Integration of satellite interferometric data in civil protection strategies for landslide studies at a regional scale

Multi-Temporal Satellite Interferometry (MTInSAR) is gradually evolving from being a tool developed by the scientific community exclusively for research purposes to a real operational technique that can meet the needs of different users involved in geohazard mitigation. This work aims at showing the innovative operational use of satellite radar interferometric products in Civil Protection Authority (CPA) practices for monitoring slow-moving landslides. We present the example of the successful ongoing monitoring system in the Valle D’Aosta Region (VAR-Northern Italy). This system exploits well-combined MTInSAR products and ground-based instruments for landslide management and mitigation strategies over the whole regional territory. Due to the critical intrinsic constraints of MTInSAR data, a robust regional satellite monitoring integrated into CPA practices requires the support of both in situ measurements and remotely sensed systems to guarantee the completeness and reliability of information. The monitoring network comprises three levels of analysis: Knowledge monitoring, Control monitoring, and Emergency monitoring. At the first monitoring level, MTInSAR data are used for the preliminary evaluation of the deformation scenario at a regional scale. At the second monitoring level, MTInSAR products support the prompt detection of trend variations of radar benchmarks displacements with bi-weekly temporal frequency to identify active critical situations where follow-up studies must be carried out. In the third monitoring level, MTInSAR data integrated with ground-based data are exploited to confirm active slow-moving deformations detected by on-site instruments. At this level, MTInSAR data are also used to carry out back analysis that cannot be performed by any other tool. From the example of the Valle D’Aosta Region integrated monitoring network, which is one of the few examples of this kind around Europe, it is evident that MTInSAR provides a great opportunity to improve monitoring capabilities within CPA activities

Remote Sensing for Natural or Man-made Disasters and Environmental Changes

Disasters can cause drastic environmental changes. A large amount of spatial data is required for managing the disasters and to assess their environmental impacts. Earth observation data offers independent coverage of wide areas for a broad spectrum of crisis situations. It provides information over large areas in near-real-time interval and supplementary at short-time and long-time intervals. Therefore, remote sensing can support disaster management in various applications. In order to demonstrate not only the efficiency but also the limitations of remote sensing technologies for disaster management, a number of case studies are presented, including applications for flooding in Germany 2013, earthquake in Nepal 2015, forest fires in Russia 2015, and searching for the Malaysian aircraft 2014. The discussed aspects comprise data access, information extraction and analysis, management of data and its integration with other data sources, product design, and organisational aspects

Improving satellite-based monitoring of the Arctic polar regions: identification of research and capacity gaps

We present a comprehensive review of the current status of remotely sensed and in situ sea ice, ocean, and land parameters acquired over the Arctic and Antarctic and identify current data gaps through comparison with the portfolio of products provided by Copernicus services. While we include several land parameters, the focus of our review is on the marine sector. The analysis is facilitated by the outputs of the KEPLER H2020 project. This project developed a road map for Copernicus to deliver an improved European capacity for monitoring and forecasting of the Polar Regions, including recommendations and lessons learnt, and the role citizen science can play in supporting Copernicus’ capabilities and giving users ownership in the system. In addition to summarising this information we also provide an assessment of future satellite missions (in particular the Copernicus Sentinel Expansion Missions), in terms of the potential enhancements they can provide for environmental monitoring and integration/assimilation into modelling/forecast products. We identify possible synergies between parameters obtained from different satellite missions to increase the information content and the robustness of specific data products considering the end-users requirements, in particular maritime safety. We analyse the potential of new variables and new techniques relevant for assimilation into simulations and forecasts of environmental conditions and changes in the Polar Regions at various spatial and temporal scales. This work concludes with several specific recommendations to the EU for improving the satellite-based monitoring of the Polar Regions

Can big data and random forests improve avalanche runout estimation compared to simple linear regression?

Accurate prediction of snow avalanche runout-distances in a deterministic sense remains a challenge due to the complexity of all the physical properties involved. Therefore, in many locations including Norway, it has been common practice to define the runout distance using the angle from the starting point to the end of the runout zone (α-angle). We use a large dataset of avalanche events from Switzerland (N = 18,737) acquired using optical satellites to calculate the α-angle for each avalanche. The α-angles in our dataset are normally distributed with a mean of 33◦ and a standard deviation of 6.1◦, which provides additional understanding and insights into α-angle distribution. Using a feature importance module in the Random Forest framework, we found the most important topographic parameter for predicting α-angles to be the average gradient from the release area to the β-point. Despite the large dataset and a modern machine learning (ML) method, we found the simple linear regression model to yield a higher performance than our ML attempts. This means that it is better to use a simple linear regression in an operational context

Calibration of DART Radiative Transfer Model with Satellite Images for Simulating Albedo and Thermal Irradiance Images and 3D Radiative Budget of Urban Environment

Remote sensing is increasingly used for managing urban environment. In this context, the H2020 project URBANFLUXES aims to improve our knowledge on urban anthropogenic heat fluxes, with the specific study of three cities: London, Basel and Heraklion. Usually, one expects to derive directly 2 major urban parameters from remote sensing: the albedo and thermal irradiance. However, the determination of these two parameters is seriously hampered by complexity of urban architecture. For example, urban reflectance and brightness temperature are far from isotropic and are spatially heterogeneous. Hence, radiative transfer models that consider the complexity of urban architecture when simulating remote sensing signals are essential tools. Even for these sophisticated models, there is a major constraint for an operational use of remote sensing: the complex 3D distribution of optical properties and temperatures in urban environments. Here, the work is conducted with the DART (Discrete Anisotropic Radiative Transfer) model. It is a comprehensive physically based 3D radiative transfer model that simulates optical signals at the entrance of imaging spectro-radiometers and LiDAR scanners on board of satellites and airplanes, as well as the 3D radiative budget, of urban and natural landscapes for any experimental (atmosphere, topography,…) and instrumental (sensor altitude, spatial resolution, UV to thermal infrared,…) configuration. Paul Sabatier University distributes free licenses for research activities. This paper presents the calibration of DART model with high spatial resolution satellite images (Landsat 8, Sentinel 2, etc.) that are acquired in the visible (VIS) / near infrared (NIR) domain and in the thermal infrared (TIR) domain. Here, the work is conducted with an atmospherically corrected Landsat 8 image and Bale city, with its urban database. The calibration approach in the VIS/IR domain encompasses 5 steps for computing the 2D distribution (image) of urban albedo at satellite spatial resolution. (1) DART simulation of satellite image at very high spatial resolution (e.g., 50cm) per satellite spectral band. Atmosphere conditions are specific to the satellite image acquisition. (2) Spatial resampling of DART image at the coarser spatial resolution of the available satellite image, per spectral band. (3) Iterative derivation of the urban surfaces (roofs, walls, streets, vegetation,…) optical properties as derived from pixel-wise comparison of DART and satellite images, independently per spectral band. (4) Computation of the band albedo image of the city, per spectral band. (5) Computation of the image of the city albedo and VIS/NIR exitance, as an integral over all satellite spectral bands. In order to get a time series of albedo and VIS/NIR exitance, even in the absence of satellite images, ECMWF information about local irradiance and atmosphere conditions are used. A similar approach is used for calculating the city thermal exitance using satellite images acquired in the thermal infrared domain. Finally, DART simulations that are conducted with the optical properties derived from remote sensing images give also the 3D radiative budget of the city at any date including the date of the satellite image acquisition

Dendrochronological dating as the basis for developing a landslide hazard map – An example from the Western Carpathians, Poland

Most landslide hazard maps are developed on the basis of an area’s susceptibility to a land-slide occurrence, but dendrochronological techniques allows one to develop maps based on past land-slide activity. The aim of the study was to use dendrochronological techniques to develop a landslide hazard map for a large area, covering 3.75 km2. We collected cores from 131 trees growing on 46 sampling sites, measured tree-ring width, and dated growth eccentricity events (which occur when tree rings of different widths are formed on opposite sides of a trunk), recording the landslide events which had occurred over the previous several dozen years. Then, the number of landslide events per decade was calculated at every sampling site. We interpolated the values obtained, added layers with houses and roads, and developed a landslide hazard map. The map highlights areas which are poten-tially safe for existing buildings, roads and future development. The main advantage of a landslide hazard map developed on the basis of dendrochronological data is the possibility of acquiring long se-ries of data on landslide activity over large areas at a relatively low cost. The main disadvantage is that the results obtained relate to the measurement of anatomical changes and the macroscopic charac-teristics of the ring structure occurring in the wood of tilted trees, and these factors merely provide in-direct information about the time of the landslide event occurrence