Landslide inventory and susceptibility modelling using geospatial tools, in Hunza-Nagar valley, northern Pakistan Landslide inventory and susceptibility modelling using geospatial tools, in Hunza-Nagar valley, northern Pakistan

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Vol15 No.6: 1354-1370

Title】Landslide inventory and susceptibility modelling using geospatial tools, in Hunza-Nagar valley, northern Pakistan

Author】Alam Sher BACHA1*; Muhammad SHAFIQUE1; Harald van der WERFF2

Addresses】1 National Centre of Excellance in Geology, University of Peshawar, Peshawar-25130, Pakistan; 2 Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, Enschede 7500 AE, The Netherlands

Corresponding author】Alam Sher BACHA

Citation】Bacha AS, Shafique M, van der Werff H (2018) Landslide inventory and susceptibility modelling using geospatial tools, in Hunza-Nagar valley, northern Pakistan. Journal of Mountain Science 15(6). https://doi.org/10.1007/s11629-017-4697-0

DOI】https://doi.org/10.1007/s11629-017-4697-0

Abstract】A comprehensive landslide inventory and susceptibility maps are prerequisite for developing and implementing landslide mitigation strategies. Landslide susceptibility maps for the landslides prone regions in northern Pakistan are rarely available. The Hunza-Nagar valley in northern Pakistan is known for its frequent and devastating landslides. In this paper, we have developed a landslide inventory map for Hunza-Nagar valley by using the visual interpretation of the SPOT-5 satellite imagery and mapped a total of 172 landslides. The landslide inventory was subsequently divided into modelling and validation data sets. For the development of landslide susceptibility map seven discrete landslide causative factors were correlated with the landslide inventory map using weight of evidence and frequency ratio statistical models. Four different models of conditional independence were used for the selection of landslide causative factors. The produced landslides susceptibility maps were validated by the success rate and area under curves criteria. The prediction power of the models was also validated with the prediction rate curve. The validation results shows that the success rate curves of the weight of evidence and the frequency models are 82% and 79%, respectively. The prediction accuracy results obtained from this study are 84% for weight of evidence model and 80% for the frequency ratio model. Finally, the landslide susceptibility index maps were classified into five different varying susceptibility zones. The validation and prediction result indicates that the weight of evidence and frequency ratio model are reliable to produce an accurate landslide susceptibility map, which may be helpful for landslides management strategies.

Keywords】Landslides; Inventory map; Susceptibility assessment; Northern Pakistan