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

Automatisierte Erkennung und Kartierung von Lawinenablagerungen mit optischen Fernerkundungsdaten

Lawinen bedrohen Gebäude sowie Verkehrsinfrastruktur im Alpenraum. Sie fordern in der Schweiz mehr Todesopfer als jeder andere Typ von Naturkatastrophen. Deshalb sind rasch verfügbare und präzise Informationen über die Lage und Reichweite von Lawinenereignissen wichtig für die Lawinenwarnung und die Entscheidungsfindung bezüglich der Sperrung von Strassen, Bergbahnen und Skipisten. Für die Evaluation der Gefahrenprognose, für die Erstellung von Kataster und Gefahrenkarten sowie für die Kalibrierung und Evaluation von Lawinenmodellen sind sie ebenfalls von grosser Bedeutung. Heute werden diese Informationen vorwiegend von Beobachtern vor Ort erhoben. Aufgrund der eingeschränkten Zugänglichkeit hochalpiner Gebiete im Winter kann aber nur ein Bruchteil aller Lawinenereignisse erfasst werden. Insbesondere kleinere bis mittlere Lawinenereignisse in abgelegenen Gebieten werden nur sporadisch kartiert. Aber gerade dieser Lawinentyp fordert die meisten Todesopfer unter der steigenden Zahl von Wintersportlern, die sich abseits der markierten Pisten bewegen. Fernerkundungssensoren können auch über schwer zugänglichem Gebiet grossflächig Daten erheben und sind deshalb ein potentielles Werkzeug, das zur Schliessung dieser Informationslücke beitragen kann. In dieser Arbeit wird systematisch untersucht, inwiefern Lawinenkegel mit räumlich hochauflösenden optischen Fernerkundungsdaten erkannt und kartiert werden können. Anhand von Feld-Spektroradiometermessungen von neun Lawinenkegeln wird analysiert, ob allgemeingültige, substantielle spektrale Unterschiede zwischen Lawinenkegel und der angrenzenden, ungestörten Schneedecke bestehen. Obwohl interessante Absorptionsfeatures im nahen Infrarotbereich des elektromagnetischen Spektrums identifiziert werden können, sind die Unterschiede kaum ausgeprägt genug, um sie mit flugzeug- oder satellitengestützten Sensoren zu erfassen. Das direktionale Reflexionsverhalten der rauen Oberfläche eines Lawinenkegels verhält sich konträr zum Reflexionsverhalten der ungestörten Schneedecke. Anhand von Daten des Luftbildscanners ADS40, aufgenommen aus unterschiedlichen Blickwinkeln, kann gezeigt werden, dass dieser Unterschied im Reflexionsverhalten der zwei Schneeoberflächentypen mit grosser Wahrscheinlichkeit genutzt werden kann, um Lawinenkegel zu detektieren. Allerdings reicht der in dieser Untersuchung verfügbare Blickwinkelunterschied von 16° nicht aus, um Lawinenkegel allein auf Basis der direktionalen Unterschiede mit genügender Genauigkeit zu kartieren. Die Texturen von Lawinenkegeln und der ungestörten Schneedecke unterscheiden sich deutlich. Eine grobe Unterscheidung ist bereits von blossem Auge möglich. Die Statistik zweiter Ordnung, welche die räumliche Verteilung von Intensitätswerten berücksichtigt, kann Texturmerkmale in digitalen Bilddaten quantitativ erfassen. Dies ist die Voraussetzung für eine automatisierte Erkennung spezifischer Texturen. Anhand von RC30 Luftbildern, aufgenommen während des Lawinenwinters 1999, werden in der Literatur beschriebene Texturmasse auf ihre Eignung für die Unterscheidung zwischen Lawinenkegel und ungestörter Schneedecke getestet. Dabei werden die massgebenden Parameter systematisch variiert, um die optimalen Einstellungen zu identifizieren. Das Texturmass Entropy erweist sich als stabilster Indikator für die Differenzierung zwischen rauen und glatten Schneeoberflächen. Weil aber auch weitere raue Schneeoberflächen, wie vom Wind modellierte Schneedecken oder künstlich angehäufter Schnee an Rändern von Skipisten, vergleichbare Texturwerte wie Lawinenkegel zeigen, reichen Texturparameter alleine nicht aus, um Lawinenkegel eindeutig zu identifizieren. Basierend auf den Erkenntnissen aus den vorangegangenen Untersuchungen wird eine Prozessierungskette entwickelt, welche spektrale und direktionale Parameter mit Texturparametern und Informationen aus Hilfsdatensätzen kombiniert. Diese Prozessierungskette wird anhand von Daten des Luftbildscanners ADS40 im Raum Davos evaluiert und verbessert. Dabei werden 94% der in drei Testgebieten vorhandenen Lawinenkegel vom Algorithmus korrekt erkannt. Auch kleinere Kegel mit einer Fläche von weniger als 2000 m2 und Kegel in Schattenhängen werden korrekt erfasst. Dieses Ergebnis zeigt das grosse Potential des entwickelten Ansatzes für die automatisierte Erkennung und Kartierung von Lawinenkegeln. Die Verfügbarkeit geeigneter Daten ist aber aufgrund der nach intensiven Schneefällen häufigen noch vorhandenen Bewölkung eingeschränkt. Zudem treten vereinzelt Fehlklassifikationen auf. Dies sind hauptsächlich vom Wind modellierte Schneedecken, künstlich angehäufter Schnee und von spärlicher Vegetation durchsetzte Flächen. Trotz diesen Einschränkungen kann der in dieser Arbeit entwickelte Ansatz in Zukunft zur Schliessung substanzieller Datenlücken beitragen. Besonders in Gebirgen von Entwicklungsländern, in denen noch kaum verlässliche Informationen über Lawinenniedergänge existieren, können damit wertvolle Informationen für die Gefahrenkartierung und die Siedlungsplanung gewonnen werden. Summary Snow-avalanches kill more people in Switzerland than any other natural hazard and threaten buildings and traffic infrastructure. Rapidly available and accurate information about the location and extent of avalanche events is important for avalanche forecasting, safety assessments for roads and ski resorts, verification of warning products as well as for hazard mapping and avalanche model calibration/validation. Today, isolated observations from individual experts in the field provide information with limited coverage. Only a fraction of all avalanche events can be recorded due to restricted accessibility of many alpine terrain sections during winter season. Information on small to medium size avalanche events within remote regions is collected only sporadically. However, these avalanches notably claim most casualties within the raising number of people pursing off-slope activities. Remote sensing instruments are able to acquire wide-area datasets even over poorly accessible regions. Therefore they are promising tools to close the above- mentioned information gap. This research systematically investigates the potential of spatially high resolved remote sensing instruments for the detection and mapping of snow-avalanche deposits. Fieldspectroradiometer data of nine avalanche deposits are analysed to identify universally valid and significant spectral offsets between avalanche deposits and the adjacent undisturbed snow cover. Promising absorption features are found in the near-infrared region of the electromagnetic spectrum. Nevertheless, the differences are unlikely to be distinct enough for a detection using air- or spaceborne remote sensing instruments. The directional reflection of rough avalanche deposit surfaces is contrary to the directional reflection of smooth undisturbed snow covers. The potential of multriangular remote sensing data for the detection and mapping of avalanche deposits is demonstrated using multiangular data acquired by the airborne scanner ADS40. However, the difference between observation angles (16°) proves to be insufficient for accurate avalanche detection solely on the base of directional properties. Therefore, auxiliary data has to be utilised. The texture of avalanche deposits and undisturbed snow cover can already be distinguished by the naked eye. Using second-order statistics, comprising the spatial distribution of the variation in pixel brightness, textural characteristics in digital image data can be quantified. This is a prerequisite for an automated detection of particular textures. Different established texture measures are tested for their discriminating potential of avalanche deposits and undisturbed snow cover using RC30 aerial images of avalanche deposits acquired within the avalanche winter 1999 in Switzerland. The control parameters such as the size of the filter box are systematically varied to find the ideal settings. The texture measure Entropy is identified as the most distinct and stable indicator to distinguish between rough and smooth snow surfaces. But avalanche deposits are not the only rough snow surfaces within the Alpine winter landscape. For example wind modeled snow surfaces or artificially piled snow at the edge of roads and ski slopes show texture characteristics similar to avalanche deposits. Consequently, a classification approach using texture information only is not sufficient for an accurate identification of avalanche deposits. Based on the findings described above, we develop an avalanche detection and mapping processing chain, combining spectral, directional and textural parameters with auxiliary datasets. The processing chain is tested and improved using data acquired by the airborne scanner ADS40 over the region of Davos, Switzerland. The accuracy assessment, based on ground reference data within three test sites, shows that 94% of all existing avalanche deposits are identified. Even small scale deposits (area < 2000 m2) and deposits within shadowed areas are detected correctly. These results demonstrate the big potential of the proposed approach for automated detection and mapping of avalanche deposits. Yet, cloud cover constrains the availability of appropriate optical remote sensing data after heavy snowfall while wind modeled snow surfaces, artificially piled snow and sparsely vegetated snow surfaces cause sporadic misclassifications. Despite these constraints, the approach developed within this research shows a big potential to fill existing gaps in avalanche information. Especially within alpine areas of developing countries with almost no reliable information on past avalanche events, such an approach may be used to acquire valuable data for hazard mapping and settlement planning

Multipath Interferences in Ground-Based Radar Data: A Case Study

Multipath interference can occur in ground-based radar data acquired with systems with a large antenna beam width in elevation in an upward looking geometry, where the observation area and the radar are separated by a reflective surface. Radiation reflected at this surface forms a coherent overlay with the direct image of the observation area and appears as a fringe-like pattern in the data. This deteriorates the phase and intensity data and therefore can pose a considerable disadvantage to many ground-based radar measurement campaigns. This poses a problem for physical parameter retrieval from backscatter intensity and polarimetric data, absolute and relative calibration on corner reflectors, the generation of digital elevation models from interferograms and in the case of a variable reflective surface, differential interferometry. The main parameters controlling the interference pattern are the vertical distance between the radar antennas and the reflective surface, and the reflectivity of this surface. We used datasets acquired in two different locations under changing conditions as well as a model to constrain and fully understand the phenomenon. To avoid data deterioration in test sites prone to multipath interference, we tested a shielding of the antennas preventing the radar waves from illuminating the reflective surface. In our experiment, this strongly reduced but did not completely prevent the interference. We therefore recommend avoiding measurement geometries prone to multipath interferences

Can big data and random forests improve avalanche runout estimation compared to simple linear regression?

Accurate prediction of snow avalanche runout-distances in a deterministic sense remains a challenge due to the complexity of all the physical properties involved. Therefore, in many locations including Norway, it has been common practice to define the runout distance using the angle from the starting point to the end of the runout zone (α-angle). We use a large dataset of avalanche events from Switzerland (N = 18,737) acquired using optical satellites to calculate the α-angle for each avalanche. The α-angles in our dataset are normally distributed with a mean of 33◦ and a standard deviation of 6.1◦, which provides additional understanding and insights into α-angle distribution. Using a feature importance module in the Random Forest framework, we found the most important topographic parameter for predicting α-angles to be the average gradient from the release area to the β-point. Despite the large dataset and a modern machine learning (ML) method, we found the simple linear regression model to yield a higher performance than our ML attempts. This means that it is better to use a simple linear regression in an operational context

Analysis of an artificially triggered avalanche at the nepheline syenite mine on Stjernøya, Alta, Northern Norway

Since 1961, a Nepheline Syenite mine is operated on the island of Stjernøya in the Altafjord, Northern Norway. The facilities are located in Lillebukt, on the southern side of the island. Above the facilities, the Nabbaren mountain is rising to a height of 727 m a.s.l. Rockfall during summer season and snow avalanches during wintertime pose potential hazards from its slopes. Due to this setting, the mining company has long experience with both physical and non-physical hazard mitigation measures. Apart from physical installations against rockfall and snow avalanches, artificial triggering of the Nabbaren avalanche forms part of this mitigation strategy.The winter of 2013/2014 was characterized by an unusual snow scarcity between December 2013 and March 20, 2014. After this date, large amounts of snow fell during a short period. Due to this new snow loading, together with intensive snowdrift, the mining company decided to artificially trigger the Nabbaren avalanche on April 8, 2014. A D4 slab avalanche was released, subsequently evolving into a mixed dry avalanche of impressive scale. In contrast to avalanches triggered in other years, this avalanche overtopped the avalanche deflecting wall at its one end causing slight damages to some of the factory installations. In order to document the avalanche, an on-site study was carried out shortly after the event. In addition, a WorldView-1 panchromatic satellite image was obtained to map the non-accessible parts of the avalanche. Here, we present findings from the field visit, from image analyses and first modellings of the avalanche run-out

New insights on permafrost genesis and conservation in talus slopes based on observations at Flüelapass, Eastern Switzerland

The talus slope at Flüelapass was the first mountain permafrost study site in Switzerland in the 1970s and the presence of ice-rich permafrost at the foot of the slope has been investigated in the context of several studies focusing on the role of snow cover distribution. We review previously developed hypotheses and present new ones using various data sources, such as temperature measurements in boreholes, a subaquatic DEM generated from unmanned aerial system (UAS) photogrammetry, terrestrial laser scan measurements of snow depth, geophysical ground investigations and automatic time-lapse photography. From this combination of data sources together with observations in the field, an interesting sequence of geomorphologic processes is established at Flüelapass. As a result we show how mass wasting processes can initiate the genesis and long-term conservation of ice-rich permafrost at the base of a talus slope

Automated avalanche mapping from SPOT 6/7 satellite imagery with deep learning: results, evaluation, potential and limitations

Spatially dense and continuous information on avalanche occurrences is crucial for numerous safety-related applications such as avalanche warning, hazard zoning, hazard mitigation measures, forestry, risk management and numerical simulations. This information is today still collected in a non-systematic way by observers in the field. Current research has explored the application of remote sensing technology to fill this information gap by providing spatially continuous information on avalanche occurrences over large regions. Previous investigations have confirmed the high potential of avalanche mapping from remotely sensed imagery to complement existing databases. Currently, the bottleneck for fast data provision from optical data is the time-consuming manual mapping. In our study we deploy a slightly adapted DeepLabV3+, a state-of-the-art deep learning model, to automatically identify and map avalanches in SPOT 6/7 imagery from 24 January 2018 and 16 January 2019. We relied on 24 778 manually annotated avalanche polygons split into geographically disjointed regions for training, validating and testing. Additionally, we investigate generalization ability by testing our best model configuration on SPOT 6/7 data from 6 January 2018 and comparing it to avalanches we manually annotated for that purpose. To assess the quality of the model results, we investigate the probability of detection (POD), the positive predictive value (PPV) and the F1 score. Additionally, we assessed the reproducibility of manually annotated avalanches in a small subset of our data. We achieved an average POD of 0.610, PPV of 0.668 and an F1 score of 0.625 in our test areas and found an F1 score in the same range for avalanche outlines annotated by different experts. Our model and approach are an important step towards a fast and comprehensive documentation of avalanche periods from optical satellite imagery in the future, complementing existing avalanche databases. This will have a large impact on safety-related applications, making mountain regions safer