The 'Stems' data are from an individual tree segmentation (Swetnam and Falk 2014) derived from the 2010 snow-off lidar and biomass-carbon allometric equations. The purpose of the dataset is to evaluate the distribution of aboveground carbon across an elevation gradient in temperature and precipitation.
The '10m Topo points' data are derived from a bare earth digital elevation model (DEM) generated from the 2010 snow-off lidar flight, these include the topographic metrics and the biomass-carbon for each pixel derived from the sum of STEMS. The purpose of the dataset is to evaluate the distribution of aboveground carbon across an elevation gradient in temperature and precipitation.
A total of three catchments in Boulder Creek were analyzed: Como Creek, Gordon Gulch, and Betasso Preserve.
Forest carbon reservoirs in complex terrain along an elevation-climate gradient spanning an 11 Celsius range in mean annual temperature (MAT) and a 50 cm yr-1 range in mean annual precipitation (MAP) did not exhibit the expected response of increasing in size with greater MAP and idealized MAT. Within catchments, the distribution of mean and peak carbon storage doubled in size for valleys versus ridges. These results suggest spatial variations in carbon storage relate more to topographically mediated water availability, as well as aspect (energy-balance) and topographic curvature (a proxy for soil depth and depth to ground water), than elevation-climate gradients. Consequently, lateral redistribution of precipitation across topographic position may either moderate or exacerbate regional climatic controls over ecosystem productivity and tree-level responses during drought.
Description of the data production are given in the Supplemental Information in Swetnam et al. (in review) and are online at: Mapping aboveground biomass from individual tree segmentation data
GIS, Topography, Morphometry, Carbon, Biomass
XML is in ISO-19115 geographic metadata format, compatible with ESRI Geoportal Server.
Citation for This Dataset
Swetnam and Falk, Application of metabolic scaling theory to reduce error in local maxima tree segmentation from aerial LiDAR, 2014, Forest Ecology and Management, 323, 158-167.
Citation for This Webpage
Tyson Lee Swetnam; Paul Brooks; Holly Barnard; Adrian Harpold; Erika Gallo (2010). "CZO Dataset: Como Creek - Topographic Carbon Storage, GIS/Map Data, LiDAR, Land Cover (2010)." Retrieved 08 Apr 2020, from http://criticalzone.org/boulder/data/dataset/5137/
Department of Energy DE-SC0006968
National Science Foundation NSF-1331408
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† Dataset Creators. Defined as the people who are responsible for designing, collecting, analyzing and providing quality assurance for a dataset. The creators of a dataset are analogous to the authors of a publication, and datasets should be cited in an analogous manner following the emerging international guidelines described at http://www.datacite.org/whycitedata.