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Modeling land use/cover change based on LCM model for a semi-arid area in the Latian Dam Watershed (Iran)

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Abstract

The monitoring and modeling of changes, based on a time-series LULC approach, is fundamental for planning and managing regional environments. The current study analyzed the LULC changes as well as estimated future scenarios for 2027 and 2037. To achieve accuracy in predicting LULC changes, the Land Change Modeler (LCM) was used for the Latian Dam Watershed, which is located approximately in the northeast of Tehran. The LULC time-series technique was specified utilizing four atmospherically endorsed surface reflectance Landsat images for the years t1 (1987), t2 (1998), t3 (2007), and t4 (2017) to authenticate the LULC predictions, so to obtain estimates for t5 (2027) and t6 (2037). The LULC classes identified in the watershed were water bodies, build-up areas, vegetated areas, and bare lands. The dynamic modeling of the LULC was based on a multi-layer perceptron (MLP), the neural network in LCM, which presented good results with an average accuracy rate equivalent to 84.89 percent. The results of the LULC change analysis showed an increase in the build-up area and a decrease in bare lands and vegetated areas within the duration of the study period. The results of this research could help in the formulation of public policies designed to conserve environmental resources in the Latian Dam Watershed and, consequently, minimize the risks of the fragmentation of orchards and vegetated areas. Also, careful regional planning ensuring the preservation of natural landscapes and open spaces is critical to creating a resilient regional environment and sustainable development.

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Abbreviations

LULC:

Land use/land cover

LCM:

Land Change Modeler

CA–Markov:

Cellular automata–Markov chain

CVC:

Cramer’s V coefficient

MLP:

Multi-layer perceptron

MOLA:

Multi-objective land allocation

ANN:

Artificial neural networks

MCE:

Multi-criteria evaluation

References

  • Alqadhi, S., Mallick, J., Talukdar, S., Bindajam, A. A., Saha, T. K., Ahmed, M., & Khan, R. A. (2021). Combining logistic regression-based hybrid optimized machine learning algorithms with sensitivity analysis to achieve robust landslide susceptibility mapping. Geocarto International, 1–26.

  • Al-sharif, A. A., & Pradhan, B. (2014). Monitoring and predicting land use change in Tripoli Metropolitan City using an integrated Markov chain and cellular automata models in GIS. Arabian Journal of Geosciences, 7(10), 4291–4301. https://doi.org/10.1007/s12517-013-1119-7

    Article  Google Scholar 

  • Anand, J., Gosain, A. K., & Khosa, R. (2018). Prediction of land use changes based on Land Change Modeler and attribution of changes in the water balance of Ganga basin to land use change using the SWAT model. Science of the Total Environment, 644, 503–519. https://doi.org/10.1016/j.scitotenv.2018.07.017

    Article  CAS  Google Scholar 

  • 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

    Article  Google Scholar 

  • Araya, Y. H., & Cabral, P. (2010). Analysis and modeling of urban land cover change in Setúbal and Sesimbra. Portugal. Remote Sensing, 2(6), 1549–1563. https://doi.org/10.3390/rs2061549

    Article  Google Scholar 

  • Ayele, G., Hayicho, H., & Alemu, M. (2019). Land use land cover change detection and deforestation modeling: In Delomena District of Bale Zone, Ethiopia. Journal of Environmental Protection, 10(4), 532–561. https://doi.org/10.4236/jep.2019.104031

  • Azari, M., Tayyebi, A., Helbich, M., & Reveshty, M. A. (2016). Integrating cellular automata, artificial neural network, and fuzzy set theory to simulate threatened orchards: Application to Maragheh. Iran. Giscience & Remote Sensing, 53(2), 183–205. https://doi.org/10.1080/15481603.2015.1137111

    Article  Google Scholar 

  • Bai, Y., Ochuodho, T. O., & Yang, J. (2019). Impact of land use and climate change on water-related ecosystem services in Kentucky, USA. Ecological Indicators, 102, 51–64. https://doi.org/10.1016/j.ecolind.2019.01.079

    Article  Google Scholar 

  • Belal, A. A., & Moghanm, F. S. (2011). Detecting urban growth using remote sensing and GIS techniques in Al Gharbiya governorate, Egypt. The Egyptian Journal of Remote Sensing and Space Science, 14(2), 73–79. https://doi.org/10.1016/j.ejrs.2011.09.001

  • Chen, J., Gong, P., He, C., Pu, R., & Shi, P. (2003). Land-use/land-cover change detection using improved change-vector analysis. Photogrammetric Engineering & Remote Sensing, 69(4), 369–379. https://doi.org/10.14358/PERS.69.4.369

  • Darabi, H., Jalali, D. (2018). Illuminating the formal–informal dichotomy in land development on the basis of transaction cost theory. Planning Theory, 18(1), 100–121.

  • Eastman, J. R. (2009). IDRISI Taiga guide to GIS and image processing. Clark Labs Clark University, Worcester, MA. Retrieved May 20, 2021, from https://scirp.org/reference/referencespapers.aspx?referenceid=1636053

  • Eastman, J. R. (2014). IDRISI selva tutorial. Retrieved May 20, 2021, from http://uhulag.mendelu.cz/files/pagesdata/eng/gis/idrisi_selva_tutorial.pdf

  • Eastman, J. R., & He, J. (2020). A regression-based procedure for Markov transition probability estimation in land change modeling. Land, 9(11), 407.

    Article  Google Scholar 

  • Eyoh, A., Olayinka, D. N., Nwilo, P., Okwuashi, O., Isong, M., & Udoudo, D. (2012). Modelling and predicting future urban expansion of Lagos, Nigeria from remote sensing data using logistic regression and GIS. International Journal of Applied, 2(5), 1–9. Retrieved May 20, 2021, from https://www.semanticscholar.org/paper/Modelling-and-Predicting-Future-Urban-Expansion-of-Eyoh-Olayinka/cc472e841da2365042d3d780d6d5d1bca2de643f 

  • Fahad, S., Li, W., Lashari, A. H., Islam, A., Khattak, L. H., & Rasool, U. (2021). Evaluation of land use and land cover spatio-temporal change during rapid urban sprawl from Lahore, Pakistan. Urban Climate, 39. https://doi.org/10.1016/j.uclim.2021.100931

  • Falahatkar, S., Hosseini, S. M., Salman Mahini, A., & Ayoubi, Sh. (2016). Prediction of land use/cover change by using LCM model. Journal of Environmental Research, 13(7), 163–174.

    Google Scholar 

  • Falahatkar, S., Soffianian, A. R., Khajeddin, S. J., Ziaee, H. R., & Nadoushan, M. A. (2011). Integration of remote sensing data and GIS for prediction of land cover map. International journal of Geomatics and Geosciences, 1(4), 847–864. Retrieved May 20, 2021, from https://www.indianjournals.com/ijor.aspx?target=ijor:ijggs&volume=1&issue=4&article=015

  • Fearnside, P. M. (2016). Environmental and social impacts of hydroelectric dams in Brazilian Amazonia: Implications for the aluminum industry. World Development, 77, 48–65. https://doi.org/10.1016/j.worlddev.2015.08.015

  • Gholamalifard, M., Joorabian Shooshtari, S., Hosseini Kahnuj, S. H., & Mirzaei, M. (2013). Land cover change modeling of coastal areas of Mazandaran Province using LCM in a GIS environment. Journal of Environmental Studies, 38(4), 109–124. https://doi.org/10.22059/JES.2013.29867

  • 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(20–22), 3761–3772. https://doi.org/10.1016/j.ecolmodel.2011.09.009

    Article  Google Scholar 

  • Guo, A., Zhang, Y., & Hao, Q. (2020). Monitoring and simulation of dynamic spatiotemporal land use/cover changes. Complexity, 2020.

  • Halmy, M. W. A., Gessler, P. E., Hicke, J. A., & Salem, B. B. (2015). Land use/land cover change detection and prediction in the north-western coastal desert of Egypt using Markov-CA. Applied Geography, 63, 101–112. https://doi.org/10.1016/j.apgeog.2015.06.015

    Article  Google Scholar 

  • Hamdy, O., Zhao, S., Salheen, M. A., & Eid, Y. Y. (2017). Analyses the driving forces for urban growth by using IDRISI® Selva models Abouelreesh-Aswan as a case study. International Journal of Engineering and Technology, 9(3), 226. https://doi.org/10.7763/IJET.2017.V9.975

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Hasan, S. S., Deng, X., Li, Z., & Chen, D. (2017). Projections of future land use in Bangladesh under the background of baseline, ecological protection and economic development. Sustainability, 9(4), 505.

    Article  Google Scholar 

  • Hoyer, R., & Chang, H. (2014). Assessment of freshwater ecosystem services in the Tualatin and Yamhill basins under climate change and urbanization. Applied Geography, 53, 402–416. https://doi.org/10.1016/j.apgeog.2014.06.023

    Article  Google Scholar 

  • Iacono, M., Levinson, D., El-Geneidy, A., & Wasfi, R. (2015). A Markov chain model of land use change. TeMA Journal of Land Use, Mobility and Environment, 8(3), 263–276.

    Google Scholar 

  • 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. https://doi.org/10.1016/j.ecolind.2018.01.047

    Article  Google Scholar 

  • Jensen, J. R. (1996). Introductory digital processing: A remote sensing perspective; Prentice-Hall: Upper Saddle River. NJ.

    Google Scholar 

  • Jiang, X., Lu, D., Moran, E., Calvi, M. F., Dutra, L. V., & Li, G. (2018). Examining impacts of the Belo Monte hydroelectric dam construction on land-cover changes using multitemporal Landsat imagery. Applied Geography, 97, 35–47.

  • Kafy, A. A., Naim, M. N. H., Subramanyam, G., Ahmed, N. U., Al Rakib, A., Kona, M. A., & Sattar, G. S. (2021). Cellular automata approach in dynamic modelling of land cover changes using RapidEye images in Dhaka. Bangladesh. Environmental Challenges, 4, 100084.

    Article  Google Scholar 

  • Keshtkar, H., & Voigt, W. (2015). A spatiotemporal analysis of landscape change using an integrated Markov chain and cellular automata models. Modeling Earth Systems and Environment, 2(1), 1–13. https://doi.org/10.1007/s40808-015-0068-4

    Article  Google Scholar 

  • Keshtkar, H., & Voigt, W. (2016). Potential impacts of climate and landscape fragmentation changes on plant distributions: Coupling multi-temporal satellite imagery with GIS-based cellular automata model. Ecological Informatics, 32, 145–155. https://doi.org/10.1016/j.ecoinf.2016.02.002

    Article  Google Scholar 

  • Labs, C. (2016). TerrSet software; Clark Labs: Worcester. MA.

    Google Scholar 

  • Lausch, A., & Herzog, F. (2002). Applicability of landscape metrics for the monitoring of landscape change: issues of scale, resolution and interpretability. Ecological Indicators, 2(1–2), 3–15. https://doi.org/10.1016/S1470-160X(02)00053-5

  • Lee, Y., & Chang, H. (2011, June). The simulation of land use change by using CA-Markov model: A case study of Tainan City, Taiwan. In 2011 19th International Conference on Geoinformatics (pp. 1–4). IEEE. https://doi.org/10.1109/GeoInformatics.2011.5980819

  • Leta, M. K., Demissie, T. A., & Tränckner, J. (2021). Modeling and prediction of land use land cover change dynamics based on Land Change Modeler (LCM) in Nashe Watershed, Upper Blue Nile Basin. Ethiopia. Sustainability, 13(7), 3740.

    Article  Google Scholar 

  • Liu, G., Jin, Q., Li, J., Li, L., He, C., Huang, Y., & Yao, Y. (2017). Policy factors impact analysis based on remote sensing data and the CLUE-S model in the Lijiang River Basin, China. CATENA, 158, 286–297. https://doi.org/10.1016/j.catena.2017.07.003

    Article  Google Scholar 

  • Mas, J. F., Kolb, M., Paegelow, M., Olmedo, M. T. C., & 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

    Article  Google Scholar 

  • Mishra, V. N., & Rai, P. K. (2016). A remote sensing aided multi-layer perceptron-Markov chain analysis for land use and land cover change prediction in Patna district (Bihar). India. Arabian Journal of Geosciences, 9(4), 1–18.

    Google Scholar 

  • Moghadam, H. S., & Helbich, M. (2013). Spatiotemporal urbanization processes in the megacity of Mumbai, India: A Markov chains-cellular automata urban growth model. Applied Geography, 40, 140–149. https://doi.org/10.1016/j.apgeog.2013.01.009

    Article  Google Scholar 

  • Mozumder, C., Tripathi, N. K., & Losiri, C. (2016). Comparing three transition potential models: A case study of built-up transitions in North-East India. Computers, Environment and Urban Systems, 59, 38–49. https://doi.org/10.1016/j.compenvurbsys.2016.04.009

    Article  Google Scholar 

  • Muller, M. R., & Middleton, J. (1994). A Markov model of land-use change dynamics in the Niagara Region, Ontario. Canada. Landscape Ecology, 9(2), 151–157. https://doi.org/10.1007/BF00124382

    Article  Google Scholar 

  • Nasiri, V., Darvishsefat, A., Rafiee, R., Shirvany, A., & Avatefi Hemmat, M. (2018). Land use change modeling through an integrated multi-layer perceptron neural network and Markov chain analysis (case study: Arasbaran region, Iran). Journal of Forestry Research. https://doi.org/10.1007/s11676-018-0659-9

    Article  Google Scholar 

  • Ngoy, K., Qi, F., & Shebitz, D. (2021). Analyzing and predicting land use and land cover changes in New Jersey using multi-layer perceptron–Markov chain model. Earth, 2, 845–870. https://doi.org/10.3390/earth2040050

    Article  Google Scholar 

  • Rawat, J. S., & Kumar, M. (2015). Monitoring land use/cover change using remote sensing and GIS techniques: A case study of Hawalbagh block, district Almora, Uttarakhand, India. The Egyptian Journal of Remote Sensing and Space Science, 18(1), 77–84. https://doi.org/10.1016/j.ejrs.2015.02.002

    Article  Google Scholar 

  • Rawat, J. S., Biswas, V., & Kumar, M. (2013). Changes in land use/cover using geospatial techniques: A case study of Ramnagar town area, district Nainital, Uttarakhand, India. The Egyptian Journal of Remote Sensing and Space Science, 16(1), 111–117. https://doi.org/10.1016/j.ejrs.2013.04.002

    Article  Google Scholar 

  • Rendana, M., Rahim, S. A., Idris, W. M. R., Lihan, T., & Rahman, Z. A. (2015). CA-Markov for predicting land use changes in tropical catchment area: A case study in Cameron Highland. Malaysia. Journal of Applied Sciences, 15(4), 689–695.

    Article  Google Scholar 

  • Rimal, B., Zhang, L., Keshtkar, H., Wang, N., & Lin, Y. (2017). Monitoring and modeling of spatiotemporal urban expansion and land-use/land-cover change using integrated Markov chain cellular automata model. ISPRS International Journal of Geo-Information, 6(9), 288. https://doi.org/10.3390/ijgi6090288

    Article  Google Scholar 

  • Ruíz-García, V. H., Borja de la Rosa, M. A., Gómez-Díaz, J. D., Asensio-Grima, C., Matías-Ramos, M., & Monterroso-Rivas, A. I. (2022). Forest fires, land use changes and their impact on hydrological balance in temperate forests of Central Mexico. Water, 14(3), 383.

  • Samat, N., Hasni, R., & Eltayeb Elhadry, Y. A. (2011). Modeling land use changes at the peri-urban area using geographic information system and cellular automata model. Journal of Sustainable Development, 4(6), 72–84. https://doi.org/10.5539/jsd.v4n6p72

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Shang, C., Wu, J. (2022). A legendary landscape in peril: Land use and land cover change and environmental impacts in the Wulagai River Basin, Inner Mongolia. Journal of Environmental Management, 301, 113816.

  • Shen, L., Li, J., Wheate, R., Yin, J., & Paul, S. (2020). Multi-layer perceptron neural network and Markov chain based geospatial analysis of land use and land cover change. Journal of Environmental Informatics Letters. https://doi.org/10.3808/jeil.202000023

    Article  Google Scholar 

  • Sinha, S., Sharma, L. K., & Nathawat, M. S. (2015). Improved land-use/land-cover classification of semi-arid deciduous forest landscape using thermal remote sensing. The Egyptian Journal of Remote Sensing and Space Science, 18(2), 217–233. https://doi.org/10.1016/j.ejrs.2015.09.005

    Article  Google Scholar 

  • Siroosi, H., Heshmati, G., & Salmanmahiny, A. (2020). Can empirically based model results be fed into mathematical models? MCE for neural network and logistic regression in tourism landscape planning. Environment, Development and Sustainability, 22(4), 3701–3722. https://doi.org/10.1007/s10668-019-00363-y

    Article  Google Scholar 

  • Soares-Filho, B., Rodrigues, H., & Follador, M. (2013). A hybrid analytical-heuristic method for calibrating land-use change models. Environmental Modelling & Software, 43, 80–87. https://doi.org/10.1016/j.envsoft.2013.01.010

    Article  Google Scholar 

  • Subedi, P., Subedi, K., & Thapa, B. (2013). Application of a hybrid cellular automaton–Markov (CA-Markov) model in land-use change prediction: A case study of Saddle Creek Drainage Basin. Florida. Applied Ecology and Environmental Sciences, 1(6), 126–132.

    Article  Google Scholar 

  • Thapa, R. B. (2009). Spatial process of urbanization in Kathmandu valley (p. 153). Nepal. University of Tsukuba.

    Google Scholar 

  • Upadhyay, T. P., Solberg, B., & Sankhayan, P. L. (2006). Use of models to analyse land-use changes, forest/soil degradation and carbon sequestration with special reference to Himalayan region: A review and analysis. Forest Policy and Economics, 9(4), 349–371. https://doi.org/10.1016/j.forpol.2005.10.003

    Article  Google Scholar 

  • USGS (United States Geological Survey) Earth Explorer, Landsat Data Archive. (2017). Retrieved May 20, 2021, from https://earthexplorer.usgs.gov/

  • Woldemichael, A. T., Hossain, F., Pielke Sr, R., & Beltrán‐Przekurat, A. (2012). Understanding the impact of dam‐triggered land use/land cover change on the modification of extreme precipitation. Water Resources Research, 48(9).

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Acknowledgements

We would like to thank Mohammad Javad Nikkhah and Abbas Najafi for their assistance as ENVI and TerrSet professionals. Also, all authors appreciate the respected reviewers who will support us with their valuable comments.

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Authors and Affiliations

  1. Department of Environmental Science, Faculty of Natural Resources and Environment, Science and Research Branch, Islamic Azad University (IAU), Tehran, Iran

    Banafsheh Shafie & Amir Hossein Javid

  2. Department of Environmental Design, Tehran University, Tehran, Iran

    Homa Irani Behbahani & Hassan Darabi

  3. Department of Mathematics, Science and Research Branch, Islamic Azad University (IAU), Tehran, Iran

    Farhad Hosseinzadeh Lotfi

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  1. Banafsheh Shafie
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  2. Amir Hossein Javid
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  3. Homa Irani Behbahani
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  5. Farhad Hosseinzadeh Lotfi
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Contributions

Banafsheh Shafie: conceptualization, data curation, formal analysis, investigation, methodology, software, validation, visualization, writing – original draft. Amir Hossein Javid: conceptualization, methodology, review and editing. Homa Irani Behbahani: conceptualization, methodology. Hassan Darabi: investigation, methodology, review and editing. Farhad Hosseinzadeh Lotfi: investigation, methodology, software.

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Correspondence to Amir Hossein Javid.

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Shafie, B., Javid, A.H., Behbahani, H.I. et al. Modeling land use/cover change based on LCM model for a semi-arid area in the Latian Dam Watershed (Iran). Environ Monit Assess 195, 363 (2023). https://doi.org/10.1007/s10661-022-10876-1

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