Sep 18, 2015 - Figure 2-4: Exemplification of the manual expert mapping for the case of ...... data and their validation through field reconnaissance and optical ...
Towards an automated snow property and avalanche mapping system (ASAM) Avalanche recognition and snow variable retrieval, version 2 (Technical report) 20130092-04-R 18 September 2015 Revision: 0
Front page image: Avalanche on the island of Stjernøya, Altafjorden, Northern Norway, April 8, 2014 – Regula Frauenfelder, NGI
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Project Project title: Document title:
Document No.: Date: Revision/Rev. date:
Towards an automated snow property and avalanche mapping system (ASAM) Avalanche recognition and snow variable retrieval, version 2 (Technical report) 20130092-04-R 18 September 2015 0
Client Client: Client’s contact person: Contract references:
ESA PRODEX office @ ESTEC Dr S. Gidlund C4000107724 NGI C4000107694 NR C4000107664 NORUT C4000107722 NVE
Project manager:
Regula Frauenfelder, NGI
Consortium partner representatives (in alphabetical order):
Regula Frauenfelder, Eirik Malnes, Karsten Müller, Rune Solberg
Report contributors (in alphabetical order):
Rune Engeset, Markus Eckerstorfer, Regula Frauenfelder, Heidi Hindberg, Matthew J. Lato, Siri Øyen Larsen, Eirik Malnes, Karsten Müller, ArntBørre Salberg, Rune Solberg, Øivind Due Trier, Hannah Vickers
Reviewed by:
Galina Ragulina, NGI
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Please use the following reference to this report: Norwegian Geotechnical Institute NGI (2015): Towards an automated snow property and avalanche mapping system (ASAM) - Avalanche recognition and snow variable retrieval, version 2 (Technical report). Prepared by: Frauenfelder, R., Malnes E., Solberg, R., Müller, K.; based on contributions from: Engeset, R., Eckerstorfer, M., Frauenfelder, R., Hindberg, H., Lato, M.J., Larsen, S.Ø., Malnes, E., Müller, K., Salberg, A.-B., Ragulina, G., Solberg, R., Trier, Ø.D., Vickers, H. NGI Report no. 20130092-04-R. 127 p.
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Summary This technical report is the second of two deliverables within the ESA PRODEX project "Towards an automated snow property and avalanche mapping system ASAM" (contract nos. C4000107724, C4000107694, C4000107664, C4000107722). The reports gives a summary of the main project achievements in the ASAM project. All partners contributed with their respective findings which can be found in the corresponding sections of the main thematic chapters.
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Contents 1
Introduction 1.1 About the ASAM project 1.2 Background
8 8 9
2
WP-2: Avalanche detection and mapping from VHR optical data 10 2.1 NVE 10 2.2 NGI 12 2.2.1 Provision of avalanche expert knowledge ................................ 12 2.2.2 Pattern recognition of avalanches using optical data ............... 16 2.2.3 Experiments and results ........................................................... 17 2.3 NR 30 2.3.1 Pattern recognition of avalanches using optical data ............... 30 2.3.2 Experiments and results ........................................................... 32 2.3.3 Conclusions .............................................................................. 39
3
WP-2: Avalanche detection and mapping from VHR SAR data 40 3.1 NR 40 3.1.1 Detection of avalanches using the reference image method .... 40 3.1.2 Experiments and results ........................................................... 41 3.1.3 Conclusions .............................................................................. 48 3.2 Norut 49 3.2.1 Electromagnetic backscatter model of avalanche snow ........... 49 3.2.2 Avalanche debris detection using Radarsat-2 data ................... 50 3.2.3 Avalanche debris detection using Sentinel-1A data ................. 59
4
WP-3: Automatic snow variable retrieval 68 4.1 NVE 68 4.2 NGI 69 4.2.1 Large avalanche events during last 15 years ............................ 69 4.2.2 Frequent avalanche sites........................................................... 70 4.2.3 Fatal avalanches due to surface hoar ........................................ 70 4.3 NR 72 4.3.1 Algorithms ................................................................................ 72 4.3.2 In situ data sources ................................................................... 77 4.3.3 Results ...................................................................................... 78 4.4 Norut 94 4.4.1 Wet snow detection with Envisat ASAR and Radarsat-2 ........ 94 4.4.2 Wet snow detection with TerraSAR-X .................................... 96 4.4.3 Wet snow detection using Sentinel-1 ....................................... 97 4.4.4 Historical datasets .................................................................... 99 4.4.5 Retrieval of snow water equivalent using TerraSAR-X........... 99 4.5 NR and Norut 100 4.5.1 Analysis of optical data and SAR consistency ....................... 100 4.5.2 Analysis of surface hoar and snowmelt.................................. 115 4.5.3 Towards a multi-sensor product ............................................. 117
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5
Recommendations
121
6
External data sources and credits
122
7
References
123
8
Acronyms and definitions
126
9
Publications, dissemination and outreach
127
Review and reference page
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1
Introduction
1.1
About the ASAM project
The project “Towards an Automated Snow property and Avalanche Mapping system” (ASAM) is a two year project (2013-2015) which deals with the applicability of remote sensing methods for avalanche applications. The project is funded by the Norwegian Space Centre (NSC) through ESA's PRODEX-programme (PROgramme de Développement d'EXpériences scientifiques) of the European Space Agency (ESA). The project is coordinated by the Norwegian Geotechnical Institute (hereafter called NGI) and carried out together with the Norwegian Computing Centre (hereafter called NR), the Norut Northern Research Centre (hereafter called Norut), and the Norwegian Water Resources and Energy Directorate (hereafter called NVE). The main goal of the ASAM project is to explore and develop methodologies supporting the vision of a future service providing national authorities with hind-cast and near real-time snow and avalanche information retrieved from Earth Observation (EO) data. The project is organised in three work packages (WP), with WP2 being concerned with “Pattern recognition of avalanches” and WP3 with “Snow variable retrieval”. Both WP2 and WP3 are technical work packages. WP1 takes care of the administrative and management aspects of the project. Information about the project as well as a list of reports and publications on its results is available on the project's webpage: http://www.ngi.no/en/Project-pages/ASAM/ (cf. Figure 1-1).
Figure 1-1: Project homepage available at: http://www.ngi.no/en/Project-pages/ASAM/
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1.2
Background
During winter season snow avalanches pose a risk to settlements and infrastructure in many alpine regions, very much so also in the Norwegian mountains. Each year, snow avalanches hit populated areas and parts of the transport network, leading to damage of buildings and infrastructure and sometimes resulting in fatalities. As recreational backcountry use is increasing, more and more fatal avalanche events involving backcountry skiers and snow mobile drivers are observed. Much of the Norwegian country is remote, and knowing exactly where avalanches have taken place, or are likely to take place next time, is often a challenge for the authorities. Earth observation satellites represent, therefore, a potentially important source of information. The main goal of the ASAM project is to explore and develop methodologies supporting the vision of a future service providing national authorities with hind-cast and near real-time snow and avalanche information retrieved from EO data. The following tasks have been set to support the main goal: • Develop sufficiently mature pattern recognition techniques to detect the outline of avalanches in high-resolution (HR) and very high resolution (VHR) optical satellite data. • Investigate whether Synthetic Aperture Radar (SAR) data could be used for the detection of avalanches, and if yes, to what extent. • Develop and validate retrieval algorithms for snow variables of the snowpack relevant for avalanche risk prediction and warning.
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2
WP-2: Automatic avalanche detection and mapping from VHR optical data
2.1
NVE
During recent years NVE established an open access system for field data collection, storage and retrieval. The system is called regObs, and includes interfaces on a web site (www.regobs.no), API (api.nve.no) and an APP (available on Appstore and Google Play). About 40 000 avalanche-related observations were submitted during the winters 2012/2013– 2014/2015, including data on avalanches relevant to WP2. Figure 2-1, Figure 2-2 and Figure 2-3 visualize the interface of regObs on the respective app for iPhones, and the interface on the internet. These data were available and exploitable to the ASAM project and proved to be of great value both for WP2 and WP3.
Figure 2-1: Interface of the regObs-app on an iPhone. Daily avalanche bulletins for 25 regions in Norway are published on varsom.no during the period December 1st to May 31st. Four forecasters are on duty daily. Each of them is responsible for 6-7 regions on average. Field observations reported via regObs, data from weather stations, meteorological models as well as snow pack models accessible via xgeo.no make the basis for the bulletin. The bulletin includes a danger level, a flash header, the type of avalanche problem(s) to be expected, and a descriptive text. The amount of avalanches within a forecasting region (the so called "avalanche activity") is a very useful quality control for the avalanche forecast. The probability of an avalanche release is related to the danger level and an increase in avalanche activity is expected at higher danger levels.
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Figure 2-2: Introduction page of the regObs web interface.
Figure 2-3: Example for a regObs observation of a medium sized slab avalanche close to Stryn, registered on March 18, 2013.
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2.2
NGI
2.2.1
Provision of avalanche expert knowledge
One of the tasks for NGI within the ASAM project was to provide its expert-knowledge in avalanche science, such as avalanche mapping, among others. Validated avalanche maps were a precondition to refine both NGI's (see section 2.2.2) and NR's (section 2.3) automatic detection and mapping algorithms, as well as for the work carried out by Norut (section 3.2). Firstly, a project-tailored classification for “avalanche type”, “avalanche age” and discrimination of the individual “parts of an avalanche” (release zone, path, deposition zone) was established (Table 2-1). All avalanches visible to the human eye in the available VHR optical imagery have been mapped within a GIS environment and been assigned with the according attributes. Thus, all avalanches were mapped with the same set of attributes. Figure 2-4 exemplifies the mapping results for the case of the Loen scene (one of the training datasets). Within the ASAM-project, only the classification for “avalanche type” was used, while the other two classification attributes are available for potential future refinements of the algorithms.
Figure 2-4: Exemplification of the manual expert mapping for the case of the Loen scene (municipality of Stryn, Sogn og Fjordane county, Norway). The colours and codes are shown in Table 2-1. Satellite image: Copyright © DigitalGlobe/WorldView-1. Table 2-1: Project-tailored classification for “avalanche type”, “avalanche age” and discrimination of the different “parts of avalanche” (release zone, path, deposition zone) used in the ASAM avalanche mapping within a GIS-environment. Type
Code
Age
Code
Section
Code
Slab
1
New
1
Deposit + path
1
Point release
2
Old
2
Deposit
2
Path
3
A further task for NGI was to identify and acquire suitable VHR optical data for the project. An overview of the data acquired and finally found feasible for further analyses, is given in Table 2-2. All used images are depicting areas in Norway, except the High Tatra image, which covers the eastern part of the High Tatra mountains in Slovakia. A total of 514 avalanches had been identified and mapped in the available imagery (cf. Table 2-3).
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Avalanche maps resulting from the pattern recognition work done by NR (section 2.3) were quality controlled and examples of NGI's proprietary avalanche hazard maps were provided to Norut in order to assist their attempts to automatically detect avalanche debris by SAR (section 3.2.2). For some of the datasets, ancillary field data were available. The Stjernøya image (northernmost image in Figure 2-6) captures an avalanche which was artificially released on April 8, 2014 (Figure 2-5). On April 11, 2014, a team from NGI investigated this avalanche by detailed fieldwork, obtaining, among others, a GPS-track along its central flow line and another one along its orographic left edge. These data, together with the WV-1 image, and video footage from the release were used to assess size, volume and speed of the avalanche (Frauenfelder et al., 2014).
Figure 2-5: Freeze frame taken from video footage of the April 8, 2014 avalanche. Image credit: Sibelco Nordic; see http://www.nrk.no/nordnytt/sprengte-los-enormt-snoskred1.11658148 for the video footage. In 2014 we were able to obtain a large WorldView-1 dataset (two portions of approximately 17 x 15 km each) covering parts of the Slovakian Tatra mountains. The imagery was provided to the ASAM project free-of-charge by the Slovakian Avalanche Prevention Centre, together with GPS-tracks of selected major avalanches from this avalanche cycle and a detailed digital elevation model. The imagery captured a large avalanche cycle in spring 2009. There were avalanches observed in almost every gully and on many slopes, both small ones, as well as such with a 100-year return period (personal communication by Marek Biskupič, Avalanche Prevention Centre, Slovakia, July 2013). The imagery was acquired on April 2, 2009; the easternmost image portion is almost cloud-free (cf. Figure 2-7), while the westernmost is heavily cloud-covered in the avalanche affected areas. The latter was, therefore, not used within the ASAM project.
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Testing
Training
Table 2-2: Image datasets. Location
Sensor
Acquisition date Spatial resolution
Hellesylt
QuickBird
April 16, 2005
0.6m
Loen
WorldView-1 April 12, 2010
0.5m
High Tatra (lower right) WorldView-1 April 2, 2009
0.5m
Dalsfjorden
QuickBird
April 3, 2005
0.6m
Eikesdalsfjorden
QuickBird
April 13, 2011
0.6m
High Tatra (rest)
WorldView-1 April 2, 2009
0.5m
Stjernøya
WorldView-1 April 17, 2013
0.5m
Testing
Traini ng
Table 2-3: Avalanches digitally mapped as ground truth data in the used datasets.
Total
Location
Manually mapped avalanches
Hellesylt
19
Loen
40
Dalsfjorden
18
Eikesdalsfjorden
39
High Tatra (rest)
322
Stjernøya
75 514
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Figure 2-6: Location of exploited VHR imagery from Norway.
Figure 2-7: WV-1 imagery, captured on April 2nd, 2009, covering parts of the High Tatra, Slovakia. The inset shows a randomly selected detail view. Data: Courtesy by Avalanche Prevention Center, Slovakia; Copyright © DigitalGlobe/WorldView-1.
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2.2.2
Pattern recognition of avalanches using optical data
In the ASAM project, NGI has refined the object-based approach for automatic avalanche detection in VHR optical satellite imagery presented by Lato et al. (2012). The prerequisite in the further development of the algorithm is that it is stable, repeatable and logical, i.e., avoiding “black box” processing. One characteristic of snow avalanches is that they are relatively easily visually distinguishable in a panchromatic image, provided that the image is uniformly exposed. As illustrated in Figure 2-8, the snow avalanche is readily identifiable, even for a non-expert. However, although the avalanche is easily identifiable, there are regions within the avalanche deposit that are nearly indistinguishable from fresh undisturbed snow. From an image interpretation perspective, the target – the snow avalanche runout-zone deposit – is distinguishable based on its signature with respect to the rest of the image as a whole, not just its surrounding pixels. Thus, for an automated snow avalanche detection algorithm to be successful, it must be capable of an image interpretation that looks at neighborhoods of pixels in comparison to the rest of the image, not simply at individual pixels in isolation. The method researched and developed represents an approach to map snow avalanche deposits in which the only information required is the orthorectified imagery. The algorithms employ object-based image analysis techniques. The development of the classification algorithms was done using an iterative approach with successive refinement of the input variables. In effect, hundreds of independent variables were tested, as well as the order and combination of the variables. The algorithm development was based on a methodical trial and error process. Further, the development was performed by using small sections of larger images, which were necessary to accommodate the large number of iterative development in the formulation of the algorithm (for further details on the original algorithms, cf. Lato et al., 2012).
Figure 2-8: Snow avalanche deposits in the High Tatra region, Slovakia. Flow direction is from the upper right to the centre of the image. Satellite image: Copyright © DigitalGlobe/ WorldView-1.
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The desire to assess the image as a whole while evaluating neighborhoods of pixels led to the use of hierarchical object-oriented image classification tools. Hierarchical object-oriented image analysis tools are designed to use similar cognitive reasoning as the human mind, which employs segmentation and classification methodologies (e.g., Benz et al., 2004; Willhauck, 2000). Object-oriented image classification differs from pixel based classification in that the assignment of membership is done using soft classifiers that employ fuzzy logic (Benz et al., 2004). The requirement for membership does not demand complete agreement between the data and the classifier, rather a value between zero and one is calculated, where zero represents improbability of agreement and one represents complete agreement. An excellent overview of image classification techniques and the advantages of object-oriented classification has been compiled by Yan (2003). We used the software package eCognition, Version 9 (Trimble, 2014). The eCognition software has the ability to use both spatial and spectral information when conducting the image classification, making it an ideal candidate for automated detection of snow avalanches from VHR optical imagery. Unlike the algorithm developed by NR (cf. section 2.3) and previously published research, our results are based exclusively on the segmentation and classification of the information visible in the optical imagery. Information derived from digital terrain models with respect to slope and aspect are not considered, neither are vegetation masks or other masking tools/layers utilized. 2.2.3
Experiments and results
2.2.3.1 Image data The final validation of methods developed in the project was performed in accordance with NR, so that we used the same datasets for training and testing our algorithms (cf. Table 2-2). Avalanche experts at NGI made the “ground truth maps” for the training imagery, as well as for all the test images (cf. section 2.2.1 and Table 2-3). 2.2.3.2 Training and parameter tuning The focus of the work in the ASAM project was to test to which extent the original algorithm developed for QuickBird imagery (Lato et al., 2012) would perform well on other types of VHR imagery, namely imagery from the WorldView satellites. The image-processing workflow developed in eCognition for the automated identification of snow avalanche deposits relies on six different variable types, as identified in Table 2-4. The process is sequential and the order of segmentation and classification of the image is critical to the success and efficiency of the algorithm. The variables used in the algorithm act as logical gates, as illustrated in Figure 2-9. The successive gates progressively classify the image; each step eliminates regions of the image that are not representative of avalanches. The processing workflow is optimized in such a way that if a pixel fails to meet the requirements of a certain logical gate, it is excluded from all future processing steps. This ensures a time efficient processing chain while exploiting the nature of the software to compare pixel neighbourhoods within the entire image. The fuzzy logic employed by the object-oriented image classification allows the user to easily change the classifiers to accommodate irregularities in the image. This
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is important when working with images that have a variable illumination, such as regions in the sun and shade. Table 2-4: Variables used in algorithm developed in eCognition for the automated detection and mapping of snow avalanches in VHR panchromatic imagery. Table from Lato et al. (2012) Variable
Purpose
GLCM Entropy
Eliminate regions with a high level of order
GLCM Dissimilarity
Eliminate regions with minimal contrast to surrounding pixels (e.g., fresh snow)
Brightness
Eliminate dark pixels (e.g., trees, rocks)
Contrast
Eliminate sharp boundaries (snow beside a rock)
Similarity
Eliminate small groupings of pixels that are too small to represent an avalanche
Neighbour distance
Fill in small gaps surrounded by known avalanche pixels
Figure 2-9: eCognition workflow steps for the identification of avalanche signatures from VHR optical satellite imagery. Figure modified after Lato et al. (2012).
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Four of the six variables used in the process described herein are standard image processing variables: brightness, contrast, similarity, and neighbour distance. The other two variables, grey level co-occurrence matrix (GLCM) entropy and GLCM dissimilarity, use information with respect to the texture of the image as defined by Haralick et al. (1973) and Haralick (1979). The GLCM assesses how frequently different combinations of pixels, containing different levels of grey, appear within an image. The two GLCM variables used in the automated detection algorithm are dissimilarity and entropy. GLCM entropy is a measure of the randomness/ disorder and intensity of the grey value distribution. The entropy values are maximized when the distribution of the magnitude of the grey level values is low, and are small when the distribution of the grey level values is random (Partio et al., 2002). The GLCM dissimilarity variable is a measure of the contrast of an individual pixel from its neighbouring pixels. The value increases linearly, as opposed to an exponential increase like GLCM contrast; the dissimilarity value will be high if the local region has a high degree of variation (Cleve et al., 2008; Hall-Beyer, 2007). Due to the variable amount of shadowing encountered when processing satellite data, which is caused by the time of year, time of day and geographic location of the data collection, an additional processing step is added. This step, identified in Figure 2-9 as ‘GLCM Entropy normalized by brightness’, enables local shadowing/brightness to be removed and only the relative GLCM matrix to be processed. A visual example of the processing chain is illustrated in Figure 2-10. At the first step the algorithm assumes that each pixel in the image has the possibility of representing avalanche snow. As the image is processed through various steps, pixels are eliminated as being not representative of avalanche snow. The final step selects only those pixels that pass each gate, and are therefore considered to be representative of a snow avalanche deposit. A significant challenge encountered during the image processing of the WV-1 image was to work with high bit-depth images, specifically 16-bit images. Unlike previous images, generally between 8- and 14-bit pixel depths, 16-bit images store the panchromatic color information across a wider spectrum of values. This allows for more detailed information, especially at the black end of the spectrum. However, when processed using the algorithms designed for QuickBird data, the original 16-bit WV image could not be processed and had to be resampled to 8- to 14-bit pixel depths.
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Initial Data
Stage 1
Stage 2
Stage 3
Stage 4
Resultant Image
Figure 2-10: General processing steps and staged results for avalanche deposit identification in eCognition. The process illustrates the workflow as shown in Figure 2-9. The steps illustrate the progressive segmentation and classification of the image, resulting in an image with only avalanche deposits identified. Imagery: Copyright © Leica-Geosystems AG. 2.2.3.3 Evaluation on test images The imagery and analyses reported below represent images that were only used for testing, not for the training of the algorithm. The algorithm development did not involve comparison of the automatically detected avalanches to the manually mapped avalanches; this comparison was only completed as a final comparative test. As no virtual ground-truth data (such as field measurements) are available – except for the case of two avalanches in the Stjernøya image – the manual digitising completed by an avalanche expert is used as the ‘truth’ for accuracy evaluations. This is acceptable since a visual identification from imagery represents the stateof-practice method for avalanche deposit mapping. As the replication of human image recognition capability through computer-vision and object oriented image analysis is extremely complicated, a discussion of false positives versus false negatives warrants discussion. The decision to make an algorithm aggressive with the possibility of producing conservative results versus a more relaxed process, that results in liberal results must be evaluated and discussed. In some fields of application it might be sufficient to know the frequency/size distribution of snow avalanche activity within one order of magnitude, while in other application fields this would clearly not be sufficient. In general, it can be said that – depending on the needs of the end user – it is best to produce robust functional algorithms and then develop finer methodologies as more data are tested and further experience is gained.
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To quantify the performance of the algorithm on the test image, we defined the following performance metrics (cf. Congalton and Green, 1999): • Area error of commission: The fraction of area incorrectly classified as avalanche with respect to the total area classified as avalanche. • Area error of omission: The fraction of area incorrectly classified as non-avalanche with respect to the total true avalanche area. NR defined two additional metrics: • Object error of commission: The fraction of segments incorrectly classified as avalanche with respect to the total number of segments classified as avalanche. • Object error of omission: The fraction of true avalanches incorrectly classified as nonavalanche with respect to the total number of true avalanches. Since our algorithm does not allow to identify individual avalanches, but rather differs between avalanche snow pixels and non-avalanche snow pixels only, the latter two metrics were not applicable for our algorithm. Classification results from the object-based detection methodology are shown in Figure 2-11 to Figure 2-14, for the four test scenes Eikesdalsfjorden, Dalsfjorden, High Tatra, and Stjernøya respectively. Some close-up results are shown in Figure 2-15 to Figure 2-17. Performance metrics of the algorithm for these test datasets are summarized in Table 2-5.
Figure 2-11: Detected avalanches (blue areas) in the Eikesdalsfjorden image, areas erroneously classified as avalanches (purple areas) and areas where the algorithm failed to detect true avalanches (orange areas). Yellow outlines represent avalanches manually identified in the image by NGI's avalanche experts. Satellite image: Copyright © DigitalGlobe/Quickbird. Page 22
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Figure 2-12: Detected avalanches (blue areas) in the Dalsfjorden image, areas erroneously classified as avalanches (purple areas) and areas where the algorithm failed to detect true avalanches (orange areas). Yellow outlines represent avalanches manually identified in the image by NGI's avalanche experts. Satellite image: Copyright © DigitalGlobe/Quickbird.
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Figure 2-13: Detected avalanches (blue areas) in the High Tatra image, areas erroneously classified as avalanches (purple areas) and areas where the algorithm failed to detect true avalanches (orange areas). Yellow outlines represent avalanches manually identified in the image by NGI's avalanche experts and green lines identify the 12 randomly selected regions of interests. Satellite image: Copyright © DigitalGlobe/ WorldView-1.
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Figure 2-13, continued: Detected avalanches (blue areas) in the High Tatra image, areas erroneously classified as avalanches (purple areas) and areas where the algorithm failed to detect true avalanches (orange areas). Yellow outlines represent avalanches manually identified in the image by NGI's avalanche experts and green lines identify the 12 randomly selected regions of interests. Satellite image: Copyright © DigitalGlobe/WorldView-1.
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Figure 2-14: Detected avalanches (blue areas) in the Stjernøya image, areas erroneously classified as avalanches (purple areas) and areas where the algorithm failed to detect true avalanches (orange areas). Yellow outlines represent avalanches manually identified in the image by NGI's avalanche experts and green lines identify the region of interest. Satellite image: Copyright © DigitalGlobe/WorldView-1.
Figure 2-15: Close-up views of the avalanches detected in the Dalsfjorden image. Color coding as in the figures above. Yellow outlines represent avalanches manually identified in the image by NGI's avalanche experts. (The close-ups represent approximately the same as those in Figure 2-25 in section 2.3). Satellite image: Copyright © DigitalGlobe/Quickbird.
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Figure 2-16: Close-up view of the avalanches detected in the Eikesdalsfjorden image (in blue), areas erroneously classified as avalanches (purple areas) and areas where the algorithm failed to detect true avalanches (orange areas). Yellow outlines represent avalanches manually identified in the image by NGI's avalanche experts. (The close-up represents the same as the one shown in Figure 2-26 in section 2.3). Satellite image: Copyright © DigitalGlobe/Quickbird. From the overview images (Figure 2-11 to Figure 2-14), we see that the algorithm provides reasonably good results for the Eikesdalsfjorden and High Tatra images, whereas it performs poorly on the Dalsfjorden image and very poorly on the Stjernøya image. In the latter two cases it largely fails to detect the “true” avalanches, while at the same time classifying much of nonavalanche snow as avalanches. This overestimation can also be seen in the case of the Eikesdalsfjorden image, whereas it seems to be less of a problem in the High Tatra image. From the summary of performance metrics (Table 2-5), we observe that in the Eikesdalsfjorden and High Tatra images, where the results look visually good, we are failing to detect 33% and 59% of the avalanche areas, respectively. For the Dalsfjorden and Stjernøya images the algorithm fails to detect most of the avalanches (73% and 96% area error of omission, respectively). One explanation for the bad performance on the Dalsfjorden image might be that the region is heavily populated by deciduous trees, which the algorithms obviously identifies as snow avalanche material due to their high level of apparent disorder. It is unclear why the algorithm performs so unsatisfactorily on the Stjernøya image. Since NR got a comparable result for this image (cf. section 2.3), the cause might lie in the inherent characteristics of this image such as its original grey level spectrum which is distinctively lower than for the other images; as a consequence, too much information might have gotten lost during the initial image normalization (to 8 bit pixel depth).
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Table 2-5: Performance metrics of avalanche detection algorithm applied to a set of test images. Image
Area error of commission (in %)
Area error of omission (in %)
Dalsfjorden
68
73
Eikesdalsfjorden
72
33
High Tatra
22
59
Stjernøya
45
96
Figure 2-17: Close-up views of the avalanches detected in the High Tatra image (in blue), areas erroneously classified as avalanches (purple areas) and areas where the algorithm failed to detect true avalanches (orange areas). Yellow outlines represent avalanches manually identified in the image by NGI's avalanche experts. (The close-ups represent the same areas as those shown in Figure 2-27 and Figure 2-28 in section 2.3). Satellite image: Copyright © DigitalGlobe/WorldView-1.
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2.2.3.4 Conclusions The results illustrate the principal capability of hierarchical object-oriented image processing for the automated detection of snow avalanche deposits from VHR optical imagery. The ability of the eCognition software to be programmed in a manner that mimics a human cognitive process is ideal for an automated avalanche deposit detection as the characteristics that make a snow avalanche identifiable can be ‘taught’ to the program and rapidly tested. However, our results show as well that the performance of the resulting algorithm is highly sensitive to the input data (sensor type, original bit depths, etc.) and might perform well on some images, while performing weakly on other images, therewith still in need of considerable improvements before its use within an operational setting can be deemed to be realistic. The asymmetry of errors, or omission versus commission errors, is critical for the development of any image-classification algorithm. The main question hereby is if false positives (errors of commission) and false negatives (errors of omission) are equally negative to a mapping programme. Would a decision maker rather know that all avalanches have been mapped, and some might be falsely done so; or that regions might be prone to avalanches that are not identified in the database? A high number of false positives may result in a lost public space, or increased road closures; while a high number of false negatives may lead to increased accidents and a false sense of public safety. The weighting of these values can only be assigned through discussions with decision and policy makers in consultation with those responsible for the designing of the detection algorithm. These are the larger philosophical questions to be addressed in future research on this topic with decision makes and policy planners.
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2.3
NR
2.3.1
Pattern recognition of avalanches using optical data
In the ASAM project, NR has further developed the textural filter based approach for automatic avalanche detection in VHR optical satellite imagery presented by Larsen et al. (2010). The key part of this detection algorithm involves texture analysis, seeking to distinguish avalanche snow from other relevant terrain cover types, such as smooth snow, rugged snow, trees and rocks. The texture characteristics of the avalanche-affected snow are extracted by convolving the image with a set of 12 multi-scale multi-directional filters. Six of these filters are oriented in the same direction as the terrain aspect, which is estimated from a digital elevation model, and the other six filters are oriented in the vertical direction. The reason for using vertical filters was that early experiments indicated that vertical filters provided useful features for distinguishing sparse forest from avalanches (Figure 2-18). In order to reduce the dimension of the filtered image, each 4×4 window is merged to a single pixel by selecting the max value. The filtered responses for each pixel are also normalized according to Varma and Zisserman (2005), i.e.: 𝐹𝐹(𝒙𝒙) ← 𝐹𝐹(𝒙𝒙)
‖𝐹𝐹(𝒙𝒙)‖2 0.03 �� , ‖𝐹𝐹(𝒙𝒙)‖2
�log �1 +
where F(x) denotes the filtered image at pixel location x.
Figure 2-18: Comparison of filter responses from two texture classes (avalanche and sparse forest) regarding both aspect and vertical orientation. Avalanche snow is enhanced in the aspect direction, but not in the vertical direction, while sparse forest is more enhanced by filtering in the vertical direction. Satellite image: Copyright © DigitalGlobe/Quickbird.
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Prior to the filtering illumination variations are reduced by subtracting a scaled shaded relief image from the panchromatic image, and normalizing the illumination compensated image to zero mean and unit variance. In the training stage, filter responses corresponding to the same texture class are used to form so-called textons. Training data from the classes: avalanche, smooth snow, rugged snow, sparse forest, forest, stones, snow in shadow, and other, are selected from filtered images covering different regions and snow conditions. The texture samples are found by selecting regions of interest (ROIs) in the images. In the training stage, the images are convolved with the filter bank to generate filter responses. The filter responses corresponding to the same class are then clustered using a K-Medoid clustering algorithm (Varma and Zisserman, 2002), and the resulting cluster means are chosen as textons. Up to 10 textons for each class and each training image are created. Textons from different texture classes and images are combined to form the texton dictionary. Slope information is added as ancillary data to the 12 dimensional filtered images. An image texton map is then constructed by means of a pixel-based classification of the image data to one of the texton clusters (Figure 2-19). Several classifiers were evaluated, but the naïve Bayes classifier provided a good compromise between accuracy and processing speed.
Figure 2-19: The texture samples (from the original image) are filtered, and then each pixel location in the multi-dimensional filter response image is mapped to the closest texton from the texton dictionary, thus producing a texton map from which the image histogram is extracted and put into the model database as a representative for the corresponding texture class. The directional filter approach, as mentioned, produces an image texton map, where a set of the textons corresponds to “avalanche snow”. In order to detect meaningful avalanche objects we evaluated the texton distribution within a small neighbourhood. Several methods were studied, including texton histogram classification using a Support Vector Machine (SVM) classifier, texton histogram χ2-matching, and the fraction of textons corresponding to avalanches within the small neighbourhood. The latter was used in this study.
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Since the avalanches in the image are generally composed of more than one identified object, we have also developed a method for merging segments that are believed to belong to the same avalanche path. The object merging algorithm is based on the avalanche run-out path. First, objects that are located in areas with a terrain slope between 25 and 60 degrees and with a certain area extent are identified, and the corresponding avalanche run-out path are estimated using the TauDEM Toolbox (http://hydrology.usu.edu/taudem/taudem5/ downloads5.0.html). Avalanche segments that are identified to be in the same run-out path are assigned to be the same avalanche. 2.3.2
Experiments and results
2.3.2.1 Image data The final validation of methods developed in the project was performed in accordance with NGI, so that we used the same datasets for training and testing our methods. Avalanche experts at NGI made the “ground truth maps” for the training imagery, as well as for all the test images (cf. section 2.2.1). An overview of the data used for validation is given in Table 2-2. All images are from Norway (Figure 2-6), except the High Tatra image (Figure 2-7), which is from Slovakia. 2.3.2.2 Training and parameter tuning Using three different training images from the regions Hellesylt, Loen and High Tatra, we extracted a set of 49 000 pixels locations that covered the classes defined above. Using these training data we estimated the texton distribution using the K-Medoid algorithm, and determined the optimal configuration by evaluating the Receiver Operating Characteristic (ROC)-plot at the output of the merging algorithm (Figure 2-20). Clearly, the ROC plot indicates that the pixel-wise detection performance should be good.
Figure 2-20: Receiver Operating Characteristic (ROC) plot of the classes 'avalanche' and 'nonavalanche' for the training data.
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2.3.2.3 Evaluation on test images Classification results from the texture based detection methodology are shown in Figure 2-21 to Figure 2-24, for the four scenes Eikesdalsfjorden, Dalsfjorden, High Tatra, and Stjernøya respectively. Some close-up results are shown in Figure 2-25 to Figure 2-28. Corresponding illustrations of the performance of the algorithm by NGI are found in Figure 2-11 to Figure 2-17. From the overview images (Figure 2-21 to Figure 2-24), we see that the algorithm only appears to provide reasonable good results for the Dalsfjorden image (Figure 2-21). For the Eikesdalsfjorden and Stjernøya images the algorithm fails to detect the “true” avalanches (Figure 2-22 and Figure 2-24), and for the High Tatra image the algorithm overestimates the amount of avalanches (Figure 2-23).
Figure 2-21: Detected avalanches (red areas) in the Dalsfjorden image. Yellow outlines represent avalanches manually identified in the image by NGI's avalanche experts. Satellite image: Copyright © DigitalGlobe/Quickbird.
Figure 2-22: Detected avalanches (red areas) in the Eikesdalsfjorden image. Yellow outlines represent avalanches manually identified in the image by NGI's avalanche experts. Satellite image: Copyright © DigitalGlobe/Quickbird.
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Figure 2-23: Detected avalanches (red areas) in the High Tatra image. Yellow outlines represent avalanches manually identified in the image by NGI's avalanche experts and green lines identify regions of interests. Satellite image: Copyright © DigitalGlobe/WorldView-1.
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Figure 2-24: Detected avalanches (red areas) in the Stjernøya image. Yellow outlines represent avalanches manually identified in the image by NGI's avalanche experts and green lines identify the region of interest. Satellite image: Copyright © DigitalGlobe/WorldView-1.
Figure 2-25: Close-up views of the avalanches detected in the Dalsfjorden image. Yellow outlines represent avalanches manually identified in the image by NGI's avalanche experts. (The close-ups represent approximately the same areas as those in Figure 2-15 in section 2.2). Satellite image: Copyright © DigitalGlobe/Quickbird. From the close-up images (Figure 2-25 to Figure 2-28), we observe that a large portion of the delineated avalanches is missed by the algorithm, even for avalanches in the Dalsfjorden image (Figure 2-25). We particularly observe that shadow areas and smooth areas in the avalanches are missed. For the Eikesdalsfjorden image (Figure 2-26), even larger parts of the avalanches
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are missed by the algorithm, in particular when the avalanche segments are thin and less rough. For the High Tatra image, on the contrary, we observe that the algorithm does a reasonably good job (even though parts of the avalanches are missing) (e.g. Figure 2-27). At the same time, it also does a very poor job by classifying large areas incorrectly as avalanches (Figure 2-28).
Figure 2-26: Close-up view of the avalanches in the Eikesdalsfjorden image. Yellow outlines represent avalanches manually identified in the image by NGI's avalanche experts. Satellite image: Copyright © DigitalGlobe/Quickbird.
Figure 2-27: Close-up views of the avalanches detected in the High Tatra image (in red). Green outlines represent avalanches manually identified in the image by NGI's avalanche experts. Satellite image: Copyright © DigitalGlobe/WorldView-1.
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Figure 2-28: Close-up views of the avalanches detected in the High Tatra image (in red). Green outlines represent avalanches manually identified in the image by NGI's avalanche experts. Satellite image: Copyright © DigitalGlobe/WorldView-1. The metrics defined to quantify the performance of the algorithm on the test image are given above (see section 2.2.3.3, p. 21). From the summary of performance metrics (Table 2-6), we observe that even in the Dalsfjorden image, where the results look visually good, we are failing to detect 54% of the avalanche areas. However, we manage to detect 87% of the avalanches. The algorithm results are opposite for the High Tatra and Stjernøya images. In the Stjernøya image the algorithm fails to detect the avalanches (96% area error of omission), while it detected too many avalanche segments (about 90%) in the High Tatra image. Table 2-6: Performance metrics of avalanche detection algorithm applied to a set of test images. We have divided the High Tatra image into two sub-images in order to speed up the processing. Image
Area error of commission (in %)
Area error of omission (in %)
Object error of commission (in %)
Object error of omission (in %)
Dalsfjorden
20
54
55
13
Eikesdalsfj.
51
88
45
47
High Tatra - 1
61
36
89
19
High Tatra - 2
73
26
96
17
Stjernøya
30
96
50
82
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2.3.3
Conclusions
The proposed algorithm worked very well on the training data, but failed to generalize to new images. For the Dalsfjorden image, the algorithm worked reasonably well, but for the High Tatra and Stjernøya images the performance was poor. For the High Tatra image, the algorithm struggled to distinguish the rugged snow from avalanches, which resulted in a severe “overdetection” of avalanches. For the Stjernøya image the situation was opposite. The algorithm was not able to detect the avalanches present in the image. The reason for this reduced performance is difficult to identify, however, one cause could be the lack of modelling capabilities of the algorithm. To compensate for that, one could extend the number of filters in the filter bank to a much large number, preferably with filters estimated from the data.
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3
WP-2: Automatic avalanche detection and mapping from VHR SAR data
3.1
NR
3.1.1
Automatic detection of avalanches using the reference image method
The proposed method relies on the hypothesis that compacted rough snow of an avalanche has very high backscatter values (σ0) compared to homogeneous snow cover and bare ground, even if the snow is wet (Wiesmann et al., 2001). This principle was also utilized by Malnes et al. (2013). As suggested by Wiesmann et al. (2001), the proposed algorithm is also based on multidate SAR images. The underlying principle is a pixel-wise comparison of the backscatter intensities of two SAR images, an event image (the one with avalanches) and a reference image. However, the algorithm assumes that both the event image and the reference image are acquired in the same beam mode, pass direction and be in the same repeat cycle. The algorithm consists of the following steps: 1. Radiometric calibration. Calibrate the SLC images to sigma-naught (σ0). 2. Multilooking. Perform pixel averaging of the SAR images. For Radarsat-2 ultra-fine SAR images, we averaged over a 7×7 window. 3. Geocoding. Geocode both event and reference image using the Range-Doppler geocoding algorithm. The geocoded SAR images are sampled to the same grid as the DEM. For the scenes evaluated here, the pixel spacing of the DEM is 10 m. Other geocoding algorithms may also be applied. 4. Ratio/difference image calculation. Calculation of the pixel-by-pixel ratio between the sigma-naught event image and sigma-naught reference image. If these images are reported in dB, the difference image is computed. Alternatively, the difference image may be computed in the slant-range domain, and then geocoded. 5. Change detection. A pixel is marked as changed if the difference minus a local mean value, normalized with respect to the local standard deviation, is larger than a given threshold. The local mean and standard deviation are computed within a sliding window of 101×101 pixels. The threshold value was determined from a set of avalanches manually delineated in the SAR images. In order to integrate spatial context (i.e. the avalanches are blob shaped), we apply a Markov random filed (MRF) in order to smooth the boundaries. First we construct two auxiliary Gaussian probability density distributions, one centered at threshold value plus 1 and the other centered at threshold value minus 1, both with standard deviation equal to two, and compute the logprobability values for each pixel. This is used as input to the iterative conditional mode algorithm in order to perform the MRF filtering. 6. Feature extraction. Extract features from potential avalanche objects and include: - average difference (in dB) between event and reference image, - average slope, - maximum slope, - average upslope contributing area, - object area, and - average contrast between object and background in the reference image.
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7. Object classification. Classify the potential objects into 'avalanche'/'non-avalanche' using a random forest classifier (Breiman, 2001). 3.1.2
Experiments and results
We now evaluate the proposed avalanche detection method to a set of Radarsat-2 SAR images. The acquisition mode is ultra-fine, with an SLC pixel spacing of 1.3×2.1m, and the polarization is HH. The SAR datasets applied are all from Troms county in Northern Norway, and were acquired in 2014 (Table 3-1). We also provide a demonstration on a pair of Sentinel-1A images. Table 3-1: The SAR datasets used in this study. All SAR data were acquired in 2014 by the Radarsat-2 sensor using the ultra-fine mode and HH polarization. Location
Date
Product
Kvaløya
Mars 4, 2014
U19
Lavangsdalen
Mars 23, 2014
U78
Storfjord
Mars 16, 2014
U74
Lakselvbugt
Mars 7, 2014
U22
Tromsdalstinden
April 1, 2014
U09
Vikøyri
Mars 9, 2014
U12
3.1.2.1 Training the algorithm The random forest classifier was trained using feature vectors corresponding to 4972 objects detected by the change detection algorithm. Objects that overlapped with avalanche polygons identified by Norut, and the features corresponding to the Norut-polygons, were assigned to the avalanche class (in total 393 objects), the remaining objects were labelled as false. The random forest classifier was trained using the randomForest toolbox in R, with the following parameters: mtry = 2, sampsize = 315 for both the avalanche class and non-avalanche class, nTree = 5001. The cutoff parameter was equal to 0.71 and was selected such that the number of out-of-bag misclassification was equal for both classes. The out-of-bag confusion matrix ( Table 3-2) indicates that that automatic algorithm agreed identified 59% of the segments identified by the human observer, whereas 3.5% of the objects not identified as avalanche by the human observer, were classified as avalanche by the automatic algorithm. Table 3-2: Out-of-bag confusion matrix. Predicted class
Actual class
Non-avalanche
Non-avalanche
Avalanche
Classification error
4420
159
0.04
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Avalanche
160
233
0.41
3.1.2.2 Results The detected avalanche objects are visualized as red polygons overlaid on the SAR images (cf. Feil! Fant ikke referansekilden. to Figure 3-5). The results are compared to manual readings performed by Norut (white polylines). The SAR images are displayed using a false colour composition, where the red, green and blue channels correspond to the reference image, event image and difference image (the one we use as a threshold to find avalanche objects), respectively. Note that the avalanche objects by Norut are not delineated using the same geocoded images as the automatic algorithm is applied to. For some cases we, therefore, observe a geometric mismatch between manual delineation and the image content. The algorithm is able to identify avalanche objects if the objects are sufficiently bright compared to its surroundings and to the references image, and if the DEM features are suitable for avalanche conditions (Feil! Fant ikke referansekilden.a and Figure 3-2). However, it should be noted that the shape of the identified avalanche may deviate from the shape manually delineated. If an avalanche does not appear as bright “blobs” compared to the reference image and its surroundings, the algorithm struggles to detect the avalanches (Figure 3-3 and Figure 3-4). For many of the manually detected avalanches an untrained human observer would not be able to identify them using the SAR images only, since they appear very weak (Figure 3-3) or look like radar layover (Figure 3-4, two upper images). However, sometimes missed detections by the automatic algorithm represent obvious errors (Figure 3-1b). The automatic algorithm detects avalanches not identified by the human observer as well (Figure 3-5). Such objects typically look like a bright “blob”, and many of the corresponding DEM features support the detection. The detection results are summarized in Table 3-3, where we see that the automatic algorithm disagrees to some extent with the manual identifications. However, many of these disagreements correspond to the cases where the blobs are weak.
Figure 3-1a: Objects identified as avalanches both manually and automatically in the Kvaløya 2014 image. Red polygons correspond to automatic detection, whereas white polylines correspond to manually detected avalanches. Radarsat-2 data were acquired under the Norwegian Rardarsat-2 agreement. Copyright © MDA/NSC/KSAT, 2014.
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Figure 3-1a, continued: Objects identified as avalanches both manually and automatically in the Kvaløya 2014 image. Red polygons correspond to automatic detection, whereas white polylines correspond to manually detected avalanches. Radarsat-2 data were acquired under the Norwegian Rardarsat-2 agreement. Copyright © MDA/NSC/KSAT, 2014.
Figure 3-1b: Objects identified as avalanches in the Kvaløya 2014 image both manually and automatically, and objects identified by manual inspection, but not identified by the automatic algorithm. Red polygons correspond to automatic detection, whereas white polylines correspond to manually detected avalanches. Radarsat-2 data were acquired under the Norwegian Rardarsat-2 agreement. Copyright © MDA/NSC/KSAT, 2014.
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Figure 3-2: Objects identified as avalanches both manually and automatically in the Lavangsdalen 2014 image. Red polygons correspond to automatic detection, whereas white polylines correspond to manually detected avalanches. Radarsat-2 data were acquired under the Norwegian Rardarsat-2 agreement. Copyright © MDA/NSC/KSAT, 2014.
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Figure 3-3: Objects identified as avalanches in the Kvaløya 2014 image by manual inspection, but not identified by the automatic algorithm. White polylines correspond to manually detected avalanches. Radarsat-2 data were acquired under the Norwegian Rardarsat-2 agreement. Copyright © MDA/NSC/KSAT, 2014.
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Figure 3-4: Objects identified as avalanches in the Lanvangsdalen 2014 image by manual inspection, but not identified by the automatic algorithm. White polylines correspond to manually detected avalanches. Radarsat-2 data were acquired under the Norwegian Rardarsat-2 agreement. Copyright © MDA/NSC/KSAT, 2014.
Figure 3-5: Objects identified as avalanches by the automatic algorithm in the Kvaløya 2014 image (left) and Lavangsdalen 2014 image (right) that were not identified manually. Radarsat-2 data were acquired under the Norwegian Rardarsat-2 agreement. Copyright © MDA/NSC/KSAT, 2014.
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Table 3-3: Comparison of the manually identified avalanches with the avalanches identified by the automatic algorithm. All imagery from 2014. Scene
No. of manually identified avalanches
No. of automatic detected avalanches
Error of omission (manually vs. automatic)
Error of commission (manually vs. automatic)
Lavangsdalen
104
65
37%
32%
Kvaløya
45
25
44%
39%
Storfjord
41
20
51%
60%
Lakselvbugt
22
9
59%
53%
We also tested NR’s automatic avalanche detection algorithm on a pair of Sentinel-1A interferometric wide swath (IW) mode images with VV polarization covering a large part of Troms in Norway (Figure 3-6). The reference image was from December 27, 2014 and the event image was from January 8, 2015. The resolution of the geocoded image was 20 m. Detected avalanches are illustrated as red polygons, and as for Radarsat-2, potential avalanche objects appear as bright blue “blobs”. The automatic algorithm did not experience problems when processing the Sentinel-1A images, indicating that the framework of NR’s automatic algorithm is suitable to process Sentinel-1A data on a larger scale. However, the avalanche detection was trained for Radarsat-2 Ultrafine images, and when applied to the Sentinel-1A images, the result was a too large fraction of false detections (Figure 3-6). It has to be noted, however, that no ground truth or manual identifications of avalanches were available.
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Figure 3-6: Test of the algorithm on a pair of Sentinel-1A images covering a large part of Troms, Norway. Red polygons show the avalanched detected by the automatic algorithm. 3.1.3
Conclusions
The automatic algorithm for detecting avalanches in SAR images is able to detect avalanches if they appear as bright blobs in the difference image. Some blobs that are identified by the human observer are missed by the algorithm, but they have often a weak contrast with respect to the background. Some blobs that were not identified by the human observer are detected by the algorithm. The status of these blobs is unclear since no field data is available. There are also some blobs that were identified by the human observer which raise the question if our hypothesis that avalanches appear as bright blobs is true. From the results presented here we conclude that an operational automatic algorithm is feasible provided enough training data is available.
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3.2
Norut
3.2.1
Electromagnetic backscatter model of snow in avalanche debris
Avalanche debris detection is possible due to increased backscatter from the debris itself, with a sharp backscatter contrast to the surrounding, undisturbed snowpack. Due to lacking theoretical models, quantification of the backscatter contributions from snow in avalanche debris is not possible. Current backscatter models only consider undisturbed snow, both wet and dry. The theory dates back to Ulaby et al. (1986), however, developments have more recently been made using a physical based numerical model (Dense Media Radiative transfer theory) (Tsang et al., 2000). In Figure 3-7 we present backscatter modelling of typical dry avalanche debris-snow with a density of 500kg/m3 and a snow grain radius of 0.7 mm. The Radiative transfer model shows that with increasing snow depth, as observable in avalanche debris, the radar backscatter increases. Besides increased snow depth, which increases volume scattering, we assume that surface roughness is the largest contributor to increased backscatter from avalanche debris.
Figure 3-7: Radar backscatter (dB) as a function of snow height (m) in dry snow conditions with a snow density of 500 kg/m3, a snow temperature of -1°C and a snow grain median radius of 0.7 mm. The soil backscatter of -18 dB is derived from the summer SAR scene.
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In Figure 3-8 we present a qualitative model of backscatter from dry and wet avalanche debris. While it is clear that air-surface backscatter from a rough surface is dominant in wet snow, we assume this to be true also in dry snow. Due to other scattering mechanisms contributing, the increase of backscatter from dry avalanche debris is larger than from wet avalanche debris. A continuation of this work will be to try to understand whether the model can be inverted in order to get an estimate of the snow depth/SWE in the avalanche deposition area.
Figure 3-8: Simplified models of backscatter contributions from snow parameters to the total backscatter for dry and wet snow, both undisturbed and in avalanche debris. Figure from Eckerstorfer and Malnes (submitted). 3.2.2
Avalanche debris detection using Radarsat-2 data
Norut has together with NGI acquired over 50 Radarsat-2 (RS-2) scenes for the seasons 2012/2013, 2013/2014 and 2014/2015 (Table 3-4). A majority of the scenes have been ordered in the Ultrafine-mode and been geocoded/multi-looked to 3x3 m spatial pixel spacing, since this gives a reasonable trade-off between resolution and SAR speckle. A minority of the scenes is in ScanSAR Narrow (SCNA), ScanSAR Wide (SCWA) mode respectively, covering some of the ASAM sites prior to observed avalanches (highlighted in grey in Table 3-4).
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Norut and NGI have worked with manual and automatic detection of avalanche debris in SAR data and their validation through field reconnaissance and optical remote sensing data. Our goal of this work was to provide a proof-of-concept that avalanche debris detection is possible with different SAR sensors. We were further interested in the understanding of the electromagnetic backscatter model from snow in avalanche debris, and to which degree SAR avalanche debris detection can assist in creating a complete avalanche activity database from a given region through an entire winter. Table 3-4: Overview over acquired SAR scenes. No.
Region
Country
Mission
Sensor
Product
1
Stryn-Kyrkjenibba
Norway
Radarsat-2
SAR
RS2_FQ20
2
Stryn
Norway
Radarsat-2
SAR
SCNA, SCWA
25
1.1-24.3.2013
3
Tromdalen/Steinfjord Norway
Radarsat-2
SAR
RS2_U24
3
2013-04-05
4
Tromdalen/Steinfjord Norway
Radarsat-2
SAR
RS2_U24
3
2013-08-26
5
Tromdalen/Steinfjord Norway
Radarsat-2
SAR
SCNA, SCWA
25
1.1-24.3.2013
6
Kattfjordeidet
Norway
Radarsat-2
SAR
RS2_U24
3
2013-04-09
7
Kattfjordet
Norway
Radarsat-2
SAR
SCNA, SCWA
25
1.1-31.3.2013
8
Kattfjordet
Norway
Radarsat-2
SAR
RS2_U16
2
2013-08-31
9
Holandsfjorden
Norway
Radarsat-2
SAR
RS2_U17
10
Ringvassøy
Norway
Radarsat-2
SAR
SCNA
25
7.4.-17.4.2013
11
Tamokdalen, Troms
Norway
Radarsat-2
SAR
RS2_U7
3
2013-12-09
12
Tromdalen/Steinfjord Norway
Radarsat-2
SAR
RS2_U19
3
2013-12-15
13
Kattfjordeidet
Norway
Radarsat-2
SAR
RS2_U19
3
2014-03-04
14
Kattfjordeidet
Norway
Radarsat-2
SAR
RS2_U5
3
2014-03-06
15
Lakselvbugt-Ulsfjord Norway
Radarsat-2
SAR
RS2_U22
3
2014-03-07
16
Vikøyri
Norway
Radarsat-2
SAR
RS2_U12
3
2014-03-09
17
Storfjord
Norway
Radarsat-2
SAR
RS2_U8
3
2014-03-15
18
Storfjord
Norway
Radarsat-2
SAR
RS2_U74
3
2014-03-16
19
Lavangsd.-Breivike.
Norway
Radarsat-2
SAR
RS2_U78
3
2014-03-23
20
Lavangsd.-Kvaløya
Norway
Radarsat-2
SAR
RS2_U12
3
2014-03-25
21
Kattfjordeidet
Norway
Radarsat-2
SAR
RS2_U19
3
2014-03-28
22
Sorbmegaisa
Norway
Radarsat-2
SAR
RS2_U78
3
2014-03-30
23
Tromsdalstinden
Norway
Radarsat-2
SAR
3
2014-04-01
28
Island
Island
Radarsat-2
SAR
3
2014-12-18
29
Island
Island
Radarsat-2
SAR
3
2014-12-18
30
Senja
Norway
Radarsat-2
SAR
3
2015-01-01
31
Kvaløya. Troms
Norway
Radarsat-2
SAR
3
2015-01-02
32
Lavangsdalen
Norway
Radarsat-2
SAR
3
2015-01-03
33
Fardalen; Longyearb. Svalbard
Radarsat-2
SAR
3
2015-01-30
34
Sogndal
Norway
Radarsat-2
SAR
3
2015-02-05
35
Gullbrå, Hordaland
Norway
Radarsat-2
SAR
3
2015-02-12
36
Oppdal
Norway
Radarsat-2
SAR
3
2015-02-11
U74
Resolution (m) 10-20
Acquisition 2013-03-24
2013-04-15
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Table 3-4, continued. No.
Region
Country
Mission
Sensor
37
Oppdal
Norway
Radarsat-2
SAR
38
Fastdalstinden
Norway
Radarsat-2
SAR
39
Røyelmyran
Norway
Radarsat-2
40
Rondvassbu
Norway
Radarsat-2
41
Ryggfonn
Norway
42
Ryggfonn
43
Vikafjell
Product
Resolution (m)
Acquisition
3
2015-02-12
U77
3
2015-02-22
SAR
U72
3
2015-02-15
SAR
U1
3
2015-04-07
Radarsat-2
SAR
U71
3
2015-04-20
Norway
Radarsat-2
SAR
U16
3
2015-04-21
Norway
Radarsat-2
SAR
U75
3
2015-04-25
3.2.2.1 Single backscatter image detection We used a single backscatter image detection method for Radarsat-2 Ultrafine (RS-2U) images acquired after the report of large avalanches in Troms in spring 2013. Manual detection of the furthest runout outline of these large avalanches was straight forward due to a sharp backscatter contrast (Figure 3-9a). However, delineation of the flanks was not possible. This is due to other strong scatters on steep mountain slopes and the missing avalanche debris, which is the only part of the avalanche that we can detect in SAR images. To improve the manual image classifications, we compiled layover masks, applied simple image enhancement techniques and corrected for the incidence angle. These techniques were critical for the application of automatic detection algorithms (explained further down). For field validation, Norut acquired airborne optical imagery over the avalanche in Kattfjordeidet, Tromsø with an optical camera mounted on an Unmanned Aerial Vehicle (UAV). The resulting high-resolution orthophoto (Figure 3-9c) allowed for validation of the SAR interpreted avalanche outlines (Figure 3-9b), with the help of ancillary data provided by NGI and by the police of the municipality of Tromsø. The two other RS-2U images from spring 2013 were acquired from Tromdalen and Steinfjord on the Senja peninsula where two large avalanches occurred. Debris of both avalanches were detectable in the SAR images and were validated by in situ observations from NGI's staff members that were on emergency duty at these events. These results were published in an ISSW proceedings paper (Malnes et al., 2013). Enlarging the RS-2U dataset with new acquisitions during winter 2013/2014 allowed for a more detailed study of both SAR avalanche debris detection in RS-2U images of different modes, as well as a deeper understanding of the backscatter contributions from avalanche debris. During an avalanche cycle in late February/early March 2014, we acquired 11 RS-2U, 2 SCWA and 2 SCNA images from areas in Troms where avalanche activity was reported (Figure 3-10). SCNW and SCNA data was included in order to investigate whether Sentinel-1A data will be suitable for avalanche debris detection. We used three detection methods, namely: 1) Single image detection, 2) Dual pole detection (only in SCNA and SCWA data) and 3) Change detection. We further used a combination of multi-sensor detection (RS-2U, SCNA, SCWA) and multi-temporal detection (covering similar areas with different sensors and sensor geometries) to manually detect avalanche debris-like features. We then ran a topographic GIS model that eliminated areas where avalanches cannot occur. Finally this left us with avalanche debris that we validated using optical Landsat-8 images, field photographs and aerial photographs (to depict strong scatters such as debris flow tracks).
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Figure 3-9: a) RS-2U backscatter draped onto a digital terrain model (DEM). The backscatter values in decibels (dB) range between -19 and +3 dB. Blue line depicts avalanche outline interpreted from SAR data. Black, dashed line depicts avalanche outline interpreted from various optical images; b) zoom-in of a); c) Orthophoto mosaic derived from a UAV reconnaissance flight over the avalanche. Figure from Malnes et al. (2013). Radarsat-2 data were acquired under the Norwegian Rardarsat-2 agreement. Copyright © MDA/NSC/KSAT, 2013.
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Figure 3-10: Multi-temporal and multi-sensor analysis of avalanche activity in the valley Lavangsdalen. a) RS-2 SCNA image from 12 February 2014; b) RS-2 SCNA image from 19 February 2014; c) RS-2 SCWA image from 2 March 2014; d) LS-8 image from March 18, 2014, in the panchromatic channel; e) RS-2U image from 23 March 2014, covering only the eastern part of the valley; f) RS-2U image from 25 March 2014, covering only the northern part of the valley. The photograph shows the avalanche encircled in blue; g) RS-2 SCWA image from 26 March 2014; h) RS-2U image from 1 April 2014, covering only the northern part of the valley. The photographs of different avalanches were taken 27 April 2014. Figure from Eckerstorfer and Malnes (submitted). Radarsat-2 data were acquired under the Norwegian Rardarsat-2 agreement. Copyright © MDA/NSC/KSAT, 2014.
Figure 3-11: Avalanche debris detection processing chain. a) SAR processing of Level 1B Radarsat-2 products and geocoding into SAR backscatter images. b) Avalanche debris-like feature detection using 1) Single image detection, 2) Dual pole detection algorithms. Manual interpretation of the results by a) Multi-sensor detection, b) Multi-temporal detection and c) Combination of both. Examples of features that can be misinterpretated as avalanche debris-like features. c) Implementation of a topographic GIS model to distinguish avalanche terrain from non-avalanche terrain. d) Validation of detected avalanche debris with field data and auxiliary remote sensing data. Figure from Eckerstorfer and Malnes (submitted).
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In total, we manually detected 546 avalanche debris-like features, of which 467 were classified as avalanche debris. 57 features were counted multiple times (multi-temporal detection), 103 features were detected using different sensors (multi-sensor detection) and 15 features were eliminated by the topographic GIS model. Using the change detection method with reference images from September 2014 (explained in detail below, in section 3.2.1.2) we were able to achieve high accuracy in our manual detections. The topographic map (Figure 3-12) shows the RS-2U ground swaths and the detected avalanche debris.
Figure 3-12: Topographic map with the avalanche forecasting regions visualized. The coloured frames depict the swath of RS-2U images, collected during March 2014. The accordingly coloured tongue-shaped features are the detected avalanche debris, counting 546. Both the RS2 SCWA and SCNA and the LS-8 images have a swath covering the entire map area. Figure from Eckerstorfer and Malnes (submitted). Background DEM: Copyright © Statens Kartverk. 3.2.2.2 Change detection method The change detection method makes use of the backscatter difference between a snow free reference image without avalanche activity and an image with avalanche activity. The reference image needs to have the same imaging geometry. Then, avalanche debris is detectable due to increased, relative backscatter, with sharp contrast to the surrounding, undisturbed snowpack. There is high confidence in correct detection as natural features would not exhibit a change in backscatter between two images. We compiled both RGB-composites, where the reference image is set in the R and B channel and the avalanche image in the G channel, enhancing avalanche debris as green features, as well as relative backscatter images (Figure 3-13). Figure 3-13 shows a comparison of avalanche debris detected in single backscatter images (left panel) and change detection images with relative backscatter (right panel). These are just examples from three RS-2U scenes showing avalanche activity in March 2014 in Troms.
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Almost all initially detected avalanche debris could be confirmed by the change detection method (between 96 and 98% correct detection), however, the change detection images allowed for detection of five more features in the image from 7 March 2014.
Figure 3-13: Comparison of avalanche debris-like feature detection in single backscatter images and change detection images. Exemplified are three areas within three different RS-2U images from a) 4 March 2014 with reference image from 12 September 2014, b) 16 march 2014 with reference image from 24 September 2014, c) 7 March 2014 with reference image from 15 September 2014. Figure from Eckerstorfer and Malnes (submitted). Radarsat-2 data were acquired under the Norwegian Rardarsat-2 agreement. Copyright © MDA/NSC/KSAT, 2014. Change detection images allow for the calculation of relative backscatter frequency distribution in avalanche debris, compared to the surrounding, undisturbed snowpack (Figure 3-14). The median relative backscatter from avalanche debris is +3.7 dB, as opposed to the median relative backscatter of -1.1 dB from the surrounding areas with a 500 m buffer. Both populations are not correlated, however, intersect in a broad relative backscatter range of +17 dB, with high probability of false detection in the range of -2 to +3 dB. The frequency distribution suggests, that a threshold of 0 dB would be best suited to distinguish avalanche debris.
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Figure 3-14: Frequency distribution of relative backscatter in avalanche debris and the surrounding, undisturbed snowpack with a 500 m buffer. Input data are the 3 March 2014 images with their associated September 2014 reference images that are exemplified in Figure 3-13. Figure from Eckerstorfer and Malnes (submitted).
3.2.2.3 Conclusions We could proof that it is possible to collect spatially upscaled data of avalanche activity from a single avalanche cycle in a given region. Satellite-borne SAR has the advantage of unbiased, spatially upscaled, high-resolution monitoring during all weather and light conditions. The limiting factors at this stage are the small ground swath, uncertain acquisition, and the high acquisition costs for RS-2 images. Furthermore, manual detection, done by an avalanche expert, however straight forward in most cases, is not possible to quantify in terms of omission and commission errors. Lastly, for operational use, there is a need for an automatic avalanche debris detection, which is highly dependent on a good understanding of the electromagnetic backscatter model from snow in avalanche debris. Nevertheless, due to the very high resolution of RS-2U data (provided for example also by TerraSAR-X and CosmoSkymed), detailed avalanche debris detection in limited areas (20 x 20 km) can be achieved with high accuracy. Sensors with lower resolution will always suffer from the inability to detect small to medium sized avalanches.
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3.2.3
Avalanche debris detection using Sentinel-1A data
3.2.3.1 Manual avalanche debris detection using Sentinel-1A Sentinel-1A and soon B, might be the working horses for operational avalanche debris detection. S-1A provides high spatial resolution (20 x 20 m), large ground swath (250 x 250 km), frequent repeat pass (12 days) and free acquisition. These were all promising prospects, and in a proof-of-concept study, newly published in The Cryosphere Discussion, we could for the first time report the detection of avalanche debris in S-1A data (Malnes et al., 2015). We acquired S-1A images in the high resolution IW mode throughout the winter 2014/2015 from many locations, both in Northern Norway and worldwide. Figure 3-15 shows an example from the valley Lavangsdalen in Troms, where the road E8 gives good field access. In a S-1A image from 27 December 2014, no avalanche activity is visible (Figure 3-15b). Using www.skrednett.no, the two elongated slope features with high backscatter are defined as debris flow tracks. During the first days of 2015, an extreme wet avalanche cycle occurred and avalanche activity was mapped in a RS-2U image from 3 January 2015 (Figure 3-15b). All these avalanche debris were then also visible in a S-1A RGB change detection image from 6 January 2015 (Figure 3-15c) (with reference image 27 December 2014), and in a single backscatter image from 8 January 2015 (Figure 3-15). On 8 January 2015 we conducted a field trip where we mapped the outline of two avalanches with a GPS and took photographs of all avalanches (Figure 3-16), validating the SAR interpretations. For the first time, we could show that medium to large avalanches were detectable using S-1A data. By comparing manual detections in S-1A data and RS-2U data, the limitations of the lower resolution S-1A data became prominent. We detected 102 avalanche debris in the RS-2U data, however, only 55 avalanches in the S-1A data covering the RS-2U ground swath were detected. The reason for this difference can partly be attributed to 1) more favorable geometry for the RS-2U image masking out less avalanche debris due to layover/shadow, and 2) differences in spatial resolution 3 m vs. 20 m, thus allowing RS-2U to detect smaller avalanche debris. We also compared the manual detected avalanches from RS-2U data with the manually detected avalanches from Sentinel-1A. Within the coverage of the RS-2U scene we were able to detect 102 avalanches. In the same coverage, we only observed 55 avalanches in the S-1A scene.
Figure 3-15: Multi-sensor and multi-temporal series of SAR images from Lavangsdalen valley, containing ascending path S-1A and RS-2U images. Collected GPS tracks of two avalanche debris are visualized with red lines. The numbers relate to field validated avalanche debris, presented in Figure 3-16. Figure from Malnes et al. (2015). Sentinel-1A data: Copyright © ESA.
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Figure 3-16: Fieldwork validation of detected avalanche debris in the valley Lavangsdalen, 8 January 2015. Avalanche debris was still clearly visible then, as the period since New Year was dry and cold. The numbers correspond to the detected avalanche debris in Figure 3-15. Figure from Malnes et al. (2015). 3.2.3.2 Towards a complete avalanche activity record The advantage of the S-1A image was, however, the significantly larger ground swath of 250 x 250 km that covered 9 out of 10 avalanche forecasting regions at once (Figure 3-17). It provided a spatial overview of the physical state of snow within the ground swath, with wet snow (purple color) in the maritime coastal zones and dry snow in the continental interior. In the S-1A image, we manually detected 489 features that are likely avalanche debris, as they appear as features with increased backscatter relative to the reference scene. The example in Figure 3-17 is only a snapshot of the avalanche activity in Troms in the winter 2014/2015. S-1A data were provided every 12 days, allowing for the creation of a complete avalanche activity dataset throughout the winter. Figure 3-18 shows 8 repeat pass RGB composites for the period December 2015 to April 2015. The wet avalanche cycle from beginning of January 2015 is visible in the RGB composite from 8 January 2014. The relatively dry period from January to the beginning of April produced only minor avalanche activity. Only towards the end of April 2015, wet avalanche cycles occurred again, which we were able to detect. In the case of Tamokdalen, four S-1A image geometries were available, resulting in data in 4 out of 12 days per repeat pass. The good availability of S-1A data and the free acquisition allowed for trying to detect some of the catastrophic avalanches that occurred in Norway in winter 2014/2015. Table 3-5 provides a summary of these analyses.
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Figure 3-17: S-1A RGB image composite, where two S-1A images with similar ascending path are merged. The reference image is from 13 December 2014, visualized in the green channel, the image from 6 January 2015 is visualized in the red and blue channel. Avalanche debris appears as green, tongues-shaped features; the furthest runout location is marked with a green point. The three rectangles are areas of interest. Figure from Malnes et al. (2015). Background DEM: Copyright © Statens Kartverk; Sentinel-1A data: Copyright © ESA.
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Figure 3-18: RGB change detection composites from the avalanche forecasting region Tamokdalen, Troms, for the winter 2014/2015. S-1A IW mode data with satellite track number 160 were used to compile change detection images with a reference image 12 days prior. The yellow circles highly noticeable avalanche activity. Sentinel-1A data: Copyright © ESA. Table 3-5: Summary of S-1A avalanche detections validated by media stories. Date S-1A
Site and date
Source of validation
Avalanche in S-1A
6.1.2015
Kaperdalen, Senja
Statens Vegvesen and nordlys.no, 7.1.2015 (road closure)
Yes, clear
24.1.2015
Fardalen, Svalbard
nrk.no, 24.1.2015 (snow scooter driver killed)
Yes, clear
12.2.2015
Storhornet, Oppdal
nrk.no, 13.2.2015 (36 reindeers killed by avalanche)
Yes, clear
12.2.2015
Oppdal Alpine centre
nrk.no, 11.2.2015 (major avalanche in ski resort).
Yes, vague
13.02.2015
Lavangsdalen, Tromsø
itromso.no, 13.02.2015 (three cars trapped by avalanche)
Yes, clear
17.02.2015
Fastdalstind, Lyngen
framtidinord.no, 17.2.2015 (German backcountry skier killed)
Yes, clear
27.2./4.4.2015
Gullbrå, Hordaland
Avisa Hordaland, 18.3.2015 (village isolated due to road closures)
Yes, clear
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3.2.3.3 Avalanche debris detection using S-1A EW mode Unfortunately, Svalbard is only covered by S-1A EW mode, with an image resolution of 40 x 40 m. This limits avalanche debris detection substantially. We have shown that large avalanche debris can be detected in RS-2 SCNA and SCWA mode images, however, only after initial detection in RS-2U data. We had some success detecting large wet avalanches that occurred in March 2015 after extreme low pressure activity. Some of these large avalanches were detectable in S-1A EW mode image as well and could be validated using www.regobs.no observations. There are currently no plans from ESA to provide IW mode images over terrestrial Svalbard, even though Norut has recommended that at least one repeat pass in ascending and descending orbit should be in IW mode. This would be valuable not only for avalanche activity monitoring, but also for glacier and interferometric monitoring. 3.2.3.4 Automatic detection of avalanche debris S-1A data A need for automated detection of avalanche debris for routine mapping and monitoring of avalanche activity clearly emerged with the availability of S-1A data. Method Each S-1A image was broken down into smaller windows of 50 x 50 pixels or 1 x 1 km, to ensure efficient computation. Then change detection images were compiled, with layover and radar shadow areas and water bodies masked out (Figure 3-19). Finally a topographic model was applied to eliminate areas where avalanches cannot occur, based on slope angle (Figure 3-19). Once the masking step has been carried out, we determined the percentage of pixels remaining in the window whose backscatter difference value exceeded a specified threshold value. In the current version of the algorithm, we required that a minimum of 1% of the total number of pixels in the window remained after the masking process must also have a difference value, which exceeded +7 dB. We then applied a median filter of window size 3x3 pixels to the difference image and ran an object classification algorithm on the image. The difference images obtained from using VV and VH polarizations were used as inputs to the features extraction step and both features objects were then combined and classified using a k-means clustering procedure. In this algorithm we used two classes since we expected a pixel to fall either into the ‘avalanche’ category or ‘not avalanche’. The algorithm was written in such a way that "class 1" was associated with the highest data values. That is to say pixels belonging to the class with high backscatter difference were assigned a value of 1, while other pixels were assigned "class 2" (Figure 3-19). The procedure was repeated for every window in the S-1A image until the entire image has been screened and classified. We recorded all pixels, which were assigned class 1 and used the result to create a final map of avalanche pixel occurrence. A final median filter of window size five was applied to the avalanche map to remove remaining noise/specklelike features, which were unlikely to correspond to avalanche debris (Figure 3-19).
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Figure 3-19: The image processing procedure carried out by the automatic detection algorithm First results and validation In Figure 3-20 we show the results of applying the automatic detection algorithm to one of the selected areas, Lavangsdalen valley. In Figure 3-20b the detected avalanche debris is illustrated in red on an elevation map of the area. Radar layover is indicated by black areas (note that some black areas correspond to low-lying regions due to the color scale). Here we have used a Sentinel-1A image which was obtained 6 January 2015 with a reference image from 25 December 2014 to form the RGB image shown in Figure 3-20a.
Figure 3-20: a) RGB composite with avalanche debris in green. The RGB-image is shown for co-polarization mode; b) The automatic avalanche detection map for Lavangsdalen, using a Sentinel-1A image obtained on 6 January 2015 and 25 December 2014. Sentinel-1A data: Copyright © ESA. In order to validate the automatic detection results we have compared the detected regions with a manually detected avalanche debris, which was validated by field reconnaissance (Figure 3-15
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and Figure 3-16). In Figure 3-21 we show, in the right hand panel, the difference between the manually and automatically detected avalanche debris, and the corresponding RGB images in the left-hand panels. Avalanche debris pixels are assigned a value of 1, i.e., a difference of -1 indicates a pixel which is detected automatically but not manually, and a difference of 1 indicates an avalanche pixel which is detected manually but not automatically. For avalanche pixels, which are detected by both the automatic and manual detection methods, the difference is equal to 0. We have reassigned a value of 2 to these pixels in order to distinguish between correctly-detected avalanche pixels and correctly-detected non-avalanche pixels which would have had a value of 0 in both cases.
Figure 3-21: The difference between the manual and automatic avalanche detection maps b) and corresponding RGB-image a) for Lavangsdalen. The difference is coded such that: -1=false detection (red); 0=no avalanche detection (lilac); +1=missed detection (light green); 2=correct detection (white). Sentinel-1A data: Copyright © ESA. We have used the difference map to calculate the percentage of pixels which are correctly detected, not detected (error of omission) or incorrectly detected (error of commission). The number of pixels in each of the three cases is normalized to the total number of manually detected avalanche pixels. Table 3-6 shows the respective percentages for Lavangsdalen, as well as Tamokdalen. We carried out similar analysis for Tamokdalen as for Lavangadalen, however we only show the Lavangsdalen results in Figure 3-20 and Figure 3-21. For both areas the omission error can be similarly large as the number of correct detections, while the potential number of misclassified pixels is smaller but not insignificant. In both areas studied, the total manually detected avalanche debris area is equal to the number of avalanche pixels multiplied by the pixel area (400 m2) i.e. 1.6 x 106 m2. The total area covered by the RGB images is approximately 3.4 x 108 m2. Table 3-6: Detection statistics for Lavangsdalen and Tamokdalen Area
Correct detection (%)
Omission error (%)
Commission error (%)
Total number of avalanche pixels (man.)
Lavangsdalen
47.1
52.9
9.7
4032
Tamokdalen
57.1
42.9
46.1
4056
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3.2.3.5 Conclusion The use of Sentinel-1A data in avalanche debris detection is already the state-of-the-art in satellite-borne SAR detection. We think that the frequent, free availability of large swath data outweighs the lower resolution compared to RS-2U, and thus limited ability to detect small to medium sized avalanches. By showing examples of avalanche activity over a large area from one extreme avalanche cycle, as well as a complete avalanche activity database for a smaller area, and the recent developments in automatic avalanche debris detection, we think that we are close to operationalizing our scientific products. Avalanche forecasting services that cover large areas and have limited manpower and / or meteorological station network would definitely benefit from the use of S-1A data.
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4
WP-3: Automatic snow variable retrieval
4.1
NVE
NVE assisted NR in scripting for retrieving relevant data (such as occurrence of wet snow, surface hoar) from regObs. Selected observers were instructed in how to record data for ASAM during the winter 2013-2014, and Norut presented the data requirements for ASAM at NVE's annual observer seminar in Tromsø December, 5th to 6th, 2013, where about 70 observers participated. NVE contributed also to the definition of near real-time monitoring sites and the calibration/validation (cal/val) sites. An alternative option for accessing regObs-data via the web interface on regobs.no is via the Application Program Interface (API). The API allows for customized and more specific search criteria. Scripts for retrieving data via the API are written in the Python programming language. The scripts are openly shared via github.com (https://github.com/kmunve/avaldata). Retrieved data can be stored in various formats; simple text files were chosen for now.
Figure 4-1: Excerpt from github.com showing a script to retrieve all snow cover observations that report on surface hoar since a specific date.
Figure 4-1 shows a Python script that retrieves all snow cover observations that report on surface hoar since a specific date. The observation’s location, time and link to the full observation on regobs.no are returned. We extracted and filtered observations of surface hoar over the last two winters to calibrate the NR surface hoar index, which resulted in 167 relevant observations.
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4.2
NGI
One of the primary tasks of NGI in WP3 was to provide base data on avalanche occurrences to the project partners and to contribute with its avalanche expert knowledge. Throughout entire WP3, NGI assisted the partners with its expert knowledge on avalanche formation, avalanche mapping, avalanche warning etc. Such work does rarely result in concrete reportable deliverables (such as figures and tables), but was a vital prerequisite for the success of the work of the partners. 4.2.1
Large avalanche events during last 15 years
In order to test the snow surface characteristics products generated by NR and Norut, NGI compiled a GIS database with more recent large avalanche events (Figure 4-2). The selection of the events was based on the prerequisite that they needed to have occurred within the time period where ERS-1/-2 and Radarsat-2 (RS-2) data are available over Norway, i.e. the last approximately 15 years. The database contains location information and metadata on 29 major avalanche events, which occurred between 2002 and 2013. This GIS database was made available to the project partners.
Figure 4-2: Screenshot of the compiled GIS database on 29 recent large avalanche events.
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4.2.2
Frequent avalanche sites
NGI has collected avalanche cases in an internal database. Of these, 33 well-known avalanche trajectories in selected regions of Sogn og Fjordane county were chosen (Figure 4-3), and a coordinate in the release zone was provided by NGI for further study of snow properties. NGI did a subjective expert evaluation (in lack of in situ data) on what could typically be an involved cause of the avalanche for these trajectories, like surface hoar. This is to be considered as a likely explanation, not a fact for individual avalanches. The defined categories were: 0 = Surface hoar is not considered as a relevant release cause in the trajectory 1 = Surface hoar is considered as a possible relevant release cause in the trajectory 2 = Surface hoar is considered as a relevant cause in the trajectory
Figure 4-3: Well-known avalanche trajectories in selected regions of Sogn og Fjordane county. 4.2.3
Fatal avalanches due to surface hoar
NGI was asked to identify avalanches where buried surface hoar was regarded as the decisive weak layer leading to the release, and where the date of the original surface hoar formation could be fairly well estimated. A thorough analysis of the snowpack where avalanches have taken place is usually only done in fatal cases.
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NGI went through all cases registered in the period 2002-2014 (snoskred.no), and found out that of 56 investigated fatal/almost fatal cases only six were with a positive confirmation for the presence of buried surface hoar (Figure 4-4). In two cases buried surface hoar was not seen as the decisive weak layer for the release (no. 2 and no. 4 in Table 4-1), which leaves four cases where buried surface hoar is registered as the weak layer. Additionally, NGI went through their internal archive for the period 1995-2002, and found one additional case with positive confirmation (no. 7 in the Table 4-1). Finally, NGI made a search in the general database for avalanche events (skrednett.no) until the year 2013 and found none (out of 12,343 reports) where surface hoar/buried surface hoar was included in the description. The few reported cases of buried surface hoar as the decisive weak layer does, however, not imply that buried surface hoar is seldom involved; it does only mean that it was not registered. An avalanche expert at NGI (with more than 40 years of experience) tells that in his experience, buried surface hoar and coarse-grained snow crystals are commonly found as the weak layers connected to avalanche releases in Norway.
Figure 4-4: Location of cases of avalanches with registered presence of buried surface hoar present, as identified by NGI (cf. Table 4-1). In case 2 and 4, buried surface hoar was, however, not identified as the decisive weak layer, but it was present in the snowpack.
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Table 4-1: Cases of avalanches with registered presence of buried surface hoar identified by NGI (see Figure 4-4 for location). In case 2 and 4, buried surface hoar was, however, not identified as the decisive weak layer, but it was present in the snowpack. No
Date of avalanche
Place
Municipal
1
22 Feb 2014
Jønndalen
Uvdal
20 Feb
2
24 Dec 2012
Tottenveggen
Hemsedal
21 Dec
3
5 Mar 2011
N.N.
Rauland
1 Mar
4
21 Apr 2008
Uløya
Lyngen
14 Apr
5
8 Mar 2008
Stavdalen, Valle i Setesdal
Aust-Agder
1-6 Mar
6
20 Nov 2002
Tronfjell
Alvdal
10 Nov
7
3 Jan 2001
Dummfossen, Leirdalen
Lom
20 Dec
4.3
Estimated date of surface hoar formation
NR
The work of NR in WP3 focuses on retrieval of snow surface temperature, snow surface grain size, snow surface wetness and snow surface hoar, and monitoring of those variables in time series of satellite data. The retrieval results are compared with in situ data from various sources. The ultimate goal of WP3 has been to use such satellite-derived data to assist in the characterisation of the snowpack for avalanche risk prediction and warning. 4.3.1
Algorithms
The algorithms behind the satellite products Surface Temperature of Snow (STS), Snow Grain Size (SGS) and Snow Surface Wetness (SSW) have been developed and tailored by NR to the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Terra (EOS AM) and Aqua (EOS PM) satellites. The MODIS instrument is acquiring data in 36 spectral bands covering the spectral range 405-14,385 nm. The spatial resolutions are 250 m for bands 1-2, 500 m for bands 3-7 and 1000 m for bands 8-36. 4.3.1.1 The STS algorithm The STS algorithm is based on an approach proposed by Key et al. (1997). In a comparison study by Amlien and Solberg (2003), this algorithm was identified as one of the best singleview techniques for retrieval of STS for polar atmospheres. The absorption of radiation in the atmosphere depends on the wavelength, and the difference between the brightness temperatures in two channels will therefore yield information about the atmospheric attenuation (Stroeve et al., 1996; Coll et al., 1994). The split-window technique aims at eliminating the atmospheric effects by utilizing this difference. The surface temperature T is estimated as a weighted sum (or difference) of the brightness temperatures observed. The split-window equation utilizes T11 measured at 11 µm (MODIS band 31) and T12 measured at 12 µm (MODIS band 32). Key’s algorithm (Key et al., 1997) is a modification of the simple
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split-window technique. An additional correction term addresses the variation of the view angle θ along a scan line and its effect on the atmospheric path length. 4.3.1.2 The SGS algorithm For SGS we have used a normalized grain size index based on work by Dozier (1989) and followed by experiments by Fily et al. (1997). MODIS bands 2 and 7 have been used as this index has been shown to be less sensitive to snow impurities. The original algorithm proposed by Dozier was based on Landsat Thematic Mapper (TM) data. The problem of radiometric terrain effects, namely the influence of slopes on the reflected light, is minimized by using ratios between two channels as an index for grain size. We have found the ratio of bands 2 and 7 as the best. Fily et al. (1997) reported that the measured data match the theoretical curves well. The ratio approach is a simple method. Signals from two channels are sufficient and information about the terrain is not needed (as it is for several other methods). Published studies do not give a calibrated ratio. One specific ratio value does not provide an exact snow grain size value, and therefore defining the actual snow grain size is a problem. However, the ratio can be used as an index of the effective grain size; the ratio increases with increasing grain size up to the point of saturation. 4.3.1.3 The SSW algorithm We have developed an approach to infer wet snow from a combination of measurements of STS and SGS from multi-temporal observations. The temperature observations give a good indication of where wet snow could be present, but are in themselves not accurate enough to provide sufficiently strong evidence of wet snow. However, if a rapid increase in the effective grain size is observed simultaneously with a snow surface temperature of approximately 0°C, then this is a strong indication of a wet snow surface. The algorithm is described in detail in Solberg et al. (2004). A good estimate of SSW is valid only for pixels completely covered with snow. A Fractional Snow Cover (FSC) map is used to restrict the pixels classified. We assume that the SSW estimate is reasonably good even if there are small areas of bare ground included. Experiments with the snow wetness algorithm have confirmed that the approach of combining STS and SGS, analysed in a time series of observations, can be used to infer wet snow, including giving an early warning of snowmelt start. Air temperature measurements from meteorological stations generally confirm the produced SSW maps. There is a potential problem observed sometimes, which is related to clouds. Non-detected clouds or cloud fractions within a pixel will usually decrease the temperature retrieved. One should be aware of this potential problem with partly cloudy SSW maps. 4.3.1.4 Towards an algorithm for snow surface hoar Experiments in the subsequent sections based on in situ reports in the crowdsourcing dataset from 2010 and in RegObs made it possible to establish a preliminary algorithm for retrieval of surface hoar and a first version of a Snow Surface Hoar (SSH) product. The crowdsourcing dataset gave a first, rough understanding (hypothesis) of the relationship between SSH, SGS
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and STS. The RegObs dataset confirmed this understanding and made it possible to refine the understanding of the relationship quantitatively. The description in the following is more to be seen as an outline of an approach and a first and preliminary version. Theory says that very large optical effective snow grain sizes could be formed from various forms of snow metamorphosis, with the largest resulting from snowmelt processes. Large optical effective snow grain sizes should also be expected from surface hoar, which is usually formed during cold conditions with a significant temperature gradient between the ground and the sky. Water molecules are transported up to the snow surface and forms large ice crystals. Such temperature gradients may typically appear during nights with a clear sky. The analysis of the two in situ datasets confirmed very well that in situ observations of extensive cover of surface hoar at the same time give the highest SGS values appearing during cold snow conditions. The analysis of the crowdsourcing dataset showed that SGS peaks in the range [90, 91] when significant surface hoar was found on the ground. The RegObs dataset included substantial more observations and extended the range to [88, 97]. Some of the reports also included information on when the surface hoar appeared and when it disappeared, which made it possible to identify roughly the transition range of SGS values from no-surface-hoar to surface-hoar. A first version of a retrieval algorithm was then based on these observations and is graphically illustrated by the example in Figure 4-5. Green boxes illustrate the range and domain of surface hoar SGS values when STS seems to be sufficiently low for the formation of surface hoar. Under these conditions snowmelt metamorphism should not appear. Blue boxes in Figure 4-5 illustrate conditions when snowmelt metamorphism might appear. Based on this, it can be seen from the sample time series that there are three periods of likely surface hoar and two periods of significant snowmelt metamorphism. Note that some degree of STS uncertainty is taken into consideration. Previous validation work (Amlien and Solberg, 2003) showed that an accuracy of about 0.5°C could be expected from STS. However, when patches of bare ground appear in the pixel, STS values above 0.0°C might easily appear. Sunlit rocks might easily reach a surface temperature of around 15-20°C in the spring, which will drive STS up. Bare ground is filtered out in the STS algorithm; however, small patches might be undetected. A preliminary and empirical temperature threshold of -4.0°C is used here. Surface hoar formation was in some cases observed when the air temperature was not very far below 0°C, but in most cases the formation takes place when the temperatures are quite low, typically in the range -10 to -25°C.
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Figure 4-5: Valid ranges for surface hoar and snowmelt metamorphism formation illustrated with an example of a time series of SGS and STS from Lemonsjøen, Vågå, in 2010. A clear upper boundary of SGS from surface hoar could not be found. The peak values seem to vary significantly, maybe not only due to the size of crystals but also orientation. The maximum possible SGS value of 100 was therefore also used as the maximum value of SGS representing surface hoar. An example of a SSH map from the Kåfjord region in northern Norway is shown in Figure 4-6. A map of the valid SGS range, likely representing surface hoar, is overlaid a snow cover map where full snow cover is shown in white and less FSC in nuances of green down to dark green representing bare ground. In this case very extensive surface hoar was reported from in situ observations along the national boarder.
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Figure 4-6: Snow map showing extensive SHH in the Kåfjord region. An example of the temporal development of SSH is shown in Figure 4-7 for the central west coast region of southern Norway. Extensive surface hoar was formed during the night before 8 February 2013, which was confirmed by in situ observations. Much of the surface hoar had disappeared already the next day, probably due to wind. This is clearly a case when surface hoar was not buried by a subsequent snowfall.
Figure 4-7: SSH for the central west coast region of southern Norway. From the data analysis in the following sections in this report SSH seems to be a fairly robust product little affected by noise. Topographic effects are eliminated by the band ratio in the SGS
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calculation. Impurities, patches of bare ground and tall vegetation all drive SGS down. No other phenomena have been found to drive SGS high up other than surface hoar and snowmelt-driven metamorphosis. The two phenomena seem to be well discriminated by the algorithm applying STS. Also, it is quite likely to observe surface hoar when it is formed since it is usually formed during clear-sky conditions with a significant temperature gradient between the ground and the sky, and when the wind conditions are not too strong (which would otherwise remove the ice crystals). This gives a fair chance of a subsequent cloud-free observation of the snow surface. The algorithm might clearly be developed further. A temporally local analysis of SGS development could be applied to detect peaks, making the algorithm less dependent on absolute values. A better understanding between various sizes and orientations of surface hoar crystals is needed to refine the algorithm. This could be done best by in situ measurements with a field spectrometer, including studies of the bidirectional reflectance distribution function (BRDF) of the surface hoar. This would require ‘chasing’ appearances of surface hoar, practically impossible without close collaboration with people frequently in the field who report immediately back when extensive surface hoar is discovered so subsequent fieldwork can take place. 4.3.1.5 The processing chain The processing chain running the retrieval algorithms described above is controlled by a processing chain framework, which retrieves data and leads them through the various steps in the chain. There are two varieties of the processing chain, one for day-time products (FSC, STS, SGS, SSW and SSH) and one for night-time products (STS only). The night-time production chain uses 30 days aggregated FSC from day-time images for snow masking. The cloud masking and the snow products FSC, STS and SGS are generated in the original MODIS acquisition (swath) geometry. Then, the cloud mask, FSC, STS and SGS are projected to UTM zone 33 and resampled to a 1 km × 1 km grid. Next, SSW is estimated in the projected, UTM grid. Sometimes, one MODIS image gives only partial coverage of the area. Also, the cloud cover may change during one day. Therefore, the snow maps (FSC, STS, SGS, SSW and SSH) are aggregated within one day, keeping the cloud-free pixels with the least off-nadir viewing angle as seen from the satellite. 4.3.2
In situ data sources
The variables derived from satellite data are compared with various sources of in situ measurements, briefly described in the following sections. 4.3.2.1 Crowdsourcing observations The Norwegian Space Centre (NSC) funded project “Forbedret snøskredvarsling med satellittdata” (Improved snow avalanche warning with satellite data) organized a crowdsourcing event where skiers reported observations of surface hoar to NGI in the winter season 20092010. Usually, the date and position of the observation with a photo and remarks were reported. 43 observations were reported. As this event took place close to the end of the project period, the data were not analysed in that project. The dataset was included in the ASAM project and
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a comparison between it and satellite observations on the days around the in situ observation were carried out. 4.3.2.2 RegObs observations NVE manages an open database (RegObs) for recording natural emergency-related observations (cf. section 2.1). The database is available via a web portal, and is used by the national warning services as well as by others who like to report observations. The database is organized into the four categories "snow", "water", "soil" and "ice". The snow observations include categories for surface observations of wet snow and surface hoar. Observations of surface hoar in the period 2011-2014 for southern Norway have been retrieved from the database and are compared with satellite observations the days around the surface observation. 4.3.2.3 Frequent avalanche sites See section 4.2.1. 4.3.2.4 Fatal avalanches due to surface hoar See section 4.2.2. 4.3.2.5 Valdresflya test site The Valdresflya mountain plateau in the south-east Jotunheimen has been chosen as a calibration and validation (cal/val) site in the project. The aim here is to study the performance of the algorithms under ‘controlled’ conditions, in particular how the performances of the STS, SGS and SSW algorithms are. The outcome of these studies is used to adapt/tune the retrieval algorithms. The mountain plateau characteristic of the site ensures minimal topographic influence and homogeneous conditions in general over several square kilometres. A comprehensive set of in situ, aircraft and satellite data has been collected since 1997 (Solberg et al., 2010). These activities have also given lots of knowledge about the region. 4.3.3
Results
4.3.3.1 Analysis of surface hoar using the crowdsourcing dataset of 2010 The crowdsourcing dataset included 43 observations of surface hoar from the period November 2009 until April 2010. A subset of 20 observations was extracted by NR where a description of each observation was provided, most including a photo. Of these, five observations in December and January were removed due to very low solar elevation. A challenge combining these in situ observations with satellite observations is the different spatial scale of the observations. Highest priority was therefore given to cases of multiple observations; reports from the same geographical region at about the same time all reporting extensive surface hoar. In some cases such reports were provided over a period of time at several locations, giving a good picture of the spatial and temporal extent of the surface hoar. The most suitable cases, 11 in total, have been extensively analysed and used for the first development and calibration/validation of a surface hoar satellite product algorithm.
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Some of the regions with surface hoar observations are in general forest covered or have relatively steep topography. In order to eliminate effects from forest and topography, frozen lakes (i.e., flat, vegetation-free areas) in the same region as the surface hoar observations were chosen to study time series of snow surface temperature (STS) and snow grain size (SGS) from satellite observations. Results for two regions are included below to illustrate the analysis and the results obtained. Åmot region, 12-14 January 2010 There are five reports from 12-14 January 2010 from the Åmot region (see reported observations in Table 4-2, in situ photos in Figure 4-8 and locations in the map in Figure 4-9). The observations span an area of about 15 km. There was a cold period, indicated by one of the reports (-20°C), and confirmed by meteorological observations (weather stations). The period was typically favorable for surface hoar formation. Some of the reports describe the crystal size as exceptional. In conclusion, the reports indicate extensive distribution of surface hoar of large to very large crystals.
Rødsmoen
Bjørasetsætra
Skjeråsen
South of Skjeråsen
Rødssætra Figure 4-8: Observer's photos of surface hoar. (Photo credit: Ørjan Venås).
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Table 4-2: Reported observations of surface hoar in the region. Date
Place
Observer’s comments
12 Jan 2010
Rødsmoen
20-40 mm crystal size
13 Jan 2010
Bjørasetsætra
Seen lots of rime frost from Rena and eastwards. 40-60 mm crystal size
14 Jan 2010
Skjeråsen
Never before seen so large crystals. 8-10 cm long and 45 cm wide. -20°C in the camp
14 Jan 2010
South of Skjeråsen
80-100 mm crystal size
14 Jan 2010
Rødssætra
30-40 mm crystal size
Figure 4-9: The locations of the surface hoar observations (black stars), weather station (blue circle) and time series of satellite observations (red cross). Background map: Copyright © Statens Kartverk. Time series of snow surface temperature (STS) and snow grain size (SGS) were extracted from processed satellite data of the lake Osensjøen about 10 km east of the observations and plotted in Figure 4-10.
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Figure 4-10: Time-series plot of SGS and STS for the lake point. Green polygon points out the time period of the in situ observations. The surface hoar was so extensive, as shown by the in situ observations, that it is plausible to assume similar conditions at Osensjøen. The time period of the in situ observations is indicated by the interval between the green lines. A peak in the SGS at the time of the observations is evident, and it reached a highest value of 89.8 on 12 January (day 12). Following this day the value starts to fall steeply. In the same period STS was around -17°C. Meteorological observations in the period 6–15 January from the weather station Rena Haugedalen (id 7010) show daily average temperatures in the interval -15 to -29°C and no precipitation. Then precipitation starts on 16 January and continues until 19 January (with a maximum of 2.9 mm/day on the 18 January). The satellite observations seem to be consistent with the in situ observed surface hoar. While it is difficult to predict the exact effect of the surface hoar on the observed optical effective snow grain size from satellite – in particular due to surface hoar’s variability in geometry and orientation – it is qualitatively expected to be high. The SGS values observed are in a range that is typical for snowmelt-driven metamorphosis. As both in situ observations (temperatures around -20°C reported on one of the days) and weather station measurements, together with the STS observations from satellite, all confirms these low temperatures, snowmelt-driven metamorphosis can be eliminated as a cause. This leaves surface hoar as the most likely explanation of the high SGS values observed. The steep fall in SGS the next days is very well explained by the weather station data. New snow is expected to give a significant reduction in the SGS, just as observed by the satellite observations. Vågå region, 14 February and 2-7 March 2010 There are four reports from 14 February and 2-7 March 2010 from the Vågå region (see reported observations in Table 4-3, in situ photos in Figure 4-11 and locations in the map in Figure 4-12).
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The observations span an area of about 25 km. The first report probably represents a separate event as it took place three weeks before the other three reported cases. The surface hoar was destroyed by wind at higher elevations in February. Table 4-3: Reported observations of surface hoar in the region. Date
Place
Observer’s comments
14 Feb 2010
Bringsfjellet
Surface hoar at lower elevations. It was blown away at higher elevations
2-7 March 2010
Melingen
Surface hoar each day
4-5 March 2010
Lemonsjøen
Surface hoar both days
6 March 2010
Sjodalen
-
Bringsfjellet
Sjodalen
Figure 4-11: Observer's photos of surface hoar. (Photo credit: Ørjan Venås).
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Figure 4-12: The locations of the surface hoar observations (black stars), weather station (blue circle) and time series of satellite observations (red crosses). Background map: Copyright © Statens Kartverk. Two time series of STS and SGS were extracted from processed satellite data of the lakes Flatningen (Figure 4-13) and Lemonsjøen (Figure 4-14) centrally located in the region of observations.
Figure 4-13: Time-series plot of SGS and STS for the Flatningen lake point. Green polygons point out the time period of the in situ observations.
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Figure 4-14: Time-series plot of SGS and STS for the Lemonsjøen lake point. Green polygons point out the time period of the in situ observations. The surface hoar was so extensive, as shown by the in situ observations, that it is plausible to assume similar conditions at both lakes. The time periods of the two in situ observation periods are indicated by the interval between the two sets of green lines. Peaks in the SGS values at the time of the observations are evident. For Flatningen they reached a maximum of 88.2 on 14 February (day 45) in the first period, and maxima of 88.9 and 88.7 on 1 and 2 March (day 60 and 61) respectively, in the second period. After the second maximum, the values decrease sharply. In these two periods STS was around -6 and -9°C respectively. For Lemonsjøen SGS values reached a maximum of 91.3 on 14 February (day 45) in the first period, and a maximum of 90.4 on both 1 and 2 March (day 60 and 61) in the second period. Again, the values decrease sharply after the second maximum. In these two periods STS was around -9 and -11°C, respectively. None of the meteorological stations in the region reported temperature and wind observations in the period. Observations of precipitation from the closest weather station, Grov-Solhaug (id 14711), show a precipitation-free period 12–16 February followed by 0.8 mm/day registered on 16 February. Furthermore, the period from 28 February until 4 March was free of precipitation as well, before a precipitation of 0.3 mm/day was recorded on 5 March and 3.6 mm the following day. The satellite observations seem to be consistent with the in situ observed surface hoar. There are clear peaks in both periods. This is consistent with precipitation-free periods, as measured by the weather stations. Also, SGS values are significantly reduced when the weather stations start to report precipitation. Even though air temperature from weather station measurements were unavailable, the SGS observations are consistent with the STS values as low temperatures are expected during the formation of the surface hoar. There was definitely no snowmelt-driven metamorphosis in the period.
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4.3.3.2 Analysis of surface hoar using a RegObs dataset NVE screened the RegObs database to identify cases of reported surface hoar. 167 observations were identified in the period October 2011 until July 2014. A subset which also includes photos taken by the observers, was extracted by NVE. This subset included 104 observations. Time, position and identifier of the database entries for both datasets were provided to NR. NR then checked each case to identify reports which could serve as “ground truth” for satellite observations. Scale-problem was one of the major problems here. RegObs reports represent in principle point observations, while the satellite observations cover vast areas with a limited spatial resolution (1 km in our case). RegObs reports that included some description of the spatial extent of the surface hoar were therefore prioritised here. Even higher priority was given to cases of multiple observations, namely to reports from the same geographical region at about the same time all reporting extensive surface hoar. In some cases such reports were provided over a period of time at several locations, giving a good picture of the spatial and temporal extent of the surface hoar. Additionally, the RegObs observations useful for this study were required to be made at the time with sufficient sunlight. Generally, observations with the sun lower than 10° above the horizon were omitted. The most suitable cases, 22 in total, were studied in detail and used for calibration and validation of a surface hoar satellite product algorithm. Results for two regions are included below to illustrate the analysis and the results obtained. Filefjell region, 7-9 February 2013 There are four reports from 7-9 February 2013 from the Filefjell region (see reported observations in Table 4-4, in situ photos in Figure 4-15 and locations in the map in Figure 4-16). One of the reports refers to the north in the region and three other to the south, of which two document east-west transects. There was a cold period, indicated by one of the reports (-18°C). The period was typically favorable for surface hoar formation. Some of the reports include information that wind has destroyed parts of the surface hoar. One of the reports also includes information about the altitudinal extent of the surface hoar. In conclusion, the reports indicate the areas with extensive distribution of surface hoar as well as the areas where surface hoar was destroyed by wind. Table 4-4: Reported observations of surface hoar in the region. ID
Date
Place
Observer’s comments
7036
7 Feb 2013
Tyin
Dominating above 1200 m.a.s.l. in westerly exposed terrain
7077
8 Feb 2013
Hemsedal
Large areas in all directions
7309
9 Feb 2013
Hol
7317
9 Feb 2013
Gurostølfjellet
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Tyin
Hemsedal
Hol
Gurostølfjellet
Figure 4-15: Observer's photos of surface hoar. (Photo credit: regObs).
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Figure 4-16: The locations of the surface hoar observations (black stars) and time series of satellite observations (red crosses). Background map: Copyright © Statens Kartverk. Time series were extracted from processed satellite data of two lakes: Otrøvatnet (Figure 4-17) close to the Tyin observation and Tyinkrysset, and Gyrinosvatnet (Figure 4-18) close to and west of Hemsedal.
Figure 4-17: Time-series plot of SGS and STS for the point at the lake Otrøvatnet. Green polygon points out the time period of the in situ observations.
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Figure 4-18: Time-series plot of SGS and STS for the point at the lake Gyrinosvatnet. Green polygon points out the time period of the in situ observations. The period 7-10 February (day 38-40) is indicated by green lines in Figure 4-17 and Figure 4-18. SGS and STS are similar and consistent for both lakes. Peak values for SGS are 89.2 for Otrøvatnet and 91.3 for Gyrinosvatnet. At the start of the period SGS is 86.1 at both sites. STS varies between -19 and -25°C in the same period. The temporal development of surface hoar for a large region around Sognefjorden, including the Filefjell region, is illustrated very well with the SSH maps in Figure 4-19. The plot in Figure 4-18 indicates that this was a short-lived phenomenon, which is confirmed with the SSH maps.
Figure 4-19: SSH maps for a large region around Sognefjorden, including the Filefjell region. West-of-Jotunheimen region, 8-15 February 2013 Four reports indicate that the surface hoar reported for the Filefjell region in the period 7-9 February was considerable. The four reports are from the period 8-15 February 2013 from the region west of Jotunheimen (see reported observations in Table 4-5, in situ photos in Figure 4-20 and locations in the map in Figure 4-21). The meteorological conditions might well have
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been favorable for the whole region from at least Voss in the south-west to Sunnmøre in the north-west. In the east-west direction, it was probably limited by the watershed in the inner part of the Scandinavian mountains (from southern Dovre to northern Hardangervidda). Table 4-5: Reported observations of surface hoar in the region. ID
Date
Place
Observer’s comments
7144
8 Feb 2013
Sunnmøre
Large quantities of surface hoar.
7354
10 Feb 2013
Voss
Surface hoar extensive (70-89% area cover) up to 1130 m.a.s.l., otherwise destroyed by wind.
7578
13 Feb 2013
Strynefjellet
Significant formation of surface hoar the last days.
7779
15 Feb 2013
Balestrand
Partly transformed surface hoar.
Sunnmøre
Strynefjellet
Voss
Figure 4-20: Observer's photos of surface hoar. (Photo credit: regObs).
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Figure 4-21: The locations of the surface hoar observations (black stars), including those in the Filefjell region (lower right corner) and time series of satellite observations (red crosses). Background map: Copyright © Statens Kartverk. Time series were extracted from processed satellite data of two lakes: Bjørkedalsvatnet (Figure 4-22) south-west of the Sunnmøre observation, and Hamlagrøvatnet (Figure 4-23) south of the Voss observation.
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Figure 4-22: Time-series plot of SGS and STS for the point at the lake Bjørkedalsvatnet. Green polygon points out the time period of the in situ observations.
Figure 4-23: Time-series plot of SGS and STS for the point at the lake Hamlagrøvatnet. Green polygon points out the time period of the in situ observations. The period 8-15 February (day 39-46) is indicated by green lines in Figure 4-22 and Figure 4-23. SGS and STS are similar and consistent for both lakes. Peak values for SGS were 88.4
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for Bjørkedalsvatnet and 91.5 for Hamlagrøvatnet, both reached on 13 February. At the start of the period the SGS values were 87.8 and 87.6 for the two sites, respectively. STS varied between about -13 and -25°C in the same period. There was a significant dip at both sites within the period. The dips were, however, not at the same time but separated three days apart. This indicates that there was either new snow covering the surface hoar or wind destroying it within the period. SSH maps of the region on 8 and 15 February are shown in Figure 4-24. The extent of the area covered by surface hoar is exceptional on the 8th, while reduced to mainly two smaller regions on the 13th.
Figure 4-24: SSH maps for the West-of-Jotunheimen region on 8 and 13 February 2013. 4.3.3.3 Analysis of fatal avalanches due to surface hoar Of the fatal avalanches due to surface hoar identified by NGI (cf. section 4.2.3), four were analysed in detail and are presented below. For the other cases, MODIS data were not processed and available for the relevant time periods. Results for two regions are included below to illustrate the analysis and the results obtained. Jønndalen, Uvdal, 22 February 2014 A fatal avalanche took place in Uvdal on 22 February 2014. Avalanche experts investigating the fatality estimated that the avalanche took place around 20 February. Analysis of a time series of SGS (Figure 4-25) indicates that surface hoar was present on 18 February. SGS value of 90 represents a ‘strong signal’, indicating that surface hoar dominates the observed area of 1 km2, maybe with large ice crystals as well.
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Figure 4-25: A time series of SGS and STS for the avalanche area. Green line points out the date of the in situ observation. Tronfjell, Alvdal, 20 November 2002 A fatal avalanche took place at Tronfjell on 20 November 2002. Avalanche experts investigating the fatality estimated that the avalanche took place around 10 November. Analysis of a time series of SGS indicates large SGS values and low temperatures on 4 and 5 November. SSH maps were produced for those days (Figure 4-26).
Figure 4-26: SSH maps showing locally significant surface hoar. The avalanche is located in the image centre (highlighted by the crossing of the two red grid lines).
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4.4
Norut
4.4.1
Wet snow detection with Envisat ASAR and Radarsat-2
Norut has a database of Envisat ASAR and RS-2 ScanSAR imagery over avalanche prone areas. We have identified several periods with avalanche activity and simultaneous acquisitions of SAR data. New acquisitions are also being programmed in RS-2 SCNA mode on a monthly basis in RS-2 background mode. These orders have a low priority and we receive only about 1/3 of the ordered images. Wet snow maps are produced on a regular basis. Currently, Norut provides these products for two regions in Norway (Lyngen and Stryn) within the "Snow, Ice and Avalanche Applications" (SNAPS) project (http://www.snaps-project.eu; Figure 4-27). These results are published online at http://www.snaps-project.eu/snow-maps/. For the ASAM project we have also set up processing chains for near real-time processing over several other sites (Tromsø/KvaløyaKattfjordeidet and Svalbard) but these are not yet published.
Figure 4-27: Time series of wet snow maps from the island of Kvaløya and parts of the Norwegian mainland. Wet snow maps from 22 January to 27 April 2013. Red indicates wet snow conditions, when the radar backscatter is
