MRI-R2: Acquisition of Airborne Remote Sensing System for Oceanographic, Terrestrial, and Environmental Research

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"This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5)."The PI?s request funding for the acquisition, integration and testing of the components of an airborne remote sensing system comprised of a waveform airborne LIDAR, a hyperspectral camera, a high-resolution video camera, a GPS/inertial motion unit, data acquisition and post-processing hardware and software, an existing IR video camera, and the labor and supplies to integrate and test the system over two years. Satellite remote sensing has enabled remarkable progress in the ocean, earth, atmospheric and environmental sciences through its ability to provide global coverage with ever increasing spatial resolution. However, the temporal coverage of low earth orbiting satellites is not optimal. This temporal coverage may be sufficient for mesoscale ocean processes with time scales of a month, but is not sufficient for ocean processes that respond to atmospheric forcing with time scales of hours to days and other submesoscale ocean processes, especially coastal processes, both physical and biological, and air-sea-land interactions in the coastal zone. In the hydrological sciences the time scales can range from hours for flash floods, to days for snowfall on mountain ranges, to months for the snowmelt into the river systems. On an even smaller scale, remote sensing of the built environment catalyzes research into more resource-efficient and sustainable cities but requires building-resolving thermal resolution of a few meters. For this range of phenomena, satellite data are very useful but not optimal, and need to be supplemented with higher resolution airborne data that are not tied to the strict schedule of a satellite orbit. This proposal addresses these needs, in research and training in these areas of science and engineering.Broader ImpactsThe data provided by the system should well support the stated research areas of coastal process and oceanography, bio, hydrology, and built areas. These are all relevant to California and to larger issues in climate and environmental change. The combination of LIDAR, a visual camera, and hyperspectral imaging is important to really get the whole story. Aside from the science that this system will enable, the data sets will be of huge interest to machine learning and robotics researchers as well. Outreach and education efforts benefit from programs in place, and seem strong. Students will love the flyovers of their schools and neighborhoods - this simple demo should be a high priority. The proposed collaboration with COSEE CA should prove fruitful and will provide the opportunity to excite a new generation of young scientists.