MODELING APPROACHES USING REMOTE SENSING DATA FROM THE NATIONAL ECOLOGICAL OBSERVATION NETWORK (NEON) AIRBORNE OBSERVATIONS

The National Ecological Observatory Network (NEON), being funded by the National Science Foundation, is a continental-scale ecological observation platform for understanding and forecasting the impacts of climate change, land use change, and invasive species on ecology. Airborne remote sensing plays a critical role in the scaling strategy underpinning the observatory design that will bridge scales from organism and stand scales captured in field samples and automated ground sensor measurements to the scale of satellite based remote sensing. The instrumentation consists of a high-fidelity imaging spectrometer measuring surface reflectance over the visible to shortwave infrared and a waveform-recording LiDAR providing spatially explicit information on regional vegetation canopy biochemistry and structure, respectively. A high-resolution digital camera is included to support land cover and land use identification at sub-meter resolution. The powerful synergy between LiDAR and spectroscopy has been exploited for detection and mapping of invasive species in Hawaii where these invasives are transforming the 3-dimensional structure of the forest ecosystem (Asner et al., 2008). In another example, LiDAR measurements of vegetation structure can be used to improve model estimates of carbon stocks. Hurtt et al. (2004) take advantage of the relationship that exists between vegetation height, and ecosystem structure and dynamics using LiDAR data as a constraint in an ecosystem demography model estimating carbon stocks and fluxes in La Selva, Costa Rica. NEON long-term measurements of the heterogeneity in vegetation structure in sampled regions across the continent and continued development of ecosystem models can extend these results to larger scales. This paper explores the potential of combing remotely sensed airborne data with ecosystem models for improved estimates of continental-scale ecosystem structure and function.