Gregory P. Asner and Christopher B. Field Department of Global Ecology Carnegie Institution of Washington 260 Panama Street Stanford, CA 94305 650.462.1047 gasner@globalecology.stanford.ed cfield@globalecology.stanford.edu http://cao.stanford.edu
Airborne remote sensing will play a key role in NEON because neither ground-based nor satellite measurements can fully capture the spatial and temporal heterogeneity of ecosystem structural and functional changes that occur at high spatial resolution (or grain size) over large geographic areas. Airborne remote sensing provides an essential perspective that bridges the gap between what is inherently a very small plot or micrometeorological tower measurement, and a very broad satellite observation. However, the information provided by airborne remote sensing depends upon the technology and algorithms employed, and a program such as NEON might consider technology-algorithm interactions in terms of accuracy, repeatability and automation.
This response to the NEON RFI focuses on two advanced remote sensing technologies and sciences, which can now be brought together in a way that will advance regional ecological research and monitoring in NEON. Each technology provides the kind of data that is sufficiently rich in information to allow for highly automated analysis techniques, including accuracy and uncertainty reporting. One technology - hyperspectral imaging (also known as imaging spectroscopy or imaging spectrometry) - can provide detailed information on the cover, abundance and concentration of biological materials and biochemicals. The other technology - waveform light detection and ranging (wLiDAR) - can provide detailed information on the cover, height, shape, and architecture of vegetation, as well as ground topography. When combined, hyperspectral imaging and wLiDAR provide one of the most powerful, and ultimately practical, set of ecosystem observations available from the airborne vantage point.
The Carnegie Institution of Washington has undertaken a pathfinding effort to fully integrate these two technologies in system called the Carnegie Airborne Observatory (CAO; http://cao.stanford.edu). The CAO is a hybrid hyperspectral and wLiDAR system providing an observational suite that simultaneously probes the biochemical and structural properties of ecosystems, and it does so in a way that facilitates an operational collection and analysis approach for programs such as NEON. The breakthroughs in the CAO development not only include its core technology, but the integration of the hardware and software used to bring the diverse data sets together during flight. In-flight data fusion facilitates advances in the critical post-processing areas of geo-orthorectification, atmospheric correction, and the rapid production of science-ready data. This has a cascading effect on our ability to generate science results, such as canopy chemistry, physiology, structure, and aboveground biomass. The remainder of this RFI response provides the background to and details on the CAO “Alpha” System design, and the critical lessons learned for NEON. We also address a critical need for additional sensor development, based on our CAO experience to date, and offer to lead this effort for NEON.