การสังเคราะห์ปัจจัยขับเคลื่อน สถานการณ์จำลอง และประเด็นการศึกษาในแบบจำลองการเปลี่ยนแปลงการใช้ที่ดินเมือง
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การวางแผนการใช้ที่ดินและการพัฒนาเมืองในอนาคตเป็นงานที่มีความซับซ้อนเนื่องจากความไม่แน่นอนจากปัจจัยสิ่งแวดล้อม สังคม และเทคโนโลยี แบบจำลองการเปลี่ยนแปลงที่ดินเมือง จึงเป็นเครื่องมือสำคัญในการจำลองและทำความเข้าใจพลวัตการเปลี่ยนแปลงการใช้ที่ดินในอนาคต การศึกษานี้ดำเนินการทบทวนวรรณกรรมอย่างเป็นระบบเพื่อวิเคราะห์ปัจจัยขับเคลื่อน รูปแบบสถานการณ์จำลอง และหัวข้อการศึกษาจากฐานข้อมูลวิชาการ 4 แหล่ง ได้บทความ 124 เรื่องในช่วงปี 2010-2024 ผลการศึกษาพบปัจจัยขับเคลื่อน 135 ปัจจัยที่แตกต่างกัน จำแนกเป็น 4 กลุ่มหลัก คือ ปัจจัยสิ่งแวดล้อมธรรมชาติ (ร้อยละ 31) ปัจจัยสิ่งแวดล้อมที่สร้างขึ้น (ร้อยละ 40.74) ปัจจัยทางสังคม-เศรษฐกิจ (ร้อยละ 17.04) และปัจจัยอื่น ๆ (ร้อยละ 10.37) โดยระยะทางจากถนนเป็นปัจจัยที่ใช้มากที่สุด (62 ครั้ง) การใช้สถานการณ์จำลองพบในร้อยละ 37.1 ของงานวิจัย โดยรูปแบบหลักคือ สถานการณ์ปกติ การอนุรักษ์สิ่งแวดล้อม และการพัฒนาแบบกระชับ หัวข้อการศึกษาแบ่งเป็น 3 กลุ่ม คือ การพยากรณ์และจำลองการขยายตัวของเมือง (49.2%) การพัฒนาแบบจำลอง (33.9%) และการศึกษาผลกระทบ (16.9%) ผลการศึกษาชี้ให้เห็นความจำเป็นในการบูรณาการปัจจัยหลายมิติ การพัฒนาสถานการณ์จำลองใหม่ และการเชื่อมโยงการวิจัยสู่การประยุกต์ใช้เชิงนโยบาย
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เกศินี นงโพธิ์, ชูเดช โลศิริ, และ สุธาทิพย์ ชวนะเวสสกุล. (2564). การติดตามและการคาดการณ์การเปลี่ยนแปลงการใช้ประโยชน์ที่ดินในอนาคตจังหวัดอุดรธานี. วารสารสังคมศาสตร์มหาวิทยาลัยศรีนครินทรวิโรฒ, 24(1), 13-34. https://ejournals.swu.ac.th/index.php/JOS/article/view/13778
นิติ เอี่ยมชื่น และ สุวิทย์ อ๋องสมหวัง. (2556). การคาดการณ์การใช้ประโยชน์ที่ดินโดยแบบจำลองคลูเอส (CLUE-S): กรณีศึกษาบริเวณพื้นที่โดยรอบกว๊านพะเยา จังหวัดพะเยา. วารสารสมาคมสำรวจข้อมูลระยะไกลและสารสนเทศภูมิศาสตร์แห่งประเทศไทย, 15(1), 26-31.
วันณัชชา เทพวงศ์, บุญศิริ สุขพร้อมสรรพ์, ธิดาภัทร อนุชาญ, และ นิติ เอี่ยมชื่น. (2565). การพยากรณ์การใช้ประโยชน์ที่ดินของเมืองชายแดนด้วยแบบจำลองฟลัส (FLUS): กรณีศึกษาอำเภอเชียงของ จังหวัดเชียงราย. วารสารวิชาการเพื่อการพัฒนานวัตกรรมเชิงพื้นที่, 3(1), 1-14.
ศรีวรรณ รื่นรมย์, มานัส ศรีวิณิช, และ นิติ เอี่ยมชื่น. (2565). การจำลองและคาดการณ์การใช้ประโยชน์ที่ดินโดยใช้แบบจำลองคลูมอนโดในจังหวัดปัตตานี. วารสารวิชาการเพื่อการพัฒนานวัตกรรมเชิงพื้นที่, 3(3), 73–90.
Achmad, A., Hasyim, S., Dahlan, B., & Aulia, D. N. (2015). Modeling of urban growth in tsunami-prone city using logistic regression: Analysis of Banda Aceh, Indonesia. Applied Geography, 62, 237-246. https://doi.org/10.1016/j.apgeog.2015.05.001
Akber, M. A., & Shrestha, R. P. (2015). Land use change and its effect on biodiversity in Chiang Rai province of Thailand. Journal of Land Use Science, 10(1), 108-128. https://doi.org/10.1080/1747423X.2013.807315
Alonso, W. (1964). Location and land use: Toward a general theory of land rent. Harvard University Press.
Allen, J., & Lu, K. (2003). Modeling and prediction of future urban growth in the Charleston region of South Carolina: A GIS-based integrated approach. Conservation Ecology, 8(2), Article 2. http://www.consecol.org/vol8/iss2/art2
Amato, F., Pontrandolfi, A., & Murgante, B. (2016). Using spatiotemporal analysis in urban sprawl assessment and prediction. In B. Murgante, S. Misra, A. M. A. C. Rocha, C. Torre, J. G. Rocha, M. I. Falcão, D. Taniar, B. O. Apduhan, & O. Gervasi (Eds.), Computational Science and Its Applications – ICCSA 2016 (pp. 758-773). Springer.
Ansari, A., & Golabi, M. H. (2019). Prediction of spatial land use changes based on LCM in a GIS environment for Desert Wetlands – A case study: Meighan Wetland, Iran. International Soil and Water Conservation Research, 7(1), 64-70. https://doi.org/10.1016/j.iswcr.2018.10.001
Batty, M. (2007). Cities and complexity: Understanding cities with cellular automata, agent-based models, and fractals. MIT Press.
Bajracharya, P. (2016). Modeling urban growth and land use change in Albuquerque using SLEUTH [Master’s thesis, University of New Mexico]. UNM Digital Repository. http://digitalrepository.unm.edu/geog_etds/26
Bengston, D. N., Fletcher, J. O., & Nelson, K. C. (2004). Public policies for managing urban growth and protecting open space: Policy instruments and lessons learned in the United States. Landscape and Urban Planning, 69(2-3), 271-286. https://doi.org/10.1016/j.landurbplan.2003.08.007
Bihamta, N., Soffianian, A., Fakheran, S., & Gholamalifard, M. (2015). Using the SLEUTH Urban Growth Model to Simulate Future Urban Expansion of the Isfahan Metropolitan Area, Iran. Journal of the Indian Society of Remote Sensing, 43(2), 407-414. https://doi.org/10.1007/s12524-014-0402-8
Brown, D. G., Johnson, K. M., Loveland, T. R., & Theobald, D. M. (2005). Rural land-use trends in the conterminous United States, 1950-2000. Ecological Applications, 15(6), 1851-1863. https://doi.org/10.1890/03-5220
Camacho Olmedo, M. T., Paegelow, M., Mas, J. F., Escobar, F. (2018). Geomatic approaches for modeling land change scenarios. In M. Camacho Olmedo, M. Paegelow, JF. Mas, F. Escobar (Eds.), Lecture Notes in Geoinformation and Cartography (pp. 1-8). Springer, Cham. https://doi.org/10.1007/978-3-319-60801-3_1
Carruthers, J. I. (2003). Growth at the fringe: The influence of political fragmentation in United States metropolitan areas. Papers in Regional Science, 82(4), 475-499. https://doi.org/10.1007/s10110-003-0148-0
Cartwright, A., Palmer, T., Taylor, A., Methner, N., & Ziervogel, G. (2018). Developing country urban areas are increasingly exposed to the impacts of climate change. Current Opinion in Environmental Sustainability, 30, 1-8.
Chaudhuri, G., & Clarke, K. C. (2013). The SLEUTH land use change model: A review. International Journal of Environmental Research and Public Health, 10(8), 3535-3561.
Chen, G., Li, X., Liu, X., Chen, Y., Liang, X., Leng, J., Xu, X., Liao, W., Qui, Y., Wu, Q., & Huang, K. (2020). Global projections of future urban land expansion under shared socioeconomic pathways. Nature Communications, 11, Article 537. https://doi.org/10.1038/s41467-020-14386-x
Conway, T. M. (2005). Current and future patterns of land-use change in the coastal zone of New Jersey. Environment and Planning B: Planning and Design, 32(6), 877-893. https://doi.org/10.1068/b31170
Correll, M. R., Lillydahl, J. H., & Singell, L. D. (1978). The effects of greenbelts on residential property values: Some findings on the political economy of open space. Land Economics, 54(2), 207-217. https://doi.org/10.2307/3146234
de Moel, H., Jongman, B., Kreibich, H., Merz, B., Penning-Rowsell, E., & Ward, P. J. (2015). Flood risk assessments at different spatial scales. Mitigation and Adaptation Strategies for Global Change, 20, 865-890. https://doi.org/10.1007/s11027-015-9654-z
Dezhkam, S., Jabbarian Amiri, B., Darvishsefat, A. A., & Sakieh, Y. (2014). Simulating the urban growth dimensions and scenario prediction through SLEUTH model: A case study of Rasht County, Guilan, Iran. GeoJournal, 79(5), 591-604. https://doi.org/10.1007/s10708-013-9515-9
Feng, Y., Liu, Y., Tong, X., Liu, M., & Deng, S. (2011). Modeling dynamic urban growth using cellular automata and particle swarm optimization rules. Landscape and Urban Planning, 102(3), 188-196. https://doi.org/10.1016/j.landurbplan.2011.04.004
Fuglsang, M., Münier, B., & Hansen, H. S. (2013). Modelling land-use effects of future urbanization using cellular automata and artificial neural networks. Environment and Planning B: Planning and Design, 40(6), 1083-1102.
Goodarzi, M. R., Abedi-Koupai, J., Hosseinifard, S. J., Ahmadi, S. H., & Tabatabaei, S. H. (2017). Comparison of auto-logistic regression and CA-Markov model in spatial modeling of land use/cover change (case study: Birjand region, Iran). Desert, 22(1), 1-12.
Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202, 18-27. https://doi.org/10.1016/j.rse.2017.06.031
Grigorescu, I., Kucsicsa, G., Popovici, E.-A., Mitrică, B., Mocanu, I., & Dumitrașcu, M. (2021). Modelling land use/cover change to assess future urban sprawl in Romania. Geocarto International, 36(7), 721-739.
Guan, Q., Wang, L., & Clarke, K. C. (2005). An artificial-neural-network-based, constrained CA model for simulating urban growth. Cartography and Geographic Information Science, 32(4), 369-380. https://doi.org/10.1559/152304005775194746
Gude, P. H., Hansen, A. J., Rasker, R., & Maxwell, B. (2007). Rates and drivers of rural residential development in the Greater Yellowstone. Landscape and Urban Planning, 77(1-2), 131-151. https://doi.org/10.1016/j.landurbplan.2005.02.004
Güneralp, B., & Seto, K. C. (2013). Futures of global urban expansion: Uncertainties and implications for biodiversity conservation. Environmental Research Letters, 8(1), Article 014025. https://doi.org/10.1088/1748-9326/8/1/014025
Han, J., Hayashi, Y., Cao, X., & Imura, H. (2015). Application of an integrated system dynamics and cellular automata model for urban growth assessment: A case study of Shanghai, China. Landscape and Urban Planning, 91(3), 133-141. https://doi.org/10.1016/j.landurbplan.2008.12.002
Hasan, S. S., Zhen, L., Miah, M. G., Ahamed, T., & Samie, A. (2020). Impact of land use change on ecosystem services: A review. Environmental Development, 34, Article 100527. https://doi.org/10.1016/j.envdev.2020.100527
Henríquez-Dole, L., Usón, T. J., Vicuña, S., Henríquez, C., Gironás, J., & Meza, F. (2018). Integrating strategic land use planning in the construction of future land use scenarios and its performance: The Maipo River Basin, Chile. Land Use Policy, 78, 353–366. https://doi.org/10.1016/j.landusepol.2018.06.045
Hilferink, M., & Rietveld, P. (1999). Land use scanner: An integrated GIS based model for long term projections of land use in urban and rural areas. Journal of Geographical Systems, 1, 155-177. https://doi.org/10.1007/s101090050010
Hsieh, H. F., & Shannon, S. E. (2005). Three approaches to qualitative content analysis. Qualitative Health Research, 15(9), 1277-1288. https://doi.org/10.1177/1049732305276687
Hua, L., Tang, L., Cui, S., & Yin, K. (2014). Simulating urban growth using the SLEUTH model in a coastal peri-urban district in China. Sustainability, 6(6), 3899-3914. https://doi.org/10.3390/su6063899
Hu, Z., & Lo, C. P. (2007). Modeling urban growth in Atlanta using logistic regression. Computers, Environment and Urban Systems, 31(6), 667-688. https://doi.org/10.1016/j.compenvurbsys.2006.11.001
Huang, K., Li, X., Liu, X., & Seto, K. C. (2019). Projecting global urban land expansion and heat island intensification through 2050. Environmental Research Letters, 14(11), Article 114037. https://iopscience.iop.org/article/10.1088/1748-9326/ab4b71
Kim, Y., & Park, K. (2015). A sustainable development model for an urban heritage area: The case of downtown Seoul, South Korea. Sustainability, 7(11), 14723-14750.
Ku, C.-A. (2016). Incorporating spatial regression model into cellular automata for simulating land use change. Applied Geography, 69, 1-9.
Lambin, E. F., & Geist, H. (Eds.). (2006). Land-use and land-cover change: Local processes and global impacts. Springer. https://doi.org/10.1007/3-540-32202-7
Landis, J. D. (1995). Imagining land use futures: Applying the California urban futures model. Journal of the American Planning Association, 61(4), 438-457. https://doi.org/10.1080/01944369508975656
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., Zhou, Y., Zhao, M., & Zhao, X. (2020). A harmonized global nighttime light dataset 1992-2018. Scientific Data, 7, Article 168. https://doi.org/10.1038/s41597-020-0510-y
Liang, X., Liu, X., Li, X., Chen, Y., Tian, H., & Yao, Y. (2018). Delineating multi-scenario urban growth boundaries with a CA-based FLUS model and morphological method. Landscape and Urban Planning, 177, 47-63. https://doi.org/10.1016/j.landurbplan.2018.04.016
Lin, Y. P., Hong, N. M., Wu, P. J., & Lin, C. J. (2007). Modeling and assessing land-use and hydrological processes to future land-use and climate change scenarios in watershed land-use planning. Environmental Geology, 53, 623-634. https://doi.org/10.1007/s00254-007-0677-y
Lin, Y. P., Chu, H.-J., Wu, C.-F., Chang, T.-K., & Chen, C.-Y. (2011). Hotspot analysis of spatial environmental pollutants using kernel density estimation and geostatistical techniques. International Journal of Environmental Research and Public Health, 8(1), 75-88. https://doi.org/10.3390/ijerph8010075
Linard, C., Gilbert, M., Snow, R. W., Noor, A. M., & Tatem, A. J. (2012). Population distribution, settlement patterns and accessibility across Africa in 2010. PLoS One, 7(2), Article e31743. https://doi.org/10.1371/journal.pone.0031743
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, Y., & Feng, Y. (2016). Simulating the impact of economic and environmental strategies on future urban growth scenarios in Ningbo, China. Sustainability, 8(10), Article 1045. https://doi.org/10.3390/su8101045
Liu, Y., Feng, Y., & Pontius, R. G. (2014). Spatially-explicit simulation of urban growth through self-adaptive genetic algorithm and cellular automata modelling. Land, 3(3), 719-738. https://doi.org/10.3390/land3030719
Liu, Y., & Yang, R. (2012). The spatial characteristics and formation mechanism of the county urbanization in China. Acta Geographica Sinica, 67(8), 1011-1020. https://www.geog.com.cn/CN/10.11821/xb201208001
Lu, D., Xu, J., Yang, D., & Zhao, J. (2017). Spatio-temporal variation and influence factors of PM2.5 concentrations in China from 1998 to 2014. Atmospheric Pollution Research, 8(6), 1151-1159. https://doi.org/10.1016/j.apr.2017.05.005
Lu, Y., Wang, X., Xie, Y., Li, K., & Xu, Y. (2016). Integrating future land use scenarios to evaluate the spatio-temporal dynamics of landscape ecological security. Sustainability, 8(12), Article 1242. https://www.mdpi.com/2071-1050/8/12/1242
Mahiny, A. S., & Clarke, K. C. (2012). Guiding SLEUTH land-use/land-cover change modeling using multicriteria evaluation: Towards dynamic sustainable land-use planning. Environment and Planning B: Planning and Design, 39(5), 925-944. https://doi.org/10.1068/b37092
Maithani, S. (2015). Neural networks-based simulation of land cover scenarios in Doon valley, India. Geocarto International, 30(2), 163-185. https://doi.org/10.1080/10106049.2014.927535
Mathioulakis, S., & Photis, Y. N. (2017). Using the SLEUTH model to simulate future urban growth in the Greater Eastern Attica area, Greece. European Journal of Geography, 8(2), 107-120. https://www.eurogeojournal.eu/index.php/egj/article/view/292
Mondal, M. S., Sharma, N., Kappas, M., & Garg, P. K. (2019). CA Markov modeling of land use land cover dynamics and sensitivity analysis to identify sensitive parameter(s). In L. Halounova & V. Šafář (Eds.), The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences (Vol. XLII-2/W13, pp. 723-729). https://doi.org/10.5194/isprs-archives-XLII-2-W13-723-2019
Munshi, T., Zuidgeest, M. H. P., Brussel, M. J. G., & van Maarseveen, M. F. A. M. (2014). Logistic regression and cellular automata-based modelling of retail, commercial and residential development in the city of Ahmedabad, India. Cities, 39, 68-86. https://doi.org/10.1016/j.cities.2014.02.007
Naghibi, F., Delavar, M. R., & Pijanowski, B. (2016). Urban growth modeling using cellular automata with multi-temporal remote sensing images calibrated by the artificial bee colony optimization algorithm. Sensors, 16(12), Article 2122. https://doi.org/10.3390/s16122122
Newman, G., Lee, J., & Berke, P. (2016). Using the land transformation model to forecast vacant land. Journal of Land Use Science, 11(4), 450-475. https://doi.org/10.1080/1747423X.2016.1162861
Nor, A. N. M., Corstanje, R., Harris, J. A., Grafius, D. R., & Siriwardena, G. M. (2017). Ecological connectivity networks in rapidly expanding cities. Heliyon, 3(6), Article e00325. https://doi.org/10.1016/j.heliyon.2017.e00325
Oguz, H. (2012). Simulating future urban growth in the city of Kahramanmaras, Turkey from 2009 to 2040. Journal of Environmental Biology, 33(2 Suppl.), 381–386.
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., ... Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372, Article n71. https://doi.org/10.1136/bmj.n71
Park, S., Clarke, K. C., Choi, C., & Kim, J. (2017). Simulating land use change in the Seoul Metropolitan Area after greenbelt elimination using the SLEUTH model. Journal of Sensors, 2017, Article 4012929. https://doi.org/10.1155/2017/4012929
Peng, W., Wang, G., Zhou, J., Zhao, J., & Yang, C. (2015). Studies on the temporal and spatial variations of urban expansion in Chengdu, western China, from 1978 to 2010. Sustainable Cities and Society, 17, 141–150. https://doi.org/10.1016/j.scs.2015.03.004
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
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
Pontius, R. G., Jr., & 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., Jr., 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
Price, B., Kienast, F., Seidl, I., Ginzler, C., Verburg, P. H., & Bolliger, J. (2015). Future landscapes of Switzerland: Risk areas for urbanisation and land abandonment. Applied Geography, 57, 32-41. https://doi.org/10.1016/j.apgeog.2014.12.009
Qiang, Y., & Lam, N. S. N. (2015). Modeling land use and land cover changes in a vulnerable coastal region using artificial neural networks and cellular automata. Environmental Monitoring and Assessment, 187, Article 57. https://doi.org/10.1007/s10661-015-4298-8
Ramachandra, T. V., Aithal, B. H., & Sanna, D. D. (2012). Insights to urban dynamics through landscape spatial pattern analysis. International Journal of Applied Earth Observation and Geoinformation, 18, 329–343. https://doi.org/10.1016/j.jag.2012.03.005
Reed, M. S., Evely, A. C., Cundill, G., Fazey, I., Glass, J., Laing, A., Newig, J., Parrish, B., Prell, C., Raymond, C., & Stringer, L. C. (2010). What is social learning? Ecology and Society, 15(4), Article r1. https://doi.org/10.5751/ES-03564-1504r01
Rimal, B., Zhang, L., Keshtkar, H., Sun, X., & Rijal, S. (2018). Quantifying the spatiotemporal pattern of urban expansion and hazard and risk area identification in the Kaski district of Nepal. Land, 7(1), Article 37. https://doi.org/10.3390/land7010037
Samie, A., Deng, X., Jia, S., & Chen, D. (2017). Scenario-based simulation on dynamics of land-use-land-cover change in Punjab Province, Pakistan. Sustainability, 9(8), Article 1285. https://doi.org/10.3390/su9081285
Saputra, M. H., & Lee, H. S. (2019). Prediction of land use and land cover changes for North Sumatra, Indonesia, using an artificial-neural-network-based cellular automaton. Sustainability, 11(11), Article 3024. https://doi.org/10.3390/su11113024
Sekovski, I., Armaroli, C., Calabrese, L., Ciavola, P., Dentale, F., Bonaldo, D., & Carniel, S. (2015). Coupling scenarios of urban growth and flood hazards along the Emilia-Romagna coast (Italy). Natural Hazards and Earth System Sciences, 15(10), 2331-2346. https://doi.org/10.5194/nhess-15-2331-2015
Seto, K. C., Güneralp, B., & Hutyra, L. R. (2012). Global forecasts of urban expansion to 2030 and direct impacts on biodiversity and carbon pools. Proceedings of the National Academy of Sciences, 109(40), 16083-16088. https://doi.org/10.1073/pnas.1211658109
Seto, K. C., Fragkias, M., Güneralp, B., & Reilly, M. K. (2011). A meta-analysis of global urban land expansion. PLoS One, 6(8), Article e23777. https://doi.org/10.1371/journal.pone.0023777
Seto, K. C., Parnell, S., & Elmqvist, T. (2013). A global outlook on urbanization. In Urbanization, biodiversity and ecosystem services: Challenges and opportunities (pp. 1-12). Springer. https://doi.org/10.1007/978-94-007-7088-1_1
Shafizadeh-Moghadam, H., Valavi, R., Shahabi, H., Chapi, K., & Shirzadi, A. (2018). Novel forecasting approaches using combination of machine learning and statistical models for flood susceptibility mapping. Journal of Environmental Management, 217, 1-11. https://doi.org/10.1016/j.jenvman.2018.03.089
Shi, Y., Wu, J., & Shi, S. (2017). Study of the simulated expansion boundary of construction land in Shanghai based on a SLEUTH model. Sustainability, 9(6), Article 876. https://doi.org/10.3390/su9060876
Shoemaker, D. A., BenDor, T. K., & Meentemeyer, R. K. (2019). Anticipating trade-offs between urban patterns and ecosystem service production: Scenario analyses of sprawl alternatives for a rapidly urbanizing region. Computers, Environment and Urban Systems, 74, 114-125. https://doi.org/10.1016/j.compenvurbsys.2018.10.003
Silva, E. A., & Clarke, K. C. (2002). Calibration of the SLEUTH urban growth model for Lisbon and Porto, Portugal. Computers, Environment and Urban Systems, 26(6), 525-552. https://doi.org/10.1016/S0198-9715(01)00014-X
Steiner, F., McSherry, L., & Cohen, J. (2000). Land suitability analysis for the upper Gila River watershed. Landscape and Urban Planning, 50(4), 199-214. https://doi.org/10.1016/S0169-2046(00)00093-1
Terzi, F. (2015). Scenario-based land use estimation: The case of Sakarya. A|Z ITU Journal of the Faculty of Architecture, 12(1), 181-203.
te Linde, A. H., Bubeck, P., Dekkers, J. E. C., de Moel, H., & Aerts, J. C. J. H. (2011). Future flood risk estimates along the river Rhine. Natural Hazards and Earth System Sciences, 11(2), 459-473. https://doi.org/10.5194/nhess-11-459-2011
Torabi Haghighi, A., Darabi, H., Shahedi, K., Solaimani, K., & Kløve, B. (2020). A scenario-based approach for assessing the hydrological impacts of land use and climate change in the Marboreh watershed, Iran. Environmental Modeling & Assessment, 25(1), 41–57. https://doi.org/10.1007/s10666-019-09665-x
Turner, B. L., II, Skole, D., Sanderson, S., Fischer, G., Fresco, L., & Leemans, R. (1995). Land-use and land-cover change: Science/research plan (IGBP Global Change Report No. 35; HDP Report No. 7). International Geosphere-Biosphere Programme.
Ullah, S., Tahir, A. A., Akbar, T. A., Hassan, Q. K., Dewan, A., Khan, A. J., & Khan, M. (2019). Remote sensing-based quantification of the relationships between land use land cover changes and surface temperature over the lower Himalayan region. Sustainability, 11(19), Article 5492. https://doi.org/10.3390/su11195492
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., Alexander, P., Evans, T., Magliocca, N. R., Malek, Z., Rounsevell, M. D., & van Vliet, J. (2019). Beyond land cover change: Towards a new generation of land use models. Current Opinion in Environmental Sustainability, 38, 77-85. https://doi.org/10.1016/j.cosust.2019.05.002
Vermeiren, K., Van Rompaey, A., Loopmans, M., Serwajja, E., & Mukwaya, P. (2012). Urban growth of Kampala, Uganda: Pattern analysis and scenario development. Landscape and Urban Planning, 106(2), 199-206. https://doi.org/10.1016/j.landurbplan.2012.03.006
Wang, H., He, S., Liu, X., Dai, L., Pan, P., Hong, S., & Zhang, W. (2013). Simulating urban expansion using a cloud-based cellular automata model: A case study of Jiangxia, Wuhan, China. Landscape and Urban Planning, 110, 99-112. https://doi.org/10.1016/j.landurbplan.2012.10.016
Wilson, C. O., & Weng, Q. (2010). Assessing surface water quality and its relation with urban land cover changes in the Lake Calumet Area, Greater Chicago. Environmental Management, 45, 1096-1111. https://doi.org/10.1007/s00267-010-9482-6
Wu, F., & Martin, D. (2002). Urban expansion simulation of Southeast England using population surface modelling and cellular automata. Environment and Planning A, 34(10), 1855-1876. https://doi.org/10.1068/a3520
Wu, F., Zhan, J., Su, H., Yan, H., & Ma, E. (2015). Scenario-based impact assessment of land use/cover and climate changes on watershed hydrology in Heihe River Basin of Northwest China. Advances in Meteorology, 2015, Article 410198. https://doi.org/10.1155/2015/410198
Wu, Q., Li, H., Wang, R., Paulussen, J., He, Y., Wang, M., Wang, B., & Wang, Z. (2006). Monitoring and predicting land use change in Beijing using remote sensing and GIS. Landscape and Urban Planning, 78(4), 322-333. https://doi.org/10.1016/j.landurbplan.2005.10.002
Xi, F., He, H. S., Clarke, K. C., Hu, Y., Wu, X., Liu, M., Shi, T., Geng, Y., & Gao, C. (2012). The potential impacts of sprawl on farmland in Northeast China: Evaluating a new strategy for rural development. Landscape and Urban Planning, 104(1), 34-46. https://doi.org/10.1016/j.landurbplan.2011.09.003
Xi, F., He, H. S., Hu, Y., Bu, R., Chang, Y., Wu, X., Liu, M., & Shi, T. (2009). Simulating the impacts of ecological protection policies on urban land use sustainability in Shenyang-Fushun, China. International Journal of Urban Sustainable Development, 1(1-2), 111-127. https://doi.org/10.1080/19463130903458326
Xiao, Y., & Watson, M. (2019). Guidance on conducting a systematic literature review. Journal of Planning Education and Research, 39(1), 93-112. https://doi.org/10.1177/0739456X17723971
Yang, X., & Lo, C. P. (2003). Modelling urban growth and landscape changes in the Atlanta metropolitan area. International Journal of Geographical Information Science, 17(5), 463-488. https://doi.org/10.1080/1365881031000086965
Yirsaw, E., Wu, W., Shi, X., Temesgen, H., & Bekele, B. (2017). Land use/land cover change modeling and the prediction of subsequent changes in ecosystem service values in a coastal area of China, the Su-Xi-Chang Region. Sustainability, 9(7), Article 1204. https://doi.org/10.3390/su9071204
Zare, M., Panagopoulos, T., & Loures, L. (2017). Simulating the impacts of future land use change on soil erosion in the Kasilian watershed, Iran. Land Use Policy, 67, 558-572. https://doi.org/10.1016/j.landusepol.2017.06.028
Zheng, H. W., Shen, G. Q., Wang, H., & Hong, J. (2015). Simulating land use change in urban renewal areas: A case study in Hong Kong. Habitat International, 46, 23-34. https://doi.org/10.1016/j.habitatint.2014.10.008
Zhou, Y., Varquez, A. C. G., & Kanda, M. (2019). High-resolution global urban growth projection based on multiple applications of the SLEUTH urban growth model. Scientific Data, 6, Article 34. https://doi.org/10.1038/s41597-019-0048-z
Zhen, L., Deng, X., Wei, Y., Jiang, Q., Lin, Y., Helming, K., Wang, C., König, H. J., & Hu, J. (2014). Future land use and food security scenarios for the Guyuan district of remote western China. iForest - Biogeosciences and Forestry, 7(6), 372-384. https://doi.org/10.3832/ifor1170-007
