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

Abstract

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.:
Climate Change Science Institute (CCSI), Oak Ridge National Laboratory (ORNL), Oak Rdige, TN (US)
Sponsoring Org.:
USDOE Office of Science (SC), Biological and Environmental Research (BER)
Subject:
54 Environmental Sciences
Keywords:
CCSI; climate datasets; Landscape Characterization; Representativeness Analysis; ORNL
OSTI Identifier:
1148699
DOI:
https://doi.org/10.15149/1148699

Citation Formats

Maddalena, Damian, Hoffman, Forrest, Kumar, Jitendra, and Hargrove, William. Landscape Characterization and Representativeness Analysis for Understanding Sampling Network Coverage. United States: N. p., 2014. 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:https://doi.org/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:https://doi.org/10.15149/1148699. https://www.osti.gov/servlets/purl/1148699. Pub date:Fri Aug 01 00:00:00 EDT 2014
@article{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},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2014},
month = {8}
}