Print PageThe National Ecological Observatory Network (NEON) is a continental-scale research platform that will collect information on ecosystems across the United States to advance our understanding and ability to forecast environmental change at the continental-scale. The dynamics of land-use and land cover and their spatial patterns play important roles in driving ecosystem change. Targeted airborne remote sensing observations made by NEON as well as geographical data sets and satellite resources produced by Federal agencies will provide information at regional and national scales. Integration of these national datasets with the targeted regional data from NEON airborne instrumentation provides a direct linkage in scaling from NEON’s distributed sensor network and in-situ field measurements coupling individual plant or canopy measurements to plot or stand level observations, and ultimately to the continental scale. NEON is developing a new data processing scheme coupling a data assimilation system with the NCAR Community Land Model (CLM) that will incorporate NEON field and airborne observations with the geospatial and satellite observations acquired and integrated through the NEON Land Use Analysis Package (LUAP).
Results/Conclusions
Recent ground, and airborne hyperspectral and LiDAR waveform data collected by NEON over the Ordway-Swisher Biological Research Station along with coincident satellite-based observations acquired by MODIS, EO-1, and Landsat will be used to prototype plot-to-regional scaling rules. These rules will be applied to transform plot-based measurements of vegetation structure and meter-scale plant functional types and structure derived from the airborne measurements to “scaled observations” on a 1-km land model grid. The coupled data assimilation-land model, currently in development, will produce estimates of soil moisture, and ecosystem water and carbon exchange across the continental United States. The ground and remote sensing data collected over Ordway-Swisher will be used to test the modeling and scaling approaches. As an example, we compare observations of leaf area index (LAI) made directly in the field with estimates from airborne and satellite remote sensing. We found that the spatial distribution of LAI from the three different metrics was generally consistent but that smaller scale measurements may have to be scaled using a calibrated offset to make inferences to larger spatial scales.
