Spatial-Temporal behavior of the reactivation of the paleolandslide in Hossein abad Village, Kalpush, Northeast of Iran

Document Type : Research Article

Authors

• Mahdieh Ghayoor Bolorfroshan ¹
• Seyed Reza Hosseinzadeh ²
• Gholam Reza Lashkaripour ³
• Masoud Minaei ⁴
• Hakimeh Morabbi Heravi ⁵

¹ PhD in Geomorphology, Ferdowsi University of Mashhad, Mashhad, Iran

² Professor in Geomorpholoy, Ferdowsi University of Mashhad, Mashhad, Iran

³ Professor in Geology, Ferdowsi University of Mashhad, Mashhad, Iran

⁴ Associate Professor in GIScience, Ferdowsi University of Mashhad, Mashhad, Iran

⁵ Associate Professor Statistics, University of Bojnord, Bojnord, Iran

10.22067/geoeh.2023.82181.1358

Abstract

Extended Abstract
Introduction
Landslides are natural hazards in mountainous regions that cause significant damage to man-made structures, such as buildings, roads, and power transmission lines. Complex geological, geomorphological, and climatic conditions lead to slope failures, where paleo landslides, formed during the Quaternary period, stabilize over time. These historical landslides often reactivate due to triggers such as earthquakes, heavy rainfall, river erosion, changes in reservoir water levels (RWL), road construction, and deforestation. Additionally, anthropogenic factors, such as construction within the landslide mass or agricultural activities on the crown of the landslide, can further destabilize these areas. The threat to human life and infrastructure is especially high when cities and villages are located on or near old landslides. Examples of this can be seen in the Vajont Dam disaster in Italy and the Three Gorges Dam in China.
In Iran, especially in the Alborz and Zagros mountain ranges, numerous ancient landslides exist that reactivate under the influence of earthquakes, heavy rainfall, and human activities. Assessing the vulnerability of buildings to such phenomena is critical. Various methods, including InSAR radar interferometry and Permanent Scatterer Interferometry (PSI), are employed to monitor slow-moving landslides and evaluate their impact on structures. This study, integrating geomorphological data, landslide kinematics, and building vulnerability assessment, focuses on the reactivation of the Hossein Abad landslide in northern Iran and analyzes its spatial-temporal behavior.
Material and Methods
Hossein Abad village, located in the Kalpush district of Miyami County in Semnan Province, is the largest village in the region. Situated on moderate slopes and atop four old landslide terrains with an average slope of 14%, the village has expanded over time. This study was conducted in four stages, involving the collection, production, processing, and interpretation of historical, field, and radar remote sensing data.
Initially, a landslide geomorphological map of the active old landslides in Hossein Abad was produced using aerial photos, geological maps, and UAV images. Then, the radar interferometry technique was applied to analyze the kinematics of the landslide movement, with displacement rate maps generated from Sentinel-1A/B radar data. Subsequently, the vulnerability of buildings in the village was assessed through field visits and questionnaires, and a vulnerability map was produced using the classification method proposed by Palmisano et al., 2016. Damage to buildings was classified based on various indicators such as material type, structural characteristics, and crack patterns. The study also analyzed the spatial relationship between landslide movement and building vulnerability, focusing on the geomorphological context of the landslides.
Results and Discussion
In the village of Hossein Abad, four old landslides were identified, and over time, the development of the village and construction activities mainly expanded on the southern slope, directly over two large, old landslides. In 2013, the Hossein Abad earthen dam was constructed near these landslides, with a capacity of approximately 16 million cubic meters of water. Heavy snowfall and rainfall in late 2018 activated these landslides, causing significant damage, including the collapse of more than 160 buildings. The main landslide, covering 22 hectares, exhibited rotational and compound movement over a marl-limestone bedrock.
The reactivation of the old landslide was confirmed through spatial analysis using permanent scatterers between 2018 and 2020. Significant horizontal and vertical displacements were observed, especially near the landslide toe and areas with visible groundwater flow. These deformations were largely driven by groundwater fluctuations influenced by the nearby dam reservoir.
In the vulnerability assessment of buildings, 67 structures were evaluated in the region. Approximately 60% of these buildings were constructed with traditional brick masonry and were located in landslide-prone areas. Many of these buildings suffered heavy damage due to ground movements and structural changes. The greatest damage occurred at the landslide toe, where upraising and settlement of the ground were most significant. The study indicates that modern concrete buildings at the toe of the landslide experienced less damage compared to older, masonry structures. Additionally, the results suggest that using lighter materials and locating buildings away from the center of landslides can mitigate damage. The investigation further emphasizes the effectiveness of integrating ground displacement monitoring with building vulnerability assessment.
 Conclusion
This study utilizes Permanent Scatterer InSAR data and building damage analysis across different zones of the Hossein Abad landslide to investigate its spatial behavior. The results show that the vulnerability of buildings in landslide-prone areas can be assessed based on the type of landslide kinematics, building resistance, and the geomorphological features of the affected area. The study indicates that the Hossein Abad landslide reactivates with each occurrence of heavy rainfall or changes in groundwater levels, causing significant damage to buildings. Even after reconstruction, similar damage is likely to occur, highlighting the ongoing instability of the region.

Keywords

• Paleo Landslide
• Building Vulnerability Assessment
• Permanent Scatterer Interferometry (PSI)
• InSAR Images
• Ground Displacement Monitoring
• Antropogenic Landslide

Main Subjects

• Sliding and mass movements

---

References

Afshari, A., Ghahroudi Tali, M., Sadough, H., & Ehteshami Moin abadi, M. (2020). Assessment slope instability around Lorestan railway by using differential synthetic aperture radar interferometry (DInSAR). Quantitative Geomorphological Research, 8(3), 183-202. [In Persian]  https://dorl.net/dor/20.1001.1.22519424.1398.8.3.11.6

Alexander, D. (1986). Landslide damage to buildings. Environmental Geology and Water Sciences, 8(3), 147-151. https://doi.org/10.1007/BF02509902 

Aslan, G., Foumelis, M., Raucoules, D., De Michele, M., Bernardie, S., & Cakir, Z. (2020). Landslide mapping and monitoring using persistent scatterer interferometry (PSI) technique in the French Alps. Remote Sensing, 12(8), 1305. https://doi.org/10.3390/rs12081305

Burland, J. B., Broms, B. B., & De Mello, V. F. (1978). Behaviour of foundations and structures. Paper presented at the 9th International conference on soil mechanics and foundation engineering, 2,495-546 Tokyo, Japan: Japanese Society of Soil Mechanics and
Foundation Engineering.

Ciampalini, A., Bardi, F., Bianchini, S., Frodella, W., Del Ventisette, C., Moretti, S., & Casagli, N. (2014). Analysis of building deformation in landslide area using multisensor PSInSAR™ technique. International Journal of Applied Earth Observation and Geoinformation, 33, 166-180. https://doi.org/10.1016/j.jag.2014.05.011

Cruden, D. M., & Varnes, D. J. (1996). Landslide types and processes, transportation research board, U.S. National Academy of Sciences, Special Report, 247: 36-75. 

Dykes, A. P., & Bromhead, E. N. (2018). The Vaiont landslide: re-assessment of the evidence leads to rejection of the consensus. Landslides, 15(9), 1815-1832. https://doi.org/10.1007/s10346-018-0996-y

Entezam, I., Rezaei, A. R., Vakilzade, Y., Mohammadi, Y., & Babazade, N. (2019).
Evaluation and analysis of the Hosein Abad Kalpush landslide.
Report TR040, National Geoscience Database of Iran. [In Persian]

Ferlisi, S., Gullà, G., Nicodemo, G., & Peduto, D. (2019). A multi-scale methodological approach for slow-moving landslide risk mitigation in urban areas, southern Italy. Euro-Mediterranean Journal for Environmental Integration, 4, 1-15. https://doi.org/10.1007/s41207-019-0110-4

Ferretti, A., Prati, C., & Rocca, F. (2001). Permanent scatterers in SAR interferometry. IEEE Transactions on Geoscience and Remote Sensing, 39(1), 8-20. https://doi.org/10.1109/36.898661

Glade, T. (2003). Vulnerability assessment in landslide risk analysis. Erde, 134(2), 123-146.

Godfrey, A., Ciurean, R. L., Van Westen, C. J., Kingma, N. C., & Glade, T. (2015). Assessing vulnerability of buildings to hydro-meteorological hazards using an expert based approach–An application in Nehoiu Valley, Romania. International Journal of Disaster Risk Reduction, 13, 229-241. https://doi.org/10.1016/j.ijdrr.2015.06.001

Grünthal, G. (1998). European macroseismic scale 1998. Luxembourg: European Seismological Commission (ESC).

Guo, C., Zhang, Y., Li, X., Ren, S., Yang, Z., Wu, R., & Jin, J. (2020). Reactivation of giant Jiangdingya ancient landslide in Zhouqu County, Gansu Province, China. Landslides, 17, 179-190. https://doi.org/10.1007/s10346-019-01266-9

Hooper, A., Segall, P. & Zebker, H. (2007). Persistent scatterer interferometric synthetic aperture radar for crustal deformation analysis with application to Volcán Alcedo, Galápagos.Journal of Geophysical Research, 112. https://doi.org/10.1029/2006JB004763

Infante, D., Di Martire, D., Confuorto, P., Tessitore, S., Tòmas, R., Calcaterra, D., & Ramondini, M. (2019). Assessment of building behavior in slow-moving landslide-affected areas through DInSAR data and structural analysis. Engineering Structures, 199, 109638. https://doi.org/10.1016/j.engstruct.2019.109638

Jian, W., Wang, Z., & Yin, K. (2009). Mechanism of the Anlesi landslide in the three
Gorges Reservoir, China. Engineering Geology, 108 (1-2), 86–95. https://doi.org/10.1016/j.enggeo.2009.06.017

Kappes, M. S., Papathoma-Koehle, M., & Keiler, M. (2012). Assessing physical vulnerability for multi-hazards using an indicator-based methodology. Applied Geography, 32(2), 577-590. https://doi.org/10.1016/j.apgeog.2011.07.002

Khodaei Geshlag, L., Roostaei, S., Mokhtari, D., & Valizadeh Kamran, K. (2021). Application of the Interferometry Synthetic Aperture Radar (InSAR) in monitoring and evaluating landslides (case study: Ahar-Varzeghan region). Journal of Geography and Planning, 25(75), 113-126. [In Persian] https://doi.org/10.22034/gp.2021.10847

Kiseleva, Е., Mikhailov, V., Smolyaninova, E., Dmitriev, P., Golubev, V., Timoshkina, E., ... & Hanssen, R. (2014). PS-InSAR monitoring of landslide activity in the Black Sea coast of the Caucasus. Procedia Technology, 16, 404-413. https://doi.org/10.1016/j.protcy.2014.10.106

Leone, F., Asté, J. P., & Leroi, E. (1996). Vulnerability assessment of elements exposed to mass-movement: working toward a better risk perception. Landslides, 1,263-270.

Li, Z., Nadim, F., Huang, H., Uzielli, M., & Lacasse, S. (2010). Quantitative vulnerability estimation for scenario-based landslide hazards. Landslides, 7, 125-134. https://doi.org/10.1007/s10346-009-0190-3

Lu, P., Catani, F., Tofani, V., & Casagli, N. (2014). Quantitative hazard and risk assessment for slow-moving landslides from Persistent Scatterer Interferometry. Landslides, 11, 685-696. https://doi.org/10.1007/s10346-013-0432-2

Mateos, R. M., Azañón, J. M., Roldán, F. J., Notti, D., Pérez-Peña, V., Galve, J. P., ... & Fernández-Chacón, F. (2017). The combined use of PSInSAR and UAV photogrammetry techniques for the analysis of the kinematics of a coastal landslide affecting an urban area (SE Spain). Landslides, 14, 743-754. https://doi.org/10.1007/s10346-016-0723-5

Nishiguchi, T., Tsuchiya, S., & Imaizumi, F. (2017). Detection and accuracy of landslide
movement by InSAR analysis using PALSAR-2 data. Landslides, 14, 1483-1490. https://doi.org/10.1007/s10346-017-0821-z

Notti, D., Galve, J. P., Mateos, R. M., Monserrat, O., Lamas-Fernández, F., Fernández-Chacón, F., ... & Azañón, J. M. (2015). Human-induced coastal landslide reactivation, Monitoring by PSInSAR techniques and urban damage survey (SE Spain). Landslides, 12, 1007-1014. https://doi.org/10.1007/s10346-015-0612-3

Palmisano, F. (2011). Landslide structural vulnerability of masonry buildings. PhD Thesis, Italy, Politecnico di Bari.

Palmisano, F., Vitone, C., & Cotecchia, F. (2016). Methodology for landslide damage assessment. Procedia Engineering, 161, 511-515. https://doi.org/10.1016/j.proeng.2016.08.679

Palmisano, F., Vitone, C., & Cotecchia, F. (2018). Assessment of landslide damage to buildings at the urban scale. Journal of Performance of Constructed Facilities, 32(4), 04018055. https://doi.org/10.1061/(ASCE)CF.1943-5509.0001201

Papathoma-Köhle, M., Neuhäuser, B., Ratzinger, K., Wenzel, H., & Dominey-Howes, D. (2007). Elements at risk as a framework for assessing the vulnerability of communities to landslides. Natural Hazards and Earth System Sciences, 7(6), 765-779. https://doi.org/10.5194/nhess-7-765-2007

Rezapour, A., Jabari, I., & Bahrami, K. (2022). Geomorphological and geological evidences of the old landslide in Mele Kabud. Quaternary Journal of Iran, 8(1, 2), 179-199. [In Persian] https://doi.org/10.22034/irqua.2022.702493

Shirani, K. (2018). Detection, monitoring and mechanism of landslide using persistent scattering ineterferometry (PSI). Journal of Water and Soil Science, 22(3),213-234. [In Persian] http://dx.doi.org/10.29252/jstnar.22.3.213

Singh, A., Kanungo, D. P., & Pal, S. (2019). Physical vulnerability assessment of buildings exposed to landslides in India. Natural Hazards, 96, 753-790. https://doi.org/10.1007/s11069-018-03568-y

Tang, H. M., Wasowski, J., Juang, C. H. )2019(. Geohazards in the three Gorges Reservoir
Area, China – Lessons learned from decades of research. Engineering Geology, 261,
105267. https://doi.org/10.1016/j.enggeo.2019.105267

Tian, J. J., Li, T. T., Pei, X. J., Ding, F., Sun, H., Xie, X. G., & Guo, J. (2022). Formation and reactivation mechanisms of large-scale ancient landslides in the Longwu River basin in the northeast Tibetan Plateau, China. Journal of Mountain Science, 19(6), 1558-1575. https://doi.org/10.1007/s11629-021-7261-x

Uzielli, M., Nadim, F., Lacasse, S., & Kaynia, A. M. (2008). A conceptual framework for quantitative estimation of physical vulnerability to landslides. Engineering Geology, 102(3-4), 251-256. https://doi.org/10.1016/j.enggeo.2008.03.011

Van Westen, C. J., Van Asch, T. W., & Soeters, R. (2006). Landslide hazard and risk zonation-why is it still so difficult?. Bulletin of Engineering geology and the Environment, 65, 167-184. https://doi.org/10.1007/s10064-005-0023-0

Wolter, A., Stead, D., Ward, B. C., Clague, J. J., & Ghirotti, M. (2016). Engineering geomorphological characterisation of the Vajont Slide, Italy, and a new interpretation of the chronology and evolution of the landslide. Landslides, 13, 1067-1081. https://doi.org/10.1007/s10346-015-0668-0

Zhang, C., Yin, Y., Dai, Z., Huang, B., Zhang, Z., Jiang, X., Tan, W., & Wang, L. (2021). Reactivation mechanism of a large-scale ancient landslide. Landslides, 18, 397-407. https://doi.org/10.1007/s10346-020-01538-9