Development of Multi-Temporal Landslide Inventory Information System for Southern Kyrgyzstan Using GIS and Satellite Remote Sensing

Golovko, Darya; Roessner, Sigrid; Behling, Robert; Wetzel, Hans‐Ulrich; Kleinschmit, Birgit

doi:10.1127/pfg/2015/0261

Cited by 20 publications

(7 citation statements)

References 21 publications

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“…The predictive performance and accuracy of susceptibility studies may thus depend partly on the mapping quality of these training landslides (Kalantar et al 2018). Yet few standards are available to warrant that landslide inventories are comparable (Corominas et al 2013;Golovko et al 2015), and even experts may differ in their subjective assessment and mapping of landslides (Van Den Eeckhaut et al 2005).…”

Section: Introductionmentioning

confidence: 99%

How robust are landslide susceptibility estimates?

et al. 2020

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Much of contemporary landslide research is concerned with predicting and mapping susceptibility to slope failure. Many studies rely on generalised linear models with environmental predictors that are trained with data collected from within and outside of the margins of mapped landslides. Whether and how the performance of these models depends on sample size, location, or time remains largely untested. We address this question by exploring the sensitivity of a multivariate logistic regression—one of the most widely used susceptibility models—to data sampled from different portions of landslides in two independent inventories (i.e. a historic and a multi-temporal) covering parts of the eastern rim of the Fergana Basin, Kyrgyzstan. We find that considering only areas on lower parts of landslides, and hence most likely their deposits, can improve the model performance by >10% over the reference case that uses the entire landslide areas, especially for landslides of intermediate size. Hence, using landslide toe areas may suffice for this particular model and come in useful where landslide scars are vague or hidden in this part of Central Asia. The model performance marginally varied after progressively updating and adding more landslides data through time. We conclude that landslide susceptibility estimates for the study area remain largely insensitive to changes in data over about a decade. Spatial or temporal stratified sampling contributes only minor variations to model performance. Our findings call for more extensive testing of the concept of dynamic susceptibility and its interpretation in data-driven models, especially within the broader framework of landslide risk assessment under environmental and land-use change.

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Section: Introductionmentioning

confidence: 99%

How robust are landslide susceptibility estimates?

et al. 2020

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“…To characterize the landslide activity in the region as fully as possible, multiple information sources need to be combined, each with its strengths and limitations [16,17]. The landslide datasets used in this study were prepared using two different methods:…”

Section: Landslide Inventorymentioning

confidence: 99%

“…For this purpose, we derived two landslide inventories: (i) a systematic multi-temporal inventory generated by automated landslide detection from time series of satellite remote sensing data and (ii) a historical inventory prepared by an expert based on the visual interpretation of high-resolution satellite remote sensing data as well as incorporating already existing knowledge on landslide occurrences. Detailed descriptions of the methodologies used for the derivation of these two inventories can be found in [1,2,16,17].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Remote-Sensing-Based Landslide Inventories for Hazard Assessment in Southern Kyrgyzstan

et al. 2017

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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.

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“…High-resolution satellite image and advanced remote sensing and spatial analysis techniques allow developing more reliable landslide inventory maps [29,35]. Methods involved in generating landslide inventories include visual interpretation of multi-temporal aerial photographs and remotely sensed images and geomorphological field mapping, expert knowledge on the geological setting in combination [36,37].…”

Section: Landslide Inventory/detectionmentioning

confidence: 99%

Remote Sensing Approaches and Related Techniques to Map and Study Landslides

Ray¹,

Lazzari²,

Olutimehin³

2020

Landslides - Investigation and Monitoring

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Landslide is one of the costliest and fatal geological hazards, threatening and influencing the socioeconomic conditions in many countries globally. Remote sensing approaches are widely used in landslide studies. Landslide threats can also be investigated through slope stability model, susceptibility mapping, hazard assessment, risk analysis, and other methods. Although it is possible to conduct landslide studies using in-situ observation, it is time-consuming, expensive, and sometimes challenging to collect data at inaccessible terrains. Remote sensing data can be used in landslide monitoring, mapping, hazard prediction and assessment, and other investigations. The primary goal of this chapter is to review the existing remote sensing approaches and techniques used to study landslides and explore the possibilities of potential remote sensing tools that can effectively be used in landslide studies in the future. This chapter also provides critical and comprehensive reviews of landslide studies focus¬ing on the role played by remote sensing data and approaches in landslide hazard assessment. Further, the reviews discuss the application of remotely sensed products for landslide detection, mapping, prediction, and evaluation around the world. This systematic review may contribute to better understanding the extensive use of remotely sensed data and spatial analysis techniques to conduct landslide studies at a range of scales.

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Development of Multi-Temporal Landslide Inventory Information System for Southern Kyrgyzstan Using GIS and Satellite Remote Sensing

Cited by 20 publications

References 21 publications

How robust are landslide susceptibility estimates?

How robust are landslide susceptibility estimates?

Evaluation of Remote-Sensing-Based Landslide Inventories for Hazard Assessment in Southern Kyrgyzstan

Remote Sensing Approaches and Related Techniques to Map and Study Landslides

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