Abstract
In the present paper, land use/land cover (LULC) change was predicted in the Greater Isfahan area (GIA), central Iran. The GIA has been growing rapidly in recent years, and attempts to simulate its spatial expansion would be essential to make appropriate decisions in LULC management plans and achieve sustainable development. Several modeling tools were employed to outline sustainable scenarios for future dynamics of LULCs in the region. Specifically, we explored past LULC changes in the study area from 1996 to 2018 and predicted its future changes for 2030 and 2050. For this purpose, we performed object-oriented and decision tree techniques on Landsat and Sentinel-2 satellite images. The CA-Markov hybrid model was utilized to analyze past trends and predict future LULC changes. LULC changes were quantitatively measured using landscape metrics. According to the results, the majority of changes were related to increasing residential areas and decreasing irrigated lands. The results indicated that residential lands would grow from 27,886.87 ha to 67,093.62 ha over1996–2050 while irrigated lands decrease from 99,799.4 ha to 50,082.16 ha during the same period of time. The confusion matrix of the 2018 LULC map was built using a total of 525 ground truth points and yielded a Kappa coefficient and overall accuracy of 78% and 82%, respectively. Moreover, the confusion matrix constructed base on the Sentinel-2 map, as a reference, to judge the predicted 2018 LULC map with a Kappa coefficient of 88%. The results of this study provide useful insights for sustainable land management. The results of this research also proved the promising capability of remote sensing algorithms, CA-Markov model and landscape metrics future LULC planning in the study area.
Similar content being viewed by others
References
Aguirre-Gutiérrez, J., Seijmonsbergen, A. C., & Duivenvoorden, J. F. (2012). Optimizing land cover classification accuracy for change detection, a combined pixel-based and object-based approach in a mountainous area in Mexico. Applied Geography, 34, 29–37.
Aquilué, N., De Cáceres, M., Fortin, M. J., Fall, A., & Brotons, L. (2017). A spatial allocation procedure to model land-use/land-cover changes: accounting for occurrence and spread processes. Ecological Modelling, 344, 73–86.
Arsanjani, J., Helbich, M., Kainz, W., & Darvishi Boloorani, A. (2012). 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.
Asgarian, A., Soffianian, A., Pourmanafi, S., & Bagheri, M. (2018). Evaluating the spatial effectiveness of alternative urban growth scenarios inprotecting cropland resources: a case of mixed agricultural-urbanized landscape in central Iran. Sustainable Cities and Society, 43, 197–207.
Barros, K., Ribeiro, C., Marcatti, G., Lorenzon, A., de Castro, N., Domingues, G., de Carvalho, J., & Santos, A. (2018). Markov chains and cellular automata to predict environments subject to desertification. Journal of Environmental Management, 225, 160–167.
Boongaling, C. G. K., Faustino-Eslava, D. V., & Lansigan, F. P. (2018). Modeling land use change impacts on hydrology and the use of landscape metrics as tools for watershed management: the case of an ungauged catchment in the Philippines. Land Use Policy, 72, 116–128.
Bryan, B. A., Nolan, M., McKellar, L., Connor, D. J., Newth, D., Harwood, T., King, D., Navarro, J., Cai, Y., Gao, L., Grundy, M., Graham, P., Ernst, A., Dunstall, S., Stock, F., Brinsmead, T., Harman, I., Grigg, J. N., Battaglia, M., Keating, B., Wonhas, A., & Hatfield-Dodds, S. (2016). Land-use and sustainability under intersecting global change and domestic policy scenarios: Trajectories for Australia to 2050. Global Environmental Change, 38, 130–152.
Chen, Y., Li, X., Liu, X., & Ai, B. (2014). Modeling urban land-use dynamics in a fast developing city using the modified logistic cellular automaton with a patch-based simulation strategy. International Journal of Geographical Information Science, 28, 234–255.
Congalton, R. G. (1991). A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment, 37, 35–46.
Conradie, B., Piesse, J., & Stephens, J. (2019). The changing environment: efficiency, vulnerability and changes in land use in the South African Karoo, 2012-2014. Environment and Development, 32, 100453.
Degife, A. W., Zabel, F., & Mauser, W. (2018). Assessing land use and land cover changes and agricultural farmland expansions in Gambella Region, Ethiopia, using Landsat 5 and Sentinel 2a multispectral data. Heliyon, 4, e00919.
Feng, Y., & Tong, X. (2018). Dynamic land use change simulation using cellular automata with spatially nonstationary transition rules. GIScience & Remote Sensing, 55, 678–698.
Freitas, M. W. D., Muñoz, P., & dos Santos, J. R. (2018). Land use and cover change modelling and scenarios in the Upper Uruguay Basin (Brazil). Ecological Modelling, 384, 128–144.
Ghavami, M., & Taleai, M. (2017). Towards a conceptual multi-agent-based framework to simulate the spatial group decision-making process. Journal of Geographical Systems, 19, 109–132.
Ghosh, P., Mukhopadhyay, A., Chanda, A., Mondal, P., Akhand, A., Mukherjee, S., Nayak, S. K., Ghosh, S., Mitra, D., Ghosh, T., & Hazra, S. (2017). Application of cellular automata and Markov-chain model in geospatial environmental modeling-a review. Remote Sensing Applications: Society and Environment, 5, 64–77.
Gomes, L. C., Bianchi, F. J. J. A., Cardoso, I. M., Schulte, R. P. O., Arts, B. J. M., & Fernandes Filho, E. I. (2020). Land use and land cover scenarios: an interdisciplinary approach integrating local conditions and the global shared socioeconomic pathways. Land Use Policy, 97, 104723.
Gounaridis, D., Chorianopoulos, I., Symeonakis, E., & Koukoulas, S. (2019). A random forest-cellular automata modelling approach to explore future land use/cover change in Attica (Greece), under different socio-economic realities and scales. Science of the Total Environment, 646, 320–335.
Guan, D., Li, H., Inohae, T., Su, W., Nagaie, T., & Hokao, K. (2011). Modeling urban land use change by the integration of cellular automaton and Markov model. Ecological Modelling, 222, 3761–3772.
Hamad, R., Balzter, H., & Kolo, K. (2018). Predicting land use/land cover changes using a CA-Markov model under two different scenarios. Sustainability, 10, 1–23.
Hasan, S. S., Sarmin, N. S., & Miah, M. G. (2020). Assessment of scenario-based land use changes in the Chittagong Hill Tracts of Bangladesh. Environment and Development, 34, 100463.
Hasegawa, T., Fujimori, S., Ito, A., Takahashi, K., & Masui, T. (2017). Global land use allocation model linked to an integrated assessment model. Science of the Total Environment, 580, 787–796.
Hipt, F., Diekkrüger, B., Steup, G., Yira, Y., Hoffmann, T., Rode, M., & Näschen, K. (2019). Modeling the effect of land use and climate change on water resources and soil erosion in a tropical West African catch-ment (Dano, Burkina Faso) using SHETRAN. Science of the Total Environment, 653, 431–445.
Hou, H., Wang, R., & Murayama, Y. (2019). Scenario-based modelling for urban sustainability focusing on changes in cropland under rapid urbanization: a case study of Hangzhou from1990 to 2035. Science of the Total Environment, 661, 422–431.
Inkoom, J. N., Frank, S., Greved, K., Walze, U., & Fürstf, C. (2018). Suitability of different landscape metrics for the assessments of patchy landscapes in West Africa. Ecological Indicators, 85, 117–127.
Isfahan Regional Water Company. (2008). Determination of water resources and consumption in Zayandehrood River Basin, report to Ministry of Energy, Islamic Republic of Iran, Isfahan (In Persian).
Islam, K., Rahman, M. 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.
Jaafari, S., Sakieh, Y., Alizadeh Shabani, A., Danehkar, A., & Nazarisamani, A. (2016). Landscape change assessment of reservation areas using remote sensing and landscape metrics (case study: Jajroud reservation, Iran). Springer: Environment, Development and Sustainability.
Jahanishakib, F., Mirkarimi, H., Salmanmahiny, A., & Poodat, F. (2018). Land use change modeling through scenario-based cellular automata Markov: improving spatial forecasting. Environmental Monitoring and Assessment, 190, 332.
Jensen, J. (2015). Introductory digital image processing, a remote sensing perspective: Chapter 13, remote sensing-derived thematic map accuracy assessment. Pearson, 4th edition, 658 p.
Jiang, W., Deng, Y., Tang, Z., Lei, X., & Chen, Z. (2017). Modelling the potential impacts of urban ecosystem changes oncarbon storage under different scenarios by linking the CLUE-S and the InVEST models. Ecological Modelling, 345, 30–40.
Joorabian Shooshtari, S., & 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.
Joorabian Shooshtari, S., Shayesteh, K., Gholamalifard, M., Azari, M., & Lopez-Moreno, J. I. (2018). Land cover change modelling in Hyrcanian forests, northern Iran: a landscape pattern and transformation analysis perspective. Cuadernos de Investigacion Geografica, 1, 1–19.
Kalantari, Z., Ferreira, C., Page, J., Goldenberg, R., Olsson, J., & Destouni, G. (2019). Meeting sustainable development challenges in growing cities: coupled social-ecological systems modeling of land use and water changes. Journal of Environmental Management, 245, 471–480.
Kindu, M., Schneider, T., Döllerer, M., Teketay, D., & Knoke, T. (2018). Scenario modelling of land use/land cover changes in Munessa-Shashemene landscape of the Ethiopian highlands. Science of the Total Environment, 622, 534–546.
Lausch, A., Blaschke, T., Haase, D., Herzog, F., Syrbe, R.-U., Tischendorf, L., & Walz, U. (2015). Understanding and quantifying landscape structure-Areviewon relevant process characteristics, data models and landscape metrics. Ecological Modelling, 295, 31–41.
Liao, J., Tang, L., Shao, G., Su, X., Chen, D., & Xu, T. (2016). Incorporation of extended neighborhood mechanisms and its impact on urban land-use cellular automata simulations. Environmental Modelling & Software, 75, 163–175.
Liao, J., Shao, G., Wang, C., Tang, L., Huang, Q., & Qiu, Q. (2019). Urban sprawl scenario simulations based on cellular automata and ordered weighted averaging ecological constraints. Ecological Indicators, 107, 105572.
Lin, B. B., Egerer, M. H., Liere, H., Jha, S., Bichier, P., & Philpott, S. M. (2018). Local- and landscape-scale land cover affects microclimate and water use in urban gardens. Science of the Total Environment, 610, 570–575.
Liu, D., Zheng, X., Zhang, C., & Wang, H. (2017). A new temporal–spatial dynamics method of simulating land-use change. Ecological Modelling, 350, 1–10.
Liu, D., Toman, E., Fuller, Z., Chen, G., Londo, A., Zhang, X., & Zhao, K. (2018). Integration of historical map and aerial imagery to characterize long-term land-use change and landscape dynamics: an object-based analysis via random forests. Ecological Indicators, 95, 595–605.
Liu, D., Zheng, X., & Wang, H. (2020). Land-use simulation and decision-support system (LandSDS): seamlessly integrating system dynamics, agent-based model, and cellular automata. Ecological Modelling, 417, 108924.
Luo, C., Li, Z., Liu, H., Li, H., Wan, R., Pan, J., & Chen, X. (2020). Differences in the responses of flow and nutrient load to isolated and coupled future climate and land use changes. Journal of Environmental Management, 256, 109918.
Madani, K., & Mariño, M. A. (2009). System dynamics analysis for managing Iran’s Zayandeh-Rud River Basin. Water Resources Management, 23, 2163–2187.
Malczewski, J. (2000). On the use of weighted linear combination method in GIS: common and best practice approaches. Transactions in GIS, 4, 5–22.
Mancosu, E., Gago-Silva, A., Barbosa, A., de Bono, A., Ivanov, E., Lehmann, A., & Fons, J. (2014). Future land-use change scenarios for the Black Sea catchment. Environmental Science & Policy, 46, 26–36.
Mas, J.-F., Kolb, M., Paegelow, M., Olmedo, M., & Houet, T. (2014). Inductive pattern-based land use/cover change models: a comparison of four software packages. Environmental Modelling & Software, 51, 94–111.
McGarigal, K. (2002). FRAGSTATS: Spatial Pattern Analysis Program for Categorical Maps (accessed 10.02.08) http://www.umass.edu/landeco/research/fragstats/fragstats.html.
Moein, M., Asgarian, A., Sakieh, Y., & Soffianian, A. (2018). Scenario-based analysis of land-use competition in central Iran: finding the trade-off between urban growth patterns and agricultural productivity. Sustainable Cities and Society, 39, 557–567.
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, 100545.
Mousazadeh, R., Ghaffarzadeh, H., Nouri, J., Gharagozlou, A., & Farahpour, M. (2015). Land use change detection and impact assessment in Anzali international coastal wetland using multi-temporal satellite images. Environmental Monitoring and Assessment, 187, 776.
Munthali, M. G., Mustak, S., Adeola, A., Botai, J., Singh, S. K., & Davis, N. (2020). Modelling land use and land cover dynamics of Dedza district of Malawi using hybrid cellular automata and Markov model. Remote Sensing Applications: Society and Environment, 17, 100276.
Murray, A. (2010). Advances in location modeling: GIS linkages and contributions. Journal of Geographical Systems, 12, 335–354.
Myint, S. W., Gober, P., Brazel, A., Grossman-Clarke, S., & Weng, Q. (2012). Per-pixel vs. object-based classification of urban land covers extraction using high spatial resolution imagery. Remote Sensing of Environment, 115, 1145–1161.
Otukei, J. R., & Blaschke, T. (2010). Land cover change assessment using decision trees, support vector machines and maximum likelihood classification algorithms. International Journal of Applied Earth Observation and Geoinformation, 12S, S27–S31.
Petropoulos, P., Kalaitzidis, G. C., & Vadrevu, K. (2012). Support vector machines and object-based classification for obtaining land-use/cover cartography from Hyperion hyperspectral imagery. Computers & Geosciences, 41, 99–107.
Pham, H., Yamaguchi, Y., & Bui, T. (2011). A case study on the relation between city planning and urban growth using remote sensing and spatial metrics. Landscape and Urban Planning, 100, 223–230.
Piling, S., Yueqing, X., Zhonglei, Y., Qingguo, L., Baopeng, X., & Jia, L. (2016). Scenario simulation and landscape pattern dynamic changes of land use in the Poverty Belt around Beijing and Tianjin: a case study of Zhangjiakou city, Hebei Province. Journal of Geographical Sciences, 26, 272–296.
Program and Budget of Isfahan. (2018). Management and planning organization of Isfahan province organization. Iran: Development Documents of Esfahan Province.
Qiu, R., Xu, W., Zhang, J., & Staenz, K. (2018). Modeling and simulating industrial land-use evolution in Shanghai, China. Journal of Geographical Systems, 20, 57–83.
Rana, V., & Suryanarayana, T. (2020). Performance evaluation of parametric and non-parametric classification algorithms for watershed scale land use/land cover mapping using principal component analysis of sentinel 2 bands. Remote Sensing Applications: Society and Environment, 19, 100351.
Ren, Y., Lu, 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.
Roose, M., & Hietala, A. (2018). Methodological Markov-CA projection of the greening agricultural landscape—a case study from 2005 to 2017 in southwestern Finland. Environmental Monitoring and Assessment, 190, 411.
Ruben, G., Zhang, K., Dong, Z., & Xia, J. (2020). Analysis and projection of land-use/land-cover dynamics through scenario-based simulations using the CA-Markov model: a case study in Guanting Reservoir Basin, China. Sustainability, 12, 3747.
Rwanga, S., & Ndambuki, J. (2017). Accuracy assessment of land use/land cover classification using remote sensing and GIS. International Journal of Geosciences, 8, 611–622.
Sakieh, Y., & Salmanmahiny, A. (2016). Performance assessment of geospatial simulation models of land-use change-a landscape metric-based approach. Environmental Monitoring and Assessment, 188, 169.
Sánchez-Espinosa, A., & Schröder, C. (2019). Land use and land cover mapping in wetlands one step closer to the ground: Sentinel-2 versus landsat 8. Journal of Environmental Management, 247, 484–498.
Shifaw, E., Sha, J., Li, X., Bao, Z., & Zhou, Z. (2019). An insight into land-cover changes and their impacts on ecosystem services before and after the implementation of a comprehensive experimental zone plan in Pingtan island, China. Land Use Policy, 82, 631–642.
Sohoulande Djebou, D. C. (2018). Toward an integrated watershed zoning framework based on the spatio-temporal variability of land-cover and climate: application in the Volta river basin. Environment and Development, 28, 55–66.
Sola, I., García-Martín, A., Sandonís-Pozo, L., Álvarez-Mozos, J., Pérez-Cabello, F., González-Audícana, M., & Montorio Llovería, R. (2018). Assessment of atmospheric correction methods for Sentinel-2 images in Mediterranean landscapes. International Journal of Applied Earth Observation and Geoinformation, 73, 63–76.
Tarawally, M., Wenbo, X., Weiming, H., Darlington Mushore, T., & Biniyam Kursah, M. (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, 100–262.
Tian, X., Wenbin, W., Qingbo, Z., Wenxia, T., Verburg, P., Peng, Y., & Liming, Y. (2018). Modeling the spatio-temporal changes in land uses and its impacts on ecosystem services in Northeast China over 2000–2050. Journal of Geographical Sciences, 28, 1611–1625.
Turner, M.G., & Gardner, R.H. (2015). Landscape ecology in theory and practice: Pattern and Process (2th ed). Springer, 482 p.
Ullah, S., Ahmad, K., Sajjad, R., Abbasi, A., Nazeer, A., & Tahir, A. (2019). Analysis and simulation of land cover changes and their impacts on land surface temperature in a lower Himalayan region. Journal of Environmental Management, 245, 348–357.
Wang, S. Q., Zheng, X. Q., & Zang, X. B. (2012). Accuracy assessments of land use change simulation based on Markov-cellular automata model. Procedia Environmental Sciences, 13, 1238–1245.
Wang, Q., Blackburn, G. A., Onojeghuo, A. O., Dash, J., Zhou, L., Zhang, Y., & Atkinson, P. M. (2017). Fusion of Landsat 8 OLI and Sentinel-2 MSI data. Geoscience and Remote Sensing, 55, 3885–3899.
Wang, C., Wang, Y., Wang, R., & Zheng, P. (2018). Modeling and evaluating land-use/land-cover change for urban planning and sustainability: a case study of Dongying city, China. Journal of Cleaner Production, 172, 1529–1534.
Wang, D., Ma, R., Xue, K., & Loiselle, S. (2019). The assessment of Landsat-8 OLI atmospheric correction algorithms for inland waters. Remote Sensing, 11, 1–23.
Wu, J., Jenerette, G. D., Buyantuyev, A., & Redman, C. L. (2011). Quantifying spatiotemporal patterns of urbanization: the case of the two fastest growing metropolitan regions in the United States. Ecological Complexity, 8, 1–8.
Wu, D., Cui, Y., & Luo, Y. (2019). Irrigation efficiency and water-saving potential considering reuse of return flow. Agricultural Water Management, 221, 519–527.
Xing, H., & Meng, Y. (2018). Integrating landscape metrics and socioeconomic features for urban functional region classification. Computers, Environment and Urban Systems, 72, 134–145.
Zhang, Q., Ban, Y., Liu, J., & Hu, Y. (2011). Simulation and analysis of urban growth scenarios for the Greater Shanghai Area, China. Computers, Environment and Urban Systems, 35, 126–139.
Zhang, H., Zeng, Y., Jin, X., Shu, B., Zhou, Y., & Yang, X. (2016). Simulating multi-objective land use optimization allocation using Multi-agent system—a case study in Changsha, China. Ecological Modelling, 320, 334–347.
Zhu, Z., Liu, L., Chen, Z., Zhang, J., & Verburg, P. (2010). Land-use change simulation and assessment of driving factors in the loess hilly region—a case study as Pengyang County. Environmental Monitoring and Assessment, 164, 133–142.
Zhuo, L., Weiguo, J., Wenjie, W., Xuan, L., & Yue, D. (2019). Exploring spatial-temporal change and gravity center movement of construction land in the Chang-Zhu-Tan urban agglomeration. Journal of Geographical Sciences, 29, 1363–1380.
Zou, L., Liu, Y., Wang, J., Yang, Y., & Wang, Y. (2019). Land use conflict identification and sustainable development scenario simulation on China’s southeast coast. Journal of Cleaner Production, 238, 117899.
Funding
This study received financial support of Iran National Sciences Foundation (INSF) of vice-presidency for Sciences and Technology (grant number: 98012996).
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Electronic supplementary material
ESM 1
(DOCX 3323 kb)
Rights and permissions
About this article
Cite this article
Motlagh, Z.K., Lotfi, A., Pourmanafi, S. et al. Spatial modeling of land-use change in a rapidly urbanizing landscape in central Iran: integration of remote sensing, CA-Markov, and landscape metrics. Environ Monit Assess 192, 695 (2020). https://doi.org/10.1007/s10661-020-08647-x
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s10661-020-08647-x