The goal is to assess water quality, by using spectral reflectance cameras carried by drones to measure sunlight reflected by water
There are remote sensing methods for assessing water quality. This application is testing application of these methods using data collected using hyper and multi spectral cameras on drones.
The method is known as ‘Ocean Colour’, and it uses remote sensing to extract water quality parameters from the colour of the water. There are some limitations to this remote sensing method, such as scattering in atmosphere and cloud coverage.
We will test using SeaBee to collect higher spatial and temporal resolution data in coastal areas, which we cannot get currently from satellite sources (current satellite for ocean colour gives 300 m resolution).
Ocean colour algorithms work well in ocean settings right now, but not as well in detailed, coastal areas (like along the coast of Norway). In these coastal settings, even if spatial resolution is good enough, the ‘edges’ (from shadows, snow cover) complicate the results. Further, there are not many satellites measuring hyperspectral reflectance, and those that do pass around once a week. We expect that using SeaBee drones will give more flexibility to measure ocean colour, even with cloud
For this application, Seabee’s multispectral (Micasense RedEdge-dual and Altum-PT) and hyperspectral (Specim AFX10) cameras were used. These cameras capture the light signal leaving the water. The light signal contains information about its optical properties of the reflected light.Using the optical property information it is then possible to extract water constituents and their concentration such as phytoplankton and mineral or organic material.
Oslofjord is the sample area, as it provides various natural events, human impacts and weekly observation data collection by FerryBox (LINK). The data are collected by flying drones on a sunny day. In 2024, the missions will collect data that will be uploaded to SeaBee Geonode. From there, the data with be processed automatically using Ocean Colour alogrithms.