# Capability Analysis of Earth Observation Data for Integrated Emergency Management
## 2 Conceptual Foundations
### Data Platforms and Providers of Satellite-Derived Information for Emergency Management  
Numerous different data platforms today offer free or subscription-based access to satellite imagery that has potential value to emergency management. For example, the Copernicus Data Space Ecosystem, provided by the European Space Agency (ESA), facilitates access to Sentinel-2, a constellation of two optical imaging satellites, Sentinel-2A and Sentinel-2B [26]. Together, they provide multi-spectral images spanning 13 spectral bands with high-resolution images of up to 10 m within a 5-day revisit time. However, while Sentinel-2 data are valuable for various Earth observation applications, they have limitations for use during emergencies in terms of spatial (too coarse to map and analyse the impact of extreme weather events on critical infrastructure) and temporal resolution (revisit time of five days is insufficient to assess rapidly changing environments and support real-time response). Moreover, as an optical sensor, Sentinel-2 only collects usable data during daylight hours, and is even then susceptible to, e.g., cloud coverage that is associated with stormy weather, thereby not providing 24-h coverage. As an example of a commercial provider, Airbus offers high-resolution optical imagery at 30 cm (Pleiades Neo, seven spectral bands) or 50 cm (Pleiades, five spectral bands), and SPOT imagery with a panchromatic resolution of 1.5 m and a multi-spectral resolution of 6 m through the Living Library on their OneAtlas platform [27]. Users can define a study area by drawing a polygon, uploading geospatial boundaries or specifying coordinates. However, costs compared to lower-resolution or freely available datasets can  
Figure 1: The UK’s Integrated Emergency Management Framework (IEMF) [25]. be a barrier to local authorities. Further, depending on the spatial area, daily imagery might not be available due to the revisit time, potentially a shortcoming for emergency management purposes. Emergency management therefore necessitates a multi-modal approach to data collation. Further potential data sources include the Moderate Resolution Imaging Spectroradiometer (MODIS), an instrument aboard NASA's Terra satellite collecting data at 250 m, 500 m and 1 km spatial resolutions [28], and Synthetic Aperture Radar (SAR), e.g., from ESA's Sentinel-1 satellites, providing continuous all-weather, day and night imagery of the Earth's surface. As Interferometric SAR can be used to detect changes in the Earth's surface with millimetre precision [29], this method could potentially be used to, e.g., detect height changes in IWSs, fallen trees and collapsed power lines. The value of satellite imagery can also be augmented and enhanced through further location-based data. For example, in the UK, the Met Office Hazard Manager provides emergency services with weather warnings, flood alerts and other hazard-related information. The European Centre for Medium-Range Weather Forecasts (ECMWF) provides the ERA5-Land dataset, which delivers hourly high-resolution information on atmospheric, land and oceanic climate variables [30]. This dataset is used for diverse applications such as climate monitoring, weather forecasting, emergency response and disaster management. Several commercial providers actively use Artificial Intelligence (AI) and machine learning algorithms to recognise specific patterns and trends in satellite imagery that help identify sites impacted by storms and support various cross-disciplinary applications, including highway infrastructure, electricity, railway and forestry. For example, LiveEO uses 3 m resolution imagery (Planet), Sentinel-2 imagery (Copernicus), in situ data (e.g., gas composition and temperature), SAR, and commercial (e.g., airborne lidar) data on demand [31]. This is used to garner information about vegetation location, height, species and vitality to evaluate the risk posed to large infrastructure network assets by identifying trees that could fall into power lines.