API src

Found 5 results.

Other language confidence: 0.9718693216778398

Global Dated Landslide Data Base during Sentinel-2 satellite data availability

This Global Dated Landslide Database (GDLDB) is part of the project WeMonitor (Weakly Supervised Deep Learning Models for Detecting and Monitoring Spatio-Temporal Anomalies in Optical and Radar Satellite Time Series), funded by the Helmholtz Imaging Platform. The aim is to develop a deep learning model that uses satellite image time series from Sentinel1/2 to automatically monitor changes caused, for example, by landslides, deforestation, large fires, dam failures, or the emergence of waste dumps. To train such a model, a reference dataset is required that shows the area and date of the changes as precise as possible. To allow for a generic and transferable model, the reference data also needs to cover the diversity of the process to be detected. Thus, the aim of the GDLDB is to comprise landslides of different sizes, shapes, and types, occurring at different seasons and in different regions with varying natural conditions and different triggering mechanisms such as rainfall and earthquake-induced landslides. To build the GDLDB, available local and regional landslide inventories from around the world are combined into one coherent database by verifying their location and date of occurrence with high-resolution remote sensing data. The selection criteria for the source inventories are the definition of the landslide location as polygons, at least a rough indication of the landslide origin date, and that the landslides occurred during the Sentinel-2 data availability from 2016 onwards. A total of 16 individual inventories are included (Table 1), one each from the USA, Dominica, Italy, Zimbabwe, southern India, Nepal, China, Papua New Guinea, and New Zealand, and two each from Kyrgyzstan, Japan, and the Philippines. In addition, a global inventory was added, including a small number of landslides from the USA, Peru, Chile, Europe, Pakistan, Nepal, India, and Taiwan, and a larger number of landslides from Indonesia. From each inventory, approximately 100 landslides were randomly selected to ensure an unbiased selection of landslides in terms of shape, size, and location. The original source inventories are produced using a variety of methods, including manual mapping in airborne data with ground verification and automatic identification in satellite remote sensing data. As a result, the mapping quality of the inventories varies greatly. In cases where landslides could not be verified by us using available optical remote sensing data (e.g. Sentinel-2, Planet Scope, and data available in Google Earth) new polygons are selected until the number of approximately 100 landslides is reached. In some inventories, the number of 100 landslides could not be guaranteed, due to a lack of suitable landslides (e.g., small size, incorrect classification) or the total number of landslides in the selected inventory was less than 100. For inventories with a lot of small landslides, that were difficult or impossible to observe, a size threshold of 1000m2 was introduced.

Data Supplement to: Cosmogenic 10Be in river sediment: where grain size matters and why

Concentrations of in-situ-produced cosmogenic 10Be in river sediment are widely used to estimate catchment-average denudation rates. Typically, the 10Be concentrations are measured in the sand fraction of river sediment. However, the grain size of bedload sediment in most bedrock rivers covers a much wider range. Where 10Be concentrations depend on grain size, denudation rate estimates based on the sand fraction alone are potentially biased. To date, knowledge about catchment attributes that may induce grain-size-dependent 10Be concentrations is incomplete or has only been investigated in modelling studies. Here we present an empirical study on the occurrence of grain-size-dependent 10Be concentrations and the potential controls of hillslope angle, precipitation, lithology, and abrasion. We first conducted a study focusing on the sole effect of precipitation in four granitic catchments located on a climate gradient in the Chilean Coastal Cordillera. We found that observed grain size dependencies of 10Be concentrations in the most-arid and most-humid catchments could be explained by the effect of precipitation on both the scouring depth of erosion processes and the depth of the mixed soil layer. Analysis of a global dataset of published 10Be concentrations in different grain sizes (n=73 catchments) – comprising catchments with contrasting hillslope angles, climate, lithology, and catchment size – revealed a similar pattern. Lower 10Be concentrations in coarse grains (defined as “negative grain size dependency”) emerge frequently in catchments which likely have thin soil and where deep-seated erosion processes (e.g. landslides) excavate grains over a larger depth interval. These catchments include steep (> 25°) and humid catchments (> 2000mm yr-1). Furthermore, we found that an additional cause of negative grain size dependencies may emerge in large catchments with weak lithologies and long sediment travel distances (> 2300–7000 m, depending on lithology) where abrasion may lead to a grain size distribution that is not representative for the entire catchment. The results of this study can be used to evaluate whether catchment-average denudation rates are likely to be biased in particular catchments.Samples from the Chilean Coastal Cordillera were processed in the Helmholtz Laboratory for the Geochemistry of the Earth Surface (HELGES). 10Be/9Be ratios were measured at the University of Cologne and normalized to the KN01-6-2 and KN01-5-3 standards. Denudation rates were calculated using a time-independent scaling scheme according to Lal (1991) and Stone (2002) (St scaling scheme) and the SLHL production rate of 4.01 at g-1 yr-1 as reported by Phillips et al. (2016)The global compilation exists of studies that measured 10Be concentrations in different grain sizes from the same sample location. We only included river basins of <5000 km2 which measured 10Be concentrations in at least one sand-sized fraction <2 mm and at least one coarser fraction >2 mm. Catchment parameters have been recalculated using a 90-m SRTM DEM.The data are presented in Excel and csv tables. Table S1 describes the characteristics of the samples catchments, Table S2 includes the grain size dependent 10Be-concentrations measured during this study and Table 3 the global compilation of grain size dependent 10Be-concentrations. All samples of this study (the Chilean Coastal Cordillera) are assigned with International Geo Sample Numbers (IGSN). The IGSN links are included in Table S2 and in the Related References Section on the DOI Landing Page. The data are described in detail in the data description file and in van Dongen et al. (2018) to which they are supplementary material to.

EMCA Landslide catalog Central Asia

TechnicalInfo

Multi-temporal landslide inventory for a study area in Southern Kyrgyzstan derived from RapidEye satellite time series data (2009 – 2013)

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.Taking up on these at the time still to be evolving opportunities, 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 optical satellite time series data (Behling et al., 2014). The developed algorithm was applied to a 7500 km² study area using RapidEye time series data which were acquired in the frame of the RESA project (Project ID 424) for the time period between 2009 and 2013. A multi-temporal landslide inventory from 1986 to 2013 derived from multi-sensor optical satellite time series data is available as separate publications (Behling et al., 2016; Behling and Roessner, 2020).The resulting multi-temporal landslide inventory being subject of this data publication is supplementary to the article of Behling et al. (2014), which describes the developed spatiotemporal landslide mapper in detail. 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 the 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 detect 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). For a detailed description of the methodology of the spatiotemporal landslide mapper, please see Behling et al. (2014).The data are provided in vector format (polygons) in form of a standard shapefile contained in the zip-file Behling_et-al_2014_landslide_inventory_SouthernKyrgyzstan_2009_2013.zip and are described in more detail in the data description file.

Multi-temporal landslide inventory for a study area in Southern Kyrgyzstan derived from multi-sensor optical satellite time series data (1986 – 2013)

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.

1