The Alps adopted in 1995 the Alpine Convention, a regional environmental protection and management initiative that considered a transnational mountain area in its geographical entirety. The aim of the convention is to foster the sustainable development of the alpine region with adequate preservation of the alpine ecosystems and their services to people. In addition, the Alpine ecosystems are also monitored following European macro-regional strategy for the Alpine region (EUSALP). EUSALP has been endorsed by the European Union in 2016 and focusses on alpine-specific challenges such as ensuring sustainable development. The strategy underlines the importance of protecting and enhancing biodiversity in the Alpine region and supports the preservation and maintenance of ecosystems and their services throughout the entire Alpine macro-region.
The alpine stakeholders have recognized and acknowledged the tremendous value of EO derived information. It can be expected that EO data will be in the near future more extensively used to support the monitoring and reporting on the status of biodiversity, nature protection and natural capital in the alpine region. However, despite the high potential of the Sentinel data only relatively few operational applications have been developed so far in this region.
The aim of the EO4Alps ecosystem project is to develop and fully deploy 6 services that focus on the common challenges related to ecosystem mapping and monitoring in the alpine region, address the specific requirements of national and regional stakeholders, and are large in scope and content to strengthen regional cooperation across countries. These services are Ecosystem mapping, Forest disturbances, Forest phenology, Forest fire recovery, Grassland management, Grassland abandonment. The core datasets including Sentinel-2 and Landsat-8 processed with FORCE and the Copernicus High Resolution Layers.
From the software perspective, a core objective of the project is to demonstrate the added value of leveraging the open, non-monolithic and federated network of platforms paradigm developed by ESA with the Network of Resources initiative to provide state of the art processing at regional scale. To this end, the system has been designed to maximize the exploitation of existing capabilities rather than developing another platform from scratch. Prominently, the project is an early adopter of the ESA OpenEO Platform, a versatile cloud-based processing and analytics environment for Earth Observation data. Through OpenEO platform, our system is able to access the required Sentinel-2 and Landsat-8 datasets already preprocessed to ARD level and organized in a Datacube. The platform allows to define rich processing graphs on top of the input data to perform processing with continental-level scalability. For processing steps that rely on existing software not practically portable to the cloud, our system is developing a container batch job processing system designed on top of the open-source Nomad orchestrator. The job processing system leverages the EODC cloud, which provides access to cloud computing Infrastucture-as-a-Service, for obtaining scalable processing resources. The results of the batch job processing are fed to a comprehensive data system built around current technologies and open source components, including: a) a Datacube component, b) a SpatioTemporal Asset Catalog (STAC), c) Object storage, d) Cloud Optimized Geotiffs and e) Geonode. Through the selected tech stack, the system opens for downstream exploitation of its assets, ranging from visualization to download to further processing, paving the way for it to become a Network of Resources provider for high-level applications based on ecosystems data.
The project “eo4alps-landslide / Geo-information Services for Landslides in the Alps has the objective of exploiting the potential of new satellite data, ground motion services and advanced modelling capacities for the assessment of gravitational hazards in the Alpine region. Many authorities and other stakeholders responsible for landslide disaster risk management are actively involved in all project phases. “eo4alps-landslide” aims at ensuring that satellite-based Earth Observation (EO) products are increasingly and more efficiently used in practice for science and operational DRM procedures.
The presentation targets the presentation of some “eo4alps-landslide” products in order to create ground motion maps, harmonised and advanced landslide inventories and susceptibility/hazard maps for the Alps. The EO-based services and products can be complemented by local datasets and terrain data from the end users directly in GEP.
The products” include 1) automatic landslide detection using satellite optical and InSAR-based services, 2) harmonised and advanced landslide catalogues resulting from the satellite based detection and local inventories, 3) susceptibility/hazard maps consisting of possible landslide source areas and landslide type-specific runout modelling. The services are generic in order to be used at several spatial scales from Tier 1 (region) to Tier 2 (municipality) and Tier 3 (local slope). Examples of products for Swiss and France will be presented and discussed. Further, interoperability of the “eo4alps-landslide” products with services of other “eo4alps” initiatives, as well as with the European Ground Motion Service (EGMS), will be discussed.
The seasonal snow cover is an important resource in mountain regions such as the Alps, the monitoring of which is crucial for water management, snow hydrology, tourism, and natural hazard mitigation. Snow has a high societal economic impact on people living in the Alps and is an important source of headwater for drainage basins downstream. AlpSnow (2020-2022) is a science activity within ESA's Alpine Regional Initiative, addressing the development of novel Earth Observation techniques and algorithms for the generation of innovative and consistent snow products optimized for specific scientific and operational applications. Input data for AlpSnow products are primarily provided by the Copernicus Sentinel satellite family, but also data of third-party missions such as the SAR missions TerraSAR-X and TanDEM-X (X-band SAR), SAOCOM (L-Band SAR), and PRISMA (hyperspectral sensor) are used. The products cover several physical parameters of the seasonal snowpack, namely snow area extent, snow surface albedo and grainsize, snow water equivalent, snow depth, snowmelt area extent and liquid water content. The usability of the products will be demonstrated within six scientific and operational use cases in the fields of meteorology, snow science, hydrology, and water management.
For development and validation of the algorithms we selected five test areas in different Alpine regions, covering a wide range of elevation zones and meteorological conditions. Each of the test areas, two of which are part of the INARCH activity, is equipped with operational field stations providing time series of meteorological and snow measurements. These are supplemented by specific activities in snow research and hydrology, including the collection of additional snow reference data for validation, as snow pits and snow transects measured during field campaigns.
In the first phase of the project several candidate algorithms for each of the snow parameters were selected, implemented, tested, and evaluated in the test areas. Based on this intercomparison we selected a set of preferred algorithms which are further developed, optimized for environments with complex topography and diverse surface cover. Regarding high resolution snow extent, we intercompare two algorithms, an advanced linear multispectral unmixing approach and a new method applying machine learning techniques. Both methods, developed within the project, are exploiting the spectral capabilities of Sentinel-2 and Landsat satellite sensors. Surface albedo and grain size products are generated adopting the algorithms from Dozier and Painter (2004) and Kokhanovsky (2015). A critical issue for both algorithms is the correction of topographic effects on the spectral irradiance and on the bidirectional reflectance distribution. The parameters wet snow extent and snow water equivalent or snow depth are derived from SAR data. For snow water equivalent two approaches are explored. The first approach develops methods for assimilation of EO snow extent products into the physical SNOWGRID model of the Austrian meteorological service (ZAMG) that is driven by numerical meteorological data and provides operationally spatially distributed snow information over the Alps. Additionally, we study the suitability and limitations of repeat-pass SAR interferometry for generating maps of snow accumulation in mountain regions with a spatial resolution in the order of about hundred meters. The interferometric phase provides a direct physical measurement of the change of the snow mass during the time span between two image acquisitions but works only for dry snow conditions. These developments are tested using L-Band SAR data from ALOS-2 PALSAR and SAOCOM and prepare for the Copernicus Expansion Mission ROSE-L. To derive maps of total snow depth we investigate the suitability of differencing DEMs from bistatic TanDEM-X data acquired during snow free conditions and conditions with maximum snow accumulation at the start of the snowmelt season.
We will present the results of the experiments dedicated to the selection of retrieval algorithms for the different snow parameters and report on the accuracy of the prototype algorithms and products developed within the project.
Snow is a crucial variable in mountain environments. In Europe, the majority of the precipitation during winter and early spring falls as snow over 1.000 m altitude, and remains stored in the snow pack until the melting season, when it returns in the hydrological cycle. Different public and private institutions are therefore interested in snow for civil or industrial purposes, such as public agencies dealing with civil protection or hydrological balances, and hydropower companies that will eventually harvest water for energy production.
One of the most important snow variables that is commonly observed is snow water equivalent (SWE). The common way to monitor SWE is to organize snow measurement campaigns in the field and sample snow depth and snow density at given control points, then spatially distribute the information using multivariate geostatistical techniques. This approach is affected by several limitations such as the cost of workforce and safety issues due to avalanche risks, which generally limit the frequency of the updates to 7-10 campaigns per winter and results in the scale of analysis being constrained to small catchment areas.
In recent years new methodologies have emerged in snow monitoring. For example, physically-based models calculate snow evolution by transforming the meteorological forcing into snow accumulation or melting according to the mass and energy balance in the snow pack. The advantage is that results are always available as they are independent on the availability of snow measurement campaigns or other blocking factors, like cloudiness, affecting satellite data. The drawbacks are the poor spatial accuracy of the snow limits, the uncertainty in the estimation of precipitation amount and phase in mountain regions and microclimatic disturbances affecting the snow, like wind accumulation and erosion.
At the same time, Earth Observation (EO) techniques have emerged thanks to the availability of open access high temporal and spatial resolution satellite data, especially relevant for the detection of the snow cover area for instance using optical images acquired from Sentinel-2 and Sentinel-3 and the Sentinel-1 SAR data. However, the approaches for SWE estimation purely based on EO data are limited by the current technological maturity of EO based methods. In addition, the presence of vegetation complicates the use of remote-sensing data alone.
In this context the eo4alps snow (https://eo4society.esa.int/projects/eo4alps-snow/) project, funded by ESA inside the Regional Initiative for the Alps, aims to provide a service of high-resolution SWE and snow depth monitoring in quasi-real time at Alpine scale. The approach is based on a hybrid technology that merges the advantages of the physical model with high-resolution high-frequency EO snow products. In particular, it is taking advantage of high resolution binary snow cover maps from Sentinel-2, wet snow cover maps from Sentinel-1 SAR data and coarser resolution daily snow cover fraction estimates from Sentinel-3 to improve the revisit frequency of the snow cover product.
The products of eo4alps snow will be available to different groups of users, such as Public Agencies and Hydropower companies, through a dedicated web application, supported by the Sentinel Hub platform that runs in the back-end, and with direct access to the data via OGC services available via the Euro Data Cube. This flexibility is intended to comply with different user requirements in terms of data access, area of interest and type of snow variable to retrieve.
Within the framework of the ESA Regional Initiatives for the Alps, the AlpGlacier project analyses capabilities to monitor glaciers in the Alps from the Copernicus Sentinel satellites. The derived products are: (a) snow cover and (b) flow velocities, both on glaciers, as well as (c) pro-glacial lakes and (d) slope instabilities, both in glacier forefields. For each product we develop algorithms working under challenging conditions, such as small glacier size and steep topography. Synergies through combination of sensors, with focus on Sentinel-1 and 2, are tested and sensor limits identified. Time series of the derived datasets are considered for trend detection and process understanding. A special focus lies on the exchange with stakeholders already involved in glacier monitoring and hazard risk to create products of use.
In a nutshell, snow cover on glaciers is mapped using a threshold-based approach, flow velocity is derived from measuring surface displacement using image correlation (offset-tracking), lakes are mapped by combining optical with SAR data and slope instabilities are mainly identified from SAR interferometry.
Specific challenges for the snow cover product are the identification and consideration of cloud cover and shadow, atmospheric and topographic correction of reflectance and deriving a suitable elevation of the end of summer snowline for patchy snow cover. For the flow velocity product using optical sensors, challenges are related to snow and cloud cover, cloud and terrain shadow and the relatively small size of most glaciers. This also applies to glacier lakes, which are very small when they form and have widely varying spectral properties, requiring a challenging combination of optical data with SAR information. The specific challenges to detect slope instabilities are the highly variable rates of movement combined with the long-lasting presence of snow cover, requiring the application of diverse InSAR processing approaches and a detailed visual analysis of the observed changes.
The presentation will show the approaches and developments for handling the challenges outlined above along with first demonstration products highlighting the progress in glacier monitoring from space.