All-weather avalanche activity monitoring from space?

Information on avalanche activity or on non-activity on local and regional scale is of great value for avalanche warning services, traffic authorities and experts responsible for safety in communities or ski resorts. In particular during bad weather condition, such information is available only very limited or not at all. The aim of ESA IAP feasibility study "Improved Alpine Avalanche Forecast Service" was to investigate existing technology to overcome this gap. Of particular interest were radar-based techniques that have the potential to operate independently of daylight and weather conditions

Multitemporal Snow Cover Mapping in Mountainous Terrain for Landsat Climate Data Record Development

A multitemporal method to map snow cover in mountainous terrain is proposed to guide Landsat climate data record (CDR) development. The Landsat image archive including MSS, TM, and ETM+ imagery was used to construct a prototype Landsat snow cover CDR for the interior northwestern United States. Landsat snow cover CDRs are designed to capture snow-covered area (SCA) variability at discrete bi-monthly intervals that correspond to ground-based snow telemetry (SNOTEL) snow-water-equivalent (SWE) measurements. The June 1 bi-monthly interval was selected for initial CDR development, and was based on peak snowmelt timing for this mountainous region. Fifty-four Landsat images from 1975 to 2011 were preprocessed that included image registration, top-of-the-atmosphere (TOA) reflectance conversion, cloud and shadow masking, and topographic normalization. Snow covered pixels were retrieved using the normalized difference snow index (NDSI) and unsupervised classification, and pixels having greater (less) than 50% snow cover were classified presence (absence). A normalized SCA equation was derived to independently estimate SCA given missing image coverage and cloud-shadow contamination. Relative frequency maps of missing pixels were assembled to assess whether systematic biases were embedded within this Landsat CDR. Our results suggest that it is possible to confidently estimate historical bi-monthly SCA from partially cloudy Landsat images. This multitemporal method is intended to guide Landsat CDR development for freshwaterscarce regions of the western US to monitor climate-driven changes in mountain snowpack extent

Operational Applications of Satellite Snowcover Observations

The history of remote sensing of snow cover is reviewed and the following topics are covered: various techniques for interpreting LANDSAT and NOAA satellite data; the status of future systems for continuing snow hydrology applications; the use of snow cover observations in streamflow forecasts by Applications Systems Verification and Transfer participants and selected foreign investigators; and the benefits of using satellite snow cover data in runoff prediction

Improving flood forecasting using multi-source remote sensing data – Report of the Floodfore project

Current remote sensing satellites can provide valuable information relevant to hydrological monitoring. And by using available in situ measurements together with the satellite data the information can be even more valuable. The FloodFore project developed new methods to estimate hydrological parameters from multi source remote sensing and in situ data. These hydrological parameters are important input to the watershed simulation model in order to improve the accuracy of its forecasts. In the project several new methods were either developed or demonstrated: satellite based snow water equivalent (SWE) estimation, weather radar based accumulated precipitation estimation, satellite based soil freezing state determination, and SWE estimation with high spatial resolution using both microwave radiometer and SAR data. Also a visualisation system for multi source information was developed to demonstrate the new products to users. The effect of the snow remote sensing estimates to the hydrological forecasting accuracy was studied for the Kemijoki river basin. The commercialisation possibilities of the results of the project were also studied

Remote sensing of snow : Factors influencing seasonal snow mapping in boreal forest region

Monitoring of snow cover in northern hemisphere is highly important for climate research and for operational activities, such as those related to hydrology and weather forecasting. The appearance and melting of seasonal snow cover dominate the hydrological and climatic patterns in the boreal and arctic regions. Spatial variability (in particular during the spring and autumn transition months) and long-term trends in global snow cover distribution are strongly interconnected to changes in Earth System (ES). Satellite data based estimates on snow cover extent are utilized e.g. in near-real-time hydrological forecasting, water resource management and to construct long-term Climate Data Records (CDRs) essential for climate research. Information on the quantitative reliability of snow cover monitoring is urgently needed by these different applications as the usefulness of satellite data based results is strongly dependent on the quality of the interpretation. This doctoral dissertation investigates the factors affecting the reliability of snow cover monitoring using optical satellite data and focuses on boreal regions (zone characterized by seasonal snow cover). Based on the analysis of different factors relevant to snow mapping performance, the work introduces a methodology to assess the uncertainty of snow cover extent estimates, focusing on the retrieval of fractional snow cover (within a pixel) during the spring melt period. The results demonstrate that optical remote sensing is well suited for determining snow extent in the melting season and that the characterizing the uncertainty in snow estimates facilitates the improvement of the snow mapping algorithms. The overall message is that using a versatile accuracy analysis it is possible to develop uncertainty estimates for the optical remote sensing of snow cover, which is a considerable advance in remote sensing. The results of this work can also be utilized in the development of other interpretation algorithms. This thesis consists of five articles predominantly dealing with quantitative data analysis, while the summary chapter synthesizes the results mainly in the algorithm accuracy point of view. The first four articles determine the reflectance characteristics essential for the forward and inverse modeling of boreal landscapes (forward model describes the observations as a function of the investigated variable). The effects of snow, snow-free ground and boreal forest canopy on the observed satellite scene reflectance are specified. The effects of all the error components are clarified in the fifth article and a novel experimental method to analyze and quantify the amount of uncertainty is presented. The five articles employ different remote sensing and ground truth data sets measured and/or analyzed for this research, covering the region of Finland and also applied to boreal forest region in northern Europe

Advances in Data Mining Knowledge Discovery and Applications

Advances in Data Mining Knowledge Discovery and Applications aims to help data miners, researchers, scholars, and PhD students who wish to apply data mining techniques. The primary contribution of this book is highlighting frontier fields and implementations of the knowledge discovery and data mining. It seems to be same things are repeated again. But in general, same approach and techniques may help us in different fields and expertise areas. This book presents knowledge discovery and data mining applications in two different sections. As known that, data mining covers areas of statistics, machine learning, data management and databases, pattern recognition, artificial intelligence, and other areas. In this book, most of the areas are covered with different data mining applications. The eighteen chapters have been classified in two parts: Knowledge Discovery and Data Mining Applications

Remote Sensing of Snow Cover Using Spaceborne SAR: A Review

The importance of snow cover extent (SCE) has been proven to strongly link with various natural phenomenon and human activities; consequently, monitoring snow cover is one the most critical topics in studying and understanding the cryosphere. As snow cover can vary significantly within short time spans and often extends over vast areas, spaceborne remote sensing constitutes an efficient observation technique to track it continuously. However, as optical imagery is limited by cloud cover and polar darkness, synthetic aperture radar (SAR) attracted more attention for its ability to sense day-and-night under any cloud and weather condition. In addition to widely applied backscattering-based method, thanks to the advancements of spaceborne SAR sensors and image processing techniques, many new approaches based on interferometric SAR (InSAR) and polarimetric SAR (PolSAR) have been developed since the launch of ERS-1 in 1991 to monitor snow cover under both dry and wet snow conditions. Critical auxiliary data including DEM, land cover information, and local meteorological data have also been explored to aid the snow cover analysis. This review presents an overview of existing studies and discusses the advantages, constraints, and trajectories of the current developments

Activities of the Remote Sensing Information Sciences Research Group

Topics on the analysis and processing of remotely sensed data in the areas of vegetation analysis and modelling, georeferenced information systems, machine assisted information extraction from image data, and artificial intelligence are investigated. Discussions on support field data and specific applications of the proposed technologies are also included

SESS Report 2021 The State of Environmental Science in Svalbard - an annual report

Executive Summary The State of Environmental Science in Svalbard (SESS) report 2021 together with its predecessors contributes to the documentation of the state of the Arctic environment in and around Svalbard, and highlights research conducted within the Svalbard Integrated Arctic Earth Observing System (SIOS). Climate change is a global problem, but many of its impacts are being felt most strongly in the Arctic. Given its remote but accessible location, Svalbard constitutes an ideal place to study the Arctic environment in general, including, more specifically, the causes and consequences of climate change. The Arctic Climate Change Update (2021) emphasised the severity of global climate change for ecosystems across the Arctic. They are undergoing radical changes regarding their structure and functioning, affecting flora, fauna and livelihoods of Arctic communities. Oceanic ecosystems and food webs are directly and indirectly altered by the warming and freshening of the Arctic Ocean. A prolonged open water period and the expansion of open water areas caused by declining sea ice affect under-ice productivity and diversity. These changes have cascading effects through ecosystems and impact the distribution, abundance and seasonality of a variety of marine species. Svalbard is located at one of the key oceanic gateways to the Arctic. This land–ice–ocean transition zone is a system particularly vulnerable to environmental changes. Svalbard’s environment is influenced by maritime processes; thus extensive observation of the ocean system is nowadays necessary. The chapter on the iMOP project reports seawater temperature and salinity variability over the last decades and indicates changes of Svalbard fjord seawater properties. The chapter highlights the role of a collaborative and supportive network of observatory operators and encourages joint planning and maintenance of future marine observatories. Arctic vegetation plays a key role in land–atmosphere interactions. Alterations can lead to ecosystem–climate feedbacks and exacerbate climate change. Extreme precipitation events are already becoming more frequent. Together with an increasing rain-to-snow ratio they impact the structure and functioning of terrestrial ecosystems. Dynamics in Arctic tundra ecosystems are expected to undergo fundamental changes with increasing temperatures as predicted by climate models. To detect, document, understand and predict those changes, COAT Svalbard provides a long-term and real-time operational observation system through ecosystem-based terrestrial monitoring. The observation system consists of six modules comprising food web pathways as well as one climate-monitoring module and focuses on two contrasting regions in Svalbard to allow for intercomparison. To date, the project has done an initial assessment of tundra ecosystems in Norway and will now begin with the long-term ecosystembased monitoring. For remote regions such as the Svalbard archipelago, terrestrial photography is a crucial addition to satellite imagery, because land-based cameras offer high temporal resolution and insensitivity towards varying weather conditions. PASSES provides an overview of cameras operating in Svalbard managed by research institutions and private companies. The survey revealed difficulties and knowledge gaps preventing the full potential of the terrestrial photography network in Svalbard from being used. Therefore, PASSES recommends the creation of a Svalbard camera system network. The effects of climate change contributed to a specific anomaly of the springtime Arctic atmosphere, namely a pronounced depletion of stratospheric ozone during March and April 2020, which can be called an Arctic ozone hole. In Svalbard, the amount of ozone loss was recorded by ground-based dedicated spectroscopic instruments measuring the total ozone column as well as the UV irradiance (EXAODEP-2020, an update of UV Ozone). The latter is important for effects on the biota. Corresponding erythemal daily doses for spring 2020 show a doubling compared to previous years with less or no ozone depletion. While the correspondence between ozone loss and increase in UV doses follows a well-known relationship, the possible later consequences of the observed springtime increase of UV doses on Svalbard’s environment need to be further studied. A particular method to observe the Svalbard environment, which has seen a very strong increase in usage during recent years, is the application of unmanned airborne or marine vehicles. The update on recent publications using these devices (UAV Svalbard) reveals that especially conventional remotely operated aerial vehicles (drones) with camera equipment are now widely used. It is recommended to SIOS to foster interdisciplinary communication among the multitude of drone users to establish exchange of information and data. New EU regulations for drone operations are being put in place from 2022 onwards also in Svalbard. Climate services are receiving more and more attention from Arctic countries, because they translate data into relevant and timely information, thereby supporting governments, societies and industries in planning and decision-making processes. SIOS contributes to climate services by providing research infrastructure with an overarching goal to develop and maintain a regional observational system for long-term measurements in and around Svalbard. The SIOS Core Data (SCD) consists of a list of essential Earth System Science variables relevant to determine environmental change in the Arctic. SCD is developed to improve the relevance and availability of scientific information addressing ESS topics for decision-making. SIOS Core Data providers have committed to maintain the observations for at least five years, to make the data publicly available, and to follow advanced principles of scientific data management and stewardship. Arctic climate change is posing risks to the safety, health and well-being of Arctic communities and ecosystems. Still, there remain gaps in our understanding of physical processes and societal implications. The authors of the SESS chapters have highlighted some unanswered questions and suggested concrete actions that should be taken to address them. The editors would like to thank the authors for their valuable contributions to the SESS Report 2021. These chapters illustrate how SIOS projects contribute to ensure the future vitality and resilience of Arctic peoples, communities and ecosystems