Examining snow avalanches using Sentinel-1 radar data: case of Gissar-Alai Mountain Range

Combining OBIA, CNN, and UAV photogrammetry for automated avalanche deposit detection and characterization

The ability to map burn severity and to understand how it varies as a function of time of year and return frequency is an important tool for landscape management and carbon accounting in tropical savannas. Different indices based on optical satellite imagery are typically used for mapping fire scars and for estimating burn severity. However, cloud cover is a major limitation for analyses using optical data over tropical landscapes. To address this pitfall, we explored the suitability of C-band Synthetic Aperture Radar (SAR) data for detecting vegetation response to fire, using experimental fires in northern Australia. Pre- and post-fire results from Sentinel-1 C-band backscatter intensity data were compared to those of optical satellite imagery and were corroborated against structural changes on the ground that we documented through terrestrial laser scanning (TLS). Sentinel-1 C-band backscatter (VH) proved sensitive to the structural changes imparted by fire and was correlated with the Normalised Burn Ratio (NBR) derived from Sentinel-2 optical data. Our results suggest that C-band SAR holds potential to inform the mapping of burn severity in savannas, but further research is required over larger spatial scales and across a broader spectrum of fire regime conditions before automated products can be developed. Combining both Sentinel-1 SAR and Sentinel-2 multi-spectral data will likely yield the best results for mapping burn severity under a range of weather conditions.

There are about eighty avalanche geocomplexes, which belong to different classes according to avalanche activity, in the mountain massif Chornohora. One of the main tasks is an investigation of natural conditions of avalanche formation because of snow mass slide process influence on the environment and human activities. Snow-avalanche formation conditions within landscape complexes of Chornohora mountain massif in Ukrainian Carpathians, depending on group of factors (landscape structure, meteorological quantity and phenomenon and other), are considered. Special attention is paid to study the morphological structure of snow and stratification structure of snow cover as the main avalanche slide factors. The landscape structure and relief of territory with avalanche activity are analyzed. All avalanche genetic types, which are identified in the Chornohora mountain massif, are located on the steep and very steep slope on old-glacial relief forms (slope steepness – 15–45° (Miller, 1966)). Avalanche activity within research territory limits of the Pozhyzhevska snow-avalanche station was analyzed and short characteristic of avalanche subperiod during research time was presented. The dynamics of snow depth and snow cower structure, temperature regime of air and snow during research period was investigated. The main indexes of meteorological phenomena, which are typical for the days when avalanche activity was identified, were determined. Analysis of meteorological quantity and phenomenon indexes is realized on the base of own research information and technical report of Pozhyzhevska snow-avalanche station. Based on the results of the investigation natural conditions of snow avalanche slide of all genetic types (polygenetic (inducted by snowfall and blizzard) and epigenetic (inducted by the melt of snow – insolation and advection process)) were determined. The typical profile of snow cover, which is characterized by avalanche sliding process, is proposed for every genetic type of avalanche. The characteristics of snow profiles are presented in figures. Key words: avalanche, Ukrainian Carpathians, snow cover, meteorological phenomena.

Abstract. Data collection for digital elevation model (DEM) generation can be carried out by two main methods in space-borne remote sensing such as stereoscopy using optical or radar satellite imagery (stereophotogrammetry, respectively radargrammetry) and interferometry based on interferometric synthetic aperture radar (InSAR) data. These techniques have advantages and disadvantages in comparison against each other. Especially filling the gaps which arise from the problem of cloud coverage in DEM generation by optical imagery, InSAR became operational in recent years and DEMs became the most demanded interferometric products. Essentially, in comparison, DEM generation from synthetic aperture radar (SAR) images is not a simple manner like generation from optical satellite imagery. Interferometric processing has several complicated steps for the production of a DEM. The quality of the data set and used software package come into prominence for the stability of the generated DEM. In the paper, the interferometric processing steps for DEM generation from InSAR data and the crucial threshold values are tried to be explained. For DEM generation, a part of Istanbul (historical peninsula and near surroundings) was selected as the test field because of data availability. The data sets of two different imaging modes (StripMap ~ 3 m resolution and High Resolution Spotlight ~ 1 m resolution) of TerraSAR-X have been used. At the implementation, besides the determination of crucial points at interferometric processing steps, to define the effect of computer software, DEM production have been performed using two different software packages in parallel and the products have been compared In the result section of the paper, besides the colorful visualizations of final products along with the height scales, accuracy evaluations have been performed for both DEMs with the help of a more accurate reference digital terrain model (DTM). This reference model has been achieved by large scale aerial photos. Normally, it has a 5 m original grid spacing, however it has been resampled at a spacing of 1 m towards the needs of the research.

The fusion of synthetic aperture radar (SAR) and optical satellite imagery poses significant challenges for ship detection due to the distinct characteristics and noise profiles of each modality. Optical imagery provides high-resolution information but struggles in adverse weather and low-light conditions, reducing its reliability for maritime applications. In contrast, SAR imagery excels in these scenarios but is prone to noise and clutter, complicating vessel detection. Existing research on SAR and optical image fusion often fails to effectively leverage their complementary strengths, resulting in suboptimal detection outcomes. This research presents a novel fusion framework designed to enhance ship detection by integrating SAR and optical imagery. This framework incorporates a detection system for optical images that utilizes Contrast Limited Adaptive Histogram Equalization (CLAHE) in combination with the YOLOv7 model to improve accuracy and processing speed. For SAR images, a customized Detection Transformer model, SAR-EDT, integrates advanced denoising algorithms and optimized pooling configurations. A fusion module evaluates the overlaps of detected bounding boxes based on intersection over union (IoU) metrics. Fused detections are generated by averaging confidence scores and recalculating bounding box dimensions, followed by robust postprocessing to eliminate duplicates. The proposed framework significantly improves ship detection accuracy across various scenarios.

The structural assessment and maintenance of bridges are critical to ensuring the longevity and safety of infrastructure, particularly in the U.S., where aging bridges present significant challenges. This review explores innovative approaches to bridge maintenance and assessment, focusing on advanced methodologies that integrate cutting-edge technologies such as Bently and MicroStation. These tools provide enhanced capabilities for evaluating deteriorating bridge components, allowing for precise structural ratings and predictive maintenance strategies. Drawing from my hands-on experience with structural ratings, this study highlights how these innovations streamline the evaluation process, offering detailed insights into the integrity of bridges. By leveraging 3D modeling, simulation, and data-driven analytics, these technologies enable engineers to assess wear and tear more accurately, predict potential failures, and optimize maintenance schedules. These advancements are instrumental in extending the lifespan of critical infrastructure while significantly reducing maintenance costs. Furthermore, the implementation of these technologies directly impacts public safety by providing more reliable data for decision-making in bridge management. The adoption of these methodologies not only enhances the efficiency of structural assessments but also facilitates proactive maintenance, minimizing the risk of catastrophic failures. The use of Bently and MicroStation in the structural assessment of bridges exemplifies the shift toward a more technologically integrated approach to infrastructure management, reflecting a broader trend in engineering practices toward digital transformation. This approach is particularly beneficial in the U.S., where a substantial portion of bridges are approaching the end of their design life, necessitating more advanced tools for structural evaluation. The findings of this study underscore the importance of adopting innovative technologies in bridge maintenance to ensure the safety, functionality, and cost-effectiveness of key infrastructure. Keywords: Structural Assessment, Bridge Maintenance, Bently, Microstation, Structural Ratings, 3D Modeling, Predictive Maintenance, Infrastructure Safety, Public Safety, U.S. Bridges.

Remote Sensing Imagery and the derived ancillary products improved the efficiency and safety of upstream oil and gas operations on the North Slope of Alaska. These Arctic regions are remote, very difficult to access in general and sometimes only seasonably accessible. Our prudent and responsible Arctic Operations require regional-reconnaissance exploration, diligent monitoring of environment such as current state of vegetation, temporal changes of terrain, water drainage system and lakes. Finally, we also need very detailed logistical-planning of field operations. Remote sensing imagery and its derived ancillary products demonstrably improved all these aspects of our Arctic Operations. For Arctic Operations, remote sensing data consisted of optical satellite and aerial imagery at various spectral and spatial resolutions, high resolution LIDAR data for digital elevation and digital surface models and synthetic aperture radar imagery (SAR). A combination of in-house and commercial software was used to ingest and process these data. The optical imagery was processed and enhanced using various spectral combinations and high pass filtering to generate the highest possible spatial-resolution for each sensor. Classic neural networks analysis was used to classify the optical imagery for vegetation. The SAR imagery was calibrated (for all polarizations) and geometrically corrected to remove layover effects. The processed optical and SAR imagery, LIDAR and ancillary products were co-registered and imported into a GIS system for final analysis and applications. The optical imagery provided information about surface feature such as lake outlines, general drainage, active channels in Colville River, general lake ice conditions, classification of vegetation types etc. The LIDAR data were used to generate slope maps (for arctic vehicles), general topographic conditions and field operations. The SAR imagery was used to monitor surface conditions when optical imagery was not available during the Arctic night conditions. SAR imagery was also used to calculate the ice thickness proxy maps for eventual field operations. All of these products contributed directly to our environmental baseline studies, improved our field operation efficiency and general safety of our Arctic Operations. For a practicing engineer (individual or team) The remote sensing data and derived products for Arctic Operations were made available via GIS system. This allowed easy integration with other data layers as well as a common background for all different disciplines to monitor progress and to contribute their learnings and ideas to the entire team.

<p>Heavy rainfall can trigger thousands of landslides, which have a significant effect on the landscape and can pose a hazard to people and infrastructure. Inventories of rainfall triggered landslides are used to improve our understanding of the physical mechanisms that cause the event, in assessing the impact of the event and in the development of hazard mitigation strategies. Inventories of rainfall-triggered landslides are most commonly generated using optical or multispectral satellite imagery, but such imagery is often obscured by cloud-cover associated with the rainfall event. Cloud-free optical satellite images may not be available until several weeks following an event. In the case where rain falls over a long period of time, for example during the monsoon season or successive typhoon events, the timing of the triggered landslides is usually poorly constrained. This lack of information on landslide timing limits both hazard mitigation strategies and our ability to model the physical processes behind the triggered landsliding.</p><p>Satellite radar has emerged recently as an alternative source of information on landslides. The removal of vegetation and movement of material due to a landslide alters the scattering properties of the Earth’s surface, thus giving landslides a signal in satellite radar imagery. Satellite radar data can be acquired in all weather conditions, and the regular and frequent acquisitions of the Sentinel-1 constellation, could allow landslide timing to be constrained to within a few days.  Satellite radar data has been successfully used in detecting the spatial distribution of landslides whose timing is known a-priori (for example those triggered by earthquakes). Here we demonstrate that time series of Sentinel-1 satellite radar images can also be used to achieve the opposite: the identification of landslide timing for an event whose spatial extent is known.</p><p>We analyse radar coherence and amplitude times series to identify changes in the time series associated with landslide occurrence. We compare pixels within each landslide with nearby pixels outside each landslide that have been identified to be similar in pre-rainfall Sentinel-1 and Sentinel-2 imagery. We test our methods on rainfall-triggered landslides in Nepal and Japan, both of which are mountainous countries that experience regular heavy rainfall events that are often obscured by cloud cover in optical satellite imagery.</p>

Snow avalanches killed more people in the Swiss alpine area during the past decades than any other natural hazard. To further improve the avalanche prediction and the protection of people and infrastructure, information about the occurrence and the distribution of avalanche activity is crucial. Nevertheless this information is missing for large parts of the Alpine area. The surface roughness of avalanche deposits differs considerably from the adjacent undisturbed snow cover and is an important factor of the directional reflectance anisotropy. The undisturbed snow-cover exhibits a strong forward scattering, while the structure of an avalanche deposit causes shadow casting and tilt effects. Therefore, the observed reflectance of avalanche deposits and undisturbed snow cover is strongly dependent on the illumination- and viewing angles. This study demonstrates the potential of multiangular remote sensing data for detecting and mapping avalanche deposits. The results indicate, that air- or spaceborne multiangular sensors are suitable for rapid detection and mapping of avalanches in inaccessible and remote regions.

The article deals the landscape structure of the Iya river basin (left tributary of the Oka river). Landscape analysis was conducted in order to identify landscape-hydrological conditions of flood formation in 2019. Since the available small-scale landscape maps for the study area did not meet the requirement of the scientific issue in detail, a landscape map was compiled at a scale of 1: 500 000. The territory is heterogeneous in its physical and geographical conditions and topographic and humidification features, which resulted in formation of different landscapes and different types of intralandscape differentiation. We used a standard set of data to create the landscape map of the Iya river basin at a scale of 1: 500 000: electronic multi-scale topographic maps, Landsat 7 and 8 satellite images, digital terrain model, slope steepness and exposure maps, small-scale landscape, geobotanical and soil maps. We based on the landscape classification, which was developed for the map “Landscapes of the South of Eastern Siberia”. The landscape diversity of the basin is represented by 39 groups of facies, united in 17 geoms and 4 classes of geoms. The landscapes of the mountainous, foothill-plain and upland-plain parts of the Iya River basin are substantially different in their appearance, characteristics of individual components, and their nature. The area of landscape units increases from southwest to northeast as the absolute height and vertical dissection of the relief decrease, their diversity increases towards the foothill part of the Iya river basin.

Meltwater retention on the surface of ice shelves may lead to hydrofracturing and ultimately their breakup and collapse. Several methods have been used to map surface meltwater on ice shelves from satellite imagery, but a comparison of the methods and a systematic analysis of the results has not been undertaken. Here we bring together two recent inventories of surface meltwater features that use machine learning technologies to map: i) ponds from both Sentinel-2 optical and Sentinel-1 SAR imagery; and ii) both ponds and slush from Landsat 8 optical imagery. We analyse the meltwater products at a bi-monthly (twice a month) timescale over six austral summers between November 2015 and March 2021 for the George VI Ice Shelf, Antarctic Peninsula. We investigate the data sets to reveal the seasonal evolution of surface and shallow subsurface meltwater in terms of the onset, cessation and therefore duration of slush and ponded water, as well as the persistency of slush and ponded water areas from year to year. We find areas where the evolution follows similar patterns from year to year, but also highly anomalous patterns and timings in other years. Finally, a systematic analysis of Sentinel 1 imagery throughout the winter seasons reveals several perennial shallow surface water bodies.

On 4 July 2019, the Ridgecrest earthquake sequence began with a series of foreshocks including an Mw 6.4 event near Searles Valley, California. This was then followed 34 hr later by an Mw 7.1 mainshock located just 15 km to the north, with the earthquake sequence resulting in a complex array of intersecting faults. This earthquake sequence poses several interesting questions including, did the stress changes induced by the Mw 6.4 foreshock trigger the Mw 7.1 mainshock and what possible mechanism(s) could explain the occurrence of widespread secondary faulting surrounding both surface ruptures? However, most of the geodetic data (such as Interferometric Synthetic Aperture Radar, light detection and ranging, and optical satellite imagery) were acquired after both events had occurred making it difficult to discern which surface fractures happened when and their possible triggering mechanism. Here, we provide a dataset composed of high-resolution optical imagery, pixel-value difference maps, .kmz fracturing mapping, and horizontal deformation maps derived from subpixel image correlation, which can uniquely separate the surface fracturing and deformation between the foreshock and mainshock events that can help answer these questions. Separate imaging of the events is made possible by the daily acquisition of optical imagery by the Planet Labs cubesat constellation, which acquired data between the two earthquakes, in the morning of 4 and 5 July, at 11.13 a.m. and 05.12 p.m. PST, respectively, with the images acquired just 40 min after the foreshock and 56 min before the mainshock, respectively. Analysis from this optical imagery reveals the location of surface faulting that allows us to map their spatial extent and determine their timing. These data which we provide here can help guide and validate field survey observations to help understand which faults ruptured when, and constrain slip inversion models for more accurate estimates of stress changes induced by the foreshock imposed on the surrounding faults.

Meltwater that pools on the surface of Arctic sea ice enhances solar absorption and accelerates further ice melt. The impact of melt ponds on energy absorption is controlled primarily through their influence on ice albedo, which is, in turn, governed in large part by the ponds' spatial coverage. This work seeks to observe the spatial coverage of melt ponds across the Arctic basin with sufficient accuracy to investigate pond‐albedo feedback and presents an improved technique to achieve this goal. We approach the problem by using the Open Source Sea Ice Processing algorithm to classify surface features in submeter resolution optical satellite imagery over select sites where such imagery is available. These data establish “true” estimates of pond coverage and the ponds' spectral reflectance. This information is then used to inform, improve, and test spectral unmixing and machine learning techniques that seek to determine melt pond coverage from more widely available, but lower resolution, optical satellite imagery (e.g., Moderate Resolution Imaging Spectroradiometer). The new machine learning approach improves accuracy from prior work and can contribute to improved efforts to validate melt pond models or understand trends in pond coverage. Nevertheless, we encounter and carefully document significant challenges to retrieving melt pond fractions from low‐resolution optical imagery. These limit accuracy to levels below that necessary for resolving climatologically important trends. We conclude that greatly expanding the collection of high‐resolution satellite imagery over sea ice is necessary to monitor melt pond coverage with the accuracy needed by the scientific community.

<p>Achieving reliable observations of avalanche debris is crucial for many applications including avalanche forecasting. The ability to continuously monitor the avalanche activity, in space and time, would provide indicators on the potential instability of the snowpack and would allow a better characterization of avalanche risk periods and zones. In this work, we use Sentinel-1 SAR (synthetic aperture radar) data and an independent in-situ avalanche inventory (as ground truth labels) to automatically detect avalanche debris in the French Alps during the remarkable winter season 2017-18. </p><p>Two main challenges are specific to this data: (i) the imbalance of the data with a small number of positive samples — or avalanche — (ii) the uncertainty of the labels coming from a separate in-situ inventory. We propose to compare two different deep learning methods on SAR image patches in order to tackle these issues: a fully supervised convolutional neural networks model and an unsupervised approach able to detect anomalies based on a variational autoencoder. Our preliminary results show that we are able to successfully locate new avalanche deposits with as much as 77% confidence on the most susceptible mountain zone (compared to 53% with a baseline method) on a balanced dataset.</p><p>In order to make an efficient use of remote sensing measurements on a complex terrain, we explore the following question: to what extent can deep learning methods improve the detection of avalanche deposits and help us to derive relevant avalanche activity statistics at different scales (in time and space) that could be useful for a large number of users (researchers, forecasters, government operators)?</p>

The purpose of the study was to date the powerful snow avalanches occurred in the avalanche catchment area in the river Korgon basin (Northwestern Altai) by the dendrochronological method. The paper presents the results of the analysis of a number of dendrochronological indicators of avalanches: the age of trees, the relation between widths of annual rings from opposite sides of the trunk, the presence of reactive (compressional) wood and traumatic resin canals, the dates of death and formation of wounds in trees, the presence of clearing effects (a sharp increase in growth) in three avalanche catchments, as well as a complex dendrochronological index of the avalanche activity. The dates of releases of the powerful avalanches were established down to 1570. It has been found that against the background of increasing amount of winter precipitation in these catchments, there are different trends in occurrence of the avalanches, which is determined by their morphological properties. In the avalanche areas with steeper slopes, due to repeated unloading of the avalanche centers in winter, the probability of releasing of powerful avalanches decreases. For the same reason, the powerful avalanches do not release during the years of maximum snow accumulation. The trends in the avalanche activity in the region under consideration had been obtained for the first time. The dendrochronological index of the avalanche activity, which is the ratio of the number of the tree growth failures to the number of the examined trees, is a good indicator of a release of a powerful avalanche even at a value of 0.85, provided that the number of examined trees in the lower part of the transit zone and in the zone of accumulation is 25–35 units. The results of this study may be used to predict the territorial differentiation of changes in the avalanche activity due to climate change. In connection with the recreational development of the Altai territory, they may also be of practical importance.