B51B-0029:
Landscape Characterization and RepresentativenessAnalysis for Understanding Sampling Network Coverage
Friday, 19 December 2014
Damian Matthew Maddalena1,2, Forrest M Hoffman3,4, Jitendra Kumar4 and William Walter Hargrove5, (1)University of North Carolina at Wilmington, Geography and Geology, Wilmington, NC, United States, (2)University of North Carolina at Wilmington, Environmental Studies, Wilmington, NC, United States, (3)University of California Irvine, Department of Earth System Science, Irvine, CA, United States, (4)Oak Ridge National Laboratory, Climate Change Science Institute, Oak Ridge, TN, United States, (5)USDA Forest Service, Eastern Forest Environmental Threat Assessment Center, Vallejo, CA, United States
Abstract:
The need to understand sensitive forested systems is amplified under climate change. Relative change can have large impacts on these sensitive systems due to low variability of climate variables under current climate regimes. Our regional and global understanding of these systems begins with understanding the data collected for scientific investigation. Sampling networks rarely conform to spatial and temporal ideals, often comprised of network sampling points which are unevenly distributed and located in less than ideal locations due to access constraints, budget limitations, or political conflict. Quantifying the global, regional, and temporal representativeness of these networks by quantifying the coverage of network infrastructure highlights the capabilities and limitations of the data collected, facilitates upscaling and downscaling for modeling purposes, and improves the planning efforts for future infrastructure investment under current conditions and future modeled scenarios. The current analysis utilizes multivariate spatiotemporal clustering (MSTC) and representativeness analysis with 4 km^2 global bioclimate data for quantitative landscape characterization and assessment of the Fluxnet, RAINFOR, and CTFS-ForestGEO networks. Results include ecoregions that highlight patterns of bioclimatic, topographic, and edaphic variables globally and quantitative representativeness maps of individual and combined networks within ecological domains, regionally, globally, and through time.
