Search for: All records

Abstract The decision to establish a network of researchers centers on identifying shared research goals. Ecologically specific regions, such as the USA’s National Ecological Observatory Network’s (NEON’s) eco-climatic domains, are ideal locations by which to assemble researchers with a diverse range of expertise but focused on the same set of ecological challenges. The recently established Great Lakes User Group (GLUG) is NEON’s first domain specific ensemble of researchers, whose goal is to address scientific and technical issues specific to the Great Lakes Domain 5 (D05) by using NEON data to enable advancement of ecosystem science. Here, we report on GLUG’s kick off workshop, which comprised lightning talks, keynote presentations, breakout brainstorming sessions and field site visits. Together, these activities created an environment to foster and strengthen GLUG and NEON user engagement. The tangible outcomes of the workshop exceeded initial expectations and include plans for (i) two journal articles (in addition to this one), (ii) two potential funding proposals, (iii) an assignable assets request and (iv) development of classroom activities using NEON datasets. The success of this 2.5-day event was due to a combination of factors, including establishment of clear objectives, adopting engaging activities and providing opportunities for active participation and inclusive collaboration with diverse participants. Given the success of this approach we encourage others, wanting to organize similar groups of researchers, to adopt the workshop framework presented here which will strengthen existing collaborations and foster new ones, together with raising greater awareness and promotion of use of NEON datasets. Establishing domain specific user groups will help bridge the scale gap between site level data collection and addressing regional and larger ecological challenges. 

Abstract Bidirectional reflectance distribution function (BRDF) effects are a persistent issue for the analysis of vegetation in airborne imaging spectroscopy data, especially when mosaicking results from adjacent flightlines. With the advent of large airborne imaging efforts from NASA and the U.S. National Ecological Observatory Network (NEON), there is increasing need for methods that are flexible and automatable across images with diverse land cover. Flexible bidirectional reflectance distribution function (FlexBRDF) is built upon the widely used kernel method, with additional features including stratified random sampling across flightline groups, dynamic land cover stratification by normalized difference vegetation index (NDVI), interpolation of correction coefficients across NDVI bins, and the use of a reference solar zenith angle. We demonstrate FlexBRDF using nine long (150–400 km) airborne visible/infrared imaging spectrometer (AVIRIS)‐Classic flightlines collected on 22 May 2013 over Southern California, where diverse land cover and a wide range of solar illumination yield significant BRDF effects. We further test the approach on additional AVIRIS‐Classic data from California, AVIRIS‐Next Generation data from the Arctic and India, and NEON imagery from Wisconsin. Comparison of overlapping areas of flightlines show that models built from multiple flightlines performed better than those built for single images (root mean square error improved up to 2.3% and mean absolute deviation 2.5%). Standardization to a common solar zenith angle among a flightline group improved performance, and interpolation across bins minimized between‐bin boundaries. While BRDF corrections for individual sites suffice for local studies, FlexBRDF is an open source option that is compatible with bulk processing of large airborne data sets covering diverse land cover needed for calibration/validation of forthcoming spaceborne imaging spectroscopy missions. 

Summary Foliar functional traits are widely used to characterize leaf and canopy properties that drive ecosystem processes and to infer physiological processes in Earth system models. Imaging spectroscopy provides great potential to map foliar traits to characterize continuous functional variation and diversity, but few studies have demonstrated consistent methods for mapping multiple traits across biomes.With airborne imaging spectroscopy data and field data from 19 sites, we developed trait models using partial least squares regression, and mapped 26 foliar traits in seven NEON (National Ecological Observatory Network) ecoregions (domains) including temperate and subtropical forests and grasslands of eastern North America.Model validation accuracy varied among traits (normalized root mean squared error, 9.1–19.4%; coefficient of determination, 0.28–0.82), with phenolic concentration, leaf mass per area and equivalent water thickness performing best across domains. Across all trait maps, 90% of vegetated pixels had reasonable values for one trait, and 28–81% provided high confidence for multiple traits concurrently.Maps of 26 traits and their uncertainties for eastern US NEON sites are available for download, and are being expanded to the western United States and tundra/boreal zone. These data enable better understanding of trait variations and relationships over large areas, calibration of ecosystem models, and assessment of continental‐scale functional diversity.