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

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

Confirmation of a non-synonymous SNP in PNPLA8 as a candidate causal mutation for Weaver syndrome in Brown Swiss cattle

Background: Bovine progressive degenerative myeloencephalopathy (Weaver syndrome) is a neurodegenerative disorder in Brown Swiss cattle that is characterized by progressive hind leg weakness and ataxia, while sensorium and spinal reflexes remain unaffected. Although the causal mutation has not been identified yet, an indirect genetic test based on six microsatellite markers and consequent exclusion of Weaver carriers from breeding have led to the complete absence of new cases for over two decades. Evaluation of disease status by imputation of 41 diagnostic single nucleotide polymorphisms (SNPs) and a common haplotype published in 2013 identified several suspected carriers in the current breeding population, which suggests a higher frequency of the Weaver allele than anticipated. In order to prevent the reemergence of the disease, this study aimed at mapping the gene that underlies Weaver syndrome and thus at providing the basis for direct genetic testing and monitoring of today's Braunvieh/Brown Swiss herds. Results: Combined linkage/linkage disequilibrium mapping on Bos taurus chromosome (BTA) 4 based on Illumina Bovine SNP50 genotypes of 43 Weaver-affected, 31 Weaver carrier and 86 Weaver-free animals resulted in a maximum likelihood ratio test statistic value at position 49,812,384 bp. The confidence interval (0.853 Mb) determined by the 2-LOD drop-off method was contained within a 1.72-Mb segment of extended homozygosity. Exploitation of whole-genome sequence data from two official Weaver carriers and 1145 other bulls that were sequenced in Run4 of the 1000 bull genomes project showed that only a non-synonymous SNP (rs800397662) within the PNPLA8 gene at position 49,878,773 bp was concordant with the Weaver carrier status. Targeted SNP genotyping confirmed this SNP as a candidate causal mutation for Weaver syndrome. Genotyping for the candidate causal mutation in a random sample of 2334 current Braunvieh animals suggested a frequency of the Weaver allele of 0.26 %. Conclusions: Through combined use of exhaustive sequencing data and SNP genotyping results, we were able to provide evidence that supports the non-synonymous mutation at position 49,878,773 bp as the most likely causal mutation for Weaver syndrome. Further studies are needed to uncover the exact mechanisms that underlie this syndrome

Mapping avalanches with satellites – evaluation of performance and completeness

The spatial distribution and size of avalanches are essential parameters for avalanche warning, avalanche documentation, mitigation measure design and hazard zonation. Despite its importance, this information is incomplete today and only available for limited areas and limited time periods. Manual avalanche mapping from satellite imagery has recently been applied to reduce this gap achieving promising results. However, their reliability and completeness have not yet been verified satisfactorily. In our study we attempt a full validation of the completeness of visually detected and mapped avalanches from optical SPOT 6, Sentinel-2 and radar Sentinel-1 imagery. We examine manually mapped avalanches from two avalanche periods in 2018 and 2019 for an area of approximately 180 km2 around Davos, Switzerland, relying on ground- and helicopter-based photographs as ground truth. For the quality assessment, we investigate the probability of detection (POD) and the positive predictive value (PPV). Additionally, we relate our results to conditions which potentially influence avalanche detection in the satellite imagery. We statistically confirm the high potential of SPOT for comprehensive avalanche mapping for selected periods (POD = 0.74, PPV = 0.88) as well as the reliability of Sentinel-1 (POD = 0.27, PPV = 0.87) for which the POD is reduced because mainly larger avalanches are mapped. Furthermore, we found that Sentinel-2 is unsuitable for the mapping of most avalanches due to its spatial resolution (POD = 0.06, PPV = 0.81). Because we could apply the same reference avalanche events for all three satellite mappings, our validation results are robust and comparable. We demonstrate that satellite-based avalanche mapping has the potential to fill the existing avalanche documentation gap over large areas, making alpine regions safer

Mapping avalanches with satellites-Evaluation of performance and completeness

pThe spatial distribution and size of avalanches are essential parameters for avalanche warning, avalanche documentation, mitigation measure design and hazard zonation. Despite its importance, this information is incomplete today and only available for limited areas and limited time periods. Manual avalanche mapping from satellite imagery has recently been applied to reduce this gap achieving promising results. However, their reliability and completeness have not yet been verified satisfactorily./p pIn our study we attempt a full validation of the completeness of visually detected and mapped avalanches from optical SPOT 6, Sentinel-2 and radar Sentinel-1 imagery. We examine manually mapped avalanches from two avalanche periods in 2018 and 2019 for an area of approximately 180 kmspan classCombining double low lineinline-formula2 around Davos, Switzerland, relying on ground-and helicopter-based photographs as ground truth. For the quality assessment, we investigate the probability of detection (POD) and the positive predictive value (PPV). Additionally, we relate our results to conditions which potentially influence avalanche detection in the satellite imagery./p pWe statistically confirm the high potential of SPOT for comprehensive avalanche mapping for selected periods (POD span classCombining double low lineinline-formulaCombining double low line 0.74, PPV span classCombining double low lineinline-formulaCombining double low line 0.88) as well as the reliability of Sentinel-1 (POD span classCombining double low lineinline-formulaCombining double low line 0.27, PPV span classCombining double low lineinline-formulaCombining double low line 0.87) for which the POD is reduced because mainly larger avalanches are mapped. Furthermore, we found that Sentinel-2 is unsuitable for the mapping of most avalanches due to its spatial resolution (POD span classCombining double low lineinline-formulaCombining double low line 0.06, PPV span classCombining double low lineinline-formulaCombining double low line 0.81). Because we could apply the same reference avalanche events for all three satellite mappings, our validation results are robust and comparable. We demonstrate that satellite-based avalanche mapping has the potential to fill the existing avalanche documentation gap over large areas, making alpine regions safer. © 2021 American Society of Mechanical Engineers (ASME). All rights reserved.ISSN:1994-0416ISSN:1994-042

Snow depth estimation at country-scale with high spatial and temporal resolution

Monitoring snow depth is important for applications such as hydrology, energy planning, ecology, and safety evaluation for outdoor winter activities. Most methods able to estimate snow depth for large regions can only do so in a spatial resolution of up to 1 km ground sampling distance (GSD). This limits their usage in high alpine areas, where this resolution fails to capture local snow distribution patterns caused by the pronounced topographical features. In this work we use a recurrent convolutional neural network to estimate snow depth at high spatial resolution (10 m GSD), weekly, and at large scale based on satellite data sources and elevation maps, without the need for measurement stations on the ground. The proposed method achieves unprecedented results for large-scale, high-resolution snow depth mapping. The resulting maps are evaluated over a period of three years against high-fidelity snow depth maps obtained with airborne photogrammetry. Finally, we also produce well-calibrated uncertainty estimates for every individual snow depth estimate via a probabilistic regression framework.ISSN:0924-271

Snow depth estimation at country-scale with high spatial and temporal resolution

Monitoring snow depth is important for applications such as hydrology, energy planning, ecology, and safety evaluation for outdoor winter activities. Most methods able to estimate snow depth for large regions can only do so in a spatial resolution of up to 1 km ground sampling distance (GSD). This limits their usage in high alpine areas, where this resolution fails to capture local snow distribution patterns caused by the pronounced topographical features. In this work we use a recurrent convolutional neural network to estimate snow depth at high spatial resolution (10 m GSD), weekly, and at large scale based on satellite data sources and elevation maps, without the need for measurement stations on the ground. The proposed method achieves unprecedented results for large-scale, high-resolution snow depth mapping. The resulting maps are evaluated over a period of three years against high-fidelity snow depth maps obtained with airborne photogrammetry. Finally, we also produce well-calibrated uncertainty estimates for every individual snow depth estimate via a probabilistic regression framework

Spatially continuous snow depth mapping by aeroplane photogrammetry for annual peak of winter from 2017 to 2021 in open areas

Information on snow depth and its spatial distribution is important for numerous applications, including natural hazard management, snow water equivalent estimation for hydropower, the study of the distribution and evolution of flora and fauna, and the validation of snow hydrological models. Due to its heterogeneity and complexity, specific remote sensing tools are required to accurately map the snow depth distribution in Alpine terrain. To cover large areas (>100 km²), airborne laser scanning (ALS) or aerial photogrammetry with large-format cameras is needed. While both systems require piloted aircraft for data acquisition, ALS is typically more expensive than photogrammetry but yields better results in forested terrain. While photogrammetry is slightly cheaper, it is limited due to its dependency on favourable acquisition conditions (weather, light conditions). In this study, we present photogrammetrically processed high-spatial-resolution (0.5 m) annual snow depth maps, recorded during the peak of winter over a 5-year period under different acquisition conditions over a study area around Davos, Switzerland. Compared to previously carried out studies, using the Vexcel UltraCam Eagle Mark 3 (M3) sensor improves the average ground sampling distance to 0.1 m at similar flight altitudes above ground. This allows for very detailed snow depth maps in open areas, calculated by subtracting a snow-off digital terrain model (DTM, acquired with ALS) from the snow-on digital surface models (DSMs) processed from the airborne imagery. Despite challenging acquisition conditions during the recording of the UltraCam images (clouds, shaded areas and fresh snow), 99 % of unforested areas were successfully photogrammetrically reconstructed. We applied masks (high vegetation, settlements, water, glaciers) to increase the reliability of the snow depth calculations. An extensive accuracy assessment was carried out using check points, the comparison to DSMs derived from unpiloted aerial systems and the comparison of snow-free DSM pixels to the ALS DTM. The results show a root mean square error of approximately 0.25 m for the UltraCam X and 0.15 m for the successor, the UltraCam Eagle M3. We developed a consistent and reliable photogrammetric workflow for accurate snow depth distribution mapping over large regions, capable of analysing snow distribution in complex terrain. This enables more detailed investigations on seasonal snow dynamics and can be used for numerous applications related to snow depth distribution, as well as serving as a ground reference for new modelling approaches and satellite-based snow depth mapping.ISSN:1994-0416ISSN:1994-042