# Capability Analysis of Earth Observation Data for Integrated Emergency Management
## 4 R2-D2 Case Study Results
### Previous Use of Satellite Imagery for Managing Selected Emergency Management Situations
Further work is required to distinguish between different landfill types, which presents problems with sites showing high variability in waste accumulation. MOD11C3-V6 is a MODIS land surface temperature (LST) product with a spatial resolution of 0.05 degrees (approximately 5 km) with daily acquisitions. The biodegradation of organic waste causes the land surface temperature of waste sites to be higher than that of their surrounding area [50], making it a valuable metric for the location of IWS. The MOD13C2 product delivers the Enhanced Vegetation Index (EVI) at monthly epochs. The EVI was chosen instead of the Normalised Difference Vegetation Index (NDVI) due to the minimal error caused by atmospheric conditions and canopy background noise [51]. Lower vegetation indices are often reported near waste sites [52; 53], indicative of IWS location. Electricity transmission faults: Several case studies have demonstrated how optical, radar and nightlight satellite imagery can help to comprehensively monitor extensive power networks, detect outages, determine the extent of power grid damage, and create risk maps to indicate vegetation risks. [PERSON] et al. [54], for example, used high-resolution Worldview-2 8-channel imagery to help monitor vegetation along power lines, which poses a risk to such infrastructure. Airborne LiDAR was used to validate the image segmentation approach for a power grid in western Norway. [PERSON] et al. [55] deployed a hybrid approach, combining airborne LiDAR with satellite data to identify vegetation risk to power lines. While LiDAR provides data at a granular level, in particular helping map tree heights, its cost and accessibility can impact its coverage and update frequency. However, commercial high-resolution multi-spectral or hyper-spectral satellite imagery can also be costly for coverage, e.g., at a city scale. [PERSON] et al. [56] provided a detailed overview of case studies using various methods (including SAR and optical imagery) to map power lines and towers, monitor vegetation in power line corridors, and identify trees that grow within the minimum vegetation clearance distance. They demonstrated how other alternative options, such as airborne lidar, can improve data quality in areas where satellite visibility is poor at ground level. [PERSON] et al. [57] demonstrated the ability of VIIRS Day/Night Band (DNB) imagery from the Suomi National Polar-orbiting Partnership to monitor power outages following Hurricane Sandy in 2012. They deployed a combination of satellite imagery and locally relevant geospatial data for infrastructure and population to produce a power outage monitoring network for continental USA and quantitatively estimate power outages requiring repair. [PERSON] et al. [58] also used VIIRS DNB imagery to detect power outages in conflict-affected areas, monitor population distribution changes, identify infrastructure damage and recovery, and assess economic impacts. Such studies often use satellite imagery together with other contextual geospatial information, such as infrastructure and population data, to improve situational awareness. The inclusion of other sensor data, such as lidar, allows improved analysis. Nonetheless, challenges remain in utilising nightlight data. These include the impact of lunar illuminance and cloud cover on nighttime lights, the temporal disparity between the event and its reflection in the data, and limited spatial coverage or spatial resolution, which might not be suitable for detecting small-scale events. Moreover, there are the associated costs involved in acquiring coverage at a city scale.