paper_2013.bib

@conference{Christie2013,
  title = {{ForWarn Forest Change Detection System Provides a Weekly Snapshot of US Forest Conditions to Aid Forest Managers}},
  author = {William M. Christie and William W. Hargrove and Steven P. Norman and Joseph P. Spruce and Jitendra Kumar and Forrest Hoffman and Sean W. Schroeder},
  booktitle = {9th Southern Forestry and Natural Resource Management GIS Conference, Athens, GA, December 8--10},
  year = {2013},
  owner = {jkumar},
  timestamp = {2013.01.23}
}

@conference{Hoffman2013b,
  title = {A Model-Inspired Sampling Network Design and Representativeness Methodology for the Arctic},
  author = {Forrest M. Hoffman and Jitendra Kumar and Stan D. Wullschleger and Larry D. Hinzman and Edward A. G. Schuur},
  booktitle = {Arctic Observing Summit (AOS) 2013},
  year = {2013},
  owner = {jkumar},
  timestamp = {2013.01.23}
}

@article{Hoffman_LandEco_2013,
  title = {Representativeness-Based Sampling Network Design for the {St}ate of {A}laska},
  author = {Forrest M. Hoffman and Jitendra Kumar and Richard T. Mills and William W. Hargrove},
  journal = {Landscape Ecology},
  year = {2013},
  number = {8},
  pages = {1567-1586},
  volume = {28},
  abstract = {Resource and logistical constraints limit the frequency and extent of environmental observa- tions, particularly in the Arctic, necessitating the development of a systematic sampling strategy to maximize coverage and objectively represent envi- ronmental variability at desired scales. A quantitative methodology for stratifying sampling domains, informing site selection, and determining the repre- sentativeness of measurement sites and networks is described here. Multivariate spatiotemporal clustering was applied to down-scaled general circulation model results and data for the State of Alaska at 4 km2 resolution to define multiple sets of ecoregions across two decadal time periods. Maps of ecoregions for the present (2000–2009) and future (2090–2099) were produced, showing how combinations of 37 character- istics are distributed and how they may shift in the future. Representative sampling locations are identified on present and future ecoregion maps. A representa- tiveness metric was developed, and representativeness maps for eight candidate sampling locations were produced. This metric was used to characterize the environmental similarity of each site. This analysis provides model-inspired insights into optimal sampling strategies, offers a framework for up-scaling measure- ments, and provides a down-scaling approach for integration of models and measurements. These tech- niques can be applied at different spatial and temporal scales to meet the needs of individual measurement 
 campaigns.},
  doi = {10.1007/s10980-013-9902-0},
  note = {\url{https://doi.org/10.1007/s10980-013-9902-0}},
  file = {pubs/Hoffman_LandEco_2013.pdf},
  owner = {jkumar},
  timestamp = {2012.07.31}
}

@article{Mills_DMESS_2013,
  title = {Identification and Visualization of Dominant Patterns and Anomalies in Remotely Sensed Vegetation Phenology Using a Parallel Tool for Principal Components Analysis},
  author = {Richard Tran Mills and Jitendra Kumar and Forrest Hoffman and William Hargrove and Joseph P. Spruce and Steve P. Norman},
  journal = {Proceedings of the 2013 International Conference on Computational Science},
  year = {2013},
  doi = {10.1016/j.procs.2013.05.411},
  note = {\url{https://doi.org/10.1016/j.procs.2013.05.411}},
  file = {pubs/Mills_ICCS_2013.pdf},
  owner = {jkumar},
  timestamp = {2013.04.01}
}