Estimating the volume of the 1978 Rissa quick clay landslide in Central Norway using historical aerial imagery

Quick clay is found across Scandinavia and is especially prominent in south-eastern and central Norway. Quick clay is prone to failure and can cause landslides with high velocities and large run-outs. The 1978 Rissa landslide is one of the best-known quick clay landslides to have occurred in the last century, both due to its size and the fact that it was captured on film. In this article, we utilise Structure from Motion Multi-View Stereo (SfM-MVS) photogrammetry to process historical aerial photography from 1964 to 1978 and derive the first geodetic volume of the Rissa landslide. We found that the landslide covered a total onshore area of 0.36 km2 and had a geodetic volume of 2.53 ± 0.52 × 106 m3 with up to 20 m of surface elevation changes. Our estimate differs profusely from previous estimates by 43–56% which can partly be accounted for our analysis not being able to measure the portion of the landslide that occurred underwater, nor account for the material deposited within the landslide area. Given the accuracy and precision of our analyses, we believe that the total volume of the Rissa landslide may have been less than originally reported. The use of modern image processing techniques such as SfM-MVS for processing historical aerial photography is recommended for understanding landscape changes related to landslides, volcanoes, glaciers, or river erosion over large spatial and temporal scales.publishedVersio

Automated detection of rock glaciers using deep learning and object-based image analysis

B Robson was supported by the Meltzer foundation and a University of Bergen grant. S MacDonell was supported by CONICYT-Programa Regional (R16A10003) and the Coquimbo Regional Government via FIC-R(2016)BIP 40000343. D. Hölbling has been supported by the Austrian Science Fund through the project MORPH (Mapping, Monitoring and Modeling the Spatio-Temporal Dynamics of Land Surface Morphology; FWF-P29461-N29). N Schaffer was financed by CONICYT-FONDECYT (3180417) and P Rastner by the ESA Dragon 4 programme (4000121469/17/I-NB).Rock glaciers are an important component of the cryosphere and are one of the most visible manifestations of permafrost. While the significance of rock glacier contribution to streamflow remains uncertain, the contribution is likely to be important for certain parts of the world. High-resolution remote sensing data has permitted the creation of rock glacier inventories for large regions. However, due to the spectral similarity between rock glaciers and the surrounding material, the creation of such inventories is typically conducted based on manual interpretation, which is both time consuming and subjective. Here, we present a novel method that combines deep learning (convolutional neural networks or CNNs) and object-based image analysis (OBIA) into one workflow based on freely available Sentinel-2 optical imagery (10 m spatial resolution), Sentinel-1 interferometric coherence data, and a digital elevation model (DEM). CNNs identify recurring patterns and textures and produce a prediction raster, or heatmap where each pixel indicates the probability that it belongs to a certain class (i.e. rock glacier) or not. By using OBIA we can segment the datasets and classify objects based on their heatmap value as well as morphological and spatial characteristics. We analysed two distinct catchments, the La Laguna catchment in the Chilean semi-arid Andes and the Poiqu catchment in the central Himalaya. In total, our method mapped 108 of the 120 rock glaciers across both catchments with a mean overestimation of 28%. Individual rock glacier polygons howevercontained false positives that are texturally similar, such as debris-flows, avalanche deposits, or fluvial material causing the user's accuracy to be moderate (63.9–68.9%) even if the producer's accuracy was higher (75.0–75.4%). We repeated our method on very-high-resolution Pléiades satellite imagery and a corresponding DEM (at 2 m resolution) for a subset of the Poiqu catchment to ascertain what difference image resolution makes. We found that working at a higher spatial resolution has little influence on the producer's accuracy (an increase of 1.0%), however the rock glaciers delineated were mapped with a greater user's accuracy (increase by 9.1% to 72.0%). By running all the processing within an object-based environment it was possible to both generate the deep learning heatmap and perform post-processing through image segmentation and object reshaping. Given the difficulties in differentiating rock glaciers using image spectra, deep learning combined with OBIA offers a promising method for automating the process of mapping rock glaciers over regional scales and lead to a reduction in the workload required in creating inventories.Publisher PDFPeer reviewe

Geosciences / Identifying Spatio-Temporal Landslide Hotspots on North Island, New Zealand, by Analyzing Historical and Recent Aerial Photography

Accurate mapping of landslides and the reliable identification of areas most affected by landslides are essential for advancing the understanding of landslide erosion processes. Remote sensing data provides a valuable source of information on the spatial distribution and location of landslides. In this paper we present an approach for identifying landslide-prone “hotspots” and their spatio-temporal variability by analyzing historical and recent aerial photography from five different dates, ranging from 1944 to 2011, for a study site near the town of Pahiatua, southeastern North Island, New Zealand. Landslide hotspots are identified from the distribution of semi-automatically detected landslides using object-based image analysis (OBIA), and compared to hotspots derived from manually mapped landslides. When comparing the overlapping areas of the semi-automatically and manually mapped landslides the accuracy values of the OBIA results range between 46% and 61% for the producers accuracy and between 44% and 77% for the users accuracy. When evaluating whether a manually digitized landslide polygon is only intersected to some extent by any semi-automatically mapped landslide, we observe that for the natural-color images the landslide detection rate is 83% for 2011 and 93% for 2005; for the panchromatic images the values are slightly lower (67% for 1997, 74% for 1979, and 72% for 1944). A comparison of the derived landslide hotspot maps shows that the distribution of the manually identified landslides and those mapped with OBIA is very similar for all periods; though the results also reveal that mapping landslide tails generally requires visual interpretation. Information on the spatio-temporal evolution of landslide hotspots can be useful for the development of location-specific, beneficial intervention measures and for assessing landscape dynamics.FFG-ASAP-847970(VLID)165265

International Journal of Disaster Risk Reduction / Spatial assessment of social vulnerability in the context of landmines and explosive remnants of war in Battambang province, Cambodia

Despite recent progress in reducing the number of victims, landmines and explosive remnants of war (ERW) cause more than 3000 causalities every year, particularly affecting the most vulnerable. Current mine action programmes, however, do not consider prevailing vulnerabilities of affected communities in their priority-setting systems. We emphasise the need to consider social vulnerability in the workflow of mine action, and apply a spatially explicit approach for its assessment at a sub-national scale in Cambodia, one of the world's most heavily affected countries. Drawing on available literature and focus group discussions with domain experts, 16 socioeconomic, demographic and distance-related vulnerability indicators were identified. The Analytical Hierarchy Process was used to obtain indicator weights, revealing that using firewood for cooking, distance to hospitals and health centres, occupation in the primary sector, poverty, conflict density, illiteracy and living in a rural area are key factors shaping social vulnerability in the context of landmines and ERW. Results were visualised using both 22 km2 grids and sub-district administrative units, a resolution often used by the Cambodian Mine Action and Victim Assistance Authority (CMAA). The results show that social vulnerability is very heterogeneous across the study area (Battambang province) with varying contributions of the underlying indicators. Significant hot spots were identified in the central, north-western, north-eastern, and southern parts of the province. The presented approach provides the means not only to assess but also monitor progress of reconstruction measures to strengthen the resilience of communities exposed to post-conflict impacts such as landmines.(VLID)231723

Assessment of Landslide-Induced Geomorphological Changes in Hítardalur Valley, Iceland, Using Sentinel-1 and Sentinel-2 Data

Publisher's version (útgefin grein)Landslide mapping and analysis are essential aspects of hazard and risk analysis. Landslides can block rivers and create landslide-dammed lakes, which pose a significant risk for downstream areas. In this research, we used an object-based image analysis approach to map geomorphological features and related changes and assess the applicability of Sentinel-1 data for the fast creation of post-event digital elevation models (DEMs) for landslide volume estimation. We investigated the Hítardalur landslide, which occurred on the 7 July 2018 in western Iceland, along with the geomorphological changes induced by this landslide, using optical and synthetic aperture radar data from Sentinel-2 and Sentinel-1. The results show that there were no considerable changes in the landslide area between 2018 and 2019. However, the landslide-dammed lake area shrunk between 2018 and 2019. Moreover, the Hítará river diverted its course as a result of the landslide. The DEMs, generated by ascending and descending flight directions and three orbits, and the subsequent volume estimation revealed that-without further post-processing-the results need to be interpreted with care since several factors influence the DEM generation from Sentinel-1 imagery.This research has been supported by the Austrian Science Fund (FWF) through the project MORPH (Mapping, monitoring and modelling the spatio-temporal dynamics of land surface morphology; FWF-P29461-N29) and the Doctoral Collage GIScience (DKW1237-N23), as well as by the Austrian Academy of Sciences (?AW) through the project RiCoLa (Detection and analysis of landslide-induced river course changes and lake formation).Peer Reviewe

Identifying Spatio-Temporal Landslide Hotspots on North Island, New Zealand, by Analyzing Historical and Recent Aerial Photography

Accurate mapping of landslides and the reliable identification of areas most affected by landslides are essential for advancing the understanding of landslide erosion processes. Remote sensing data provides a valuable source of information on the spatial distribution and location of landslides. In this paper we present an approach for identifying landslide-prone “hotspots” and their spatio-temporal variability by analyzing historical and recent aerial photography from five different dates, ranging from 1944 to 2011, for a study site near the town of Pahiatua, southeastern North Island, New Zealand. Landslide hotspots are identified from the distribution of semi-automatically detected landslides using object-based image analysis (OBIA), and compared to hotspots derived from manually mapped landslides. When comparing the overlapping areas of the semi-automatically and manually mapped landslides the accuracy values of the OBIA results range between 46% and 61% for the producer’s accuracy and between 44% and 77% for the user’s accuracy. When evaluating whether a manually digitized landslide polygon is only intersected to some extent by any semi-automatically mapped landslide, we observe that for the natural-color images the landslide detection rate is 83% for 2011 and 93% for 2005; for the panchromatic images the values are slightly lower (67% for 1997, 74% for 1979, and 72% for 1944). A comparison of the derived landslide hotspot maps shows that the distribution of the manually identified landslides and those mapped with OBIA is very similar for all periods; though the results also reveal that mapping landslide tails generally requires visual interpretation. Information on the spatio-temporal evolution of landslide hotspots can be useful for the development of location-specific, beneficial intervention measures and for assessing landscape dynamics

Geographic object‐based image analysis (GEOBIA) of the distribution and characteristics of aeolian sand dunes in Arctic Sweden

Current climate change in the Arctic is unprecedented in the instrumental record, with profound consequences for the environment and landscape. In Arctic Sweden, aeolian sand dunes have been impacted by climatic changes since their initial formation after the retreat of the last glacial ice sheet. Dune type, location and orientation can therefore be used to explore past wind patterns and landscape destabilisation in this sensitive area. However, knowledge of the full spatial extent and characteristics of these dunes is limited by their inaccessibility and dense vegetation cover. Geographic object‐based image analysis (GEOBIA) permits the semi‐automatic creation of reproducible parameter‐based objects and can be an appropriate means to systematically and spatially map these dunes remotely. Here, a digital elevation model (DEM) and its derivatives, such as slope and curvature, were segmented in a GEOBIA context, enabling the identification and mapping of aeolian sand dunes in Arctic Sweden. Analysis of the GEOBIA‐derived and expert‐accepted polygons affirms the prevalence of parabolic dune type and reveals the coexistence of simple dunes with large coalesced systems. Furthermore, mapped dune orientations and relationships to other geomorphological features were used to explore past wind directions and to identify sediment sources as well as the reasons for sand availability. The results indicate that most dune systems in Arctic Sweden were initially supplied by glaciofluvial and fluvial disturbances of sandy esker systems. Topographic control of wind direction is the dominant influence on dune orientation. Further, our approach shows that analysing the GEOBIA‐derived dune objects in their geomorphological context paves the way for successfully investigating aeolian sand dune location, type and orientation in Arctic Sweden, thereby facilitating the understanding of post‐glacial landscape (in)stability and evolution in the area.Göran Gustafsson Foundation http://dx.doi.org/10.13039/50110000342

Spatial Evaluation of a Natural Flood Management Project Using SAR Change Detection

This study proposes an evaluation method using C-band Sentinel-1 synthetic aperture radar (SAR) data to provide evidence of flood characteristic changes after the restoration of a floodplain. A portable, flexible evaluation framework has replicated previous change detection research approaches to analyse a Natural Flood Management (NFM) project on the Sussex Ouse river in southern England, conducted by the Sussex Flow Initiative (SFI), to ascertain how control measures have helped mitigate flood risk. GIS operations were conducted on the mapped results of the change detection procedure to identify how flood area, form and compactness have been affected after the NFM installation restored a floodplain to slow river flow and how these changes relate to the overall aims of the project. Innovative means were employed to verify the change detection methodology by sampling flood records from internet-published drone footage. The overall accuracy achieved using the Change Detection and Thresholding (CDAT) technique was 75%. The use of SAR data provides evidence of how NFM features function during significant flood events, providing a mapped delineation of the actual flood extent. A comprehensive scorecard has been developed to evaluate the positive and negative outcomes of the spatial changes that have manifested in post-restoration floods, in comparison to inundation before the installation. Results from this study have been included in the annual report of the SFI project to demonstrate how key features have attenuated flood waters in accordance with design intentions

Decadal Scale Changes in Glacier Area in the Hohe Tauern National Park (Austria) Determined by Object-Based Image Analysis

In this paper, we semi-automatically classify clean and debris-covered ice for 145 glaciers within Hohe Tauern National Park in the Austrian Alps for the years 1985, 2003, and 2013. We also map the end-summer transient snowline (TSL), which approximates the annual Equilibrium Line Altitude (ELA). By comparing our results with the Austrian Glacier Inventories from 1969 and 1998, we calculate a mean reduction in glacier area of 33% between 1969 and 2013. The total ice area reduced at a mean rate of 1.4 km2 per year. This TSL rose by 92 m between 1985 and 2013 to an altitude of 3005 m. Despite some limitations, such as some seasonal snow being present at higher elevations, as well as uncertainties related to the range of years that the LiDAR DEM was collected, our results show that the glaciers within Hohe Tauern National Park conform to the heavy shrinkage experienced in other areas of the European Alps. Moreover, we believe that Object-Based Image Analysis (OBIA) is a promising methodology for future glacier mapping

Earth Science Informatics / An object-based approach for semi-automated landslide change detection and attribution of changes to landslide classes in northern Taiwan

Earth observation (EO) data are very useful for the detection of landslides after triggering events, especially if they occur in remote and hardly accessible terrain. To fully exploit the potential of the wide range of existing remote sensing data, innovative and reliable landslide (change) detection methods are needed. Recently, object-based image analysis (OBIA) has been employed for EO-based landslide (change) mapping. The proposed object-based approach has been tested for a sub-area of the Baichi catchment in northern Taiwan. The focus is on the mapping of landslides and debris flows/sediment transport areas caused by the Typhoons Aere in 2004 and Matsa in 2005. For both events, pre- and post-disaster optical satellite images (SPOT-5 with 2.5 m spatial resolution) were analysed. A Digital Elevation Model (DEM) with 5 m spatial resolution and its derived products, i.e., slope and curvature, were additionally integrated in the analysis to support the semi-automated object-based landslide mapping. Changes were identified by comparing the normalised values of the Normalized Difference Vegetation Index (NDVI) and the Green Normalized Difference Vegetation Index (GNDVI) of segmentation-derived image objects between pre- and post-event images and attributed to landslide classes.(VLID)364709