Document Type : پژوهشی

Authors

1 MSc. Student of Survey Engineering- Geographic Information Systems, Lamei Gorgani Institute of Higher Education, Gorgan, Iran

2 Associate Professor, Department of Natural Resources and member of Water Management Research Center, Faculty of Agriculture and Natural Resources, University of Mohaghegh Ardabili

3 Ph.D. of Watershed Management Sciences and Engineering, Department of Watershed Management Engineering, Faculty of Natural Resources

4 M.Sc. in Civil Engineering- Geodesy, Golestan Regional Water Company, Gorgan, Iran

Abstract

Landslide, as an important natural hazards, causes damage to infrastructure and leads to economic, social and human losses. In this regard, determining the critical zones of landslides occurrence can be used in planning for damage reduction. The aim of the current research is analyzing and identifying landslide hot spots using Getis-Ord algorithm in Gharnaveh watershed, Golestan province. Therefore, the frequency and characteristics of landslides has been analyzed in different classes of slope, aspect, soil and land uses. The area, length, width, depth and height of the precipice of landslide features have been considered as the basis in hot spot analysis. The results showed that the landslide hot spots are located in the eastern part of the study area, which includes high altitude classes, rangelands and moderate slopes. Results showed that the rangeland and forest land uses, loess soils and 50-75% slope class and the northern aspect had the highest number of landslides. Also, the non-significant landslide points using the Getis-Ord method and considering landslide area criterion located in the middle and downstream of Gharnaveh watershed. Determining the landslide hotspots and affecting factors through the spatial analysis in GIS provides the defining thresholds in the landslide’s occurrence. The analysis of the landslide hotspots can be a basis for spatial planning, and risk reduction. The implemented approach can be used in the evaluation of the spatial autocorrelation of natural hazards, and in combination with the areas prone to multiple environmental hazards can predict the risk and severity of damages in the future.

Keywords

Main Subjects

 
Abdollahzadeh, A., Ownegh, M., Sadoddin, A., & Mostafazadeh, R. 2016. Comparison of two landslide-prone area determination methods in Ziarat Watershed, Golestan Province. Journal of Emergency Management, 5(1): 5-13. (in Persian).
Abdollahzadeh, A., Ownegh, M., Sadoddin, A., & Mostafazadeh, R. 2014. Development of a landslide management plan under normal and critical scenarios for Ziarat Watershed, Golestan Province. Watershed Management Research (Pajouhesh & Sazandegi), 27(3):75-84. (in Persian).
Abedini, M., & Piroozi, E. 2020. Landslide hazard zoning with u sing combination methods of hot spot, ANP and WLC (Case study: Khalkhal county). Journal of Geography and Environmental Hazards, 8(4), 19-36. doi: 10.22067/geo.v0i0.81836. (in Persian).
Alavi, S. A., Behnammorshedi, H., & Ashournejad, Q. 2018. The Analysis of Spatial Equality of Tourism Services and Attractions: A Case Study of the Province of Fars. Geography and Urban Space Development, 4(2), 63-80. doi: 10.22067/gusd.v4i2.60203. (in Persian).
Alexander, D. 2005. Vulnerability to landslides. Landslide hazard and risk, 175-198.
Ariapour, M., Bashiri, M., & Golkarian, A. 2019. Modeling of mass movements using data mining methods in the southeast of Neyshabur city, Razavi Khorasan Province. Hydrogeomorphology, 6(19), 57-77. (in Persian).
Bianchini, S., Cigna, F., Righini, G., Proietti, C., & Casagli, N. 2012. Landslide hotspot mapping by means of persistent scatterer interferometry. Environmental Earth Sciences, 67, 1155-1172.
Esfandiyari Darabadi, F., & Beheshti Javid, E. 2016. Landslides susceptibility zoning using Bayes' Theorem-ANP hybrid model (Case study: Heyran Defile). Hydrogeomorphology, 3(8), 93-111. (in Persian).
Ghashghaie, S., & Behzadi, S. 2019. Spatial statistics analysis to identify hot spots using accidental event calls services. Journal of Statistical Research of Iran JSRI, 16(1), 121-141.
Ghorbani, A., Zabihi, M., & Mostafazadeh, R. 2023. Determining the distribution pattern of spatial correlation of flood occurrence in Ardabil province using Moran's Index in GIS. RS and GIS for Natural Resources, 10.30495/GIRS.2023.1977963.2036 (in Persian).
Guzzetti, F. 2000. Landslide fatalities and the evaluation of landslide risk in Italy. Engineering Geology, 58(2), 89-107.
Hafezi Moghaddas, N., Nikudel, M., & Bahrami, K. 2011. Evaluation of collapsibility of loess deposits of Gharnaveh catchment in north of Kalale, Golestan province. Scientific Quarterly Journal of Iranian Association of Engineering Geology, 4(1&2), 39-46. (in Persian).
Hölbling, D., Betts, H., Spiekermann, R., & Phillips, C. 2016. Identifying spatio-temporal landslide hotspots on North Island, New Zealand, by analyzing historical and recent aerial photography. Geosciences, 6(4), 48.
Jaedicke, C., Van Den Eeckhaut, M., Nadim, F., Hervás, J., Kalsnes, B., Vangelsten, B. V., ... & Smebye, H. 2014. Identification of landslide hazard and risk ‘hotspots’ in Europe. Bulletin of Engineering Geology and the Environment, 73, 325-339.
Lin, S. C., Ke, M. C., & Lo, C. M. 2017. Evolution of landslide hotspots in Taiwan. Landslides, 14, 1491-1501.
Lu, P., Bai, S., Tofani, V., & Casagli, N. 2019. Landslides detection through optimized hot spot analysis on persistent scatterers and distributed scatterers. ISPRS Journal of Photogrammetry and Remote Sensing, 156, 147-159.
Moradi, H.R., Mohammdy, M., Pourghasemi, H.R., Mostafazadeh, R. (2010). Landslide hazard analysis in Golestan Province using Dempster-Shafer theory. Researches in Earth Science, 1(3): 1-14. (in Persian).
Motevalli, A., Pourghasemi, H. R., & Zabihi, M. 2018. Assessment of GIS-based machine learning algorithms for spatial modeling of landslide susceptibility: case study in Iran, 258-280.
Nadian M, Mirzaei R, Soltani Mohammadi S. 2018. Application of Moran'sI Autocorrelation in Spatial-Temporal Analysis of PM2.5 Pollutant (A case Study: Tehran City). Journal of Environmental Health Engineering, 5(3), 197-213. (in Persian).
Najafi Eigdir, A., Roostaei, S., Hejazi, A., Rajabi, M., & Jalali, N. (2021). Landslide hazard zonation using the bivariate statistical models in Nazlo-Chay Basin. Hydrogeomorphology, 8(27), 17-1. doi: 10.22034/hyd.2021.25375.1376 (in Persian).
Ord, J.K. and Getis, A. 1995. Local Spatial Autocorrelation Statistics Distributional Issues and an Application. Geographical Analysis, 27, 286-306.
Parker, R. N., Densmore, A. L., Rosser, N. J., De Michele, M., Li, Y., Huang, R., ... & Petley, D. N. 2011. Mass wasting triggered by the 2008 Wenchuan earthquake is greater than orogenic growth. Nature Geoscience, 4(7), 449-452.
Pokharel, B., Alvioli, M., & Lim, S. 2021. Assessment of earthquake-induced landslide inventories and susceptibility maps using slope unit-based logistic regression and geospatial statistics. Scientific reports, 11(1), 21333.
Saber Chenari K, Bahremand1 A, Berdi Sheikh V, Bairam Komaki C. 2019. Gully erosion hazard Zoning in the Gharnaveh Watershed, Golestan Province. Journal of Engineering Geology, 13 (1), 69-94. (in Persian).
Shahri, M., & Shariat Mohaymany, A. (2022). Identifying Spatio-temporal Patterns of Traffic Congestion Using Data Obtained from Google Maps Service Traffic Image. Iranian Journal of Remote Sensing & GIS, doi: 10.52547/gisj.2022.223633.1055. (in Persian).
Sultana, N. 2020. Analysis of landslide-induced fatalities and injuries in Bangladesh: 2000-2018. Cogent Social Sciences, 6(1), 1737402.
Wu, C. 2022. Certainty Factor Analyses and Spatiotemporal Characteristics of Landslide Evolution: Case Studies in the Chishan River Watershed in Taiwan. ISPRS International Journal of Geo-Information, 11(7), 382.
Zali, M., & Shahedi, K. 2021. Landslide sensitivity assessment using fuzzy logic approach and GIS in Neka Watershed. Water and Soil Management and Modelling, 1(1), 67-80. doi: 10.22098/mmws.2021.1183. (in Persian).