AN INTEGRATED APPROACH FOR SIMULATION AND PREDICTION OF LAND USE AND LAND COVER CHANGES AND URBAN GROWTH (CASE STUDY: SANANDAJ CITY IN IRAN)
DOI:
https://doi.org/10.2298/IJGI2203273SKeywords:
land use and land cover change, artificial neural network, logistic regression, cellular automata, SanandajAbstract
One of the growing areas in the west of Iran is Sanandaj city, the center of Kordestan province, which requires the investigation of the city's growth and the estimation of land degradation. Today, the combination of remote sensing data and spatial models is a useful tool for monitoring and modeling land use and land cover (LULC) changes. In this study, LULC changes and the impact of Sanandaj city growth on land degradation in geographical directions during the period 1989 to 2019 were investigated. Also, the accuracy of three models, artificial neural network-cellular automata (ANN-CA), logistic regression-cellular automata (LR-CA), and the weight of evidence-cellular automata (WOE-CA) for modeling LULC changes was evaluated, and the results of these models were compared with the CA-Markov model. According to the results of the study, ANN-CA, LR-CA, and WOE-CA models, with an accuracy of more than 80%, are efficient and effective for modeling LULC changes and growth of urban areas.
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References
Alimohammadi, A., Rabiei, H. R., & Zeaiean Firouzabadi, P. (2004). A new approach for modeling uncertainty in remote sensing change detection process. In B. S. Anders (Ed.), Proceedings of the 12th International Conference on Geomatics − Geospatial Information Research: Bridging the Pacific and Atlantic (pp. 505–508). T. Academic. http://giscience.hig.se/binjiang/geoinformatics/files/p503.pdf
Amini, S., Saber, M., Rabiei-Dastjerdi, H., & Homayouni, S. (2022). Urban Land Use and Land Cover Change Analysis Using Random Forest Classification of Landsat Time Series. Remote Sensing, 14(11), Article 2654. https://doi.org/10.3390/rs14112654
Aneesha Satya, B., Shashi, A., & Deva, P. (2020). Future land use land cover scenario simulation using open-source GIS for the city of Warangal, Telangana, India. Applied Geomatics, 12, 281–290. https://doi.org/10.1007/s12518-020-00298-4
Belay, T., & Mengistu, D. A. (2019). Land use and land cover dynamics and drivers in the Muga watershed, Upper Blue Nile basin, Ethiopia. Remote Sensing Applications: Society and Environment, 15, Article 100249. https://doi.org/10.1016/j.rsase.2019.100249
Bonham-Carter, G. F. (1994). Geographic Information Systems for Geoscientists, Modelling with GIS. Pergamon Press.
Boudet, F., MacDonald, G. K., Robinson, B. E., & Samberg, L. H. (2020). Rural-urban connectivity and agricultural land management across the Global South. Global Environmental Change, 60, Article 101982. https://doi.org/10.1016/j.gloenvcha.2019.101982
Das, N., Mondal, P., Sutradhar, S., & Ghosh, R. (2021). Assessment of variation of land use/land cover and its impact on land surface temperature of Asansol subdivision. The Egyptian Journal of Remote Sensing and Space Science, 24(1), 131–149. https://doi.org/10.1016/j.ejrs.2020.05.001
Dube, T., Gumindoga, W., & Chawira, M. (2014). Detection of land cover changes around Lake Mutirikwi, Zimbabwe, based on traditional remote sensing image classification techniques. African Journal of Aquatic Science, 39(1), 89–95. https://doi.org/10.2989/16085914.2013.870068
ESRI. (2019). ArcGIS (Version 10.2) [Computer software]. https://www.esri.com/en-us/arcgis/about-arcgis/overview
Exelis Visual Information Solutions. (2019). ENVI (Version 5.0.3) [Computer software]. https://envi.software.informer.com/download/#download_content
Grigoraș, G., & Urițescu, B. (2019). Land Use/Land Cover changes dynamics and their effects on Surface Urban Heat Island in Bucharest, Romania. International Journal of Applied Earth Observation and Geoinformation, 80, 115–126. https://doi.org/10.1016/j.jag.2019.03.009
Gross, D., Dubois, G., Pekel, J.-F., Mayaux, P., Holmgren, M., Prins, H. H. T., Rondinini, C., & Boitani, L. (2013). Monitoring land cover changes in African protected areas in the 21st century. Ecological Informatics, 14, 31–37. https://doi.org/10.1016/j.ecoinf.2012.12.002
Hao, Y., Chen, Z., Huang, Q., Li, F., Wang, B., & Ma, L. (2020). Bidirectional Segmented Detection of Land Use Change Based on Object-Level Multivariate Time Series. Remote Sensing, 12(3), Article 478. https://doi.org/10.3390/rs12030478
Hishe, S., Bewket, W., Nyssen, J., & Lyimo, J. (2020). Analyzing past land use land cover change and CA-Markov based future modeling in the Middle Suluh Valley, Northern Ethiopia. Geocarto International, 35(3), 225–255. https://doi.org/10.1080/10106049.2018.1516241
Iran Meteorological Organization. (2019). Climate of Sanandaj city. Retrieved October 31, 2022, from https://www.irimo.ir
Karimi, N., & Boussauw, K. (2018). Sanandaj, Iran. Cities, 72(Part B), 261–273. https://doi.org/10.1016/j.cities.2017.09.004
Khan, A., Khan, H. H., & Umar, R. (2017). Impact of land-use on groundwater quality: GIS-based study from an alluvial aquifer in the western Ganges basin. Applied Water Science, 7, 4593–4603. https://doi.org/10.1007/s13201-017-0612-7
Kourosh Niya, A., Huang, J., Kazemzadeh-Zow, A., Karimi, H., Keshtkar, H., & Naimi, B. (2020). Comparison of three hybrid models to simulate land use changes: a case study in Qeshm Island, Iran. Environmental Monitoring and Assessment, 192, Article 302. https://doi.org/10.1007/s10661-020-08274-6
Lakes, T., Müller, D., & Krüger, C. (2009). Cropland change in southern Romania: a comparison of logistic regressions and artificial neural networks. Landscape Ecology, 24, Article 1195. https://doi.org/10.1007/s10980-009-9404-2
Lee, Y., & Brody, S. D. (2018). Examining the impact of land use on flood losses in Seoul, Korea. Land Use Policy, 70, 500–509. https://doi.org/10.1016/j.landusepol.2017.11.019
Lin, Y.-P., Chu, H.-J., Wu, Ch.-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
Mitsova, D., Shuster, W., & Wang, X. (2011). A cellular automata model of land cover change to integrate urban growth with open space conservation. Landscape and Urban Planning, 99(2), 141–153. https://doi.org/10.1016/j.landurbplan.2010.10.001
Mohamed, A., & Worku, H. (2020). Simulating urban land use and cover dynamics using cellular automata and Markov chain approach in Addis Ababa and the surrounding. Urban Climate, 31, Article 100545. https://doi.org/10.1016/j.uclim.2019.100545
Mucova, S. A. R., Filho, W. L., Azeiteiro, U. M., & Pereira, M. J. (2018). Assessment of land use and land cover changes from 1979 to 2017 and biodiversity & land management approach in Quirimbas National Park, Northern Mozambique, and Africa. Global Ecology and Conservation, 16, Article e00447. https://doi.org/10.1016/j.gecco.2018.e00447
Odongo, V. O., van Oel, P. R., van der Tol, C., & Su, Z. (2019). Impact of land use and land cover transitions and climate on evapotranspiration in the Lake Naivasha Basin, Kenya. Science of the Total Environment, 682, 19–30. https://doi.org/10.1016/j.scitotenv.2019.04.062
Ouyang, W., Wu, Y., Hao, Z., Zhang, Q., Bu, Q., & Gao, X. (2018). Combined impacts of land use and soil property changes on soil erosion in a mollisol area under long-term agricultural development. Science of The Total Environment, 613–614, 798–809. https://doi.org/10.1016/j.scitotenv.2017.09.173
Pal, S., & Ziaul, S. (2017). Detection of land use and land cover change and land surface temperature in English Bazar urban center. The Egyptian Journal of Remote Sensing and Space Science, 20(1), 125–145. https://doi.org/10.1016/j.ejrs.2016.11.003
Pourghasemi, H. R., & Gokceoglu, C. (2019). Spatial Modeling in GIS and R for Earth and Environmental Sciences. Elsevier press.
QGIS Development Team. (2016). QGIS2.18-MOLUSCE Plugin software [Computer software]. https://plugins.qgis.org/plugins/molusce/
Rabiei-Dastjerdi, H., Amini, S., McArdle, G., & Homayouni, S. (2022). City-region or city? That is the question: modelling sprawl in Isfahan using geospatial data and technology. GeoJournal, 87, 1–21. https://doi.org/10.1007/s10708-021-10554-8
Regmi, A. D., Devkota, K. C., Yoshida, K., Pradhan, B., Pourghasemi, H. R., Kumamoto, T., & Akgun, A. (2014). Application of frequency ratio, statistical index, and weights-of-evidence models and their comparison in landslide susceptibility mapping in Central Nepal Himalaya. Arabian Journal of Geosciences, 7, 725–742. https://doi.org/10.1007/s12517-012-0807-z
Roy, A., & Inamdar, A. B. (2019). Multi-temporal Land Use Land Cover (LULC) change analysis of a dry semi-arid river basin in western India following a robust multi-sensor satellite image calibration strategy. Heliyon, 5(4), Article e01478. https://doi.org/10.1016/j.heliyon.2019.e01478
Sapena, M., & Ruiz, L. A. (2019). Analysis of land use/land cover spatio-temporal metrics and population dynamics for urban growth characterization. Computers, Environment and Urban Systems, 73, 27–39. https://doi.org/10.1016/j.compenvurbsys.2018.08.001
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
Shooshtari, S. J., & Gholamalifard, M. (2015). Scenario-based land cover change modeling and its implications for landscape pattern analysis in the Neka Watershed, Iran. Remote Sensing Applications: Society and Environment, 1, 1–19. https://doi.org/10.1016/j.rsase.2015.05.001
Siddiqui, A., Siddiqui, A., Maithani, S., Jha, A. K., Kumar, P., & Srivastav, S. K. (2018). Urban growth dynamics of an Indian metropolitan using CA Markov and Logistic Regression. The Egyptian Journal of Remote Sensing and Space Sciences, 21(3), 229–236. https://doi.org/10.1016/j.ejrs.2017.11.006
Statistical Center of Iran. (2016). Census Statistics. https://www.amar.org.ir
Tao, H., Xing, J., Zhou, H., Chang, X., Li, G., Chen, L., & Li, J. (2018). Impacts of land use and land cover change on regional meteorology and air quality over the Beijing-Tianjin-Hebei region, China. Atmospheric Environment, 189, 9–21, https://doi.org/10.1016/j.atmosenv.2018.06.033
United Nations, Department of Economic and Social Affairs, Population Division. (2019). World Urbanization Prospects: The 2018 Revision (ST/ESA/SER.A/420). United Nations.
U.S. Geological Survey. (2019). Landsat images time series and Shuttle Radar Topography Mission Digital elevation datasets. https://earthexplorer.usgs.gov
Valjarević, A., Filipović, D., Živković, D., Ristić, N., Božović, J., & Božović, R. (2021). Spatial Analysis of the Possible First Serbian Conurbation. Applied Spatial Analysis and Policy, 14(1), 113–134. https://doi.org/10.1007/s12061-020-09348-1
Verstegen, J. A., van der Laan, C., Dekker, S. C., Faaij, A. P. C., & Santos, M. J. (2019). Recent and projected impacts of land use and land cover changes on carbon stocks and biodiversity in East Kalimantan, Indonesia. Ecological Indicators, 103, 563–575. https://doi.org/10.1016/j.ecolind.2019.04.053
Wang, X., Zhang, B., Xu, X., & He, C. (2020). Regional water-energy cycle response to land use/cover change in the agro-pastoral ecotone, Northwest China. Journal of Hydrology, 580, Article 124246. https://doi.org/10.1016/j.jhydrol.2019.124246
White, R., & Engelen, G. (2000). High-resolution integrated modelling of the spatial dynamics of urban and regional systems. Computer, Environment and Urban Systems, 24(5), 383–400. https://doi.org/10.1016/S0198-9715(00)00012-0
Xu, J., Sharma, R., Fang, J., & Xu, Y. (2008). Critical linkages between land-use transition and human health in the Himalayan region. Environment International, 34(2), 239–247. https://doi.org/10.1016/j.envint.2007.08.004
Yang, X., Zheng, X.-Q., & Lv, L.-N. (2012). A spatiotemporal model of land use change based on ant colony optimization, Markov chain and cellular automata. Ecological Modelling, 233, 11–19. https://doi.org/10.1016/j.ecolmodel.2012.03.011
Zeaiean, P., Rabiei, H. R., & Alimohamadi, A. (2005). Detection of Land Use/Cover Changes of Isfahan by Agricultural Lands Around Urban Area Using Remote Sensing and GIS Technologies. The Journal of Spatial Planning, 9(4), 41–54. https://hsmsp.modares.ac.ir/article-21-6077-en.html
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