Automated Spatiotemporal Landslide Mapping over Large Areas Using RapidEye Time Series Data

Behling, Robert; Roessner, Sigrid; Kaufmann, Hermann; Kleinschmit, Birgit

doi:10.3390/rs6098026

Cited by 115 publications

(100 citation statements)

References 53 publications

(80 reference statements)

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“…This dataset was obtained using an automated object-oriented landslide mapping approach that utilizes multi-temporal satellite-based imagery acquired by different optical sensors (Landsat E(TM), SPOT 1-5, ASTER, IRS-1C LISS III, and RapidEye) between 1986 and 2016 [1][2][3]21]. The resulting landslide dataset is composed of 1846 polygons.…”

Section: Landslide Inventorymentioning

confidence: 99%

“…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]. This method is based on the analysis of normalized difference vegetation index (NDVI) trajectories [3] and has been successfully applied in this study area [2]. In this paper, 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.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Landslide Inventorymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2017

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“…In this regard, Haberland et al (2011) announced the compilation of new seismic data and landslide information that should be used to assess related risk in the Fergana Basin, a subregion in the south-western Tien Shan. More recently, results of remote sensing and spatial analyses applied to landslide detection in some areas of the Tien Shan (mainly near the Fergana Valley) have been published by Behling et al (2014) and Golovko et al (2014).…”

Section: Introductionmentioning

confidence: 99%

Tien Shan Geohazards Database: Earthquakes and landslides

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Strom

Torgoev³

et al. 2015

Geomorphology

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“…The most recent major failures in this area occurred in 1988 and in the period between 2003 and 2005, and they coincide with phases of high landslide activity in Southern Kyrgyzstan. The temporal evolution of the landslide activity in this area derived from the optical satellite remote-sensing time-series data exhibit regularly reoccurring landslide activity between 1990 and 2013 [19]. The SBAS results suggest that the already displaced landslide masses have been subject to subsequent reactivations, which also affect the youngest Pleistocene deposits.…”

Section: Downslope Deformations Of Landslidesmentioning

confidence: 83%

“…The use of optical satellite remote-sensing data has allowed for the spatial characterization of the predisposing factors [16,17] and the establishment of a spatially consistent multi-temporal landslide inventory system at a regional scale by integrating landslide information from various sources [18,19].…”

Section: Introductionmentioning

confidence: 99%

ALOS/PALSAR InSAR Time-Series Analysis for Detecting Very Slow-Moving Landslides in Southern Kyrgyzstan

et al. 2015

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This study focuses on evaluating the potential of ALOS/PALSAR time-series data to analyze the activation of deep-seated landslides in the foothill zone of the high mountain Alai range in the southern Tien Shan (Kyrgyzstan). Most previous field-based landslide investigations have revealed that many landslides have indicators for ongoing slow movements in the form of migrating and newly developing cracks. L-band ALOS/PALSAR data for the period between 2007 and 2010 are available for the 484 km 2 area in this study. We analyzed these data using the Small Baseline Subset (SBAS) time-series technique to assess the surface deformation related to the activation of landslides. We observed up to ±17 mm/year of LOS velocity deformation rates, which were projected along the local steepest slope and resulted in velocity rates of up to −63 mm/year. The obtained rates indicate very slow movement of the deep-seated landslides during the observation time. We also compared these movements with precipitation and earthquake records. The results suggest that the deformation peaks correlate with rainfall in the 3 preceding months and with an earthquake event. Overall, the results of this study indicated the great potential of L-band InSAR time series analysis for OPEN ACCESS Remote Sens. 2015, 7 8974 efficient spatiotemporal identification and monitoring of slope activations in this region of high landslide activity in Southern Kyrgyzstan.

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Automated Spatiotemporal Landslide Mapping over Large Areas Using RapidEye Time Series Data

Cited by 115 publications

References 53 publications

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

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

Tien Shan Geohazards Database: Earthquakes and landslides

ALOS/PALSAR InSAR Time-Series Analysis for Detecting Very Slow-Moving Landslides in Southern Kyrgyzstan

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