Simulation and Impact Assessment of Land Use and Land Cover Changes Inside and Around Protected Areas in Lorestan Province, Iran
Subject Areas : مهندسی محیط زیست
Seyede Zahra Mousavi nadarvand
1
,
Abdolrassoul Salman Mahiny
2
,
Peyman Karami
3
,
seyedhamed mirkarimi
4
,
Hamidreza Kamyab
5
1 - Department of Environmental Sciences, Faculty of Fisheries and Environmental Sciences, Gorgan University of Agricultural Sciences and Natural Resources, Iran
2 - Department of Environmental Sciences, Faculty of Fisheries and Environmental Sciences, Gorgan University of Agricultural Sciences and Natural Resources, Iran
3 - , Department of Environmental Sciences, Faculty of Natural Resources and Environment Sciences, Malayer University, Malayer, Iran
4 - professor of environmental sciences Associate
5 - , Department of Environmental Sciences, Faculty of Fisheries and Environmental Sciences, Gorgan University of Agricultural Sciences and Natural Resources, Iran
Keywords: Protected areas, statistical analysis, logistic regression model, Lorestan Province, Land Use Change,
Abstract :
Monitoring and analyzing land use and land cover (LULC) changes in protected areas and their surrounding buffer zones play an important role in sustainable habitat management and biodiversity conservation. This study examines LULC changes in four protected areas of Lorestan Province, Iran (Ghalikouh, Oshtorankouh, Sefidkouh, and Shadabkouh), together with a 20-kilometer buffer zone, over the period 1990–2050. The key novelty of this research lies in the simultaneous analysis of both the protected areas and their adjacent zones, enabling a clearer distinction between internal and external pressures and a deeper understanding of human-induced impacts. LULC maps were generated by integrating Landsat satellite imagery with the global GLC_FCS30 product at a 30-meter resolution. Future changes up to 2050 were projected using logistic regression modeling in TerrSet software. To assess the significance of LULC changes within the protected areas and their surrounding buffers, statistical tests including Friedman, Wilcoxon, proportion tests, and Chi-square were performed in SPSS at a 5% significance level. The results indicated significant changes inside the protected areas, particularly in built-up and agricultural classes, whereas forest cover and water bodies remained relatively stable. In addition, human pressures in the surrounding buffers intensified, resulting in the expansion of built-up and agricultural land uses. These findings emphasize the necessity of integrated management beyond the official boundaries of protected areas, the use of advanced remote sensing technologies, and active participation of local communities to ensure sustainable conservation of natural resources.
Ait El Haj, F., Ouadif, L. and Akhssas, A. (2023). Simulating and predicting future land-use/land cover trends using CA- Markov and LCM models. Case Studies in Chemical and Environmental Engineering, 7: 100342, doi https://doi.org/10.1016/j.cscee.2023.100342.
Bairwa, B., Sharma, R., Kundu, A., Sammen, S.S., Alsheri, F., Pande, C.B., Orban, Z. and Salem, A. (2025). Predicting changes in land use and land cover using remote sensing and land change modeler. Frontiers in Environmental Science, 13: 1540140.
Buzási, A. and Jäger, B.S. (2020). District-scale assessment of urban sustainability. Sustainable Cities and Society, 62: 102388.
Chaudhuri, G. and Mishra, N. (2016). Spatio-temporal dynamics of land cover and land surface temperature in Ganges-Brahmaputra delta: A comparative analysis between India and Bangladesh. Applied Geography, 68, doi 10.1016/j.apgeog.2016.01.002.
Chiaka, J.C., Liu, G., Li, H., Zhang, W., Wu, M., Huo, Z. and Gonella, F. (2024). Land cover changes and management effectiveness of protected areas in tropical coastal area of sub-Saharan Africa. Environmental and Sustainability Indicators, 22: 100340, doi https://doi.org/10.1016/j.indic.2024.100340.
Eastman, J.R. (2006). IDRISI Andes guide to GIS and image processing. Clark University, Worcester, 328. Eastman, J.R. (2015). TerrSet tutorial. Clark Labs, Clark University: Worcester, MA, United States.
Ghosh, S., Chatterjee, N.D. and Dinda, S. (2021). Urban ecological security assessment and forecasting using integrated DEMATEL-ANP and CA-Markov models: A case study on Kolkata Metropolitan Area, India. Sustainable Cities and Society, 68: 102773.
Imran, M. and Din, N.u. (2021). Geospatially mapping carbon stock for mountainous forest classes using InVEST model and Sentinel-2 data: a case of Bagrote valley in the Karakoram range. Arabian Journal of Geosciences, 14(9): 756, doi 10.1007/s12517-021-07023-4.
Islam, K.N., Rahman, M.M., Jashimuddin, M., Islam, K. and Zhang, Y. (2020). Impact of co-management on tree diversity and carbon sequestration in protected areas: Experiences from Bangladesh. Trees, Forests and People, 2: 100033.
Karami, P. and Tavakoli, S. (2022). Identification and analysis of areas prone to conflict with wild boar (Sus scrofa) in the vineyards of Malayer County, western Iran. Ecological Modelling, 471: 110039.
Lamichhane, S. and Shakya, N.M. (2021). Land use land cover (LULC) change projection in Kathmandu Valley using the CLUE-S model. Journal of Advanced College of Engineering and Management, 6: 221-233.
Lin, Z., Peng, S., Ma, D., Shi, S., Zhu, Z., Zhu, J., Gong, L. and Huang, B. (2024). Patterns of change, driving forces and future simulation of LULC in the Fuxian Lake Basin based on the IM-RF-Markov-PLUS framework. Sustainable Futures, 8: 100289, doi https://doi.org/10.1016/j.sftr.2024.100289.
Luiza Petroni, M., Siqueira-Gay, J. and Lucia Casteli Figueiredo Gallardo, A. (2022). Understanding land use change impacts on ecosystem services within urban protected areas. Landscape and Urban Planning, 223: 104404, doi https://doi.org/10.1016/j.landurbplan.2022.104404.
Moghadam, H.S. and 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.
Mosleh, M.K. (2025). Integrating the CA–Markov model and geospatial techniques for spatiotemporal prediction of land use/land cover dynamics in Qus District, Egypt. Modeling Earth Systems and Environment, 11(5): 1-15.
Muhammad, B.J.H., Ping, W., Mohabbat, M.J., Ahmad, I. and Islam, I. (2025). Modelling and prediction of land use land cover change dynamics based on the land change modeller in Kunar Province, Eastern Afghanistan. Scottish Geographical Journal: 1-24.
Munthali, M., Mustak, S., Adeola, A., Botai, J., Singh, S. and 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.
Parvar, Z. and Salmanmahiny, A. (2025). Assessing and predicting habitat quality under urbanization and climate pressures. Journal for Nature Conservation, 86: 126903, doi https://doi.org/10.1016/j.jnc.2025.126903.
Pokhrel, M. and Shakya, N.M. 2021. Assessment of future land use/cover change of Kathmandu Valley using two models of land change. Paper presented at the Proc. IOE Grad. Conf.
Pontius Jr, R.G., Cornell, J.D. and Hall, C.A. (2001). Modeling the spatial pattern of land-use change with GEOMOD2: application and validation for Costa Rica. Agriculture, ecosystems & environment, 85(1-3): 191-203.
Ramachandra, T., Bharath, S. and Gupta, N. (2018). Modelling landscape dynamics with LST in protected areas of Western Ghats, Karnataka. Journal of environmental management, 206: 1253-1262.
Rwanga, S. and Ndambuki, J. (2017). Accuracy Assessment of Land Use/Land Cover Classification Using Remote Sensing and GIS. International Journal of Geosciences, 08: 611-622, doi 10.4236/ijg.2017.84033.
Salem, M., Bose, A., Bashir, B., Basak, D., Roy, S., Chowdhury, I.R., Alsalman, A. and Tsurusaki, N. 2021. Urban Expansion Simulation Based on Various Driving Factors Using a Logistic Regression Model: Delhi as a Case Study. Sustainability, 13(19). Retrieved from.
Sambou, S., Lykke, A.M., Sambou, H., Guiro, I., Sambou, B. and Mbow, C. (2015). Land use-land cover change and drivers of deforestation in the Patako protected area (Center-West of Senegal). American Journal of Environmental Protection, 4(6): 306-317.
Schmitz, M.H., do Couto, E.V., Xavier, E.C., Tomadon, L.d.S., Leal, R.P. and Agostinho, A.A. (2023). Assessing the role of protected areas in the land-use change dynamics of a biodiversity hotspot. Ambio, 52(10): 1603-1617, doi 10.1007/s13280-023-01886-5.
Sobhani, P., Esmaeilzadeh, H., Barghjelveh, S., Sadeghi, S.M. and Marcu, M.V. 2022. Habitat Integrity in Protected Areas Threatened by LULC Changes and Fragmentation: A Case Study in Tehran Province, Iran. Land, 11(1). Retrieved from.
Sobhani, P., Esmaeilzadeh, H. and Mostafavi, H. (2021). Simulation and impact assessment of future land use and land cover changes in two protected areas in Tehran, Iran. Sustainable Cities and Society, 75: 103296, doi https://doi.org/10.1016/j.scs.2021.103296.
Yang, F., Yang, L., Fang, Q. and Yao, X. (2024). Impact of landscape pattern on habitat quality in the Yangtze River Economic Belt from 2000 to 2030. Ecological Indicators, 166: 112480, doi https://doi.org/10.1016/j.ecolind.2024.112480.
Zhang, X., Liu, L., Chen, X., Gao, Y., Xie, S. and Mi, J.)2021(GLC_FCS30: global land-cover product with fine classification system at 30 m using time-series Landsat imagery, Earth Syst. Sci. Data, 13, 2753–2776.
Zhang, X., Liu, L., Wu, C., Chen, X., Gao, Y., Xie, S. and Zhang, B. (2020). Development of a global 30 m impervious surface map using multisource and multitemporal remote sensing datasets with the Google Earth Engine platform. Earth System Science Data, 12(3): 1625-1648.
Zhang, X., Zhao, T., Xu, H., Wendi, L., Wang, J., Chen, X. and Liu, L. (2024). GLC_FCS30D: the first global 30 m land-cover dynamics monitoring product with a fine classification system for the period from 1985 to 2022 generated using dense-time-series Landsat imagery and the continuous change-detection method. Earth System Science Data, 16: 1353-1381, doi 10.5194/essd-16-1353-2024.
Zhou, L., Dang, X., Sun, Q. and Wang, S. (2020). Multi-scenario simulation of urban land change in Shanghai by random forest and CA-Markov model. Sustainable Cities and Society, 55: 102045.
