Abstract
Large areas in southern Kyrgyzstan are subjected to high and ongoing landslide activity; however, an objective and systematic assessment of landslide susceptibility at a regional level has not yet been conducted. In this paper, we investigate the contribution that remote sensing can provide to facilitate a quantitative landslide hazard assessment at a regional scale under the condition of data scarcity. We performed a landslide susceptibility and hazard assessment based on a multi-temporal landslide inventory that was derived from a 30-year time series of satellite remote sensing data using an automated identification approach. To evaluate the effect of the resulting inventory on the landslide susceptibility assessment, we calculated an alternative susceptibility model using a historical inventory that was derived by an expert through combining visual interpretation of remote sensing data with already existing knowledge on landslide activity in this region. For both susceptibility models, the same predisposing factors were used: geology, stream power index, absolute height, aspect and slope. A comparison of the two models revealed that using the multi-temporal landslide inventory covering the 30-year period results in model coefficients and susceptibility values that more strongly reflect the properties of the most recent landslide activity. Overall, both susceptibility maps present the highest susceptibility values for similar regions and are characterized by acceptable to high predictive performances. We conclude that the results of the automated landslide detection provide a suitable landslide inventory for a reliable large-area landslide susceptibility assessment. We also used the temporal information of the automatically detected multi-temporal landslide inventory to assess the temporal component of landslide hazard in the form of exceedance probability. The results show the great potential of satellite remote sensing for deriving detailed and systematic spatio-temporal information on landslide occurrences, which can significantly improve landslide susceptibility and hazard assessment at a regional scale, particularly in data-scarce regions such as Kyrgyzstan.
Highlights
The eastern rim of the Fergana Basin in southern Kyrgyzstan is a tectonically active region that experiences regular landslide occurrences
To evaluate the ability of individual factors to differentiate between pixels with high and low susceptibilities, the AUROC values of susceptibility models based on a single factor were calculated (Table 3)
Geology is the main factor determining the differentiation of landslide susceptibility in the study area in each of the three models
Summary
The eastern rim of the Fergana Basin in southern Kyrgyzstan is a tectonically active region that experiences regular landslide occurrences. 2017, 9, 943 by local experts, these analyses have been of a qualitative nature and concentrated on areas in the vicinity of settlements and roads Factors that complicate this task are the large size of the study area (approximately 2300 km2 ) and the limited availability of spatially detailed and up-to-date information on landslide occurrences and predisposing factors. Using time series of archived remote sensing data enables multi-temporal reconstruction of backdated landslide occurrences for large areas covering a time period of several decades Such a task requires analyzing large amounts of remote sensing data, which can only be accomplished using automated methods. We have developed such a method for the automated object-based detection of landslide occurrences using multi-sensor time series of optical satellite images [1,2]. We investigate the suitability of the resulting systematic multi-temporal landslide inventory covering a 30-year time period for conducting subsequent analyses of landslide susceptibility and hazard
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