Assessing Short-term Flood Impact on Land Use Dynamics in Iran’s Central Zagros: A Case Study of Sefid Kuh Protected Area
DOI:
https://doi.org/10.3097/LO.2024.1130Keywords:
Land-use/land-cover change detection, fragmentation analysis, Flood interference, Landscape pattern indices, Central Zagros, Sefid kuh protected areaAbstract
Floods are extreme events that can alter the land cover and land use patterns in mountainous regions, with significant consequences for biodiversity, ecosystem services, and human well-being. However, there is a lack of comprehensive and integrated studies on the short-term and long-term effects of floods on land cover dynamics in the Central Zagros region, which is a climate change hotspot and a protected area with rich flora and fauna. In this study, we aimed to assess the effects of floods on land cover changes and transitions in the Sefid Kuh Protected Area, Lorestan Province in Iran, using temporal satellite imagery from Landsat 8, land-use/land-cover change detection and fragmentation analysis, and landscape pattern indices. We also conducted fieldwork and interviews to evaluate the impact of floods on land cover from the ground and from the local people’s perspectives. Our results showed that floods caused significant disturbances and shifts in different land cover classes, such as Thin Woodlands, Thick Woodlands, Agriculture, Rock, and Snow. For the landscape pattern indices the Shannon’s Diversity Index (SHDI), Interspersion and Juxtaposition Index (IJI), Patch Density (PD), Edge Density (ED), Largest Patch Index (LPI), Aggregation Index (AI), Percentage of Land Area (PLAND), Number of Patches (NP), Total Edge (TE), Landscape Shape Index (LSI), and Splitting Index (SPLIT) have been used. Results revealed that floods reduced the diversity and heterogeneity of the landscape, increased the fragmentation and isolation of forest patches, and enhanced the aggregation and clumpiness of bare soil patches. These changes have implications for the resilience and adaptation of the study area to future flood hazards and climate change.
References
Intergraph. ERDAS IMAGINE (Version 2015) [English; Microsoft Windows]. Hexagon Geospatial. 2015. http://hexagongeospatial.com.
Almenar, J.B., Bolowich, A., Elliot, T., Geneletti, D., Sonnemann, G. and Rugani, B. (2019). Assessing habitat loss, fragmentation and ecological connectivity in Luxembourg to support spatial planning. Landscape and Urban Planning, 189, 335– 351. https://doi.org/10.1016/j.landurbplan.2019.05.004
Anderson, B.G., Rutherfurd, I.D. and Western, A.W. (2006). An analysis of the influence of riparian vegetation on the propagation of flood waves. Environmental Modelling & Software, 21(9), 1290–1296. https://doi.org/10.1016/j.envsoft.2005.04.027
Ariza, A., Robredo Irizar, M. and Bayer, S. (2018). Empirical line model for the atmospheric correction of sentinel-2A MSI images in the Caribbean Islands. European Journal of Remote Sensing, 51(1), 765–776. https://doi.org/10.1080/22797254.2018.1482732
Ayanlade, A. (2017). Remote sensing vegetation dynamics analytical methods: a review of vegetation indices techniques. Geoinformatica Polonica, 7–17. https://doi.org/10.4467/21995923GP.17.001.7188
Ayele, G.T., Tebeje, A.K., Demissie, S.S., Belete, M.A., Jemberrie, M.A., Teshome, W.M., Mengistu, D.T. and Teshale, E.Z. (2018). Time series land cover mapping and change detection analysis using geographic information system and remote sensing, Northern Ethiopia. Air, Soil and Water Research, 11, 1178622117751603. https://doi.org/10.1177/1178622117751603
Balha, A. and Singh, C.K. (2023). Comparison of maximum likelihood, neural networks, and random forests algorithms in classifying urban landscape. In Application of Remote Sensing and GIS in Natural Resources and Built Infrastructure Management, 29–38. Cham: Springer International Publishing. https://doi.org/10.1007/978-3-031-14096-9_2
Banholzer, S., Kossin, J. and Donner, S. (2014). The impact of climate change on natural disasters. Reducing disaster: Early warning systems for climate change, 21–49. https:// doi.org/10.1007/978-94-017-8598-3_2
Behling, R., Roessner, S., Foerster, S., Saemian, P., Tourian, M.J., Portele, T.C. and Lorenz, C. (2022). Interrelations of vegetation growth and water scarcity in Iran revealed by satellite time series. Scientific Reports, 12(1), 20784. https://doi.org/10.1038/s41598-022-24712-6
Bianchin, S.; Richert, E.; Heilmeier, H.; Merta, M.; & Seidler, C. (2011). Landscape metrics as a tool for evaluating scenarios for flood prevention and nature conservation. Landscape Online, 25(1): 1–15. https://doi.org/10.3097/LO.201125
Billah, M., Masum, M. F. H., & Rana, S. (2023). Random forest classifications for land use mapping to assess rapid flood damage using Sentinel-1 and Sentinel-2 data. Remote Sensing Applications: Society and Environment. https://doi.org/10.1016/j.rsase.2023.100947
Birkeland, P.W.; Colman, S.M.; Burke, R.M.; Shroba, R.R.; & Meierding, T.C. (1979). Nomenclature of alpine glacial deposits, or, what’s in a name? Geology, 7(11): 532–536. https://doi.org/10.1016/j.rsase.2023.100947
Biswas, G., Sengupta, A., Alfaisal, F.M., Alam, S., Alharbi, R.S. and Jeon, B.H. (2023). Evaluating the effects of landscape fragmentation on ecosystem services: A three-decade perspective. Ecological Informatics, 77, 102283. https://doi.org/10.1016/j.ecoinf.2023.102283
Bogaert, J. (2001). Landscape Ecology in Action. Landscape and Urban Planning, 53(1–4): 183–184. https://doi.org/10.1007/978-94-011-4082-9
Bolan, S., Padhye, L.P., Jasemizad, T., Govarthanan, M., Karmegam, N., Wijesekara, H., Amarasiri, D., Hou, D., Zhou, P., Biswal, B.K. and Balasubramanian, R. (2023). Impacts of climate change on the fate of contaminants through extreme weather events. Science of The Total Environment, 168388. https://doi.org/10.1016/j.scitotenv.2023.168388
Chalid, A. (2020). Evaluation of flood control in the landuse changess in the Cisangkuy sub-watershed bandung regency, Indonesia, 7(19): 5541–5549. https://doi.org/10.1088/1755-1315/708/1/012029
Chavez, P.S. (1988). An improved dark-object subtraction technique for atmospheric scattering correction of multispectral data. Remote Sensing of Environment, 24(3): 459–479. https://doi.org/10.1016/0034-4257(88)90019-3
Chen, Y., Wang, Y., Zhang, Y., Luan, Q. and Chen, X. (2020). Flash floods, land-use change, and risk dynamics in mountainous tourist areas: A case study of the Yesanpo Scenic Area, Beijing, China. International Journal of Disaster Risk Reduction, 50, 101873. https://doi.org/10.1016/j.ijdrr.2020.101873
Coalson, J.E., Anderson, E.J., Santos, E.M., Madera Garcia, V., Romine, J.K., Luzingu, J.K., Dominguez, B., Richard, D.M., Little, A.C., Hayden, M.H. and Ernst, K.C. (2021). The complex epidemiological relationship between flooding events and human outbreaks of mosquito-borne diseases: a scoping review. Environmental health perspectives, 129(9), 096002. https://doi.org/10.1289/EHP8887
Coppin, P.; Jonckheere, I.; Nackaerts, K.; Muys, B.; & Lambin, E. (2004). Digital change detection methods in ecosystem monitoring: A review. International Journal of Remote Sensing, 25(9): 1565–1596. https://doi.org/10.1080/0143116031000101675
Crossman, N.D., Nedkov, S. and Brander, L. (2019). Water flow regulation for mitigating river and coastal flooding. SEEA EEA Revision, 15.
Croke, J., Thompson, C. and Fryirs, K. (2017). Prioritising the placement of riparian vegetation to reduce flood risk and end-of-catchment sediment yields: Important considerations in hydrologically-variable regions. Journal of Environmental Management, 190, 9–19. https://doi.org/10.1016/j.jenvman.2016.12.046
Dar, I.; Qadir, J.; & Shukla, A.( 2019). Estimation of LST from multi-sensor thermal remote sensing data and evaluating the influence of sensor characteristics. Annals of GIS, 25(3): 263–281. https://doi.org/10.1080/19475683.2019.1623318
Darvishsefat, A.A.; & Tajvidi, M. (2006). Atlas of Protected Areas of Iran. University of Tehran. In persian.
FAO 1995, Gregorio, A. (2005). Land Cover Classification System Classification Concepts and User Manual Software Version (2); Food and Agriculture Organization of the United Nations. Rome, Italy .
Farina, A. (2000). Landscape ecology in action. Kluwer Verlag, Dordrecht . 10.1007/978-94-011-4082-9
Firouz, E. (2005). complete fauna of Iran. IB Tauris. In persian.
Gabriels, K., Willems, P., and Van Orshoven, J. (2022). A comparative flood damage and risk impact assessment of land use changes. Natural Hazards and Earth System Sciences, 22(2), 395–410. https://doi.org/10.5194/nhess-22-395-2022
Gao, L., Zhang, B., Zhang, X. and Li, J. (2005). July. Study on the spectral characteristics of building materials covered by different paint. In Proceedings. 2005 IEEE International Geoscience and Remote Sensing Symposium, 2005. IGARSS’05. 5 : 3208-3211. https://doi.org/10.1109/IGARSS.2005.1526523
Goodarzi, M.; Pourhashemi, M.; & Azizi, Z. (2019). Investigation on Zagros forests cover changes under the recent droughts using satellite imagery. Journal of Forest Science, 65(1): 9–17. https://doi.org/10.17221/61/2018-JFS
Guttman, N.B. (1999). Accepting the standardized precipitation index: a calculation algorithm 1. JAWRA Journal of the American Water Resources Association, 35(2), 311–322. https://doi.org/10.1111/j.1752-1688.1999.tb03592
Haddad, N.M., Brudvig, L.A., Clobert, J., Davies, K.F., Gonzalez, A., Holt, R.D., Lovejoy, T.E., Sexton, J.O., Austin, M.P., Collins, C.D. and Cook, W.M. (2015). Habitat fragmentation and its lasting impact on Earth’s ecosystems. Science advances, 1(2), p.e1500052. https://doi.org/10.1126/sciadv.1500052
Hallegatte, S. (2016). Shock waves: managing the impacts of climate change on poverty. World Bank Publications. 10.1596/978-1-4648-0673-5
Herzog, F.; & Lausch, A. (2001). Supplementing land-use statistics with landscape metrics: some methodological considerations. Environmental monitoring and assessment, 72: 37–50. https://doi.org/10.1596/978-1-4648-0673-5
Huang, S., Tang, L., Hupy, J.P., Wang, Y. and Shao, G. (2021). A commentary review on the use of normalized difference vegetation index (NDVI) in the era of popular remote sensing. Journal of Forestry Research, 32(1), 1–6. https://doi.org/10.1007/s11676-020-01155-1
IPCC. (2021). Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Chapter 11: Weather and Climate Extreme Events in a Changing Climate. Cambridge University Press. https://doi.org/10.1017/9781009157896
Iran Meteorological Organization (IRIMO). (2018). Precipitation Data for Lorestan. Available at: http://www.irimo.ir
Island, W.; Ohimain, E.I.; Izah, S.C.; & Otobotekere, D. (2014). Selective Impacts of the 2012 Water Floods on the Vegetation and Wildlife of Selective impacts of the 2012 water floods on the vegetation and wildlife of Wilberforce Island , Nigeria. (January). https://doi.org/10.11648/j.ijema.20140202.13
Jaeger, J.A.G. (2000). Landscape division, splitting index, and effective mesh size: new measures of landscape fragmentation. Landscape Ecology, 15(2): 115–130. https://doi.org/10.1023/A:1008129329289
Jones, J.A. and Driscoll, C.T. (2022). Long-term ecological research on ecosystem responses to climate change. BioScience, 72(9), 814–826. https://doi.org/10.1093/biosci/biac021
Jönsson, A.M.; Lagergren, F.; & Smith, B. (2015). Forest management facing climate change - an ecosystem model analysis of adaptation strategies. Mitigation and Adaptation Strategies for Global Change, 20(2): 201–220. https://doi.org/10.1007/s11027-013-9487-6
Kayitesi, N.M., Guzha, A.C. and Mariethoz, G. (2022). Impacts of land use land cover change and climate change on river hydro-morphology-a review of research studies in tropical regions. Journal of Hydrology, 615, 128702. https://doi.org/10.1016/j.jhydrol.2022.128702
Lin, W.; Sun, Y.; Nijhuis, S.; & Wang, Z. (2020). Science of the Total Environment Scenario-based flood risk assessment for urbanizing deltas using future land-use simulation (FLUS): Guangzhou Metropolitan Area as a case study. Science of the Total Environment 739: 139899. https://doi.org/10.1016/j.scitotenv.2020.139899
Liu, B., Song, W., Meng, Z. and Liu, X. (2023). Review of Land Use Change Detection—A Method Combining Machine Learning and Bibliometric Analysis. Land, 12(5), 1050.4. https://doi.org/10.3390/land12051050
Liu, J., Coomes, D.A., Gibson, L., Hu, G., Liu, J., Luo, Y., Wu, C. and Yu, M. (2019). Forest fragmentation in China and its effect on biodiversity. Biological Reviews, 94(5), 1636–1657. https://doi.org/10.1111/brv.12519
Lorestan Governorate. (2019). Official response to the request of the Special Committee for the National Flood Report. Available at: https://un-spider.org/sites/default/files/session6-27-arash-malekian-special-remoting-committee-on-iran-floods-2019.pdf
Lorestan Province Crisis Management Coordination Council. (2019). Actions and Activities of the Lorestan Province Crisis Management Coordination Council - April 2019. Available at: https://reliefweb.int/report/iran-islamic-republic/islamic-republic-iran-floods-response-plan-april-2019
Mandala, M., Hakim, F.L., Indarto, I. and Kurnianto, F.A. (2024). Land Use and Land Cover Change in East Java Indonesia from 1972 to 2021: Learning from Landsat. Environmental Research, Engineering and Management, 80(3), 57–69. https://doi.org/10.5755/j01.erem.80.3.35362
McGarigal, K., Cushman, S.A., Neel, M.C. and Ene, E. (2002). FRAGSTATS: spatial pattern analysis program for categorical maps. Computer software program produced by the authors at the University of Massachusetts, Amherst. http://www.umass.edu/landeco/research/fragstats/fragstats.html
McGarigal, K., Cushman, S.A. and Ene, E. (2012). FRAGSTATS v4: spatial pattern analysis program for categorical and continuous maps. Computer software program produced by the authors at the University of Massachusetts, Amherst, 15, 153–162.
McGarigal, K.; & Marks, B.J. (1995). FRAGSTATS: spatial pattern analysis program for quantifying landscape structure. General Technical Report - US Department of Agriculture, Forest Service 351(PNW-GTR-351): 122. https://doi.org/10.2737/PNW-GTR-351
Meurant, M.; Gonzalez, A.; Doxa, A.; & Albert, C.H. (2018). Selecting surrogate species for connectivity conservation. Biological Conservation, 227(September): 326–334. https:// doi.org/10.1016/j.biocon.2018.09.028
Modrick, T.M. and Georgakakos, K.P. (2015). The character and causes of flash flood occurrence changes in mountainous small basins of Southern California under projected climatic change. Journal of Hydrology: Regional Studies, 3, .312– 336. https://doi.org/10.1016/j.ejrh.2015.02.003
Mostafazadeh, R. and Talebi Khiavi, H. (2024). Landscape change assessment and its prediction in a mountainous gradient with diverse land-uses. Environment, Development and Sustainability, 26(2), 3911–3941. https://doi.org/10.1007/s10668-022-02862-x
Noroozi, J., Talebi, A., Doostmohammadi, M., Rumpf, S.B., Linder, H.P. and Schneeweiss, G.M. (2018). Hotspots within a global biodiversity hotspot-areas of endemism are associated with high mountain ranges. Scientific reports, 8(1), 10345. https://doi.org/10.1038/s41598-018-28504-9
Noroozi, J., Talebi, A. and Doostmohammadi, M. (2020). Plant Biogeography and Vegetation of High Mountains of Central and South-West Asia1. In: J. Noroozi, ed., The Alborz Mountain Range. Plant and Vegetation, vol. 17. Springer Nature Switzerland AG. https://doi.org/10.1007/978-3-30-45212-4_4
Oliver E. J. Wing, Paul D. Bates, Christopher C. Sampson, Andrew M. Smith, Kris A. Johnson, T.A.E. (2017). Validation of a 30 m resolution flood hazard model of the conterminous United States. Water Resources Research. https://doi.org/10.1002/2017WR020917
Paper, D. (2019). Resilient Infrastructure Working Group. Prieto-Amparan, J.A., Villarreal-Guerrero, F., Martinez-Salvador, M., Manjarrez-Domínguez, C., Santellano-Estrada, E. and Pinedo-Alvarez, A. (2018). Atmospheric and radiometric correction algorithms for the multitemporal assessment of grasslands productivity. Remote Sensing, 10(2), 219. https://doi.org/10.3390/rs10020219
Rane, N.L., Achari, A., Choudhary, S.P. and Giduturi, M. (2023). Effectiveness and Capability of Remote Sensing (RS) and Geographic Information Systems (GIS): A Powerful Tool for Land use and Land Cover (LULC) Change and Accuracy Assessment. International Journal of Innovative Science and Research Technology, 8: 286–295. https://www.doi.org/10.56726/IRJMETS38104
Rwanga, S.S. and Ndambuki, J.M. (2017). Accuracy assessment of land use/land cover classification using remote sensing and GIS. International Journal of Geosciences, 8(04), p.611.Saura S, M.-M.J. 2001. Sensitivity of landscape pattern metrics to map spatial extent. Photogram Eng Remote Sense, 67: 1027–1036. https://doi.org/10.4236/ijg.2017.84033
Schaller, N.; Kay, A.L.; Lamb, R.; Massey, N.R.; Van Oldenborgh, G.J.; Otto, F.E.L.; Sparrow, S.N.; Vautard, R.; Yiou, P.; Ashpole, I.; Bowery, A.; Crooks, S.M.; Haustein, K.; Huntingford, C.; Ingram, W.J.; Jones, R.G.; Legg, T.; Miller, J.; Skeggs, J.; Wallom, D.; Weisheimer, A.; Wilson, S.; Stott, P.A.; & Allen, M.R. (2016). Human influence on climate in the 2014 southern England winter floods and their impacts. Nature Climate Change, 6(6): 627–634. https://doi.org/10.1038/nclimate2927
Stolpe, H., Ngoc Ha, N. and Jolk, C. (2022). Regional Water and Land Use Planning: Systematic Planning Support. Water Conservation-Inevitable Strategy. 10.5772/ intechopen.102713
Song, C., Woodcock, C. E., Seto, K. C., Lenny, M. P.,& Macomber, S.A. (2001). Classification and change detection using Landdsat TM data. Remote sensing of Environment, 75(2): 230–244. https://doi.org/10.1016/S0034-4257(00)00169-3
SRCIF. (2019). The 2019 Iran Floods Narrative (In Persian).: 44– 45 p. Lorestan.
Talukdar, S., Singha, P., Mahato, S., Pal, S., Liou, Y.A. and Rahman, A. (2020). Land-use land-cover classification by machine learning classifiers for satellite observations—A review. Remote Sensing, 12(7), 1135. https://doi.org/10.3390/rs12071135
Tucker, C.J. (1979). Red and photographic infrared near combinations for monitoring vegetation. Remote Sensing of Environment, 8(2): 127–150. https://doi.org/10.1016/0034-4257(79)90013-0
Turner, Monica G., Virginia H. Dale, and R.H.G. (1989). Predicting across scales: theory development and testing. Landscape ecology, 3: 245–252. https://doi.org/10.1007/BF00131542
Vaghefi, S.A.; Keykhai, M.; Jahanbakhshi, F.; Sheikholeslami, J.; Ahmadi, A.; Yang, H.; & Abbaspour, K.C. (2019). The future of extreme climate in Iran. Scientific Reports, 9(1): 1–11. https://doi.org/10.1038/s41598-018-38071-8
Verbesselt, J.; Hyndman, R.; Newnham, G.; & Culvenor, D. (2010). Detecting trend and seasonal changes in satellite image time series. Remote sensing of Environment, 114(1): 106–115. https://doi.org/10.1016/j.rse.2009.08.014
Vicente-Serrano, S.M., Beguería, S. and López-Moreno, J.I. (2010). A multiscalar drought index sensitive to global warming: the standardized precipitation evapotranspiration index. Journal of climate. 23(7), pp.1696-1718. https://doi.org/10.1175/2009JCLI2909.1
Walz, U. (2008). Monitoring of landscape change and functions in Saxony (Eastern Germany)-Methods and indicators. Ecological Indicators, 8(6): 807–817. https://doi.org/10.1016/j.ecolind.2007.09.006
Wang, C.; Chen, J.; Wu, J.; Tang, Y.; Shi, P.; Black, T.A.; & Zhu, K. (2017). Remote Sensing of Environment A snow-free vegetation index for improved monitoring of vegetation spring green-up date in deciduous ecosystems. Remote Sensing of Environment, 196: 1–12. https://doi.org/10.1016/j.rse.2017.04.031
Waylen, P., Southworth, J., Gibbes, C. and Tsai, H. (2014). Time series analysis of land cover change: Developing statistical tools to determine significance of land cover changes in persistence analyses. Remote Sensing, 6(5), 4473–4497. https://doi.org/10.3390/rs6054473
Whited, D.C., Lorang, M.S., Harner, M.J., Hauer, F.R., Kimball, J.S. and Stanford, J.A. (2007). Climate, hydrologic disturbance, and succession: drivers of floodplain pattern. Ecology, 88(4), 940–953. https://doi.org/10.1890/05-1149
Wu, Y.; Li, S.; & Yu, S. (2016). Monitoring urban expansion and its effects on land use and land cover changes in Guangzhou city, China. Environmental monitoring and assessment 188: 1–15. https://doi.org/10.1007/s10661-015-5069-2
Zhao, R.; Liu, X.; & Xu, W. (2020). Integration of coseismic deformation into WebGIS for near real-time disaster evaluation and emergency response. Environmental Earth Sciences 79: 1–11. https://doi.org/10.1007/s12665-020-09153-6
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Soheyl Moradi , Hossein Moradi , Jafar Dolatshah , Azita Rezvani
This work is licensed under a Creative Commons Attribution 4.0 International License.
