Remote sensing with satellites – A new era for risk management
Although satellite images have been available for decades, insurers have been reluctant to introduce them into their risk management. To leverage the full potential of this source of data and information, providers and users need to work together more closely.
The American Landsat programme and the French SPOT series have been providing images from orbit for civil applications since the 1980s. Thanks to the growing number of national and commercial providers in the remote sensing market, interested parties today can call on a variety of satellite systems, for example the European Union‘s Copernicus project. Seven specially developed satellite missions, the Copernicus Sentinels, form the centrepiece of the project‘s space component. These generate radar and spectral images for earth observation purposes, and to monitor the oceans and atmosphere. Besides satellite pictures, images of the surface of the earth are also provided by aircraft and drones at a lower altitude. Whereas satellites are suitable for observing large areas, such as flood areas, the advantages with drones lie in studying smaller sites and industrial complexes.
Resolution – The magic word in the industry
A major benefit of having such a variety of data providers is that temporal resolution has improved significantly in recent years. In the past, a weekly fly-by cycle was the rule. Nowadays, many regions of the earth are overflown on an almost daily basis by at least one system, which then supplies the relevant images. The challenge is to pick out the suitable data from a highly fragmented jungle of providers. Spatial resolution, in other words the ability to show details, is a key criterion with digital satellite images. The greater the number of grid cells (pixels) that are available to cover the area of interest, the more clearly defined the image will be. Whereas in the past, spatial resolutions in the tens of metres were standard, objects today can be identified in the decimetre range. This is known as VHR (Very High Resolution). With this level of detail, buildings and infrastructure, and even individual vehicles, can be clearly identified. The downside of this is that it involves a significantly larger volume of data, although this should pose no problem, at least for the analysis of smaller areas. As well as temporal and spatial resolution, spectral resolution plays a key role in remote sensors. This depends on the electromagnetic radiation wavelengths (e.g. visible light, near or far infrared) that are covered by the sensors. Satellites usually take pictures on different spectral channels. If they cover the visible light spectrum, the resulting image is similar to the view from an airplane. In contrast, infrared channels can provide information on the state of vegetation or plant vitality. This can be very useful for agricultural insurance and for estimating harvests. Thermal images show differences in temperature and are used to study climate-related issues or to monitor thermal leaks at power plants, for example. The various spectral channels also include radar images using the SAR principle (Synthetic Aperture Radar). The key advantage with these is that they are independent of weather conditions, since they allow images to be taken under the cloud cover.
Satellite data form part of what is called spatial data, because they include georeferencing. For this reason, they are a prime source of data for the analysis of natural hazards, and for short-term weather and long-term climate monitoring. Satellite data are also used in the Munich Re client and service tools, primarily as a visualisation and orientation background, such as those from Google Earth. This program has been available since 2005 and is an integral part of many map applications today. NATHAN (Natural Hazards Assessment Network), the Munich Re hazard assessment tool, also relies on such visualisation techniques. Somewhat less “visible” is the satellite information contained in complex data analysis, as for example in the NATHAN global natural hazard maps. This is also true of our hailstorm zones, wildfire map, and detailed flood zones, among others. The latter use high-resolution digital terrain models derived from satellite images. Satellite data first found their way into underwriting or risk management through post-event or post-disaster applications. They make use of up-to-date images to determine the area affected, and ideally even the loss intensity. Using geoanalytical procedures, these footprints can then be compared with a company‘s own exposure. This facilitates prompt and realistic loss estimates in property and agricultural insurance. Today’s improved temporal resolution also helps provide imaging technology support for monitoring tasks. Applications are also conceivable in the engineering sector, for example to observe the progress of construction projects or the development and status of infrastructure installations.
Formulate requirements precisely
In the past, the dialogue between risk managers and the data or service providers has not always been entirely successful. Part of the reason for this was that each side had little understanding of the other, and insufficient attention was paid to the requirements of the risk managers on the one hand, or to the technical limitations of the providers on the other. With the advance of big data and data analytics in an increasing number of business sectors, many new providers and start-ups are knocking on doors, offering solutions for the insurance industry. To ensure successful cooperation, the technical options on the provider side to meet the requirements of the risk managers must first be sounded out. Demands are also placed on the risk experts. They must give clear specifications to the data suppliers in order to make the most of the potential for technical innovation. Requests such as “we need better claims data” are clearly too imprecise in this context, and will invariably lead to disappointment on both sides. Nor should the difficulty be underestimated of the process between receiving the image raw data and ending up with usable underwriting information. In many instances, complex image processing and interpretation methods are involved. You can contact our team of geodata and satellite experts if you would like to find out more about this subject.