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Title: Landscape Characterization and Representativeness Analysis for Understanding Sampling Network Coverage

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 work presented here utilizes multivariate spatiotemporal clustering analysis and representativeness analysis for quantitative landscape characterization and assessment of the Fluxnet, RAINFOR, and ForestGEO networks. Results include ecoregions that highlight patterns of bioclimatic, topographic, and edaphic variables and quantitative representativeness maps of individual and combined networks.
Authors:
; ; ;
Publication Date:
DOE Contract Number:
DE-AC05-00OR22725
Product Type:
Dataset
Research Org(s):
Climate Change Science Institute (CCSI), Oak Ridge National Laboratory (ORNL), Oak Rdige, TN (US)
Collaborations:
PNL, BNL,ANL,ORNL
Sponsoring Org:
USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23)
Subject:
54 Environmental Sciences; CCSI; climate datasets; Landscape Characterization; Representativeness Analysis; ORNL
OSTI Identifier:
1148699

Maddalena, Damian, Hoffman, Forrest, Kumar, Jitendra, and Hargrove, William. Landscape Characterization and Representativeness Analysis for Understanding Sampling Network Coverage. United States: N. p., Web. doi:10.15149/1148699.
Maddalena, Damian, Hoffman, Forrest, Kumar, Jitendra, & Hargrove, William. Landscape Characterization and Representativeness Analysis for Understanding Sampling Network Coverage. United States. doi:10.15149/1148699.
Maddalena, Damian, Hoffman, Forrest, Kumar, Jitendra, and Hargrove, William. 2014. "Landscape Characterization and Representativeness Analysis for Understanding Sampling Network Coverage". United States. doi:10.15149/1148699. https://www.osti.gov/servlets/purl/1148699.
@misc{osti_1148699,
title = {Landscape Characterization and Representativeness Analysis for Understanding Sampling Network Coverage},
author = {Maddalena, Damian and Hoffman, Forrest and Kumar, Jitendra and Hargrove, William},
abstractNote = {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 work presented here utilizes multivariate spatiotemporal clustering analysis and representativeness analysis for quantitative landscape characterization and assessment of the Fluxnet, RAINFOR, and ForestGEO networks. Results include ecoregions that highlight patterns of bioclimatic, topographic, and edaphic variables and quantitative representativeness maps of individual and combined networks.},
doi = {10.15149/1148699},
year = {2014},
month = {8} }
  1. The Climate Change Science Institute (CCSI) was formed in 2009 to integrate climate science activities across Oak Ridge National Laboratory. Approximately, 130 scientists are doing research in the areas of (i) earth system modeling, (ii) data integration, dissemination, and informatics, (iii) integrative ecosystem scienceterrestrial ecosystem and carbon cycle science, and (iv) climate impacts, adaptation, and vulnerability science. CCSI works to strengthen the predictive capabilities and effectiveness of climate and biogeochemical models and develop useful climate adaptation and mitigation tools and information in collaboration with land-energy-water system stakeholders.
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