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Multi-Scenario Land Use and Land Cover (LULC) Change Projection Framework Using Markov Chain and PLUS Integrated Model


Multi-Scenario Land Use and Land Cover (LULC) Change Projection Framework Using Markov Chain and PLUS Integrated Model

Marey, Ahmed (2023) Multi-Scenario Land Use and Land Cover (LULC) Change Projection Framework Using Markov Chain and PLUS Integrated Model. Masters thesis, Concordia University.

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The spatial distribution of urban land use has undergone significant transformations due to rapid urbanization. Assessing the dynamic and complex interactions of land use and land cover (LULC) can help planners and policymakers understand the extent and effects of those changes. This study proposes a novel framework for land use and land cover (LULC) change through the integration of patch-generating land use simulation (PLUS) and Markov Chain (MC) model under different scenarios. Various simulations have been conducted for the island of Montreal, Quebec, Canada using regional land use types under the five shared socioeconomic pathways (SSPs) for the year of 2028. In addition, a comparative study was conducted between three major cities in Canada: Toronto, Ottawa, and Montreal, in which global land use types were used to project LULC change in 2030 based on historical trends. Different accuracy measures were calculated to validate our model and compared to the accuracy of other models reported in the literature. Our findings show that our model achieved a higher figure of merit (FoM) than other models and was able to simulate LULC change without the need for expert knowledge in the field. The results of this multi-scenario simulation and ecological, environmental effect study can be used as a reference for future regional territorial spatial planning and policy formulation. The integration of the PLUS and Markov Chain models is shown to be quite applicable to the projection and assessment of urban spatial land use patterns.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Building, Civil and Environmental Engineering
Item Type:Thesis (Masters)
Authors:Marey, Ahmed
Institution:Concordia University
Degree Name:M.A. Sc.
Program:Building Engineering
Date:11 August 2023
Thesis Supervisor(s):Wang, Liangzhu (Leon) and Goubran, Sherif
Keywords:Patch-Generating Land Use Simulation (PLUS); Markov Chain; Land Use and Land Cover (LULC); Cellular Automata (CA); Spatial Pattern
ID Code:992853
Deposited By: Ahmed Marey
Deposited On:14 Nov 2023 19:29
Last Modified:14 Nov 2023 19:29


Advancing Land Change Modeling: Opportunities and Research Requirements (p. 18385). (2014). National Academies Press. https://doi.org/10.17226/18385
Almulhim, A. I., Bibri, S. E., Sharifi, A., Ahmad, S., & Almatar, K. M. (2022). Emerging Trends and Knowledge Structures of Urbanization and Environmental Sustainability: A Regional Perspective. Sustainability, 14(20), 13195. https://doi.org/10.3390/su142013195
Ambarwulan, W., Yulianto, F., Widiatmaka, W., Rahadiati, A., Tarigan, S. D., Firmansyah, I., & Hasibuan, M. A. S. (2023). Modelling land use/land cover projection using different scenarios in the Cisadane Watershed, Indonesia: Implication on deforestation and food security. The Egyptian Journal of Remote Sensing and Space Science, 26(2), 273–283. https://doi.org/10.1016/j.ejrs.2023.04.002
An, L. (2012). Modeling human decisions in coupled human and natural systems: Review of agent-based models. Ecological Modelling, 229, 25–36. https://doi.org/10.1016/j.ecolmodel.2011.07.010
Anderson, J., Hardy, E., & Roach, J. (1976). A Land Use and Land Cover Classification System for Use with Remote Sensor Data (Professional Paper) [Professional Paper].
Andrewartha, H. G., & Birch, C. (1982). The distribution and abundance of Animals. University of Chicago Press.
Antoni, J. P., Judge, V., Vuidel, G., & Klein, O. (2018). Constraint Cellular Automata for Urban Development Simulation: An Application to the Strasbourg-Kehl Cross-Border Area. In M. T. Camacho Olmedo, M. Paegelow, J.-F. Mas, & F. Escobar (Eds.), Geomatic Approaches for Modeling Land Change Scenarios (pp. 293–306). Springer International Publishing. https://doi.org/10.1007/978-3-319-60801-3_14
Auch, R. F., Wellington, D. F., Taylor, J. L., Stehman, S. V., Tollerud, H. J., Brown, J. F., Loveland, T. R., Pengra, B. W., Horton, J. A., Zhu, Z., Midekisa, A. A., Sayler, K. L., Xian, G., Barber, C. P., & Reker, R. R. (2022). Conterminous United States Land-Cover Change (1985–2016): New Insights from Annual Time Series. Land, 11(2), 298. https://doi.org/10.3390/land11020298
Bai, X. (2016). Eight energy and material flow characteristics of urban ecosystems. Ambio, 45(7), 819–830. https://doi.org/10.1007/s13280-016-0785-6
Bakker, M. M., Govers, G., Kosmas, C., Vanacker, V., Oost, K. van, & Rounsevell, M. (2005). Soil erosion as a driver of land-use change. Agriculture, Ecosystems & Environment, 105(3), 467–481. https://doi.org/10.1016/j.agee.2004.07.009
Balbontin, D. T. (2014). Analyse de la détérioration de la forêt de la Cordillère de la Costa dans le sud chilien: Géomatique et modélisation prospective appliquée sur une forêt patrimoniale de la province d’Osorno (41o 15’—41o 00’ latitude Sud).
Balk, D., Tagtachian, D., Jiang, L., Marcotullio, P., Cook, E. M., Jones, B., Mustafa, A., & McPhearson, T. (2022). Frameworks to envision equitable urban futures in a changing climate: A multi-level, multidisciplinary case study of New York City. Frontiers in Built Environment, 8, 949433. https://doi.org/10.3389/fbuil.2022.949433
Batty, M. (1997). Cellular Automata and Urban Form: A Primer. Journal of the American Planning Association, 63(2), 266–274. https://doi.org/10.1080/01944369708975918
Batty, M., Couclelis, H., & Eichen, M. (1997). Urban Systems as Cellular Automata.
Batty, M., Xie, Y., & Sun, Z. (1999). Modeling urban dynamics through GIS-based cellular automata. Computers, Environment and Urban Systems, 23(3), 205–233. https://doi.org/10.1016/S0198-9715(99)00015-0
Berling-Wolff, S., & Wu, J. (2004). Modeling urban landscape dynamics: A review: Modeling urban landscapes. Ecological Research, 19(1), 119–129. https://doi.org/10.1111/j.1440-1703.2003.00611.x
Bibri, S. E., Krogstie, J., & Kärrholm, M. (2020). Compact city planning and development: Emerging practices and strategies for achieving the goals of sustainability. Developments in the Built Environment, 4, 100021. https://doi.org/10.1016/j.dibe.2020.100021
Bielecka & Jenerowicz. (2019). Intellectual Structure of CORINE Land Cover Research Applications in Web of Science: A Europe-Wide Review. Remote Sensing, 11(17), 2017. https://doi.org/10.3390/rs11172017
Brown, D. G., Verburg, P. H., Pontius, R. G., & Lange, M. D. (2013). Opportunities to improve impact, integration, and evaluation of land change models. Current Opinion in Environmental Sustainability, 5(5), 452–457. https://doi.org/10.1016/j.cosust.2013.07.012
Bürgi, M., Hersperger, A. M., & Schneeberger, N. (2005). Driving forces of landscape change – current and new directions.
Burnicki, A. C., Brown, D. G., & Goovaerts, P. (2010). Propagating error in land-cover-change analyses: Impact of temporal dependence under increased thematic complexity. International Journal of Geographical Information Science, 24(7), 1043–1060. https://doi.org/10.1080/13658810903279008
Calvin, K. V., Snyder, A., Zhao, X., & Wise, M. (2022). Modeling land use and land cover change: Using a hindcast to estimate economic parameters in gcamland v2.0. Geoscientific Model Development, 15(2), 429–447. https://doi.org/10.5194/gmd-15-429-2022
Camacho Olmedo, M. T., Paegelow, M., Mas, J. F., & Escobar, F. (2018). Geomatic Approaches for Modeling Land Change Scenarios. An Introduction. In M. T. Camacho Olmedo, M. Paegelow, J.-F. Mas, & F. Escobar (Eds.), Geomatic Approaches for Modeling Land Change Scenarios (pp. 1–8). Springer International Publishing. https://doi.org/10.1007/978-3-319-60801-3_1
Carlson, T. N., & Traci Arthur, S. (2000). The impact of land use — land cover changes due to urbanization on surface microclimate and hydrology: A satellite perspective. Global and Planetary Change, 25(1–2), 49–65. https://doi.org/10.1016/S0921-8181(00)00021-7
Cebecauer, T., & Hofierka, J. (2008). The consequences of land-cover changes on soil erosion distribution in Slovakia. Geomorphology, 98(3–4), 187–198. https://doi.org/10.1016/j.geomorph.2006.12.035
Chapin III, F. S., Zavaleta, E. S., Eviner, V. T., Naylor, R. L., Vitousek, P. M., Reynolds, H. L., Hooper, D. U., Lavorel, S., Sala, O. E., Hobbie, S. E., Mack, M. C., & Díaz, S. (2000). Consequences of changing biodiversity. Nature, 405(6783), 234–242. https://doi.org/10.1038/35012241
Chen, X., Vierling, L., & Deering, D. (2005). A simple and effective radiometric correction method to improve landscape change detection across sensors and across time. Remote Sensing of Environment, 98(1), 63–79. https://doi.org/10.1016/j.rse.2005.05.021
Chen, Y., Li, X., Wang, S., & Liu, X. (2012). Defining agents’ behaviour based on urban economic theory to simulate complex urban residential dynamics. International Journal of Geographical Information Science, 26(7), 1155–1172. https://doi.org/10.1080/13658816.2011.626780
Cities—United Nations Sustainable Development Action 2015. (n.d.). United Nations Sustainable Development. Retrieved July 26, 2023, from https://www.un.org/sustainabledevelopment/cities/
Clarke, K. (2004). The Limits of Simplicity: Toward Geocomputational Honesty in Urban Modeling.
Clarke, K. C., Hoppen, S., & Gaydos, L. (1997). A self-modifying cellular automaton model of historical urbanization in the San Francisco Bay area. Environment and Planning B: Planning and Design, 24(2), 247–261. https://doi.org/10.1068/b240247
Cobbinah, P. B., Erdiaw-Kwasie, M. O., & Amoateng, P. (2015). Rethinking sustainable development within the framework of poverty and urbanisation in developing countries. Environmental Development, 13, 18–32. https://doi.org/10.1016/j.envdev.2014.11.001
Colditz, R. R., Acosta-Velázquez, J., Díaz Gallegos, J. R., Vázquez Lule, A. D., Rodríguez-Zúñiga, M. T., Maeda, P., Cruz López, M. I., & Ressl, R. (2012). Potential effects in multi-resolution post-classification change detection. International Journal of Remote Sensing, 33(20), 6426–6445. https://doi.org/10.1080/01431161.2012.688148
Conway, T. M. (2009). The impact of class resolution in land use change models. Computers, Environment and Urban Systems, 33(4), 269–277. https://doi.org/10.1016/j.compenvurbsys.2009.02.001
Coppin, P., Jonckheere, I., Nackaerts, K., Muys, B., & Lambin, E. (2004). Review ArticleDigital change detection methods in ecosystem monitoring: A review. International Journal of Remote Sensing, 25(9), 1565–1596. https://doi.org/10.1080/0143116031000101675
Costanza, R., & Ruth, M. (1998). Using Dynamic Modeling to Scope Environmental Problems and Build Consensus. Environmental Management, 22(2), 183–195. https://doi.org/10.1007/s002679900095
Crooks, A. T., & Heppenstall, A. J. (2012). Introduction to Agent-Based Modelling. In A. J. Heppenstall, A. T. Crooks, L. M. See, & M. Batty (Eds.), Agent-Based Models of Geographical Systems (pp. 85–105). Springer Netherlands. https://doi.org/10.1007/978-90-481-8927-4_5
Cuo, L., Lettenmaier, D. P., Alberti, M., & Richey, J. E. (2009). Effects of a century of land cover and climate change on the hydrology of the Puget Sound basin. Hydrological Processes, 23(6), 907–933. https://doi.org/10.1002/hyp.7228
Dambeebo, D., & Jalloh, C. A. (2018). Sustainable Urban Development and Land Use Management: Wa Municipality in Perspective, Ghana. Journal of Sustainable Development, 11(5), 235. https://doi.org/10.5539/jsd.v11n5p235
de Koning, G. H. J., Verburg, P. H., Veldkamp, A., & Fresco, L. O. (1999). Multi-scale modelling of land use change dynamics in Ecuador. Agricultural Systems, 61(2), 77–93. https://doi.org/10.1016/S0308-521X(99)00039-6
Dietzel, C., & Clarke, K. C. (2007). Toward Optimal Calibration of the SLEUTH Land Use Change Model. Transactions in GIS, 11(1). https://doi.org/10.1111/j.1467-9671.2007.01031.x
Dimyati, M., Mizuno, K., Kobayashi, S., & Kitamura, T. (1996). An analysis of land use/cover change in Indonesia. International Journal of Remote Sensing, 17(5), 931–944. https://doi.org/10.1080/01431169608949056
Dolley, J., Marshall, F., Butcher, B., Reffin, J., Robinson, J. A., Eray, B., & Quadrianto, N. (2020). Analysing trade-offs and synergies between SDGs for urban development, food security and poverty alleviation in rapidly changing peri-urban areas: A tool to support inclusive urban planning. Sustainability Science, 15(6), 1601–1619. https://doi.org/10.1007/s11625-020-00802-0
Données géoréférencées. (n.d.). Observatoire du Grand Montréal | CMM. Retrieved April 30, 2023, from https://observatoire.cmm.qc.ca/produits/donnees-georeferencees/
Ebi, K. L., Hallegatte, S., Kram, T., Arnell, N. W., Carter, T. R., Edmonds, J., Kriegler, E., Mathur, R., O’Neill, B. C., Riahi, K., Winkler, H., Van Vuuren, D. P., & Zwickel, T. (2014). A new scenario framework for climate change research: Background, process, and future directions. Climatic Change, 122(3), 363–372. https://doi.org/10.1007/s10584-013-0912-3
Ehrensperger, A., De Bremond, A., Providoli, I., & Messerli, P. (2019). Land system science and the 2030 agenda: Exploring knowledge that supports sustainability transformation. Current Opinion in Environmental Sustainability, 38, 68–76. https://doi.org/10.1016/j.cosust.2019.04.006
Enemark, S. (2007). Integrated land-use management for sustainable development. Proceedings of the Joint FIG Commission, 3.
Engelen, G., White, R., Uljee, I., & Drazan, P. (1995). Using cellular automata for integrated modelling of socio-environmental systems. Environmental Monitoring and Assessment, 34(2), 203–214. https://doi.org/10.1007/BF00546036
Environment and Climate Change Canada. (2021). Canadian environmental sustainability indicators: Land-use change. Environment and Climate Change Canada - Environnement et changement climatique Canada.
Environment and Climate Change Canada. (2023). Canadian Environmental Sustainability Indicators: Greenhouse gas emissions.
Evans, T. P., Phanvilay, K., Fox, J., & Vogler, J. (2011). An agent-based model of agricultural innovation, land-cover change and household inequality: The transition from swidden cultivation to rubber plantations in Laos PDR. Journal of Land Use Science, 6(2–3), 151–173. https://doi.org/10.1080/1747423X.2011.558602
Feranec, J., Hazeu, G., Christensen, S., & Jaffrain, G. (2007). Corine land cover change detection in Europe (case studies of the Netherlands and Slovakia). Land Use Policy, 24(1), 234–247. https://doi.org/10.1016/j.landusepol.2006.02.002
Foley, J. A., DeFries, R., Asner, G. P., Barford, C., Bonan, G., Carpenter, S. R., Chapin, F. S., Coe, M. T., Daily, G. C., Gibbs, H. K., Helkowski, J. H., Holloway, T., Howard, E. A., Kucharik, C. J., Monfreda, C., Patz, J. A., Prentice, I. C., Ramankutty, N., & Snyder, P. K. (2005). Global Consequences of Land Use. Science, 309(5734), 570–574. https://doi.org/10.1126/science.1111772
Gallardo, M., & Cocero, D. (2023). Using the European CORINE Land Cover Database: A 2011–2021 Specific Review. In Sustainable Development Goals in Europe (pp. 303–325). Springer.
García-Álvarez, D., & Camacho Olmedo, M. T. (2017). Changes in the methodology used in the production of the Spanish CORINE: Uncertainty analysis of the new maps. International Journal of Applied Earth Observation and Geoinformation, 63, 55–67. https://doi.org/10.1016/j.jag.2017.07.001
García-Álvarez, D., Camacho Olmedo, M. T., & Paegelow, M. (2019). Sensitivity of a common Land Use Cover Change (LUCC) model to the Minimum Mapping Unit (MMU) and Minimum Mapping Width (MMW) of input maps. Computers, Environment and Urban Systems, 78, 101389. https://doi.org/10.1016/j.compenvurbsys.2019.101389
García-Álvarez, D., Camacho Olmedo, M. T., Paegelow, M., & Mas, J. F. (Eds.). (2022). Land Use Cover Datasets and Validation Tools: Validation Practices with QGIS. Springer International Publishing. https://doi.org/10.1007/978-3-030-90998-7
Gaur, S., Bandyopadhyay, A., & Singh, R. (2021a). From Changing Environment to Changing Extremes: Exploring the Future Streamflow and Associated Uncertainties Through Integrated Modelling System. Water Resources Management, 35(6), 1889–1911. https://doi.org/10.1007/s11269-021-02817-3
Gaur, S., Bandyopadhyay, A., & Singh, R. (2021b). Projecting land use growth and associated impacts on hydrological balance through scenario-based modelling in the Subarnarekha basin, India. Hydrological Sciences Journal, 66(14), 1997–2010. https://doi.org/10.1080/02626667.2021.1976408
Gaur, S., Krishna, Ch. N. T., Bandyopadhyay, A., & Singh, R. (2022). Diagnosing the Combined Impact of Climate and Land Use Land Cover Changes on the Streamflow in a Mountainous Watershed. In B. Yadav, M. P. Mohanty, A. Pandey, V. P. Singh, & R. D. Singh (Eds.), Sustainability of Water Resources (Vol. 116, pp. 343–357). Springer International Publishing. https://doi.org/10.1007/978-3-031-13467-8_22
Gaur, S., Mittal, A., Bandyopadhyay, A., Holman, I., & Singh, R. (2020). Spatio-temporal analysis of land use and land cover change: A systematic model inter-comparison driven by integrated modelling techniques. International Journal of Remote Sensing, 41(23), 9229–9255. https://doi.org/10.1080/01431161.2020.1815890
Gaur, S., & Singh, R. (2023). A Comprehensive Review on Land Use/Land Cover (LULC) Change Modeling for Urban Development: Current Status and Future Prospects. Sustainability, 15(2), 903. https://doi.org/10.3390/su15020903
Geist, H. J., & Lambin, E. F. (2004). Dynamic Causal Patterns of Desertification. BioScience, 54(9), 817. https://doi.org/10.1641/0006-3568(2004)054[0817:DCPOD]2.0.CO;2
Giri, C. P. (Ed.). (2016). Role of Remote Sensing for Land-Use and Land-Cover Change Modeling. In Remote Sensing of Land Use and Land Cover (0 ed., pp. 246–263). CRC Press. https://doi.org/10.1201/b11964-21
Giri, C., Zhu, Z., & Reed, B. (2005). A comparative analysis of the Global Land Cover 2000 and MODIS land cover data sets. Remote Sensing of Environment, 94(1), 123–132. https://doi.org/10.1016/j.rse.2004.09.005
Government of Canada, S. C. (2017, November 15). Illustrated Glossary—Census metropolitan area (CMA) and census agglomeration (CA). https://www150.statcan.gc.ca/n1/pub/92-195-x/2021001/geo/cma-rmr/cma-rmr-eng.htm
Government of Canada, S. C. (2022, February 9). Census Profile, 2021 Census of Population. https://www12.statcan.gc.ca/census-recensement/2021/dp-pd/prof/index.cfm?Lang=E
Grinblat, Y., Gilichinsky, M., & Benenson, I. (2016). Cellular Automata Modeling of Land-Use/Land-Cover Dynamics: Questioning the Reliability of Data Sources and Classification Methods. Annals of the American Association of Geographers, 106(6), 1299–1320. https://doi.org/10.1080/24694452.2016.1213154
Gustafson, E. J., Shifley, S. R., Mladenoff, D. J., Nimerfro, K. K., & He, H. S. (2000). Spatial simulation of forest succession and timber harvesting using LANDIS. Canadian Journal of Forest Research, 30(1), 32–43. https://doi.org/10.1139/x99-188
Gutman, G., Janetos, A. C., Justice, C. O., Moran, E. F., Mustard, J. F., Rindfuss, R. R., Skole, D., Turner, B. L., & Cochrane, M. A. (Eds.). (2004). Land Change Science: Observing, Monitoring and Understanding Trajectories of Change on the Earth’s Surface (Vol. 6). Springer Netherlands. https://doi.org/10.1007/978-1-4020-2562-4
Gutman, G., Justice, C., Sheffner, E., & Loveland, T. (2012). The NASA Land Cover and Land Use Change Program. In G. Gutman, A. C. Janetos, C. O. Justice, E. F. Moran, J. F. Mustard, R. R. Rindfuss, D. Skole, B. L. Turner, & M. A. Cochrane (Eds.), Land Change Science (Vol. 6, pp. 17–29). Springer Netherlands. https://doi.org/10.1007/978-1-4020-2562-4_2
Hameed, A. A. S. (2021). Urban and Regional Planning Strategies to Achieve Sustainable Urban Development: (Subject review). International Journal of Advances in Scientific Research and Engineering, 07(03), 22–27. https://doi.org/10.31695/IJASRE.2021.33980
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. https://doi.org/10.3390/su7044260
Hansen, H. S. (2010). Modelling the future coastal zone urban development as implied by the IPCC SRES and assessing the impact from sea level rise. Landscape and Urban Planning, 98(3–4), 141–149. https://doi.org/10.1016/j.landurbplan.2010.08.018
Hansen, M. C., Stehman, S. V., & Potapov, P. V. (2010). Quantification of global gross forest cover loss. Proceedings of the National Academy of Sciences, 107(19), 8650–8655. https://doi.org/10.1073/pnas.0912668107
Harlan, S. L., Declet-Barreto, J. H., Stefanov, W. L., & Petitti, D. B. (2013). Neighborhood Effects on Heat Deaths: Social and Environmental Predictors of Vulnerability in Maricopa County, Arizona. Environmental Health Perspectives, 121(2), 197–204. https://doi.org/10.1289/ehp.1104625
Hay, J., & Mimura, N. (2006). Supporting climate change vulnerability and adaptation assessments in the Asia-Pacific region: An example of sustainability science. Sustainability Science, 1(1), 23–35. https://doi.org/10.1007/s11625-006-0011-8
He, C., Shi, P., Chen, J., Li, X., Pan, Y., Li, J., Li, Y., & Li, J. (2005). Developing land use scenario dynamics model by the integration of system dynamics model and cellular automata model. Science in China Series D: Earth Sciences, 48(11), 1979–1989. https://doi.org/10.1360/04yd0248
He, C., Zhao, Y., Tian, J., & Shi, P. (2013). Modeling the urban landscape dynamics in a megalopolitan cluster area by incorporating a gravitational field model with cellular automata. Landscape and Urban Planning, 113, 78–89. https://doi.org/10.1016/j.landurbplan.2013.01.004
He, Z., Deng, M., Cai, J., Xie, Z., Guan, Q., & Yang, C. (2020). Mining spatiotemporal association patterns from complex geographic phenomena. International Journal of Geographical Information Science, 34(6), 1162–1187. https://doi.org/10.1080/13658816.2019.1566549
Heistermann, M., Müller, C., & Ronneberger, K. (2006). Land in sight?Achievements, deficits and potentials of continental to global scale land-use modeling. Agriculture, Ecosystems & Environment, 114(2–4), 141–158. https://doi.org/10.1016/j.agee.2005.11.015
Hewitt, R., Van Delden, H., & Escobar, F. (2014). Participatory land use modelling, pathways to an integrated approach. Environmental Modelling & Software, 52, 149–165. https://doi.org/10.1016/j.envsoft.2013.10.019
Hoffmann, J. (2021). Demographic change and land use. Sustainable Land Management in a European Context, 63.
Hu, H., Liu, W., & Cao, M. (2008). Impact of land use and land cover changes on ecosystem services in Menglun, Xishuangbanna, Southwest China. Environmental Monitoring and Assessment, 146(1–3), 147–156. https://doi.org/10.1007/s10661-007-0067-7
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
Huang, Q., He, C., Liu, Z., & Shi, P. (2014). Modeling the impacts of drying trend scenarios on land systems in northern China using an integrated SD and CA model. Science China Earth Sciences, 57(4), 839–854. https://doi.org/10.1007/s11430-013-4799-7
Islam, K., Rahman, Md. F., & Jashimuddin, M. (2018). Modeling land use change using Cellular Automata and Artificial Neural Network: The case of Chunati Wildlife Sanctuary, Bangladesh. Ecological Indicators, 88, 439–453. https://doi.org/10.1016/j.ecolind.2018.01.047
Jokar Arsanjani, J., Helbich, M., Kainz, W., & Darvishi Boloorani, A. (2013). Integration of logistic regression, Markov chain and cellular automata models to simulate urban expansion. International Journal of Applied Earth Observation and Geoinformation, 21, 265–275. https://doi.org/10.1016/j.jag.2011.12.014
Joshi, N., Baumann, M., Ehammer, A., Fensholt, R., Grogan, K., Hostert, P., Jepsen, M., Kuemmerle, T., Meyfroidt, P., Mitchard, E., Reiche, J., Ryan, C., & Waske, B. (2016). A Review of the Application of Optical and Radar Remote Sensing Data Fusion to Land Use Mapping and Monitoring. Remote Sensing, 8(1), 70. https://doi.org/10.3390/rs8010070
Kalfas, D. G., Zagkas, D. T., Dragozi, E. I., & Melfou, K. Κ. (2021). An approach of landsenses ecology and landsenseology in Greece. International Journal of Sustainable Development & World Ecology, 28(8), 677–692. https://doi.org/10.1080/13504509.2021.1920061
Kalfas, D. G., Zagkas, D. T., Dragozi, E. I., & Zagkas, T. D. (2020). Estimating value of the ecosystem services in the urban and peri-urban green of a town Florina-Greece, using the CVM. International Journal of Sustainable Development & World Ecology, 27(4), 310–321. https://doi.org/10.1080/13504509.2020.1714786
Kalfas, D. G., Zagkas, D. T., Raptis, D. I., & Zagkas, T. D. (2019). The multifunctionality of the natural environment through the basic ecosystem services in the Florina region, Greece. International Journal of Sustainable Development & World Ecology, 26(1), 57–68. https://doi.org/10.1080/13504509.2018.1489910
Kalteh, A. M., Hjorth, P., & Berndtsson, R. (2008). Review of the self-organizing map (SOM) approach in water resources: Analysis, modelling and application. Environmental Modelling & Software, 23(7), 835–845. https://doi.org/10.1016/j.envsoft.2007.10.001
Kline, J. D., Moses, A., Lettman, G. J., & Azuma, D. L. (2007). Modeling forest and range land development in rural locations, with examples from eastern Oregon. Landscape and Urban Planning, 80(3), 320–332. https://doi.org/10.1016/j.landurbplan.2006.10.017
Kocabas, V., & Dragicevic, S. (2006). Assessing cellular automata model behaviour using a sensitivity analysis approach. Computers, Environment and Urban Systems, 30(6), 921–953. https://doi.org/10.1016/j.compenvurbsys.2006.01.001
Kok, K., & Winograd, M. (2002). Modelling land-use change for Central America, with special reference to the impact of hurricane Mitch. Ecological Modelling, 149(1–2), 53–69. https://doi.org/10.1016/S0304-3800(01)00514-2
Kolb, M., Mas, J.-F., & Galicia, L. (2013). Evaluating drivers of land-use change and transition potential models in a complex landscape in Southern Mexico. International Journal of Geographical Information Science, 27(9), 1804–1827. https://doi.org/10.1080/13658816.2013.770517
Lambin, E. F. (1997). Modelling and monitoring land-cover change processes in tropical regions. Progress in Physical Geography: Earth and Environment, 21(3), 375–393. https://doi.org/10.1177/030913339702100303
Lambin, E. F., & Geist, H. J. (2008). Land-use and land-cover change: Local processes and global impacts. Springer Science & Business Media.
Lambin, E. F., Geist, H. J., & Lepers, E. (2003). Dynamics of Land-Use and Land-Cover Change in Tropical Regions. Annual Review of Environment and Resources, 28(1), 205–241. https://doi.org/10.1146/annurev.energy.28.050302.105459
Lawler, J. J., O’Connor, Raymond. J., Hunsaker, C. T., Jones, K. B., Loveland, T. R., & White, D. (2004). The effects of habitat resolution on models of avian diversity and distributions: A comparison of two land-cover classifications. Landscape Ecology, 19(5), 517–532. https://doi.org/10.1023/B:LAND.0000036151.28327.01
Leemans, R. (Ed.). (2013). Ecological Systems: Selected Entries from the Encyclopedia of Sustainability Science and Technology. Springer New York. https://doi.org/10.1007/978-1-4614-5755-8
Li, Q., Meng, Q., Cai, J., Yoshino, H., & Mochida, A. (2009). Applying support vector machine to predict hourly cooling load in the building. Applied Energy, 86(10), 2249–2256. https://doi.org/10.1016/j.apenergy.2008.11.035
Li, X., Chen, G., Liu, X., Liang, X., Wang, S., Chen, Y., Pei, F., & Xu, X. (2017). A New Global Land-Use and Land-Cover Change Product at a 1-km Resolution for 2010 to 2100 Based on Human–Environment Interactions. Annals of the American Association of Geographers, 107(5), 1040–1059. https://doi.org/10.1080/24694452.2017.1303357
Li, X., Chen, Y., Liu, X., Li, D., & He, J. (2011). Concepts, methodologies, and tools of an integrated geographical simulation and optimization system. International Journal of Geographical Information Science, 25(4), 633–655. https://doi.org/10.1080/13658816.2010.496370
Li, X., & Gar-On Yeh, A. (2004). Data mining of cellular automata’s transition rules. International Journal of Geographical Information Science, 18(8), 723–744. https://doi.org/10.1080/13658810410001705325
Li, X., & Gong, P. (2016). Urban growth models: Progress and perspective. Science Bulletin, 61(21), 1637–1650. https://doi.org/10.1007/s11434-016-1111-1
Li, X., Lin, J., Chen, Y., Liu, X., & Ai, B. (2013). Calibrating cellular automata based on landscape metrics by using genetic algorithms. International Journal of Geographical Information Science, 27(3), 594–613. https://doi.org/10.1080/13658816.2012.698391
Li, X., Liu, X., & Yu, L. (2014). A systematic sensitivity analysis of constrained cellular automata model for urban growth simulation based on different transition rules. International Journal of Geographical Information Science, 28(7), 1317–1335. https://doi.org/10.1080/13658816.2014.883079
Li, X., Yang, Q., & Liu, X. (2008). Discovering and evaluating urban signatures for simulating compact development using cellular automata. Landscape and Urban Planning, 86(2), 177–186. https://doi.org/10.1016/j.landurbplan.2008.02.005
Li, X., & Yeh, A. G.-O. (2000). Modelling sustainable urban development by the integration of constrained cellular automata and GIS. International Journal of Geographical Information Science, 14(2), 131–152. https://doi.org/10.1080/136588100240886
Li, X., & Yeh, A. G.-O. (2002). Neural-network-based cellular automata for simulating multiple land use changes using GIS. International Journal of Geographical Information Science, 16(4), 323–343. https://doi.org/10.1080/13658810210137004
Li, X., & Yeh, A. G.-O. (2004). Analyzing spatial restructuring of land use patterns in a fast growing region using remote sensing and GIS. Landscape and Urban Planning, 69(4), 335–354. https://doi.org/10.1016/j.landurbplan.2003.10.033
Liang, X., Guan, Q., Clarke, K. C., Liu, S., Wang, B., & Yao, Y. (2021). Understanding the drivers of sustainable land expansion using a patch-generating land use simulation (PLUS) model: A case study in Wuhan, China. Computers, Environment and Urban Systems, 85, 101569. https://doi.org/10.1016/j.compenvurbsys.2020.101569
Ligtenberg, A., Wachowicz, M., Bregt, A. K., Beulens, A., & Kettenis, D. L. (2004). A design and application of a multi-agent system for simulation of multi-actor spatial planning. Journal of Environmental Management, 72(1–2), 43–55. https://doi.org/10.1016/j.jenvman.2004.02.007
Lin, Y.-P., Chu, H.-J., Wu, C.-F., & Verburg, P. H. (2011). Predictive ability of logistic regression, auto-logistic regression and neural network models in empirical land-use change modeling – a case study. International Journal of Geographical Information Science, 25(1), 65–87. https://doi.org/10.1080/13658811003752332
Liu, M., Hu, Y., Zhang, W., Zhu, J., Chen, H., & Xi, F. (2011). Application of land-use change model in guiding regional planning: A case study in Hun-Taizi River Watershed, Northeast China. Chinese Geographical Science, 21(5), 609–618. https://doi.org/10.1007/s11769-011-0497-6
Liu, S., Huang, G., Wei, Y., & Qu, Z. (2022). Monitoring and Assessing Land Use/Cover Change and Ecosystem Service Value Using Multi-Resolution Remote Sensing Data at Urban Ecological Zone. Sustainability, 14(18), 11187. https://doi.org/10.3390/su141811187
Liu, X., Li, X., Liu, L., He, J., & Ai, B. (2008). A bottom‐up approach to discover transition rules of cellular automata using ant intelligence. International Journal of Geographical Information Science, 22(11–12), 1247–1269. https://doi.org/10.1080/13658810701757510
Liu, X., Li, X., Shi, X., Wu, S., & Liu, T. (2008). Simulating complex urban development using kernel-based non-linear cellular automata. Ecological Modelling, 211(1–2), 169–181. https://doi.org/10.1016/j.ecolmodel.2007.08.024
Liu, X., Li, X., Shi, X., Zhang, X., & Chen, Y. (2010). Simulating land-use dynamics under planning policies by integrating artificial immune systems with cellular automata. International Journal of Geographical Information Science, 24(5), 783–802. https://doi.org/10.1080/13658810903270551
Liu, X., Liang, X., Li, X., Xu, X., Ou, J., Chen, Y., Li, S., Wang, S., & Pei, F. (2017). A future land use simulation model (FLUS) for simulating multiple land use scenarios by coupling human and natural effects. Landscape and Urban Planning, 168, 94–116. https://doi.org/10.1016/j.landurbplan.2017.09.019
Liu, X., Ma, L., Li, X., Ai, B., Li, S., & He, Z. (2014). Simulating urban growth by integrating landscape expansion index (LEI) and cellular automata. International Journal of Geographical Information Science, 28(1), 148–163. https://doi.org/10.1080/13658816.2013.831097
Liu, Y., Kong, X., Liu, Y., & Chen, Y. (2013). Simulating the Conversion of Rural Settlements to Town Land Based on Multi-Agent Systems and Cellular Automata. PLoS ONE, 8(11), e79300. https://doi.org/10.1371/journal.pone.0079300
Liu, Y., Lv, X., Qin, X., Guo, H., Yu, Y., Wang, J., & Mao, G. (2007). An integrated GIS-based analysis system for land-use management of lake areas in urban fringe. Landscape and Urban Planning, 82(4), 233–246. https://doi.org/10.1016/j.landurbplan.2007.02.012
Liu, Y., & Phinn, S. R. (2003). Modelling urban development with cellular automata incorporating fuzzy-set approaches. Computers, Environment and Urban Systems, 27(6), 637–658. https://doi.org/10.1016/S0198-9715(02)00069-8
Liu, Z., Verburg, P. H., Wu, J., & He, C. (2017). Understanding Land System Change Through Scenario-Based Simulations: A Case Study from the Drylands in Northern China. Environmental Management, 59(3), 440–454. https://doi.org/10.1007/s00267-016-0802-3
Longley, P. (2010). Global Mapping Of Human Settlement: Experiences, Datasets, and Prospects: Book Reviews. The Photogrammetric Record, 25(130), 205–207. https://doi.org/10.1111/j.1477-9730.2010.00574_3.x
Loveland, T., Ohlen, D., Brown, J., & Reed, B. (1999). AnAnalysisof the IGBPGlobal Land-GovGehr aracterizatiPonrocess.
Lu, C., Qi, X., Zheng, Z., & Jia, K. (2022). PLUS-Model Based Multi-Scenario Land Space Simulation of the Lower Yellow River Region and Its Ecological Effects. Sustainability, 14(11), 6942. https://doi.org/10.3390/su14116942
Lu, D., Mausel, P., Brondízio, E., & Moran, E. (2004). Change detection techniques. International Journal of Remote Sensing, 25(12), 2365–2401. https://doi.org/10.1080/0143116031000139863
Lu, D., & Weng, Q. (2007). A survey of image classification methods and techniques for improving classification performance. International Journal of Remote Sensing, 28(5), 823–870. https://doi.org/10.1080/01431160600746456
Marshall, C. H., Pielke, R. A., Steyaert, L. T., & Willard, D. A. (2004). The Impact of Anthropogenic Land-Cover Change on the Florida Peninsula Sea Breezes and Warm Season Sensible Weather. Monthly Weather Review, 132(1), 28–52. https://doi.org/10.1175/1520-0493(2004)132<0028:TIOALC>2.0.CO;2
Mas, J. F., Paegelow, M., & Camacho Olmedo, M. T. (2018). LUCC Modeling Approaches to Calibration. In M. T. Camacho Olmedo, M. Paegelow, J.-F. Mas, & F. Escobar (Eds.), Geomatic Approaches for Modeling Land Change Scenarios (pp. 11–25). Springer International Publishing. https://doi.org/10.1007/978-3-319-60801-3_2
Mas, J.-F., Kolb, M., Paegelow, M., Camacho Olmedo, M. T., & Houet, T. (2014). Inductive pattern-based land use/cover change models: A comparison of four software packages. Environmental Modelling & Software, 51, 94–111. https://doi.org/10.1016/j.envsoft.2013.09.010
Matson, P. A., Parton, W. J., Power, A. G., & Swift, M. J. (1997). Agricultural Intensification and Ecosystem Properties. Science, 277(5325), 504–509. https://doi.org/10.1126/science.277.5325.504
Metropolis, N., & Ulam, S. (2023). The Monte Carlo Method.
Meyer, W. B., & Turner, B. L. (1992). Human Population Growth and Global Land-Use/Cover Change.
Milà I Canals, L., Bauer, C., Depestele, J., Dubreuil, A., Freiermuth Knuchel, R., Gaillard, G., Michelsen, O., Müller-Wenk, R., & Rydgren, B. (2007). Key Elements in a Framework for Land Use Impact Assessment Within LCA (11 pp). The International Journal of Life Cycle Assessment, 12(1), 5–15. https://doi.org/10.1065/lca2006.05.250
Moran, E. F., Skole, D. L., & Turner, B. L. (2012). The Development of the International Land-Use and Land-Cover Change (LUCC) Research Program and Its Links to NASA’s Land-Cover and Land-Use Change (LCLUC) Initiative. In G. Gutman, A. C. Janetos, C. O. Justice, E. F. Moran, J. F. Mustard, R. R. Rindfuss, D. Skole, B. L. Turner, & M. A. Cochrane (Eds.), Land Change Science (Vol. 6, pp. 1–15). Springer Netherlands. https://doi.org/10.1007/978-1-4020-2562-4_1
Müller, D., & Munroe, D. K. (2014). Current and future challenges in land-use science. Journal of Land Use Science, 9(2), 133–142. https://doi.org/10.1080/1747423X.2014.883731
Munroe, D. K., & Müller, D. (2020). Land-system science to support achieving the sustainable development goals. Journal of Land Use Science, 15(4), 477–481. https://doi.org/10.1080/1747423X.2020.1783085
Musakwa, W., & Niekerk, A. V. (2013). Implications of land use change for the sustainability of urban areas: A case study of Stellenbosch, South Africa. Cities, 32, 143–156. https://doi.org/10.1016/j.cities.2013.01.004
Mushore, T. D., Mutanga, O., Odindi, J., & Dube, T. (2017). Assessing the potential of integrated Landsat 8 thermal bands, with the traditional reflective bands and derived vegetation indices in classifying urban landscapes. Geocarto International, 32(8), 886–899. https://doi.org/10.1080/10106049.2016.1188168
Natural Resources Canada. (n.d.). Canadian Wildland Fire Information System | Canadian National Fire Database (CNFDB). Retrieved July 25, 2023, from https://cwfis.cfs.nrcan.gc.ca/ha/nfdb
Nedd, R., Light, K., Owens, M., James, N., Johnson, E., & Anandhi, A. (2021). A Synthesis of Land Use/Land Cover Studies: Definitions, Classification Systems, Meta-Studies, Challenges and Knowledge Gaps on a Global Landscape. Land, 10(9), 994. https://doi.org/10.3390/land10090994
NRC. (2014). Advancing land change modeling: Opportunities and research requirements. National Research Council Washington, DC.
Nukala, R., & Mutz, D. (2015). Strategic approach for sustainable land use in an emerging country—Case of India. Presentation at the 2015 World Bank Conference on Land and Poverty, 1–21.
Nuñez, M. N., Ciapessoni, H. H., Rolla, A., Kalnay, E., & Cai, M. (2008). Impact of land use and precipitation changes on surface temperature trends in Argentina. Journal of Geophysical Research, 113(D6), D06111. https://doi.org/10.1029/2007JD008638
Odindi, J., Mutanga, O., Abdel-Rahman, E. M., Adam, E., & Bangamwabo, V. (2017). Determination of urban land-cover types and their implication on thermal characteristics in three South African coastal metropolitans using remotely sensed data. South African Geographical Journal, 99(1), 52–67. https://doi.org/10.1080/03736245.2015.1117015
Odum, E. P. (1975). Ecology. Rinehart and Winston.
OECD. (2018). https://www.oecd.org/
Okin, G. S., Murray, B., & Schlesinger, W. H. (2001). Degradation of sandy arid shrubland environments: Observations, process modelling, and management implications. Journal of Arid Environments, 47(2), 123–144. https://doi.org/10.1006/jare.2000.0711
O’Neill, B. C., Kriegler, E., Ebi, K. L., Kemp-Benedict, E., Riahi, K., Rothman, D. S., Van Ruijven, B. J., Van Vuuren, D. P., Birkmann, J., Kok, K., Levy, M., & Solecki, W. (2017). The roads ahead: Narratives for shared socioeconomic pathways describing world futures in the 21st century. Global Environmental Change, 42, 169–180. https://doi.org/10.1016/j.gloenvcha.2015.01.004
O’Neill, B. C., Kriegler, E., Riahi, K., Ebi, K. L., Hallegatte, S., Carter, T. R., Mathur, R., & van Vuuren, D. P. (2014). A new scenario framework for climate change research: The concept of shared socioeconomic pathways. Climatic Change, 122(3), 387–400. https://doi.org/10.1007/s10584-013-0905-2
O’Sullivan, D., & Perry, G. L. W. (2013). Spatial Simulation: Exploring Pattern and Process.
Ottawa as the Seat of Government—The Physical and Administrative Setting—House of Commons Procedure and Practice, Third edition, 2017—ProceduralInfo—House of Commons of Canada. (n.d.). Retrieved July 31, 2023, from https://www.ourcommons.ca/procedure/procedure-and-practice-3/ch_06_1-e.html
Overmars, K. P., & Verburg, P. H. (2005). Analysis of land use drivers at the watershed and household level: Linking two paradigms at the Philippine forest fringe. International Journal of Geographical Information Science, 19(2), 125–152. https://doi.org/10.1080/13658810410001713380
Paegelow, M., & Camacho, M. O. (2008). Advances in geomatic simulations for environmental dynamics. In M. Paegelow & M. T. C. Olmedo (Eds.), Modelling Environmental Dynamics (pp. 3–54). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-540-68498-5_1
Parker, D. C., Manson, S. M., Janssen, M. A., Hoffmann, M. J., & Deadman, P. (2003). Multi-Agent Systems for the Simulation of Land-Use and Land-Cover Change: A Review. Annals of the Association of American Geographers, 93(2), 314–337. https://doi.org/10.1111/1467-8306.9302004
Patel, S. K., Verma, P., & Shankar Singh, G. (2019). Agricultural growth and land use land cover change in peri-urban India. Environmental Monitoring and Assessment, 191(9), 600. https://doi.org/10.1007/s10661-019-7736-1
Perica, S., & Foufoula-Georgiou, E. (1996). Model for multiscale disaggregation of spatial rainfall based on coupling meteorological and scaling descriptions. Journal of Geophysical Research: Atmospheres, 101(D21), 26347–26361. https://doi.org/10.1029/96JD01870
Pijanowski, B. C., Alexandridis, K. T., & Müller, D. (2006). Modelling urbanization patterns in two diverse regions of the world. Journal of Land Use Science, 1(2–4), 83–108. https://doi.org/10.1080/17474230601058310
Pijanowski, B. C., Brown, D. G., Shellito, B. A., & Manik, G. A. (2002). Using neural networks and GIS to forecast land use changes: A Land Transformation Model. Computers, Environment and Urban Systems, 26(6), 553–575. https://doi.org/10.1016/S0198-9715(01)00015-1
Poelmans, L., & Van Rompaey, A. (2010). Complexity and performance of urban expansion models. Computers, Environment and Urban Systems, 34(1), 17–27. https://doi.org/10.1016/j.compenvurbsys.2009.06.001
Pongratz, J., Dolman, H., Don, A., Erb, K., Fuchs, R., Herold, M., Jones, C., Kuemmerle, T., Luyssaert, S., Meyfroidt, P., & Naudts, K. (2018). Models meet data: Challenges and opportunities in implementing land management in Earth system models. Global Change Biology, 24(4), 1470–1487. https://doi.org/10.1111/gcb.13988
Pontius, G. R., & Malanson, J. (2005). Comparison of the structure and accuracy of two land change models. International Journal of Geographical Information Science, 19(2), 243–265. https://doi.org/10.1080/13658810410001713434
Pontius, R. G., Boersma, W., Castella, J.-C., Clarke, K., De Nijs, T., Dietzel, C., Duan, Z., Fotsing, E., Goldstein, N., Kok, K., Koomen, E., Lippitt, C. D., McConnell, W., Mohd Sood, A., Pijanowski, B., Pithadia, S., Sweeney, S., Trung, T. N., Veldkamp, A. T., & Verburg, P. H. (2008). Comparing the input, output, and validation maps for several models of land change. The Annals of Regional Science, 42(1), 11–37. https://doi.org/10.1007/s00168-007-0138-2
Pontius, R. G., Huffaker, D., & Denman, K. (2004). Useful techniques of validation for spatially explicit land-change models. Ecological Modelling, 179(4), 445–461. https://doi.org/10.1016/j.ecolmodel.2004.05.010
Pontius, R. G., & Millones, M. (2011). Death to Kappa: Birth of quantity disagreement and allocation disagreement for accuracy assessment. International Journal of Remote Sensing, 32(15), 4407–4429. https://doi.org/10.1080/01431161.2011.552923
Pontius, R. G., & Schneider, L. C. (2001). Land-cover change model validation by an ROC method for the Ipswich watershed, Massachusetts, USA. Agriculture, Ecosystems & Environment, 85(1–3), 239–248. https://doi.org/10.1016/S0167-8809(01)00187-6
Popp, A., Calvin, K., Fujimori, S., Havlik, P., Humpenöder, F., Stehfest, E., Bodirsky, B. L., Dietrich, J. P., Doelmann, J. C., Gusti, M., Hasegawa, T., Kyle, P., Obersteiner, M., Tabeau, A., Takahashi, K., Valin, H., Waldhoff, S., Weindl, I., Wise, M., … Vuuren, D. P. van. (2017). Land-use futures in the shared socio-economic pathways. Global Environmental Change, 42, 331–345. https://doi.org/10.1016/j.gloenvcha.2016.10.002
Rahman, A., Kumar, S., Fazal, S., & Siddiqui, M. A. (2012). Assessment of Land use/land cover Change in the North-West District of Delhi Using Remote Sensing and GIS Techniques. Journal of the Indian Society of Remote Sensing, 40(4), 689–697. https://doi.org/10.1007/s12524-011-0165-4
Reba, M., & Seto, K. C. (2020). A systematic review and assessment of algorithms to detect, characterize, and monitor urban land change. Remote Sensing of Environment, 242, 111739. https://doi.org/10.1016/j.rse.2020.111739
Ren, Y., Lü, Y., Comber, A., Fu, B., Harris, P., & Wu, L. (2019). Spatially explicit simulation of land use/land cover changes: Current coverage and future prospects. Earth-Science Reviews, 190, 398–415. https://doi.org/10.1016/j.earscirev.2019.01.001
Rindfuss, R. R., Walsh, S. J., Turner, B. L., Fox, J., & Mishra, V. (2004). Developing a science of land change: Challenges and methodological issues. Proceedings of the National Academy of Sciences, 101(39), 13976–13981. https://doi.org/10.1073/pnas.0401545101
Rounsevell, M. D. A., Arneth, A., Alexander, P., Brown, D. G., De Noblet-Ducoudré, N., Ellis, E., Finnigan, J., Galvin, K., Grigg, N., Harman, I., Lennox, J., Magliocca, N., Parker, D., O’Neill, B. C., Verburg, P. H., & Young, O. (2014). Towards decision-based global land use models for improved understanding of the Earth system. Earth System Dynamics, 5(1), 117–137. https://doi.org/10.5194/esd-5-117-2014
Sala, O. E., Stuart Chapin, F., Iii, Armesto, J. J., Berlow, E., Bloomfield, J., Dirzo, R., Huber-Sanwald, E., Huenneke, L. F., Jackson, R. B., Kinzig, A., Leemans, R., Lodge, D. M., Mooney, H. A., Oesterheld, M., Poff, N. L., Sykes, M. T., Walker, B. H., Walker, M., & Wall, D. H. (2000). Global Biodiversity Scenarios for the Year 2100. Science, 287(5459), 1770–1774. https://doi.org/10.1126/science.287.5459.1770
Sang, L., Zhang, C., Yang, J., Zhu, D., & Yun, W. (2011). Simulation of land use spatial pattern of towns and villages based on CA–Markov model. Mathematical and Computer Modelling, 54(3–4), 938–943. https://doi.org/10.1016/j.mcm.2010.11.019
Santé, I., García, A. M., Miranda, D., & Crecente, R. (2010). Cellular automata models for the simulation of real-world urban processes: A review and analysis. Landscape and Urban Planning, 96(2), 108–122. https://doi.org/10.1016/j.landurbplan.2010.03.001
Schéma d’aménagement et de développement de l’agglomération de Montréal. (n.d.).
Schmalzbauer, B., & Visbeck, M. (2016). 2. German future earth summit-Conference summary report Berlin, 28th & 29th of January 2016.
Schulp, C. J. E., Nabuurs, G.-J., & Verburg, P. H. (2008). Future carbon sequestration in Europe—Effects of land use change. Agriculture, Ecosystems & Environment, 127(3–4), 251–264. https://doi.org/10.1016/j.agee.2008.04.010
Secretariat, T. B. of C., & Secretariat, T. B. of C. (n.d.). 2020 Land Cover of Canada—Open Government Portal. Retrieved August 1, 2023, from https://open.canada.ca/data/en/dataset/ee1580ab-a23d-4f86-a09b-79763677eb47
Serneels, S., Said, M. Y., & Lambin, E. F. (2001). Land cover changes around a major east African wildlife reserve: The Mara Ecosystem (Kenya). International Journal of Remote Sensing, 22(17), 3397–3420. https://doi.org/10.1080/01431160152609236
Shi, M., Wu, H., Fan, X., Jia, H., Dong, T., He, P., Baqa, M. F., & Jiang, P. (2021). Trade-Offs and Synergies of Multiple Ecosystem Services for Different Land Use Scenarios in the Yili River Valley, China. Sustainability, 13(3), 1577. https://doi.org/10.3390/su13031577
Shi, T., Huang, Y., Wang, H., Shi, C.-E., & Yang, Y.-J. (2015). Influence of urbanization on the thermal environment of meteorological station: Satellite-observed evidence. Advances in Climate Change Research, 6(1), 7–15. https://doi.org/10.1016/j.accre.2015.07.001
Shi, Z., Fonseca, J. A., & Schlueter, A. (2017). A review of simulation-based urban form generation and optimization for energy-driven urban design. Building and Environment, 121, 119–129. https://doi.org/10.1016/j.buildenv.2017.05.006
Singh, A. (1989). Review Article Digital change detection techniques using remotely-sensed data. International Journal of Remote Sensing, 10(6), 989–1003. https://doi.org/10.1080/01431168908903939
Singh, R. K., Sinha, V. S. P., Joshi, P. K., & Kumar, M. (2021). A multinomial logistic model-based land use and land cover classification for the South Asian Association for Regional Cooperation nations using Moderate Resolution Imaging Spectroradiometer product. Environment, Development and Sustainability, 23(4), 6106–6127. https://doi.org/10.1007/s10668-020-00864-1
Sinha, P., & Kumar, L. (2013). Markov Land Cover Change Modeling Using Pairs of Time-Series Satellite Images. Photogrammetric Engineering & Remote Sensing, 79(11), 1037–1051. https://doi.org/10.14358/PERS.79.11.1037
Soares-Filho, B. S., Nepstad, D. C., Curran, L. M., Cerqueira, G. C., Garcia, R. A., Ramos, C. A., Voll, E., McDonald, A., Lefebvre, P., & Schlesinger, P. (2006). Modelling conservation in the Amazon basin. Nature, 440(7083), 520–523. https://doi.org/10.1038/nature04389
Sohl, T. L., & Claggett, P. R. (2013). Clarity versus complexity: Land-use modeling as a practical tool for decision-makers. Journal of Environmental Management, 129, 235–243. https://doi.org/10.1016/j.jenvman.2013.07.027
Sohl, T. L., Sayler, K. L., Drummond, M. A., & Loveland, T. R. (2007). The FORE-SCE model: A practical approach for projecting land cover change using scenario-based modeling. Journal of Land Use Science, 2(2), 103–126. https://doi.org/10.1080/17474230701218202
Sophie, B., Pierre, D., Eric, V. B., Martin, H., Lammert, K., Vasileios, K., & Olivier, A. (2011). Producing global land cover maps consistent over time to respond the needs of the climate modelling community. 2011 6th International Workshop on the Analysis of Multi-Temporal Remote Sensing Images (Multi-Temp), 161–164. https://doi.org/10.1109/Multi-Temp.2011.6005073
Sreedhar, Y., Nagaraju, A., & Murali Krishna, G. (2016). An Appraisal of Land Use/Land Cover Change Scenario of Tummalapalle, Cuddapah Region, India—A Remote Sensing and GIS Perspective. Advances in Remote Sensing, 05(04), 232–245. https://doi.org/10.4236/ars.2016.54019
Statistics Canada. (2022a). Canada’s large urban centres continue to grow and spread. Statistics Canada.
Statistics Canada. (2022b). Population growth in Canada’s rural areas, 2016 to 2021. Statistics Canada.
Statistics Canada. (2023). Population Projections for Canada (2021 to 2068), Provinces and Territories (2021 to 2043). Statistics Canada.
Stepputat, F., & Van Voorst, R. (2016). Cities on the agenda: Urban governance and sustainable development. DIIS Report.
Stone, B., Hess, J. J., & Frumkin, H. (2010). Urban Form and Extreme Heat Events: Are Sprawling Cities More Vulnerable to Climate Change Than Compact Cities? Environmental Health Perspectives, 118(10), 1425–1428. https://doi.org/10.1289/ehp.0901879
Surya, B., Ahmad, D. N. A., Sakti, H. H., & Sahban, H. (2020). Land Use Change, Spatial Interaction, and Sustainable Development in the Metropolitan Urban Areas, South Sulawesi Province, Indonesia. Land, 9(3), 95. https://doi.org/10.3390/land9030095
Talukdar, S., Singha, P., Mahato, S., Shahfahad, 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. https://doi.org/10.3390/rs12071135
Tangen, K. (1999). The climate change negotiations: Buenos Aires and beyond. Global Environmental Change, 9(3), 175–178. https://doi.org/10.1016/S0959-3780(99)00008-4
Tarawally, M., Wenbo, X., Weiming, H., Mushore, T. D., & Kursah, M. B. (2019). Land use/land cover change evaluation using land change modeller: A comparative analysis between two main cities in Sierra Leone. Remote Sensing Applications: Society and Environment, 16, 100262. https://doi.org/10.1016/j.rsase.2019.100262
Tilman, D., Fargione, J., Wolff, B., D’Antonio, C., Dobson, A., Howarth, R., Schindler, D., Schlesinger, W. H., Simberloff, D., & Swackhamer, D. (2001). Forecasting Agriculturally Driven Global Environmental Change. Science, 292(5515), 281–284. https://doi.org/10.1126/science.1057544
Tobler, W. R. (1970). A Computer Movie Simulating Urban Growth in the Detroit Region. Economic Geography, 46, 234. https://doi.org/10.2307/143141
Tong, X., & Feng, Y. (2020). A review of assessment methods for cellular automata models of land-use change and urban growth. International Journal of Geographical Information Science, 34(5), 866–898. https://doi.org/10.1080/13658816.2019.1684499
Turner, B. L., Lambin, E. F., & Reenberg, A. (2007). The emergence of land change science for global environmental change and sustainability. Proceedings of the National Academy of Sciences, 104(52), 20666–20671. https://doi.org/10.1073/pnas.0704119104
Uejio, C. K., Wilhelmi, O. V., Golden, J. S., Mills, D. M., Gulino, S. P., & Samenow, J. P. (2011). Intra-urban societal vulnerability to extreme heat: The role of heat exposure and the built environment, socioeconomics, and neighborhood stability. Health & Place, 17(2), 498–507. https://doi.org/10.1016/j.healthplace.2010.12.005
United Nations. (2017). World population projected to reach 9.8 billion in 2050, and 11.2 billion in 2100 | UN DESA | United Nations Department of Economic and Social Affairs. https://www.un.org/development/desa/en/news/population/world-population-prospects-2017.html
United Nations. (2018). 68% of the world population projected to live in urban areas by 2050, says UN | UN DESA | United Nations Department of Economic and Social Affairs. https://www.un.org/development/desa/en/news/population/2018-revision-of-world-urbanization-prospects.html
US EPA, O. (2017, November 2). Land Use [Reports and Assessments]. https://www.epa.gov/report-environment/land-use
Van Delden, H., Van Vliet, J., Rutledge, D. T., & Kirkby, M. J. (2011). Comparison of scale and scaling issues in integrated land-use models for policy support. Agriculture, Ecosystems & Environment, 142(1–2), 18–28. https://doi.org/10.1016/j.agee.2011.03.005
Van Vliet, J., Bregt, A. K., Brown, D. G., Van Delden, H., Heckbert, S., & Verburg, P. H. (2016). A review of current calibration and validation practices in land-change modeling. Environmental Modelling & Software, 82, 174–182. https://doi.org/10.1016/j.envsoft.2016.04.017
van Vuuren, D. P., Kriegler, E., O’Neill, B. C., Ebi, K. L., Riahi, K., Carter, T. R., Edmonds, J., Hallegatte, S., Kram, T., Mathur, R., & Winkler, H. (2014). A new scenario framework for Climate Change Research: Scenario matrix architecture. Climatic Change, 122(3), 373–386. https://doi.org/10.1007/s10584-013-0906-1
Veeraswamy, G., Nagaraju, A., Balaji, E., & Sreedhar, Y. (2017). Land use and Land Cover analysis using Remote Sensing and GIS:A case study In Gudur area , Nellore District, Andhra Pradesh, India. 04(17).
Verburg, P. H., Kok, K., Pontius Jr, R. G., & Veldkamp, A. (2006). Modeling land-use and land-cover change. In Land-use and land-cover change: Local processes and global impacts (pp. 117–135). Springer.
Verburg, P. H., Neumann, K., & Nol, L. (2011). Challenges in using land use and land cover data for global change studies: LAND USE AND LAND COVER DATA FOR GLOBAL CHANGE STUDIES. Global Change Biology, 17(2), 974–989. https://doi.org/10.1111/j.1365-2486.2010.02307.x
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
Verburg, P. H., Schot, P. P., Dijst, M. J., & Veldkamp, A. (2004). Land use change modelling: Current practice and research priorities. GeoJournal, 61(4), 309–324. https://doi.org/10.1007/s10708-004-4946-y
Verburg, P. H., Soepboer, W., Veldkamp, A., Limpiada, R., Espaldon, V., & Mastura, S. S. A. (2002). Modeling the Spatial Dynamics of Regional Land Use: The CLUE-S Model. Environmental Management, 30(3), 391–405. https://doi.org/10.1007/s00267-002-2630-x
Verburg, P. H., Van De Steeg, J., Veldkamp, A., & Willemen, L. (2009). From land cover change to land function dynamics: A major challenge to improve land characterization. Journal of Environmental Management, 90(3), 1327–1335. https://doi.org/10.1016/j.jenvman.2008.08.005
Verheye, W. H. (2009). Land use, land cover and soil sciences-volume IV: Land use management and case studies. EOLSS Publications.
Verma, P., & Raghubanshi, A. S. (2018). Urban sustainability indicators: Challenges and opportunities. Ecological Indicators, 93, 282–291. https://doi.org/10.1016/j.ecolind.2018.05.007
Verma, P., Singh, R., Singh, P., & Raghubanshi, A. S. (2020). Urban ecology – current state of research and concepts. In Urban Ecology (pp. 3–16). Elsevier. https://doi.org/10.1016/B978-0-12-820730-7.00001-X
Vipond, R. C. (2017). Making a global city: How one Toronto school embraced diversity. University of Toronto Press.
Vitousek, P. M., Mooney, H. A., Lubchenco, J., & Melillo, J. M. (1997). Human Domination of Earth’s Ecosystems. 277.
Vlek, P. L., Khamzina, A., & Tamene, L. D. (2017). Land degradation and the sustainable development goals: Threats and potential remedies.
Wang, J., Bretz, M., Dewan, M. A. A., & Delavar, M. A. (2022). Machine learning in modelling land-use and land cover-change (LULCC): Current status, challenges and prospects. Science of The Total Environment, 822, 153559. https://doi.org/10.1016/j.scitotenv.2022.153559
Weng, Q. (2001). A remote sensing?GIS evaluation of urban expansion and its impact on surface temperature in the Zhujiang Delta, China. International Journal of Remote Sensing, 22(10), 1999–2014. https://doi.org/10.1080/713860788
White, R., & Engelen, G. (1993). Cellular Automata and Fractal Urban Form: A Cellular Modelling Approach to the Evolution of Urban Land-Use Patterns. Environment and Planning A: Economy and Space, 25(8), 1175–1199. https://doi.org/10.1068/a251175
White, R., Engelen, G., & Uljee, I. (1997). The use of constrained cellular automata for high-resolution modelling of urban land-use dynamics. Environment and Planning B: Planning and Design, 24(3), 323–343. https://doi.org/10.1068/b240323
Wolf, J., Bindraban, P. S., Luijten, J. C., & Vleeshouwers, L. M. (2003). Exploratory study on the land area required for global food supply and the potential global production of bioenergy. Agricultural Systems, 76(3), 841–861. https://doi.org/10.1016/S0308-521X(02)00077-X
Wu, F. (1998). Polycentric Urban Development and Land-Use Change in a Transitional Economy: The Case of Guangzhou. Environment and Planning A: Economy and Space, 30(6), 1077–1100. https://doi.org/10.1068/a301077
Wu, F. (1999). GIS-based simulation as an exploratory analysis for space-time processes. Journal of Geographical Systems, 1(3), 199–218. https://doi.org/10.1007/s101090050012
Wu, F. (2002). Calibration of stochastic cellular automata: The application to rural-urban land conversions. International Journal of Geographical Information Science, 16(8), 795–818. https://doi.org/10.1080/13658810210157769
Wu, H., Ye, L.-P., Shi, W.-Z., & Clarke, K. C. (2014). Assessing the effects of land use spatial structure on urban heat islands using HJ-1B remote sensing imagery in Wuhan, China. International Journal of Applied Earth Observation and Geoinformation, 32, 67–78. https://doi.org/10.1016/j.jag.2014.03.019
Wu, L., Yang, Y., Yang, H., Xie, B., & Luo, W. (2023). A Comparative Study on Land Use/Land Cover Change and Topographic Gradient Effect between Mountains and Flatlands of Southwest China. Land, 12(6), 1242. https://doi.org/10.3390/land12061242
Xiang, W.-N., & Clarke, K. C. (2003). The Use of Scenarios in Land-Use Planning. Environment and Planning B: Planning and Design, 30(6), 885–909. https://doi.org/10.1068/b2945
Xiao, J., Shen, Y., Ge, J., Tateishi, R., Tang, C., Liang, Y., & Huang, Z. (2006). Evaluating urban expansion and land use change in Shijiazhuang, China, by using GIS and remote sensing. Landscape and Urban Planning, 75(1–2), 69–80. https://doi.org/10.1016/j.landurbplan.2004.12.005
Xu, L., Herold, M., Tsendbazar, N.-E., Masiliūnas, D., Li, L., Lesiv, M., Fritz, S., & Verbesselt, J. (2022). Time series analysis for global land cover change monitoring: A comparison across sensors. Remote Sensing of Environment, 271, 112905. https://doi.org/10.1016/j.rse.2022.112905
Yamashita, R., & Hoshino, S. (2018). Development of an agent-based model for estimation of agricultural land preservation in rural Japan. Agricultural Systems, 164, 264–276. https://doi.org/10.1016/j.agsy.2018.05.004
Yan, J., Wang, L., Song, W., Chen, Y., Chen, X., & Deng, Z. (2019). A time-series classification approach based on change detection for rapid land cover mapping. ISPRS Journal of Photogrammetry and Remote Sensing, 158, 249–262. https://doi.org/10.1016/j.isprsjprs.2019.10.003
Yang, J., Tang, W., Gong, J., Shi, R., Zheng, M., & Dai, Y. (2023). Simulating urban expansion using cellular automata model with spatiotemporally explicit representation of urban demand. Landscape and Urban Planning, 231, 104640. https://doi.org/10.1016/j.landurbplan.2022.104640
Yao, Y., Liang, H., Li, X., Zhang, J., & He, J. (2017). SENSING URBAN LAND-USE PATTERNS BY INTEGRATING GOOGLE TENSORFLOW AND SCENE-CLASSIFICATION MODELS. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLII-2/W7, 981–988. https://doi.org/10.5194/isprs-archives-XLII-2-W7-981-2017
Yeh, A. G.-O., & Li, X. (1998). Sustainable land development model for rapid growth areas using GIS. International Journal of Geographical Information Science, 12(2), 169–189. https://doi.org/10.1080/136588198241941
Yi, S., Zhou, Y., & Li, Q. (2022). A New Perspective for Urban Development Boundary Delineation Based on the MCR Model and CA-Markov Model. Land, 11(3), 401. https://doi.org/10.3390/land11030401
Yuan, D. (1999). Survey of multispectral methods for land-cover change analysis. Remote Sensing Change Detection: Environmental Monitoring Methods and Application.
Zerriffi, H., Reyes, R., & Maloney, A. (2023). Pathways to sustainable land use and food systems in Canada. Sustainability Science, 18(1), 389–406. https://doi.org/10.1007/s11625-022-01213-z
Zhang, S., Guan, Z., Liu, Y., & Zheng, F. (2022). Land Use/Cover Change and Its Relationship with Regional Development in Xixian New Area, China. Sustainability, 14(11), 6889. https://doi.org/10.3390/su14116889
Zhu, Z. (2017). Change detection using landsat time series: A review of frequencies, preprocessing, algorithms, and applications. ISPRS Journal of Photogrammetry and Remote Sensing, 130, 370–384. https://doi.org/10.1016/j.isprsjprs.2017.06.013
Zhu, Z., & Woodcock, C. E. (2014). Continuous change detection and classification of land cover using all available Landsat data. Remote Sensing of Environment, 144, 152–171. https://doi.org/10.1016/j.rse.2014.01.011
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