Assessment of Land-use Changes and Landscape Pattern through Landsat Data in the Chalus Watershed, North of Iran

Document Type : Original Research Article

Authors

Department of Environmental Sciences, Faculty of Marine and Environmental Sciences, University of Mazandaran, Babolsar, Iran

Abstract

Land use change represents a critical challenge, potentially altering the landscape pattern. This study aims to evaluate land-use changes in the Chalus watershed in northern Iran and analyze its landscape patterns from 1982 to 2022. Land-use maps were generated using Landsat 3, 5, 7, and 8 imagery within the Google Earth Engine platform, and the changes were assessed with the Land Change Modeler (LCM) in TerrSet. Key landscape metrics, including patch density (PD), number of patches (NP), largest patch index (LPI), landscape shape index (LSI), edge density (ED), and patch cohesion index (PCI), were measured at the landscape scale (entire watershed) using Fragstats. The findings revealed that the rangeland, forest, agricultural land, built-up areas, and water bodies experienced changes of +23736, -25124, +274, +1016, and +99 ha, respectively, from 1982 to 2022. The results indicate that significant changes occurred across the watershed landscape regarding patch number, density, shape, and size, demonstrating substantial habitat fragmentation over this period. The study findings demonstrate that development trends over the past four decades have led to increases in land-use change within the region, which in turn has perpetuated landscape fragmentation and a reduction in natural habitats. This study identified the expansion of built-up areas and agricultural activities as significant contributors to the intensification of habitat fragmentation. Consequently, strategic measurements and planning are essential to prevent further fragmentation and degradation of the landscape.

Keywords

Main Subjects

References

Ambarwulan, W., Yulianto, F., Widiatmaka, W., Rahadiati, A., Tarigan, S.D., Firmansyah, I. & Hasibuan, M.A.S., 2023. Modelling land use/land cover projection using different scenarios in the Cisadane Watershed, Indonesia: Implication on deforestation and food security. The Egyptian Journal of Remote Sensing and Space Science, 26(2), 273-283.‏
Arora, A., Pandey, M., Mishra, V.N., Kumar, R., Rai, P. K., Costache, R., Punia, M. & Di, L., 2021. Comparative evaluation of geospatial scenario-based land change simulation models using landscape metrics. Ecological Indicators, 128, 107810.‏
Bachri, S., Shrestha, R.P., Irawan, L.Y., Masruroh, H., Prastiwi, M.R.H., Billah, E.N. & Hidiyah, T.M., 2024. Land Use Change Simulation Model Using a Land Change Modeler in Anticipation of the Impact of the Semeru Volcano Eruption Disaster in Indonesia. Environmental Challenges, 100862.‏
Basse, R.M., Omrani, H., Charif, O., Gerber, P. & Bódis, K., 2014. Land use changes modelling using advanced methods: Cellular automata and artificial neural networks. The spatial and explicit representation of land cover dynamics at the cross-border region scale. Applied Geography, 53, 160-171.‏
Bogale, T., Damene, S., Seyoum, A. & Haregeweyn, N., 2024. Land use land cover change intensity analysis for sustainable natural resources management: The case of northwestern highlands of Ethiopia. Remote Sensing Applications: Society and Environment, 101170.‏
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.‏
Burkhard, B., Kroll, F., Müller, F. & Windhorst, W., 2009. Landscapes' capacities to provide ecosystem services-A concept for land-cover based assessments. Landscape Online, 15-15.‏
Eastman, J.R., 2012. IDRISI selva manual. Clark University, Worcester
El Jeitany, J., Nussbaum, M., Pacetti, T., Schroder, B. & Caporali, E., 2024. Landscape metrics as predictors of water-related ecosystem services: Insights from hydrological modeling and data-based approaches applied on the Arno River Basin, Italy. Science of The Total Environment, 954, 176567.‏
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.‏
Kumar, M., Denis, D.M., Singh, S.K., Szabo, S. & Suryavanshi, S., 2018. Landscape metrics for assessment of land cover change and fragmentation of a heterogeneous watershed. Remote Sensing Applications: Society and Environment, 10, 224-233.‏
Lafortezza, R., Coomes, D.A., Kapos, V. & Ewers, R.M., 2010. Assessing the impacts of fragmentation on plant communities in New Zealand: scaling from survey plots to landscapes. Global Ecology and Biogeography, 19(5), 741-754.‏
Liu, T. & Yang, X., 2015. Monitoring land changes in an urban area using satellite imagery, GIS and landscape metrics. Applied Geography, 56, 42-54.‏ ‏
McGarigal, K., Cushman, S.A., Neel, M.C. & Ene, E., 2002. FRAGSTATS: spatial pattern analysis program for categorical maps. Computer software program produced by the authors at the University of Massachusetts, Amherst. Available at the following web site: www. umass. edu/landeco/research/fragstats/fragstats. html, 6.‏
Mulatu, K., Hundera, K. & Senbeta, F., 2024. Analysis of land use/land cover changes and landscape fragmentation in the Baro-Akobo Basin, Southwestern Ethiopia. Heliyon, 10(7).‏
Nehzak, H.K., Aghaei, M., Mostafazadeh, R. & Rabiei-Dastjerdi, H., 2022. Evaluation of land use change predictions using CA-Markov model and management scenarios. In Computers in Earth and Environmental Sciences (pp. 105-115). Elsevier.‏
Paul, B. K., & Rashid, H., 2017. Land use change and coastal management. Climatic Hazards in Coastal Bangladesh, 183-207.‏
Qi, J., Chen, J., Lafortezza, R., & Lin, Z., 2018. Remote Sensing for Ecosystem Sustainability. Elsevier, 186-201.https://doi.org/10.1016/B978-0-12-409548-9.10428-2.
Sertel, E., Topaloglu, R. H., Şalli, B., Yay Algan, I. & Aksu, G. A., 2018. Comparison of landscape metrics for three different level land cover/land use maps. ISPRS International Journal of Geo-Information, 7(10), 408.‏
Shim, S.H., & Choi, J.H., 2024. Building an XGBoost model based on landscape metrics and meteorological data for nonpoint source pollution management in the Nakdong River watershed. Ecological Indicators, 165, 112156.
Sisay, G., Gessesse, B., Furst, C., Kassie, M. & Kebede, B., 2023. Modeling of land use/land cover dynamics using artificial neural network and cellular automata Markov chain algorithms in Goang watershed, Ethiopia. Heliyon, 9(9).
Sithole, S. M., Musakwa, W., Magidi, J. & Kibangou, A.Y., 2024. Characterising landcover changes and urban sprawl using geospatial techniques and landscape metrics in Bulawayo, Zimbabwe (1984–2022). Heliyon. https://doi.org/10.1016/j.heliyon.2024.e27275.
Sun, B. & Zhou, Q., 2016. Expressing the spatiotemporal pattern of farmland change in arid lands using landscape metrics. Journal of Arid Environments, 124, 118-127.‏
Urban, D.L., 2006. Landscape Ecology. In Encyclopedia of Environmetrics (eds A.H. El-Shaarawi and W.W. Piegorsch). https://doi.org/10.1002/9780470057339.val005
USGS., 2020. https://earthexplorer.usgs.gov.

Files

Share

How to cite

Statistics