan urban growth trajectory. Frontiers in Forests and Global Change, (7), 1345047.
• Behera, M. D., 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–A geo-information based approach. Journal of earth system science, (121), 1011-1024.
• Beroho, M., Briak, H., Cherif, E. K., Boulahfa, I., Ouallali, A. & et al. (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.
• Bulut, S. (2023). Uydu görüntüsü ve uzaktan algılama teknikleri ile arazi kullanım sınıflarının belirlenmesi. Anadolu Orman Araştırmaları Dergisi, 9(2), 150-156.
• 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.
• Çakir, G., Sivrikaya, F. & Keleş, S. (2008). Forest cover change and fragmentation using Landsat data in Macka state forest enterprise in Turkey. Environmental Monitoring and Assessment, 137(1–3), 51-66. https://doi.org/ 10.1007/s10661-007-9728-9
• Chen, Y., Li, X., Liu, X. & Ai, B. (2014). Modeling urban land-use dynamics in a fast developing city using the modified logistic cellular automaton with a patch-based simulation strategy. International Journal of Geographical Information Science, 28(2), 234-255.
• Chim, K., Tunnicliffe, J., Shamseldin, A. & Ota, T. (2019). Land use change detection and prediction in upper Siem Reap River, Cambodia. Hydrology, 6(3), 1-23. https://doi.org/10.3390/hydrology6030064.
• Choukiker, S.K. & Dohare, D. (2021). A literature review on land use land cover changes detection using remote sensing and GIS. International Journal for Research in Applied Science & Engineering Technology. 9(3), https://doi.org/10.22214/ijraset.2021. 33349
• Cole, B., Smith, G. & Balzter, H. (2018). Acceleration and fragmentation of CORINE land cover changes in the United Kingdom from 2006–2012 detected by Copernicus IMAGE2012 satellite data. International Journal of Applied Earth Observation and Geoinformation, (73), 107-122.
• Demir, M. (2021). CORINE sistemine göre Kars ilinde arazi örtüsü/arazi kullanımı, değişimi ve projeksiyonu. Coğrafya Dergisi, (43), 93-110.
• Demirel, D. & Sivrikaya, F. (2023). Monitoring and Mapping Temporal and Spatial Land Use/Land Cover Change: A Case Study from Inebolu (Türkiye). Journal of Green Technology and Environment, 1(2), 13-21.
• Dey, N. N., Al Rakib, A., Kafy, A. A. & Raikwar, V. (2021). Geospatial modelling of changes in land use/land cover dynamics using Multi-layer Perceptron Markov chain model in Rajshahi City, Bangladesh. Environmental Challenges, (4), 100148.
• Eastman, J. R. (2012). Idrisi selva tutorial. Idrisi Production, Clark Labs-Clark University, 45(January), 51-63.
• 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.
• FAO (2021). Land use statistics and indicators statistics. Global, regional and country trends 1990-2019. FAOSTAT Analytical Brief Series, 28. Rome.
• Ghosh, P., Mukhopadhyay, A., Chanda, A., Mondal, P., Akhand, A., Mukherjee, S. & et al. (2017). Application of Cellular automata and Markov-chain model in geospatial environmental modeling-A review. Remote Sensing Applications: Society and Environment, (5), 64-77.
• Guan, D., Gao, W., Watari, K. & Fukahori, H. (2008). Land use change of Kitakyushu based on landscape ecology and Markov model. Journal of Geographical Sciences, (18), 455-468.
• Gull, A. & Mahmood, S. (2022). Spatio-temporal analysis and trend prediction of land cover changes using Markov chain model in Islamabad, Pakistan. Advanced GIS, 2(2), 52-61.
• Hamad, R., Balzter, H. & Kolo, K. (2018). Predicting land use/land cover changes using a CA-Markov model under two different scenarios. Sustainability, 10 (10), 3421.
• Hoek, G., Beelen, R., De Hoogh, K., Vienneau, D., Gulliver, J., Fischer, P. & Briggs, D. (2008). A review of land-use regression models to assess spatial variation of outdoor air pollution. Atmospheric environment, 42(33), 7561-7578.
• Hossein, M. K., Ahmed, A., Uddin, M. J., Saifullah, A. S. M., Babu, M. A. H. & Sarker, T. (2023). Prediction of land use and land cover changes (LULC) and urban growth analysis in Dhaka Metropolitan Area (DMA) using CA-Markov model and geospatial techniques. Arabian Journal of Geosciences, 16(8), 468.
• Hua, A. K. (2017). Application of CA-Markov model and land use/land cover changes in Malacca River watershed, Malaysia. Applied Ecology & Environmental Research, 15(4)
• 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.
• Hussain, S., Lu, L., Mubeen, M., Nasim, W., Karuppannan, S. & et al. (2022). Spatiotemporal variation in land use land cover in the response to local climate change using multispectral remote sensing data. Land, 11(5), 595. https://doi.org/10.3390/land11050595.
• 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.
• Işinkaralar, Ö. (2023a). Arazi Örtüsü Değişiminin CORINE Verisiyle Modellenmesi: Ankara İlinin Kentsel Büyüme Tahmini. Artium, 11(1), 54-60.
• Işınkaralar, Ö. (2023b). Simulation of urban growth’s pressure on urban blue-green space using the CORINE Database for Kocaeli, Türkiye. Forestist.
• Javaid, K., Ghafoor, G. Z., Sharif, F., Shahid, M. G., Shahzad, L. & et al. (2023). Spatio-temporal analysis of land use land cover change and its impact on land surface temperature of Sialkot City, Pakistan. Scientific Reports, 13(1), 22166.
• Kaptan, S. (2021). Changes in forest areas and land cover and their causes using ıntensity analysis: The case of Alabarda Forest Planning Unit. Environmental Monitoring and Assessment, 193(7), 1-17. https://doi.org/10.1007/ s10661-021-09089-9
• Kaptan, S., Aksoy, H. & Durkaya, B. (2022). Estimation of uneven-aged forest stand parameters, crown closure and land use/cover using the Landsat 8 OLI satellite image. Geocarto International, 37(5), 1408-1425.
• 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.
• Khawaldah, H. A., Farhan, I. & Alzboun, N. M. (2020). Simulation and prediction of land use and land cover change using GIS, remote sensing and CA-Markov model. Global Journal of Environmental Science and Management, 6(2), 215-232.
• 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 automaton and Markov chain model (CA-Markov). Sustainability, 12(24), 10452.
• Kuleli, T. & Bayazıt, S. (2022). Land cover change detection in the Turkish coastal zone based on 28-year (1990–2018) Corine data. Environmental Monitoring and Assessment, 194(12), 846.
• Mansour, S., Alahmadi, M., Atkinson, P. M. & Dewan, A. (2022). Forecasting of built-up land expansion in a Desert Urban Environment. Remote Sensing, 14(9), 2037. https://doi.org/10.3390/rs14092037
• Moghadam, H. S. & Helbich, M. (2013). Spatiotemporal urbanization processes in the megacity of Mumbai, India: A Markov chains-cellular automata urban growth model. Applied Geography, 40, 140-149. https://doi.org/ 10.1016/j.apgeog.2013.01.009.
• Mubea, K., Goetzke, R. & Menz, G. (2014). Applying cellular automata for simulating and assessing urban growth scenario based in Nairobi, Kenya. International Journal of Advanced Computer Science and Applications, (5), 1-13.
• Munthali, M. G., Davis, N., Adeola, A. M., Botai, J. O., Kamwi, J. M. & et al. (2019). Local perception of drivers of land-use and land-cover change dynamics across Dedza District, Central Malawi Region. Sustainability, 11(3), 832.
• Nadaf, F. M. & Gaonkar, V. G. P. (2021). Spatio-temporal monitoring and predicting the Land Use/Land Cover Transformations using Cellular Automata (CA)–Markov Model: A case study of Urban Canacona, Goa-India. Bulletin of Environment, Pharmacology and Life Sciences, (10), 204-213.
• Nath, B., Wang, Z., Ge, Y., Islam, K., P. 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.
• Nguyen, T. T. H. & Ngo, T. T. P. (2018). Land use/land cover change prediction in Dak Nong Province based on remote sensing and Markov Chain Model and Cellular Automata. Journal of Vietnamese Environment, 9(3), 132-140.
• Özdemir, S., Gülsoy, S., & Mert, A. (2020). Predicting the effect of climate change on the potential distribution of Crimean Juniper. Kastamonu University Journal of Forestry Faculty, 20(2), 133-142.
• Öztürk, D. (2013). Hücresel otomat-markov zinciri yöntemiyle samsun kıyı alanlarındaki mekansal değişimlerin modellenmesi. TMMOB Harita ve Kadastro Mühendisleri Odası, 14, 14-17.
• Rimal, B., Zhang, L., Keshtkar, H., Sun, X. & Rijal, S. (2018). Quantifying the spatiotemporal pattern of urban expansion and hazard and risk area identification in the Kaski District of Nepal. Land, 7(1), 37.
• Roy, S., Farzana, K., Papia, M. & Hasan, M. (2015). Monitoring and prediction of land use/land cover change using the integration of Markov chain model and cellular automation in the Southeastern Tertiary Hilly Area of Bangladesh. International Journal of Sciences : Basic and Applied Research, 24(4), 125-148.
• 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. Modeling Earth Systems and Environment, 7(1), 57-70.
• Singh, S. K., Mustak, S., Srivastava, P. K., Szabó, S. & Islam, T. (2015). Predicting spatial and decadal LULC changes through cellular automata Markov chain models using earth observation datasets and geo-information. Environmental Processes, 2, 61-78.
• Sivrikaya, F., Çakır, G., Kadioğullari, A., Keleş, S., Başkent, E. Z. & Terzioğlu, S. (2007). Evaluating land use/land cover changes and fragmentation in the Camili forest planning unit of northeastern Turkey from 1972 to 2005. Land Degradation & Development, 18(4), 383-396. https://doi.org/ 10.1002/ldr.782
• 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, 1073-1091.
• Surabuddin Mondal, M., Sharma, N., Kappas, M. & Garg, P. K. (2019). Ca Markov modeling of land use land cover dynamics and sensitivity analysis to identify sensitive parameter (S). The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 42, 723-729.
• Svestac, C., Herbei, M. V. & Sala, F. (2020). Land cover change detection in timis county based on corine land cover dabases from 1990-2018. Research Journal of Agricultural Science, 52 (3).
• Tariq, A., Yan, J. & Mumtaz, F. (2022). Land change modeler and CA-Markov chain analysis for land use land cover change using satellite data of Peshawar, Pakistan. Physics and Chemistry of the Earth, Parts A/B/C, 128, 103286.
• Toplu, R. (2024). Modeling the temporal and spatial land use change using a cellular automata-markov model. (MSC Thesis, Kastamonu University, Institute of Science).
• Üstün Topal, T. & Kurt Konakoğlu, S. S. (2023). Investigations of Spatial and Temporal Land Use/Land Cover Changes in Trabzon Province (1990-2018) Using CORINE Maps and Landscape Metrics. Journal of Anatolian Environmental and Animal Sciences, 8(3), 536-546. https://doi.org/10.35229/jaes.1353548
• Viana, C. M. & Rocha, J. (2018). Spatiotemporal analysis and scenario simulation of agricultural land use land cover using GIS and a Markov chain model. In Geospatial Technologies for All: short papers, posters and poster abstracts of the 21th AGILE Conference on Geographic Information Science. 21th AGILE Conference on Geographic Information Science.
• Winkler, K., Fuchs, R., Rounsevell, M. & Herold, M. (2021). Global land use changes are four times greater than previously estimated. Nature communications, 12(1), 2501.
• Xu, J., Mu, M., Liu, Y., Zhou, Z., Zhuo, H. & et al. (2023). Assessing 30-year land use and land cover change and the driving forces in Qian Jiang, China, using multitemporal remote sensing images. Water, 15(18), 3322.
• Yagoub, M. M. & Al Bizreh, A. A. (2014). Prediction of land cover change using Markov and cellular automata models: case of Al-Ain, UAE, 1992-2030. Journal of the Indian Society of Remote Sensing, 42, 665-671.
• Zadbagher, E., Becek, K. & Berberoglu, S. (2018). Modeling land use/land cover change using remote sensing and geographic information systems: case study of the Seyhan Basin, Turkey. Environmental monitoring and assessment, 190, 1-15.
• Žoncová, M. & Masný, M. (2022). Comparison of land cover spatial trend model and real land cover changes: case study of Slovak Republic. Geocarto International, 37(26), 13500-13517.