Multi-temporal landslide inventory for a study area in Southern Kyrgyzstan derived from multi-sensor optical satellite time series data (1986 – 2013)
Cite as:
Behling, Robert; Roessner, Sigrid (2020): Multi-temporal landslide inventory for a study area in Southern Kyrgyzstan derived from multi-sensor optical satellite time series data (1986 – 2013). V. 1.0. GFZ Data Services. https://doi.org/10.5880/GFZ.1.4.2020.002
Status
I N R E V I E W : Behling, Robert; Roessner, Sigrid (2020): Multi-temporal landslide inventory for a study area in Southern Kyrgyzstan derived from multi-sensor optical satellite time series data (1986 – 2013). V. 1.0. GFZ Data Services. https://doi.org/10.5880/GFZ.1.4.2020.002
Abstract
Multi-temporal landslide inventories are important information for the understanding of landslide dynamics and related predisposing and triggering factors, and thus a crucial prerequisite for probabilistic hazard and risk assessment. Despite the great importance of these inventories, they do not exist for many landslide prone regions in the world. In this context, the recently evolving global-scale availability of high temporal and spatial resolution optical satellite imagery (RapidEye, Sentinel-2A/B, planet) has opened up new opportunities for the creation of these multi-temporal inventories.
To derive such multi-temporal landslide inventories, a semi-automated spatiotemporal landslide mapper was developed at the Remote Sensing Section of the GFZ Potsdam being capable of deriving post-failure landslide objects (polygons) from multi-sensor optical satellite time series data (Behling et al., 2016). The developed approach represents an extension of the original methodology (Behling et al., 2014, Behling and Roessner, 2020) and facilitates the integration of optical time series data acquired by different satellite systems. The goal of combining satellite data originating from variable sensor systems has been the establishment of longest possible time series for retrospective systematic assessment of multi-temporal landslide activity at highest possible temporal and spatial resolution. We applied the developed approach to a 2500 km² study area in Southern Kyrgyzstan using an optical satellite database acquired by the Landsat TM/ETM+, SPOT 1/5, IRS1-C LISSIII, ASTER, and RapidEye sensor systems covering a time period between 1986 and 2013. A multi-temporal landslide inventory from 2009-2013 derived from RapidEye satellite time series data is available as separate publications (Behling et al., 2014; Behling and Roessner, 2020).
The resulting systematic multi-temporal landslide inventory being subject of this data publication is supplementary to the article of Behling et al. (2016), which describes the extended spatiotemporal landslide mapper in detail. This multi-sensor approach prioritizes most suitable images within the available multi-sensor satellite time series using parameters, such as spatial resolution, cloud coverage, similarity of sensor characteristics and seasonality related to vegetation characteristics with the goal of establishing a robust back-bone time series for initial detection of possible landslide objects. In a second step, this initial analysis gets more refined in order to achieve the best possible approximation of the date of landslide occurrence. For a more detailed description of the methodology of the extended spatiotemporal landslide mapper, please see Behling et al. (2016).
In general, this landslide mapper detects landslide objects by analyzing temporal NDVI-based vegetation cover changes and relief-oriented parameters in a rule-based approach combining pixel- and object-based analysis. Typical landslide-related vegetation changes comprise abrupt disturbances of vegetation cover in the result of the actual failure as well as post-failure revegetation which usually happens at a slower pace compared to vegetation growth in the surrounding undisturbed areas, since the displaced landslide masses are susceptible to subsequent erosion and reactivation processes. The resulting landslide-specific temporal surface cover dynamics in form of temporal trajectories is used as input information to identify freshly occurred landslides and to separate them from other temporal variations in the surrounding vegetation cover (e.g., seasonal vegetation changes or changes due to agricultural activities) and from permanently non-vegetated areas (e.g., urban non-vegetated areas, water bodies, rock outcrops).
The data are provided in vector format (polygons) in form of a standard shapefile contained in the zip-file 2020-002_Behling_et-al_2016_landslide_inventory_SouthernKyrgyzstan_1986_2013.zip and are described in more detail in the associated data description.
Authors
Behling, Robert;GFZ German Research Centre for Geosciences, Potsdam, Germany
Roessner, Sigrid;GFZ German Research Centre for Geosciences, Potsdam, Germany
Contact
Behling, Robert
(Post-Doc); GFZ German Research Centre for Geosciences, Potsdam, Germany; ➦
Roessner, Sigrid
(Senior Scientist); GFZ German Research Centre for Geosciences, Potsdam, Germany; ➦
Funders
Bundesministerium für Bildung und Forschung:
PROGRESS (03IS2191G)
Deutsches Zentrum für Luft- und Raumfahrt:
RESA (424)
Keywords
multi-temporal landslide inventory, optical satellite remote sensing, time series analysis, Kyrgyzstan, Central Asia, RapidEye satellite system, multi-sensor, environmental data > geo-referenced data > GIS digital format, land > geomorphic process > landslide, methodology > telemetry > remote sensing, monitoring > monitoring technique > GIS digital technique > image classification > supervised image classification
affiliation (affiliationIdentifier=0000-0002-5298-1547 affiliationIdentifierScheme=ORCID): GFZ German Research Centre for Geosciences, Potsdam, Germany
affiliation (affiliationIdentifier=0000-0002-6940-9694 affiliationIdentifierScheme=ORCID): GFZ German Research Centre for Geosciences, Potsdam, Germany
titles
title: Multi-temporal landslide inventory for a study area in Southern Kyrgyzstan derived from multi-sensor optical satellite time series data (1986 – 2013)
affiliation (affiliationIdentifier=0000-0002-5298-1547 affiliationIdentifierScheme=ORCID): GFZ German Research Centre for Geosciences, Potsdam, Germany
contributor (contributorType=Producer)
contributorName (nameType=Personal): Behling, Robert
affiliation (affiliationIdentifier=0000-0002-5298-1547 affiliationIdentifierScheme=ORCID): GFZ German Research Centre for Geosciences, Potsdam, Germany
contributor (contributorType=Researcher)
contributorName (nameType=Personal): Behling, Robert
affiliation (affiliationIdentifier=0000-0002-5298-1547 affiliationIdentifierScheme=ORCID): GFZ German Research Centre for Geosciences, Potsdam, Germany
affiliation (affiliationIdentifier=0000-0002-6940-9694 affiliationIdentifierScheme=ORCID): GFZ German Research Centre for Geosciences, Potsdam, Germany
affiliation (affiliationIdentifier=0000-0002-6940-9694 affiliationIdentifierScheme=ORCID): GFZ German Research Centre for Geosciences, Potsdam, Germany
affiliation (affiliationIdentifier=0000-0002-6940-9694 affiliationIdentifierScheme=ORCID): GFZ German Research Centre for Geosciences, Potsdam, Germany
contributor (contributorType=ContactPerson)
contributorName (nameType=Personal): Behling, Robert
affiliation (affiliationIdentifier=0000-0002-5298-1547 affiliationIdentifierScheme=ORCID): GFZ German Research Centre for Geosciences, Potsdam, Germany
contributor (contributorType=ContactPerson)
contributorName: Roessner, Sigrid
affiliation (affiliationIdentifier= affiliationIdentifierScheme=): GFZ German Research Centre for Geosciences, Potsdam, Germany
CharacterString: Multi-temporal landslide inventory for a study area in Southern Kyrgyzstan derived from multi-sensor optical satellite time series data (1986 – 2013)
CharacterString: Multi-temporal landslide inventories are important information for the understanding of landslide dynamics and related predisposing and triggering factors, and thus a crucial prerequisite for probabilistic hazard and risk assessment. Despite the great importance of these inventories, they do not exist for many landslide prone regions in the world. In this context, the recently evolving global-scale availability of high temporal and spatial resolution optical satellite imagery (RapidEye, Sentinel-2A/B, planet) has opened up new opportunities for the creation of these multi-temporal inventories.
To derive such multi-temporal landslide inventories, a semi-automated spatiotemporal landslide mapper was developed at the Remote Sensing Section of the GFZ Potsdam being capable of deriving post-failure landslide objects (polygons) from multi-sensor optical satellite time series data (Behling et al., 2016). The developed approach represents an extension of the original methodology (Behling et al., 2014, Behling and Roessner, 2020) and facilitates the integration of optical time series data acquired by different satellite systems. The goal of combining satellite data originating from variable sensor systems has been the establishment of longest possible time series for retrospective systematic assessment of multi-temporal landslide activity at highest possible temporal and spatial resolution. We applied the developed approach to a 2500 km² study area in Southern Kyrgyzstan using an optical satellite database acquired by the Landsat TM/ETM+, SPOT 1/5, IRS1-C LISSIII, ASTER, and RapidEye sensor systems covering a time period between 1986 and 2013. A multi-temporal landslide inventory from 2009-2013 derived from RapidEye satellite time series data is available as separate publications (Behling et al., 2014; Behling and Roessner, 2020).
The resulting systematic multi-temporal landslide inventory being subject of this data publication is supplementary to the article of Behling et al. (2016), which describes the extended spatiotemporal landslide mapper in detail. This multi-sensor approach prioritizes most suitable images within the available multi-sensor satellite time series using parameters, such as spatial resolution, cloud coverage, similarity of sensor characteristics and seasonality related to vegetation characteristics with the goal of establishing a robust back-bone time series for initial detection of possible landslide objects. In a second step, this initial analysis gets more refined in order to achieve the best possible approximation of the date of landslide occurrence. For a more detailed description of the methodology of the extended spatiotemporal landslide mapper, please see Behling et al. (2016).
In general, this landslide mapper detects landslide objects by analyzing temporal NDVI-based vegetation cover changes and relief-oriented parameters in a rule-based approach combining pixel- and object-based analysis. Typical landslide-related vegetation changes comprise abrupt disturbances of vegetation cover in the result of the actual failure as well as post-failure revegetation which usually happens at a slower pace compared to vegetation growth in the surrounding undisturbed areas, since the displaced landslide masses are susceptible to subsequent erosion and reactivation processes. The resulting landslide-specific temporal surface cover dynamics in form of temporal trajectories is used as input information to identify freshly occurred landslides and to separate them from other temporal variations in the surrounding vegetation cover (e.g., seasonal vegetation changes or changes due to agricultural activities) and from permanently non-vegetated areas (e.g., urban non-vegetated areas, water bodies, rock outcrops).
The data are provided in vector format (polygons) in form of a standard shapefile contained in the zip-file 2020-002_Behling_et-al_2016_landslide_inventory_SouthernKyrgyzstan_1986_2013.zip and are described in more detail in the associated data description.
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;GFZ German Research Centre for Geosciences, Potsdam, Germany
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