Integrating GIS and NetLogo-based modelling for simulating land-cover change scenarios in Mashhad Metropolitan Area, Iran
DOI:
https://doi.org/10.12775/bgss-2025-0014Keywords
land cover, geography, planning & development, Mashhad Metropolitan Area, Support Vector Machine, Agent-Based ModelingAbstract
This paper presents an agent-based model (ABM) integrated with GIS and NetLogo to simulate land-cover changes in the Mashhad Metropolitan Area (MMA) of Northeast Iran until 2030. It has two main objectives: first, to monitor land-cover changes from 2000 to 2020 using the Support Vector Machine (SVM) algorithm, which reveals an increase in built-up areas of 21,303 hectares (58.92%), alongside decreases in barren lands and green spaces of 15,922 hectares (25.73%) and 7,229 hectares (15.86%), respectively. Second, it conducts spatial simulations of land-cover changes projected for 2030 through the ABM in NetLogo, exploring four scenarios. Results indicate that Mashhad is expanding toward the north and west, with informal settlements encroaching on green spaces, while improved road access is expected to further accelerate urban land expansion.
References
Abar, S., Theodoropoulos, G.K., Lemarinier, P., & O’Hare, G. M. (2017). Agent Based Modelling and Simulation tools: A review of the state-of-art software. Computer Science Review, 24: 13-33. DOI: https://doi.org/10.1016/j.cosrev.2017.03.001.
Achter, S., Borit, M., Cottineau, C., Meyer, M., Polhill, J.G., & Radchuk, V. (2024). How to conduct more systematic reviews of agent-based models and foster theory development-Taking stock and looking ahead. Environmental Modelling & Software, 173: 105867. DOI: https://doi.org/10.1016/j.envsoft.2023.105867.
Angel, S., Parent, J., Civco, D. L., Blei, A., & Potere, D. (2011). The dimensions of global urban expansion: Estimates and projections for all countries, 2000–2050. Progress in Planning, 75(2): 53-107. DOI: https://doi.org/10.1016/j.progress.2011.04.001.
Arabi Aliabad, F., Shojaei, S., Zare, M., & Ekhtesasi, M.R. (2019). Assessment of the fuzzy ARTMAP neural network method performance in geological mapping using satellite images and Boolean logic. International journal of environmental science and technology, 16: 3829-3838. DOI: https://doi.org/10.1007/s13762-018-1795-7.
Bartik, T.J. (2012). Including jobs in benefit-cost analysis. Annual Review of Resource Economics, 4(1): 55-73. DOI: https://doi.org/10.1146/annurev-resource-110811-114520.
Batty, M. (1998). Urban evolution on the desktop: simulation with the use of extended cellular automata. Environment and planning A, 30(11): 1943-1967. DOI: https://doi.org/10.1068/a301.
Beerman, J.T., Beaumont, G.G., & Giabbanelli, P.J. (2023). A framework for the comparison of errors in agent-based models using machine learning. Journal of computational science, 72: 102119. DOI: https://doi.org/10.1016/j.jocs.2023.102119.
Cao, Z., Zhu, J., Tang, B., & Chen, T. (2023). System dynamics simulation of occupational health and safety management causal model based on NetLogo. Heliyon, 9(8). DOI: https://doi.org/10.1016/j.heliyon.2023.e18752.
Chachondhia, P., Shakya, A., & Kumar, G. (2021). Performance evaluation of machine learning algorithms using optical and microwave data for LULC classification. Remote Sensing Applications: Society and Environment, 23: 100599. DOI: https://doi.org/10.1016/j.rsase.2021.100599.
Chen, D., Ma, A., & Zhong, Y. (2023). Semi-supervised knowledge distillation framework for global-scale urban man-made object remote sensing mapping. International Journal of Applied Earth Observation and Geoinformation, 122: 103439. DOI: https://doi.org/10.1016/j.jag.2023.103439.
Coronese, M., Occelli, M., Lamperti, F., & Roventini, A. (2023). AgriLOVE: agriculture, land-use and technical change in an evolutionary, agent-based model. Ecological Economics, 208: 107756. DOI: https://doi.org/10.1016/j.ecolecon.2023.107756.
Daams, M.N., Banquet, A., Delbouve, P., & Veneri, P. (2023). Consistent metropolitan boundaries for the remote sensing of urban land. Remote Sensing of Environment, 297: 113789. DOI: https://doi.org/10.1016/j.rse.2023.113789.
Dadashpoor, H., Jahanzad, N., & Jalili, H. (2016). Analysis and forecasting of the integrated spatial structure in Mashhad metropolitan region in the period 1996-2041. Motaleate Shahri, 5(18): 51-62.
Darrel Jenerette, G., & Potere, D. (2010). Global analysis and simulation of land-use change associated with urbanization. Landscape ecology, 25: 657-670.
Derkenbaeva, E., Hofstede, G.J., van Leeuwen, E., & Vega, S.H. (2023). Simulating households’ energy transition in Amsterdam: An agent-based modeling approach. Energy Conversion and Management, 294: 117566. DOI: https://doi.org/10.1016/j.enconman.2023.117566.
De Souza Silva, I., Nascimento, D.T.F., de Araújo Romão, P., da Silva, G.D.F.N., Sales, M.M., & da Luz, M.P. (2023). Proposition of LULC mapping in progressive detailing for the surroundings of hydroelectric powerplant reservoirs: Case study for the Batalha (Brazil). International Journal of Applied Earth Observation and Geoinformation, 118: 103218. DOI: https://doi.org/10.1016/j.jag.2023.103218.
De Visscher, L., De Baets, B., & Baetens, J. M. (2023). A critical review of common pitfalls and guidelines to effectively infer parameters of agent-based models using Approximate Bayesian Computation. Environmental Modelling & Software, 105905. DOI: https://doi.org/10.1016/j.envsoft.2023.105905.
Dicks, M., Paskaramoorthy, A., & Gebbie, T. (2024). A simple learning agent interacting with an agent-based market model. Physica A: Statistical Mechanics and its Applications, 633: 129363. DOI: https://doi.org/10.1016/j.physa.2023.129363.
Dixon, B., & Candade, N. (2008) Multispectral landuse classification using neural networks and support vector machines: one or the other, or both? International Journal of Remote Sensing, 29(4): 1185-1206. DOI: https://doi.org/10.1080/01431160701294661.
Fleiss, J.L., Levin, B., & Paik, M.C. (2013). Statistical methods for rates and proportions. John Wiley & Sons.
Fu, X., Wang, X., & Yang, Y. J. (2018). Deriving suitability factors for CA-Markov land use simulation model based on local historical data. Journal of environmental management, 206: 10-19. DOI: https://doi.org/10.1016/j.jenvman.2017.10.012.
General population and housing censuses of Iran. (2016). Iran’s official statistics website. Available at: https://amar.org.ir.
Gomes, E., Banos, A., Abrantes, P., Rocha, J., & Schläpfer, M. (2020). Future land use changes in a peri-urban context: Local stakeholder views. Science of the total environment, 718: 137381. DOI: https://doi.org/10.1016/j.scitotenv.2020.137381.
Gong, S., Dong, X., Wang, K., Lei, B., Jia, Z., Qin, J., & Cao, R. (2023). Agent-based modelling with geographically weighted calibration for intra-urban activities simulation using taxi GPS trajectories. International Journal of Applied Earth Observation and Geoinformation, 122: 103368. DOI: https://doi.org/10.1016/j.jag.2023.103368.
Han, H., Yang, C., & Song, J. (2015). Scenario simulation and the prediction of land use and land cover change in Beijing, China. Sustainability, 7(4): 4260-4279. DOI: https://doi.org/10.3390/su7044260.
Huang, D., Huang, J., & Liu, T. (2019). Delimiting urban growth boundaries using the CLUE-S model with village administrative boundaries. Land use policy, 82: 422-435. DOI: https://doi.org/10.1016/j.landusepol.2018.12.028.
Huang, Y., Li, L., & Yu, Y. (2018). Does urban cluster promote the increase of urban eco-efficiency? Evidence from Chinese cities. Journal of Cleaner Production, 197: 957-971. DOI: https://doi.org/10.1016/j.jclepro.2018.06.251.
Innocenti, E., Detotto, C., Idda, C., & Prunetti, D. (2019). Urban, agricultural and touristic land use patterns: combining spatial econometrics and ABM/LUCC. In 2019 4th World Conference on Complex Systems (WCCS). IEEE. DOI: 10.1109/ICoCS.2019.8930776.
Irwin, E.G., & Geoghegan, J. (2001). Theory, data, methods: developing spatially explicit economic models of land use change. Agriculture, Ecosystems & Environment, 85(1-3): 7-24. DOI: https://doi.org/10.1016/S0167-8809(01)00200-6.
Islam, M.R., & Esraz-Ul-Zannat, M. (2023). Remote sensing-based investigation of coastal LULC dynamics in the coastal region of Bangladesh. Remote Sensing Applications: Society and Environment, 31: 100982. DOI: https://doi.org/10.1016/j.rsase.2023.100982.
Jasim, B.S., Jasim, O.Z., & AL-Hameedawi, A.N. (2024). Evaluating land use land cover classification based on machine learning algorithms. Engineering and Technology Journal, 42(5): 557-568. DOI: https://doi.org/10.30684/etj.2024.144585.1638.
Jijo, B.T., & Abdulazeez, A. M. (2021). Classification based on decision tree algorithm for machine learning. evaluation, 6: 7. DOI: https://doi.org/10.38094/jastt20165.
Kafy, A.A., Shuvo, R.M., Naim, M.N.H., Sikdar, M.S., Chowdhury, R.R., Islam, M.A., & Kona, M.A. (2021). Remote sensing approach to simulate the land use/land cover and seasonal land surface temperature change using machine learning algorithms in a fastest-growing megacity of Bangladesh. Remote Sensing Applications: Society and Environment, 21: 100463. DOI: https://doi.org/10.1016/j.rsase.2020.100463.
Kamran, M., Khokhar, M.F., Waheed, A., Khan, J.A., & Khayyam, U. (2022). Exploring the Nexus between Land Use Land Cover (LULC) Changes and Population Growth in Planned and Unplanned Cities. Available at SSRN 4261709. DOI: https://doi.org/10.1016/j.heliyon.2023.e13297.
Kamusoko, C., Aniya, M., Adi, B., & Manjoro, M. (2009). Rural sustainability under threat in Zimbabwe–simulation of future land use/cover changes in the Bindura district based on the Markov-cellular automata model. Applied Geography, 29(3): 435-447. DOI: https://doi.org/10.1016/j.apgeog.2008.10.002.
Karali, E., Rounsevell, M.D., & Doherty, R. (2011). Integrating the diversity of farmers’ decisions into studies of rural land-use change. Procedia Environmental Sciences, 6: 136-145.
Kimelberg, S.M., & Williams, E. (2013). Evaluating the importance of business location factors: The influence of facility type. Growth and Change, 44(1): 92-117. DOI: https://doi.org/10.1111/grow.12003.
Koomen, E., & Buurman, J.J.G. (2002). Economic theory and land prices in land use modeling. In Papers of 5th AGILE conference on Geographic Information Science.
Kucsicsa, G., Popovici, E.A., Bălteanu, D., Grigorescu, I., Dumitraşcu, M., & Mitrică, B. (2019). Future land use/cover changes in Romania: regional simulations based on CLUE-S model and CORINE land cover database. Landscape and ecological engineering, 15(1): 75-90. DOI: https://doi.org/10.1007/s11355-018-0362-1.
Lai, Y. (2023). Optimization of urban and rural ecological spatial planning based on deep learning under the concept of sustainable development.Results in Engineering, 19: 101343. DOI: https://doi.org/10.1016/j.rineng.2023.101343.
Lang, D., & Ertsen, M.W. (2023). Modelling farmland dynamics in response to farmer decisions using an advanced irrigation-related agent-based model. Ecological Modelling, 486: 110535. DOI: https://doi.org/10.1016/j.ecolmodel.2023.110535.
Lee, I.P.A., Eldakar, O.T., Gogarten, J.P., & Andam, C.P. (2023). Protocol for an agent-based model of recombination in bacteria playing a public goods game. STAR protocols, 4(4): 102733. DOI: https://doi.org/10.1016/j.xpro.2023.102733.
Li, F., Xie, Z., Clarke, K.C., Li, M., Chen, H., Liang, J., & Chen, Z. (2019). An agent-based procedure with an embedded agent learning model for residential land growth simulation: The case study of Nanjing, China. Cities, 88: 155-165. DOI: https://doi.org/10.1016/j.cities.2018.10.008.
Li, F., Yigitcanlar, T., Nepal, M., Nguyen, K., & Dur, F. (2023). Machine learning and remote sensing integration for leveraging urban sustainability: A review and framework. Sustainable Cities and Society, 104653. DOI: https://doi.org/10.1016/j.scs.2023.104653.
Li, Y., Cai, Y., Fu, Q., Zhang, X., Wan, H., & Yang, Z. (2024). Dynamics of Land Use/Land Cover Considering Ecosystem Services for a Dense-Population Watershed Based on a Hybrid Dual-Subject Agent and Cellular Automaton Modeling Approach. Engineering, 37: 182-195. DOI: https://doi.org/10.1016/j.eng.2023.10.015.
Liang, X., Liu, X., Li, D., Zhao, H., & Chen, G. (2018). Urban growth simulation by incorporating planning policies into a CA-based future land-use simulation model. International Journal of Geographical Information Science, 32(11): 2294-2316. DOI: https://doi.org/10.1080/13658816.2018.1502441.
Liu, D., Zheng, X., & Wang, H. (2020). Land-use Simulation and Decision-Support system (LandSDS): Seamlessly integrating system dynamics, agent-based model, and cellular automata. Ecological Modelling, 417: 108924. DOI: https://doi.org/10.1016/j.ecolmodel.2019.108924.
Mahmoodzadeh, H., & Naghdbishi, A. (2021). Comparative study of the efficiency of the urban development Agent based model and Markov chain Cellular Automata model for urban development simulation in the Neyshabur. Geography and Development, 19(64): 291-314. DOI: 10.22111/J10.22111.2021.6352.
Mashhad Municipality. (2020). Statistics of Mashhad, 2020, Assistance of Planning and Human Capital Development of Mashhad Municipality under the supervision ofStatistics Management, Mashhad: Mashhad Municipality.
Marini, N., Otálora, S., Müller, H., & Atzori, M. (2021). Semi-supervised training of deep convolutional neural networks with heterogeneous data and few local annotations: An experiment on prostate histopathology image classification. Medical image analysis, 73: 102165. DOI: https://doi.org/10.1016/j.media.2021.102165.
Mohammed, A.M., & Ukai, T. (2023). Agent-based modelling for spatiotemporal patterns of urban land expansion around university campuses. Modeling Earth Systems and Environment, 9(1): 1119-1133.
Morshed, S.R., Fattah, M.A., Haque, M.N., & Morshed, S.Y. (2022) Future ecosystem service value modelling with land cover dynamics by using machine learning based Artificial Neural Network model for Jashore city, Bangladesh. Physics and Chemistry of the Earth, Parts a/b/c, 126: 103021. DOI: https://doi.org/10.1016/j.pce.2021.103021.
Motieyan, H., & Mesgari, M.S. (2018). An agent-based modeling approach for sustainable urban planning from land use and public transit perspectives. Cities, 81: 91-100. DOI: https://doi.org/10.1016/j.cities.2018.03.018.
Motlaq, M.A., & Abbaszadeh, G. (2012). The physical development of Mashhad city and its environmental impacts. Environment and Urbanization ASIA, 3(1): 79-91. DOI: https://doi.org/10.1177/097542531200300105.
Mungai, L.M., Messina, J.P., Zulu, L.C., & Snapp, S. (2022) Modelling Spatiotemporal Patterns of Land Use/Land Cover Change in Central Malawi Using a Neural Network Model. Remote Sensing, 14(14): 3477. DOI: https://doi.org/10.3390/rs14143477.
Nehzak, H.K., Aghaei, M., Mostafazadeh, R., & Rabiei-Dastjerdi, H. (2022). Assessment of machine learning algorithms in land use classification. In Computers in Earth and Environmental Sciences. Elsevier. DOI: https://doi.org/10.1016/B978-0-323-89861-4.00022-1.
Nganro, S., Trisutomo, S., Barkey, R., Ali, M., Imura, H., Onishi, A., Tsai, P.-I., & Mahamud, M.A. (2021). Prediction of Future Land Use and Land Cover (LULC) in Makassar City. Tataloka, 23(2): 183-189.
Njoku, E.A., & Tenenbaum, D.E. (2022). Quantitative assessment of the relationship between land use/land cover (LULC), topographic elevation and land surface temperature (LST) in Ilorin, Nigeria. Remote Sensing Applications: Society and Environment, 27: 100780. DOI: https://doi.org/10.1016/j.rsase.2022.100780.
Nucci, L., Rahnama, M.R., & Heydari, A. (2016). Analysis the Process and Priorities of Urbanization in the Mashhad city After Islamic Revolution of Iran. International journal, 2(4). Available at: http://www.ijhcs.com/index.php/ijhcs/index.
Ortiz, A.F.C., Rodas, K.A.C., Pineda, A.P.C., Sandoval, J., Amaguaya, D.J.V., Gallo, A.D.V., Rosero, O.D.M., & Bustamante, A.F.M. (2022). Simulador para recrear las dinámicas urbanas de Tena con Netlogo, antes de la intervención del espacio público (Simulator to recreate the urban dynamics of Tena with Netlogo, before the intervention of the public space – in Spanish). Eídos, 14(20): 39-44. DOI: https://doi.org/10.29019/eidos.v14i20.1005.
Ouma, Y., Nkwae, B., Moalafhi, D., Odirile, P., Parida, B., Anderson, G., & Qi, J. (2022). Comparison of machine learning classifiers for multitemporal and multisensor mapping of urban LULC features. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 43: 681-689. DOI: https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-681-2022.
Park, K. (2023). Regreening suburbia: An analysis of urban greening approaches in US sprawl retrofitting projects. Urban Forestry & Urban Greening, 88: 128092. DOI: https://doi.org/10.1016/j.ufug.2023.128092.
Pham, T.D., Xia, J., Ha, N.T., Bui, D.T., Le, N.N., & Takeuchi, W. (2019). A review of remote sensing approaches for monitoring blue carbon ecosystems: Mangroves, seagrasses, and salt marshes during 2010–2018. Sensors, 19(8): 1933. DOI: https://doi.org/10.3390/s19081933.
Potić, I., & Potić, M. (2017). Remote sensing machine learning algorithms in environmental stress detection-case study of Pan-European south section of corridor ten in Serbia. Bulletin of Natural Sciences Research, 7(2). DOI: https://doi.org/10.5937/univtho7-14957.
Qiu, R., Xu, W., Zhang, J., & Staenz, K. (2018). Modelling and simulating urban residential land development in Jiading New City, Shanghai. Applied Spatial Analysis and Policy, 11: 753-777.
Rahnama, M. R. (2021a). Forecasting land-use changes in Mashhad Metropolitan area using Cellular Automata and Markov chain model for 2016-2030. Sustainable Cities and Society, 64: 102548. DOI: https://doi.org/10.1016/j.scs.2020.102548.
Rahnama, M. R. (2021b). Simulation of land use land cover change in Melbourne metropolitan area from 2014 to 2030: Using multilayer perceptron neural networks and Markov chain model. Australian Planner, 57(1): 36-49. DOI: https://doi.org/10.1080/07293682.2021.1920994.
Rahnama, M.R., & Wyatt, R. (2021). Projecting Land use change with neural network and GIS in northern Melbourne for 2014–2050. Australian Geographer, 52(2): 149-170. DOI: https://doi.org/10.1080/00049182.2021.1920088.
Razaque, A., Ben Haj Frej, M., Almi’ani, M., Alotaibi, M., & Alotaibi, B. (2021). Improved support vector machine enabled radial basis function and linear variants for remote sensing image classification. Sensors, 21(13): 4431. DOI: https://doi.org/10.3390/s21134431.
Ritu, S.M., Sarkar, S.K., & Zonaed, H. (2023). Prediction of Padma riverbank shifting and its consequences on LULC changes. Ecological Indicators, 156: 111104. DOI: https://doi.org/10.1016/j.ecolind.2023.111104.
Salem, M.B., Ettabaa, K.S., & Bouhlel, M.S. (2018). An unsupervised classification approach for hyperspectral images based adaptive spatial and spectral neighborhood selection and graph clustering. Procedia Computer Science, 126: 743-750. DOI: https://doi.org/10.1016/j.procs.2018.08.008.
Santarsiero, V., Nolè, G., Lanorte, A., Tucci, B., Cillis, G., & Murgante, B. (2022). Remote sensing and spatial analysis for land-take assessment in Basilicata Region (Southern Italy). Remote Sensing, 14(7): 1692. DOI: https://doi.org/10.3390/rs14071692.
Secchi, D., Grimm, V., Herath, D.B., & Homberg, F. (2024). Modeling and theorizing with agent-based sustainable development. Environmental Modelling & Software, 171: 105891. DOI: https://doi.org/10.1016/j.envsoft.2023.105891.
Shivakumar, B.R. (2021) Land cover mapping capability of chaincluster, K-means, and ISODATA techniques—A case study. In Advances in VLSI, Signal Processing, Power Electronics, IoT, Communication and Embedded Systems: Select Proceedings of VSPICE 2020. Springer Singapore. DOI: https://doi.org/10.3390/rs14071692.
Talukdar, S., Singha, P., Mahato, S., Pal, S., Liou, Y.A., & Rahman, A. (2020). Land-use land-cover classification by machine learning classifiers for satellite observations—A review. Remote Sensing, 12(7): 1135. DOI: https://doi.org/10.3390/rs12071135.
Tassi, A., & Vizzari, M. (2020). Object-oriented lulc classification in google earth engine combining snic, glcm, and machine learning algorithms. Remote Sensing, 12(22): 3776. DOI: https://doi.org/10.3390/rs12223776.
Tayyebi, A., & Pijanowski, B.C. (2014). Modelling multiple land use changes using ANN, CART and MARS: Comparing tradeoffs in goodness of fit and explanatory power of data mining tools. International journal of applied earth observation and geoinformation, 28: 102-116. DOI: https://doi.org/10.1016/j.jag.2013.11.008.
Teimouri, M.S., Forghani, A., & Baghban, S. (2022). A Geospatial Analysis Approach for Mapping and Ranking of Tourist Corridors in Mashhad Metropolis Iran. World Journal of Geo-matics and Geosciences, 2(1): 1–23. DOI: 10.31586/wjgg.2022.240.
United Nations Department of Economic and Social Affairs. (2015). Transforming our world: the 2030 agenda for sustainable development.
Yu, Q., Wu, W., Yang, P., Tang, H., Zhou, Q., & Chen, Z. (2013). Progress of agent-based agricultural land change modeling: A review. Shengtai Xuebao/Acta Ecologica Sinica, 33(6), 1690-1700.
Vahidi, H., & Yan, W. (2014). Towards spatially explicit agent-based model for simulation of informal transport infrastructure indirect growth dynamic in informal settlements. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 40: 273-279. DOI: https://doi.org/10.5194/isprsarchives-XL-2-W3-273-2014.
Valbuena, D., Verburg, P.H., Bregt, A.K., & Ligtenberg, A. (2010). An agent-based approach to model land-use change at a regional scale. Landscape ecology, 25: 185-199. DOI: https://doi.org/10.1007/s10980-009-9380-6.
Walker, R. (2004). Theorizing land-cover and land-use change: the case of tropical deforestation. International regional science review, 27(3): 247-270. DOI: https://doi.org/10.1177/0160017604266026.
Wang, Z., Lin, L., Zhang, B., Xu, H., Xue, J., Fu, Y., Zeng, Y., & Li, F. (2023). Sustainable urban development based on an adaptive cycle model: A coupled social and ecological land use development model. Ecological Indicators, 154: 110666. DOI: https://doi.org/10.1016/j.ecolind.2023.110666.
Wang, C., Zhan, J., & Xin, Z. (2020). Comparative analysis of urban ecological management models incorporating low-carbon transformation. Technological Forecasting and Social Change, 159: 120190. DOI: https://doi.org/10.1016/j.techfore.2020.120190.
Wang, S. Q., Zheng, X. Q., & Zang, X. B. (2012). Accuracy assessments of land use change simulation based on Markov-cellular automata model. Procedia Environmental Sciences, 13, 1238-1245. DOI: https://doi.org/10.1016/j.proenv.2012.01.117.
Wilensky, U. (1999). NetLogo. Center for connected learning and computerbased modeling, Northwestern University, Evanston. Available at: http://ccl.northwestern.edu/netlogo/.
Yousefi, S., Mirzaee, S., Almohamad, H., Al Dughairi, A. A., Gomez, C., Siamian, N., Alrasheedi, M., & Abdo, H. G. (2022). Image classification and land cover mapping using sentinel-2 imagery: optimization of SVM parameters. Land, 11(7): 993. DOI: https://doi.org/10.3390/land11070993.
Zhaka, L., & Xhina, E. (2021). Canada’s Provinces Geospatial Data Visualization Using NetLogo and GIS. International Journal of Scientific and Technological Research, 7.
Zarin, T., & Esraz-Ul-Zannat, M. (2023). Assessing the potential impacts of LULC change on urban air quality in Dhaka city. Ecological Indicators, 154: 110746. DOI: https://doi.org/10.1016/j.ecolind.2023.110746.
Zeraatpisheh, M., Ayoubi, S., Brungard, C.W., & Finke, P. (2019). Disaggregating and updating a legacy soil map using DSMART, fuzzy c-means and k-means clustering algorithms in Central Iran. Geoderma, 340: 249-258. DOI: https://doi.org/10.1016/j.geoderma.2019.01.005.
Zhang, L., Han, R., & Cao, H. (2021). Understanding Urban Land Growth through a Social-Spatial Perspective. Land, 10(4): 348. DOI: https://doi.org/10.3390/land10040348.
Zhang, X., & Li, P. (2014). Lithological mapping from hyperspectral data by improved use of spectral angle mapper. International Journal of Applied Earth Observation and Geoinformation, 31: 95-109. DOI: https://doi.org/10.1016/j.jag.2014.03.007.
Zhang, W., & Wang, M.Y. (2018). Spatial-temporal characteristics and determinants of land urbanization quality in China: Evidence from 285 prefecture-level cities. Sustainable Cities and Society, 38: 70–79. DOI: https://doi.org/10.1016/j.scs.2017.12.011.
Zhao, L., Liu, X., Xu, X., Liu, C., & Chen, K. (2022). Three-Dimensional Simulation Model for synergistically simulating urban horizontal expansion and vertical growth. Remote Sensing, 14(6): 1503. DOI: https://doi.org/10.3390/rs14061503.
Zhao, M., Zhou, Y., Li, X., Cheng, W., Zhou, C., Ma, T., & Huang, K. (2020). Mapping urban dynamics (1992–2018) in Southeast Asia using consistent nighttime light data from DMSP and VIIRS. Remote Sensing of Environment, 248: 111980. DOI: https://doi.org/10.1016/j.rse.2020.111980.
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