Using eCognition Definiens for automated detection of snow avalanches from optical imagery

Detection of avalanches from remotely collected optical imagery has been tested through analysis of image properties such as brightness, contrast, and different measures of texture. There have been few publications on the subject, providing an excellent opportunity for new developments. The work conducted at NGI in 2011 aimed at detecting fresh snow avalanches from very-high resolution (VHR) optical imagery. The research presented in this Technical Note has been supported by the Ministry of Petroleum and Energy (OED) through the Norwegian Water Resources and Energy Directorate (NVE).Norges Forskningsråd (NFR

Using eCognition Definiens for automated detection of snow avalanche deposits from very high resolution optical imagery - New developments

The identification of snow avalanche deposits from high resolution optical satellite imagery had been the focus of the project "avalRS” which NGI, together with the Norwegian Computing Centre and Statens Veivesen, had carried out for the European Space Agency (2008-2011; e.g., Frauenfelder et al., 2011). The algorithms developed have produced variable results, often working well in certain situations and poorly in others. In 2011 using the object oriented image processing software eCognition, NGI developed two prototype algorithms on its own. The two algorithms were developed for (i) QuickBird satellite imagery, and (ii) Leica ADS-40 airborne imagery (cf. Lato and Frauenfelder, 2012).Aspart of the continuation of this research program, the algorithms developed in 2011 were published in the journal *Natural Hazards and Earth System Sciences* (Lato et al., 2012a) as well as presented at International conferences, e.g., at the "International Snow Science Workshop 2012” in Anchorage, Alaska (Lato et al., 2012b). Overall the developments have been accepted well within the community, the preliminary results demonstrate the possibility of numerous research and commercial applications. In parallel with the publication and presentation of the research results in 2012, new satellite images containing snow avalanche deposits were tested with the algorithms in eCognition. An overview of the data, the region it represents, as well as a discussion of the results is included in this document.Norges vassdrags- og energidirektorat (NVE), Region Ves

Consolidation of requirements - Analysis of questionnaire

This report presents a synthesis of the results of a questionnaire distributed to personnel from the Norwegian Public Roads Administration (NPRA) and several county road administrations. The aim of the survey was to evaluate the current and potential use of uncrewed aircraft systems (UAS), or drones, for assessing roads exposed to landslide, rockfall, and avalanche hazards. Furthermore, the survey aimed to identify the challenges or barriers to uptake. The recipients of the survey were identified as key persons in their organisations working with avalanche danger in the road sector, either as responsible persons for avalanche danger assessments or as operators/managers of avalanche-prone roads. Results from a total of 36 respondents to the questionnaire are summarized and some recommendations are proposed.The Norwegian Research Council, Project: 32103

Automated Avalanche Deposit Mapping From VHR Optical Imagery

Using eCognition we developed an algorithm to automatically detect and map avalanche deposits in Very High Resolution (VHR) optical remote sensing imagery acquired from satellites and airplanes. The algorithm relies on a cluster-based object-oriented image interpretation approach which employs segmentation and classification methodologies to identify avalanche deposits. The algorithm is capable of detecting avalanche deposits of varying size, composition, and texture. A discrete analysis of one data set (airborne imagery collected near Davos, Switzerland) demonstrates the capability of the algorithm. By comparing the automated detection results to the manually mapped results for the same image, 33 of the 35 manually digitized slides were correctly identified by the automated method. The automated mapping approach characterized 201 667 m2, of the image as being representative of a fresh snow avalanche, roughly 8.5% of the image. Through a spatial intersection between the manually mapped avalanches and the automatically mapped avalanches, 184 432 m2, or 89%, of the automatically mapped regions are spatially linked to the manually mapped regions. The rate of false positive was less than 1% of the pixels in the image. The initial results of the algorithm are promising, future development and implementation is currently being evaluated. The ability to automatically identify the location and extent of avalanche deposits using VHR optical imagery can assist in the development of detailed regional maps of zones historically prone to avalanches. This in turn can help to validate issued avalanche warnings

Could retrieval of snow layer formation by optical satellite remote sensing help avalanche forecasting? Presentation of first results

Of special interest within the field of avalanche research and avalanche warning are properties related to snow grain type and snow grain size at the surface. In continental and intermountain avalanche climates weak layers or interfaces are the main cause of avalanches. Knowledge about such weak layers helps to increase the precision of avalanche forecasting. Some of these potential weak layers form on the snow surface and are preserved until burial. Optical satellite sensors measure reflected sunlight at different wavelengths. The near-infrared region is sensitive to the optical grain size of the snow. Due to the distinct size and shape characteristics of potential weak layers such as, for example, surface hoar, their reflectance is quite different from new snow in general. If the weather permits optical observations it should, therefore, be possible to detect such layers by remote sensing. We present the results of a pilot study where in situ measured surface snow grain characteristics are compared to snow grain characteristics as derived from multispectral data from the MODIS satellite sensor. The pilot study showed that parallel in situ snow measurements and snow analyses exploiting data from MODIS are possible for the selected test sites in Norway. The study aims at establishing a relationship between the satellite-observed snow grain size index (SGS) variable and the snow grain size and shape as measured in the field. Based on satellite and in situ data measured over several years, we intend to establish a snow grain evolution model. The model will be used as an input to the avalanche forecasting model

The avalanche situation in a special winter. Review of the 09/10 season in Norway.

The 2009/10 season featured significantly different snow conditions compared to the usually maritime snow pack in most of Norway. A stable high-pressure system over large parts of Europe led to low temperatures and limited precipitation in the country. Along the coast a continuous snow cover was observed for several weeks while the mountains received down to 20% of the 1971-2000 normal precipitation values. The snowpack had several weeks to develop extensive layers of depth hoar also in areas where this is usually not observed. The weather situation promoted the development of surface hoar in many locations as well, especially on the eastern side of the mountains. Still, the danger level was moderate most days of the winter. Similar conditions lead to the catastrophic avalanche winter in 1979. Based on that experience awareness was high and both road closures and evacuations were implemented in several occasions when heavy snowfalls were to load the weak base of the well established persistent weak layers. The observed avalanches were often hard slab avalanches of medium size occurring on slopes where avalanches are not observed in normal winters. The five registered fatalities during the past season were all back country skiing accidents and most of the avalanches were at least partly released in the week base of the depth hoar layers. The daily presence of avalanche observations caused intensive media coverage of the special snow situation. Journalists and the public soon adopted the international danger scale and communication between avalanche experts and the media improved significantly as a result of this season

First geophysical investigations to study a fragile Pomor cultural heritage site at Russekeila – Kapp Linné), Svalbard

With climate warming, the cultural heritage sites of the Arctic are in great danger. Extensive research is needed to study such sites. The archaeological site at Russekeila – Kapp Linné, Svalbard was selected for the survey as previous research had highlighted its vulnerability to cryospheric hazards. The main objectives of the survey were (i) to register the precise surface and subsurface locations of cultural heritage (CH) (remains of an 18th century Russian Pomor trapper's hut) objects within the study area, (ii) to determine the impact of coastal erosion on the CH objects and (iii) to understand the near-surface stratigraphy of the site. The geophysical surveys were carried out using a Ground Penetrating Radar (GPR) instrument with two shielded antennas of 500 MHz and 800 MHz centre frequencies. Only weak anomalies were observed at the intersections with wooden drifts, which can be explained by the low contrast between the relative dielectric constant values of the driftwood and the background soil. The depth extent of the driftwood within the soil was understood from the processed GPR data to a depth of approximately 25 cm. A near-surface stratigraphy of the site morphology, including thaw depth, saturated and unsaturated sediments and soil cover, was established based on multiple reflectors observed to 2 m depth. Loose sediments are indicated by reflectors to a depth of approximately 20 cm. Unsaturated fine sediments, which show a stronger signal compared to the underlying saturated sand layers, can be observed from about 1.2 m depth. No reflectors are shown below the thaw depth.First geophysical investigations to study a fragile Pomor cultural heritage site at Russekeila – Kapp Linné), SvalbardpublishedVersio

Experiments with remote sensing in the context of avalanche warning and detection

In Proceedings of Advances in Avalanche Forecasting, Podbanské, Slovakia, 22 October 2012Two Norwegian projects carried out by NGI and NR have investigated and experimented with the potential of using remote sensing for avalanche warning and detection: The Norwegian Space Centre (NSC) supported project “Improved Avalanche Warning Using Satellite Data” (2008-2010) and the European Space Agency (ESA) funded project “Avalanche Inventory for Decision Support and Hind-cast - AvalRS” (2008–2011)

Avalanche debris detection using satellite-borne radar and optical remote sensing

The mountainous fjord landscape around Tromsø in Northern Norway is prone to high avalanche activity during the snow season. Large avalanches pose a hazard to infrastructure, such as buildings and roads, located between the steep mountainsides and the fjords. To forecast the spatial and temporal extent of avalanche events, knowledge of past activity is critical. For this, a complete avalanche record is needed, however, difficult to achieve. We hypothesize, that the use of satellite data can assist in mapping avalanches over large areas. During and shortly after an intense avalanche cycle in the county of Troms in March 2014, we obtained 11 high-resolution Radarsat-2 Ultrafine scenes centered over large observed avalanches, together with one Landsat-8 scene and four Radarsat-2 SCN/SCW scenes with coarser resolution, covering the entire county. We detected avalanche debris-like features visually, by applying two detection algorithms that make use of the increased backscatter in avalanche debris. This backscatter increase is due to increased snow water equivalent and surface roughness. In addition to the multi-sensor approach using high- to mediumresolution satellite data, we also used a multi-temporal approach. Repeated acquisitions of satellite data from the same area enabled redetection of avalanche debris-like features by change detection methods and, thus, confirmation of their existence. In this study, we show the usability of satellite radar data in detecting avalanches over a large area with medium resolution. Since ultra-high resolution is not available in an operational context today, our hypothesis based on our results using Radarsat-2 is that the ESA Sentinel-1 satellite could provide sufficient coverage and resolution to detect medium and large sized avalanches

The eye in the sky: Avalanche mapping from space

The Seward Highway in Alaska has over one hundred avalanche paths spread out along a 150 km major transportation corridor, which traverses three different avalanche climatic regimes. This coupled with a small staff can make avalanche debris detection and mapping difficult. With the use of satellite imaging we may have a reliable means of detecting and recording avalanche deposits. During the winter of 2016 the Seward Highway recorded an unprecedented amount of glide avalanche releases. Using SAR imagery we can accurately detect avalanche debris, further aiding in mitigation strategies and avalanche hazard management