As we seek to better understand our world and the impacts of a changing climate, the importance of satellite imagery or remote sensing cannot be underestimated. Recently news came from a team of researchers at Scripps Institution of Oceanography at UC San Diego of the changing ice sheet pattern in Antarctica. The research team found by studying data from four separate European Space Agency (ESA) satellite missions, NASA ice velocity data, and outputs from NASA computer models that these ice shelves have experienced a loss of nearly 4,000 gigatons since 1994 — producing an amount of meltwater that can nearly fill the Grand Canyon — as a result of melting from increased heat in the ocean under the ice shelves (UCSD, 2020). This is just one example of using remote sensing to answer questions about how changing climate can impact our world.

The research team at Icebreaker One have been investigating the use of remote sensing across the SERI project and potential benefits these techniques can bring to stakeholders and beneficiaries. Focussing our immediate work on the insurance sector we’ve seen examples of the use of remote sensing in the field of catastrophe modelling, exposure management and risk engineering to event response and claims control. Some of these examples can be used to guide our work to define climate-ready financial products within the SERI project for property insurance, e.g. 4EI used remote sensing data to develop a building heat index across the UK. Could such a dataset be used in addition to existing data sets (such as flooding hazards, landslide susceptibility) and help better define financial products such as mortgages and home insurance? We don’t know yet if climate change is causing more weather weirdness but a project to use supercomputers to re-run climate models has been proposed by the Met Office.

Figure 1: some uses of remote sensing within property catastrophe insurance

Other examples include Skytek, a technology company that uses remote sensing technology to support shipping insurance businesses. They use earth observation data to monitor storms and severe weather events, to track ships offshore and assess their proximity to potential disastrous storms. They also use data to guide insurance underwriting and post storm claims. Combining the ship’s tracking system (AIS which also uses Global Positioning System (GPS) to define the ship’s location and satellite imagery of upcoming weather events allows insurers to identify ships which may be at risk and monitor their response to storm alerts (Skytech, 2020). The shocking events in Lebanon have been well documented in the mainstream media and there are a number of before and after satellite images published and offered as free data by major satellite image providers (e.g. Maxar). That data is very useful for damage analysis for post-event insurance claims which may be used following man-made disasters or natural catastrophes.

From extreme weather events and disaster response to ship emissions, a topic which is of high importance to the International Maritime Organisation and their recent focus on cutting sulphur oxide emissions. The use of UAV (unmanned aerial vehicles) remote sensing is evolving. Companies such as Martek Marine uses drones to assess emissions from ships. Another example is EMSA that uses UAV remote sensing with gas sensors to monitor sulphur emissions on the coast of Denmark. One way the shipping industry is looking to reduce emissions is by moving to LNG (Liquified Petroleum Gas).

As the IMO (2016) states, “the use of LNG is considered to have significant environmental advantages. An LNG fuelled ship reduces the emissions of NOx by 85% to 90% (using a gas only engine), and SOx and particles by close to 100% compared to today’s conventional fuel oil. In addition, LNG fuelled ships may result in a net reduction of greenhouse gas (GHG) emissions”.

Decarbonisation of shipping is one of the product ideas of the SERI project – we are exploring how remote sensing and related technologies can be used to develop financial products.

The agriculture sector is one of the heaviest greenhouse emitters and remote sensing can be used to estimate emissions. One example by SEGES using Web Map Service (WMS) imagery highlights how machine learning algorithms were applied to remote sensing imagery to detect in an automated way over 26,000 slurry tanks and assess ammonia emissions across 34,000 farms and over 42,933 km2 land in Denmark in 2019.

From the studies mentioned above we can see that remote sensing data is usually used within each sector as derived data, i.e. it’s not the raw data outputs that are most useful but those obtained by processing and analysing imagery information to gain climate and environmental insights, monitor and detect changes. Creating climate-ready financial products offers an opportunity to use or develop derived information,  e.g. thermal or greenhouse gas emissions calculations, flood hazard mapping, land-use etc.

Through our research and collaboration with SERI project partners and stakeholders, we hope to be able to use remote sensing to inform our development of climate-ready financial products in a sandbox/test-bed environment over the coming months. We are currently collecting use cases from other fields that may contribute to climate-ready financial products innovation. This will be an exciting journey –please reach out if you’d like to learn more. We’d love to hear from you!