Land use/cover change modeling with emphasis on built-up land growth with the help of CA-Markov model integration and multi-criteria decision analysis based on GIS. (Case study: Aras River watershed)

Document Type : Original Article

Authors

1 M.Sc. Student, Department of Geography, Ferdowsi University of Mashhad, Geographic Information Science/System and Remote Sensing Laboratory (GISSRS: Lab), Ferdowsi University of Mashhad, Mashhad, Iran

2 Associate Prof., Department of Geography, Ferdowsi University of Mashhad, Geographic Information Science/System and Remote Sensing Laboratory (GISSRS: Lab), Ferdowsi University of Mashhad, Mashhad, Iran

Abstract

Introduction: In recent years, global population growth and urban expansion have led to significant land use and cover changes. These changes have numerous detrimental consequences, such as increasing surface temperatures, deforestation, desertification, degradation of ecosystem services, biodiversity loss, and threats to food security. Therefore, monitoring and modeling these changes are essential for optimal land management and sustainable utilization of natural resources. Given that the Aras River Basin has undergone significant transformations over time, particularly in built-up land developments, this research focuses on modeling land use/land cover changes in this area.
                  
Materials and Methods: Initially, land use maps for the region were extracted for the years 2000 and 2020 from the Globeland30 project of the China National Geomatics Center. Subsequently, two maps were prepared to illustrate the potential growth of built-up land based on a land development scenario. This was achieved using advanced decision-making analysis methods based on GIS, including BWM and MEREC. Finally, these two maps, along with the land use maps, were combined to form the input for the CA-Markov model. The modeling process was carried out twice: once using the BWM + CA-Markov combination, and again using the CA-Markov + MEREC combination for the year 2040.
 
Results and Discussion: The examination of the results demonstrated that in the output of the combined BWM + CA-Markov model, the extent of built-up land increased from 603 square kilometers in 2020 to over 930 square kilometers in 2040. Meanwhile, this figure was approximately 829 square kilometers in the output of the MEREC + CA-Markov model. Furthermore, the final results obtained from the intersection of these combined models also indicated an increase in the extent of this land from 603 square kilometers in 2020 to 930 square kilometers in 2040.
Conclusion: The continuous growth of built-up land in this basin can lead to the destruction of environmental resources and pose threats to ecosystems. The findings of this study provide relevant managers with valuable insights for optimal management of future conditions and the provision of necessary infrastructure.

Keywords


Aalıjahan, M., Salahi, B., & Hatami, D. (2021). Investigating the relationship between changes in atmospheric greenhouse gases and discharge fluctuations in the Basin of Aras River. lnternational Journal of Geography and Geography Education, (44), 461-474.  https://doi.org/10.32003/igge.852263
Alsharif, M., Alzandi, A. A., Shrahily, R., & Mobarak, B. (2022). Land Use Land Cover Change Analysis for Urban Growth Prediction Using Landsat Satellite Data and Markov Chain Model for Al Baha Region Saudi Arabia. Forests, 13(10), 1530.  https://doi.org/10.3390/f13101530
Arif, M., Sengupta, S., Mohinuddin, S., & Gupta, K. (2023). Dynamics of land use and land cover change in peri urban area of Burdwan city, India: a remote sensing and GIS based approach. GeoJournal, 1-25.  https://doi.org/10.1007/s10708-023-10860-3
Aslami, F., & Ghorbani, A. (2018). Object-based land-use/land-cover change detection using Landsat imagery: a case study of Ardabil, Namin, and Nir counties in northwest Iran. Environmental monitoring and assessment, 190, 1-14. https://doi.org/10.1007/s10661-018-6751-y
Camara, M., Jamil, N., Abdullah, A., & Hashim, R. (2020). Integrating cellular automata Markov model to simulate future land use change of a tropical basin. Global Journal of Environmental Science and Management, 6(3), 403-414.  https://doi.org/10.22034/gjesm.2020.03.09
Chen, H., Tackie, E. A., Ahakwa, I., Musah, M., Salakpi, A., Alfred, M., & Atingabili, S. (2022). Does energy consumption, economic growth, urbanization, and population growth influence carbon emissions in the BRICS? Evidence from panel models robust to cross-sectional dependence and slope heterogeneity. Environmental Science and Pollution Research, 29(25), 37598-37616.  https://doi.org/10.1007/s11356-021-17671-4
Chen, J., Chen, J., Liao, A., Cao, X., Chen, L., Chen, X., He, C., Han, G., Peng, S., & Lu, M. (2015). Global land cover mapping at 30 m resolution: A POK-based operational approach. ISPRS Journal of Photogrammetry and Remote Sensing, 103, 7-27.  https://doi.org/10.1016/j.isprsjprs.2014.09.002
Chen, Z., Huang, M., Zhu, D., & Altan, O. (2021). Integrating remote sensing and a markov-FLUS model to simulate future land use changes in Hokkaido, Japan. Remote Sensing, 13(13), 2621.  https://doi.org/10.3390/rs13132621
Christensen, M., & Jokar Arsanjani, J. (2020). Stimulating implementation of sustainable development goals and conservation action: predicting future land use/cover change in Virunga National Park, Congo. Sustainability, 12(4), 1570.  https://doi.org/10.3390/su12041570
Geranian, H. (2022). Determination of Potential Mineralization Areas by Hybrid Multi-Criteria Decision-Making Methods in the Khoynehrud Region of East Azerbaijan. Journal of Mineral Resources Engineering, 7(2), 25-46.  https://doi.org/10.30479/JMRE.2022.14635.1470
Gurbuz, M., & Cilek, A. (2023). Analysis of Urban Land Use Change Using Remote Sensing and Different Change Detection Techniques: the Case of Ankara Province. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 48, 515-520.  https://doi.org/10.5194/isprs-archives-XLVIII-M-1-2023-515-2023
Hajihoseini, M., Morid, S., Emamgholizadeh, S., Amirahmadian, B., Mahjoobi, E., & Gholami, H. (2023). Conflict and cooperation in Aras International Rivers Basin: status, trend, and future. Sustainable Water Resources Management, 9(1), 28.  https://doi.org/10.1007/s40899-022-00799-7
He, Y., Wu, W., Xie, X., Ke, X., Song, Y., Zhou, C., Li, W., Li, Y., Jing, R., & Song, P. (2023). Land Use/Cover Change Prediction Based on a New Hybrid Logistic-Multicriteria Evaluation-Cellular Automata-Markov Model Taking Hefei, China as an Example. Land, 12(10), 1899.  https://doi.org/10.3390/land12101899
Hishe, S., Bewket, W., Nyssen, J., & Lyimo, J. (2020). Analysing past land use land cover change and CA-Markov-based future modelling in the Middle Suluh Valley, Northern Ethiopia. Geocarto International, 35(3), 225-255.  https://doi.org/10.1080/10106049.2018.1516241
Hua, A. (2017). Application of CA-Markov model and land use/land cover changes in Malacca River watershed, Malaysia. Applied Ecology & Environmental Research, 15(4).   https://doi.org/10.15666/aeer/1504_605622
Isinkaralar, O., Varol, C., & Yilmaz, D. (2022). Digital mapping and predicting the urban growth: integrating scenarios into cellular automata—Markov chain modeling. Applied Geomatics, 1-11.  https://doi.org/10.1007/s12518-022-00464-w
Jana, A., Jat, M. K., Saxena, A., & Choudhary, M. (2022). Prediction of land use land cover changes of a river basin using the CA-Markov model. Geocarto International, 37(26), 14127-14147.  https://doi.org/10.1080/10106049.2022.2086634
Jones, G. L., & Qin, Q. (2022). Markov chain Monte Carlo in practice. Annual Review of Statistics and Its Application, 9, 557-578.  https://doi.org/10.1146/annurev-statistics-040220-090158
Kamaraj, M., & Rangarajan, S. (2022). Predicting the future land use and land cover changes for Bhavani basin, Tamil Nadu, India, using QGIS MOLUSCE plugin. Environmental Science and Pollution Research, 1-12.  https://doi.org/10.21203/rs.3.rs-616393/v1
Karimzadeh Motlagh, Z., Lotfi, A., Pourmanafi, S., & Ahmadizadeh, S. (2022). Evaluation and Prediction of Land-Use Changes using the CA_Markov Model. Geography and Environmental Planning, 33(2), 63-80.  https://doi.org/10.22108/gep.2022.130601.1458
Keshavarz-Ghorabaee, M., Amiri, M., Zavadskas, E. K., Turskis, Z., & Antucheviciene, J. (2021). Determination of objective weights using a new method based on the removal effects of criteria (MEREC). Symmetry, 13(4), 525.  https://doi.org/10.3390/sym13040525
Khoshnoodmotlagh, S., Verrelst, J., Daneshi, A., Mirzaei, M., Azadi, H., Haghighi, M., Hatamimanesh, M., & Marofi, S. (2020). Transboundary basins need more attention: Anthropogenic impacts on land cover changes in aras river basin, monitoring and prediction. Remote Sensing, 12(20), 3329.  https://doi.org/10.3390/rs12203329
Kisamba, F. C., & Li, F. (2023). Analysis and modelling urban growth of Dodoma urban district in Tanzania using an integrated CA–Markov model. GeoJournal, 88(1), 511-532.  https://doi.org/10.1007/s10708-022-10617-4
Kourosh Niya, A., Huang, J., Kazemzadeh-Zow, A., Karimi, H., Keshtkar, H., & Naimi, B. (2020). Comparison of three hybrid models to simulate land use changes: a case study in Qeshm Island, Iran. Environmental monitoring and assessment, 192, 1-19.  https://doi.org/10.1007/s10661-020-08274-6
Latue, P. C., & Rakuasa, H. (2023). Analysis of Land Cover Change Due to Urban Growth in Central Ternate District, Ternate City using Cellular Automata-Markov Chain. Journal of Applied Geospatial Information, 7(1), 722-728.  https://doi.org/10.30871/jagi.v7i1.4653
Li, J., Dong, S., Li, Y., Wang, Y., Li, Z., & Li, F. (2022). Effects of land use change on ecosystem services in the China–Mongolia–Russia economic corridor. Journal of Cleaner Production, 360, 132175.  https://doi.org/10.1016/j.jclepro.2022.132175
Liping, C., Yujun, S., & Saeed, S. (2018). Monitoring and predicting land use and land cover changes using remote sensing and GIS techniques—A case study of a hilly area, Jiangle, China. PloS one, 13(7), e0200493.  https://doi.org/10.1371/journal.pone.0200493
Lu, L., Xue, Q., Zhang, X., Qin, C., & Jia, L. (2023). Spatiotemporal Variation and Quantitative Attribution of Carbon Storage Based on Multiple Satellite Data and a Coupled Model for Jinan City, China. Remote Sensing, 15(18), 4472.  https://doi.org/10.3390/rs15184472
Minaei, F., Minaei, M., Kougias, I., Shafizadeh-Moghadam, H., & Hosseini, S. A. (2021). Rural electrification in protected areas: A spatial assessment of solar photovoltaic suitability using the fuzzy best worst method. Renewable Energy, 176, 334-345.  https://doi.org/10.1016/j.renene.2021.05.087
Minaei, M., & Kainz, W. (2016). Watershed land cover/land use mapping using remote sensing and data mining in Gorganrood, Iran. ISPRS International journal of geo-information, 5(5), 57.  https://doi.org/10.3390/ijgi5050057
Mirzaei, M., Jafari, A., Verrlest, J., Haghighi, M., Zargarnia, A. H., Khoshnoodmotlagh, S., Azadi, H., & Scheffran, J. (2020). Trans-boundary land cover changes and its influences on water crisis: Case study of the Aras River. Applied Geography, 124, 102323.  https://doi.org/10.1016/j.apgeog.2020.102323
Mokarram, M., & Pham, T. M. (2023). Prediction of drought-driven land use/land cover changes in the Bakhtegan Lake watershed of Iran using Markov chain cellular automata model and remote sensing data. Natural Hazards, 116(1), 1291-1314.  https://doi.org/10.1007/s11069-022-05721-0
Moradi, E., & Sharifi, A. (2023). Assessment of forest cover changes using multi-temporal Landsat observation. Environment, development and sustainability, 25(2), 1351-1360.  https://doi.org/10.1007/s10668-021-02097-2
Mostafazadeh, R., & Talebi khiavi, H. (2022). Landscape change assessment and its prediction in a mountainous gradient with diverse land‑uses. Environment Development and Sustainability. https://doi.org/10.1007/s10668-022-02862-x
Mustafa, U., & Ghasemlouni̇a, R. (2021). Flood prioritization watersheds of The Aras River, based on geomorphometric properties: Case study Iğdır Province. Jeomorfolojik Araştırmalar Dergisi(6), 21-40.  https://doi.org/10.46453/jader.781152
Nasehi, F., Hassani, A., Monavvari, M., Karbassi, A., & Khorasani, N. (2013). Evaluating the metallic pollution of riverine water and sediments: a case study of Aras River. Environmental monitoring and assessment, 185, 197-203.  https://doi.org/10.1007/s10661-012-2543-y
Ngo, T. D. (2023). Demographic trends and population health: tackling inequality in a world of eight billion people. In (Vol. 8, pp. e012137): BMJ Specialist Journals. https://doi.org/10.1136/bmjgh-2023-012137
Pertuack, S., & Latue, P. C. (2023). Geographic Artificial Intelligence and Unmanned Aerial Vehicles Application for Correlation Analysis of Settlement Density and Land Surface Temperature in Panggang Island Jakarta. Buana Jurnal Geografi, Ekologi Dan Kebencanaan, 1(1), 39-47.  https://doi.org/ 10.56211/buana.v1i1.340
Rabby, Y. W., Li, Y., Abedin, J., & Sabrina, S. (2022). Impact of land use/land cover change on landslide susceptibility in Rangamati municipality of Rangamati District, Bangladesh. ISPRS International journal of geo-information, 11(2), 89.  https://doi.org/10.3390/ijgi11020089
Rahman, M. M., & Szabó, G. (2022). Sustainable Urban Land-Use Optimization Using GIS-Based Multicriteria Decision-Making (GIS-MCDM) Approach. ISPRS International journal of geo-information, 11(5), 313.  https://doi.org/10.3390/ijgi11050313
Rakuasa, H., Sihasale, D. A., Somae, G., & Latue, P. C. (2023). Prediction of Land Cover Model for Central Ambon City in 2041 Using the Cellular Automata Markov Chains Method. Jurnal Geosains dan Remote Sensing, 4(1), 1-10.  https://doi.org/10.23960/jgrs.2023.v4.i1.85
Rezaei, J. (2015). Best-worst multi-criteria decision-making method. Omega, 53, 49-57.  https://doi.org/10.1016/j.omega.2014.11.009
Samat, N. (2014). Monitoring the expansion of built-up areas in Seberang Perai region, Penang State, Malaysia. IOP Conference Series: Earth and Environmental Science,  https://doi.org/10.1088/1755-1315/18/1/012180
Sanakhan, A., Solgi, A., Sorbi, A., & Arian, M. (2020). Survey of active tectonic: the influence of river morphotectonic in Aras Basin. Arabian Journal of Geosciences, 13, 1-20.  https://doi.org/10.1007/s12517-020-05484-7
Shafizadeh-Moghadam, H., Minaei, M., Feng, Y., & Pontius Jr, R. G. (2019). GlobeLand30 maps show four times larger gross than net land change from 2000 to 2010 in Asia. International Journal of Applied Earth Observation and Geoinformation, 78, 240-248.  https://doi.org/10.1016/j.jag.2019.01.003
Shah, M. I., Abbas, S., Olohunlana, A. O., & Sinha, A. (2023). The impacts of land use change on biodiversity and ecosystem services: An empirical investigation from highly fragile countries. Sustainable Development, 31(3), 1384-1400.  https://doi.org/10.1002/sd.2454
Shukla, A. K., Ojha, C. S. P., Mijic, A., Buytaert, W., Pathak, S., Garg, R. D., & Shukla, S. (2018). Population growth, land use and land cover transformations, and water quality nexus in the Upper Ganga River basin. Hydrology and Earth System Sciences, 22(9), 4745-4770.  https://doi.org/10.5194/hess-22-4745-2018
Sohail, M. T., Manzoor, Z., Ehsan, M., Al-Ansari, N., Khan, M. B., Shafi, A., Ullah, J., Hussain, A., Raza, D., & Usman, U. (2023). Impacts of urbanization, LULC, LST, and NDVI changes on the static water table with possible solutions and water policy discussions: A case from Islamabad, Pakistan. Frontiers in Environmental Science, 11, 1018500.  https://doi.org/10.3389/fenvs.2023.1018500
Sonu, T., & Bhagyanathan, A. (2022). The impact of upstream land use land cover change on downstream flooding: A case of Kuttanad and Meenachil River Basin, Kerala, India. Urban Climate, 41, 101089  . https://doi.org/10.1016/j.uclim.2022.101089
Talebi khiavi, H., Mostafazadeh, R., Asadi, M., & Asbaghian, K. (2022). Temporal land use change and its economic values under competing driving forces in a diverse land use configuration. Arabian Journal of Geosciences, 1597. https://doi.org/10.1007/s12517-022-10890-0
Tavangar, S., Moradi, H., Massah Bavani, A., & Gholamalifard, M. (2021). A futuristic survey of the effects of LU/LC change on stream flow by CA–Markov model: A case of the Nekarood watershed, Iran. Geocarto International, 36(10), 1100-1116.  https://doi.org/10.1080/10106049.2019.1633419
Vaddiraju, S. C., Talari, R., Bhavana, K., & Apsana, S. (2023). Future Land Use Land Cover Scenario Simulation Using Open-Source GISFor The Saroor Nagar Watershed, Telangana, India.  https://doi.org/10.21203/rs.3.rs-3091123/v1
Vu, T.-T., & Shen, Y. (2021). Land-use and land-cover changes in dong trieu district, vietnam, during past two decades and their driving forces. Land, 10(8), 798.  https://doi.org/10.3390/land10080798
Xiao, T., Ran, F., Li, Z., Wang, S., Nie, X., Liu, Y., Yang, C., Tan, M., & Feng, S. (2023). Sediment organic carbon dynamics response to land use change in diverse watershed anthropogenic activities. Environment International, 172, 107788.  https://doi.org/10.1016/j.envint.2023.107788
Xu, J., Zhang, L., Li, J., Cao, Z., Yang, H., & Chen, X. (2021). Probabilistic estimation of variogram parameters of geotechnical properties with a trend based on Bayesian inference using Markov chain Monte Carlo simulation. Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, 15(2), 83-97.  https://doi.org/10.1080/17499518.2020.1757720
Yulianto, F., Maulana, T., & Khomarudin, M. R. (2018). Analysis of the dynamics of land use change and its prediction based on the integration of remotely sensed data and CA-Markov model, in the upstream Citarum Watershed, West Java, Indonesia. International Journal of Digital Earth.  https://doi.org/10.1080/17538947.2018.1497098
Zarandian, A., Badamfirouz, J., Musazadeh, R., Rahmati, A., & Azimi, S. B. (2018). Scenario modeling for spatial-temporal change detection of carbon storage and sequestration in a forested landscape in Northern Iran. Environmental monitoring and assessment, 190(8), 474.  https://doi.org/10.1007/s10661-018-6845-6
Zarandian, A., Mohammadyari, F., Mirsanjari, M. M., & Visockiene, J. S. (2023). Scenario modeling to predict changes in land use/cover using Land Change Modeler and InVEST model: a case study of Karaj Metropolis, Iran. Environmental monitoring and assessment, 195(2), 273.  https://doi.org/10.1007/s10661-022-10740-2
Zhang, C., Yao, D., Zhen, Y., Li, W., & Li, K. (2022). Mismatched Relationship between Urban Industrial Land Consumption and Growth of Manufacturing: Evidence from the Yangtze River Delta. Land, 11(9), 1390.  https://doi.org/10.3390/land11091390
Zhang, Z., Hörmann, G., Huang, J., & Fohrer, N. (2023). A Random Forest-Based CA-Markov Model to Examine the Dynamics of Land Use/Cover Change Aided with Remote Sensing and GIS. Remote Sensing, 15(8), 2128.  https://doi.org/10.3390/rs15082128