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GeoComputation 2009 [chapter]

Yong Xue, Forrest M. Hoffman, Dingsheng Liu
2009 Lecture Notes in Computer Science  
The tremendous computing requirements of today's algorithms and the high costs of high-performance supercomputers drive us to share computing resources. The emerging computational Grid technologies are expected to make feasible the creation of a computational environment handling many PetaBytes of distributed data, tens of thousands of heterogeneous computing resources, and thousands of simultaneous users from multiple research institutions (Giovanni et al. . GeoComputation is about using
more » ... s different types of geographical and environmental data and developing relevant tools within the overall context of a computational scientific approach. It is concerned with new computational techniques, algorithms, and paradigms that are dependent upon and can take advantage of Grid Computing. It includes spatial data analysis, dynamic modeling, simulation, space-time dynamics and visualization and virtual reality. This conference will offer presentations from a variety of sources, both local, national and international and will enable you to network with others working in similar fields. Grid computing technology is a new method for processing remotely sensed data. Jianwen Ai et al. in their paper "Grid Workflow Modeling for Remote Sensing Retrieval Service with Tight Coupling" discusses some application cases based on Grid computing for Geo-sciences and the application limit of Grid in remote sensing, and provides a method for Grid Workflow modeling for remote sensing. Tight-coupling remote sensing algorithms cannot be scheduled by a Grid platform directly. Therefore, we need an interactive graphical tool to present the executing relationships of algorithms and to generate automatically the corresponding submitted description files for a Grid platform. Image resampling, which is frequently used in remote sensing processing procedures, is a time-consuming task. Parallel computing is an effective way to speed up
doi:10.1007/978-3-642-01973-9_38 fatcat:tafxqi442vhqhj2uiw44gidtdm

A Fractal Landscape Realizer for Generating Synthetic Maps

William Hargrove, Forrest M. Hoffman, Paul M. Schwartz
2002 Conservation ecology  
doi:10.5751/es-00371-060102 fatcat:noapfsnyd5gfvlbckoitphzqym

A Practical Map-Analysis Tool for Detecting Potential Dispersal Corridors

William W. Hargrove, Forrest M. Hoffman, Rebecca A. Efroymson
2005 Landscape Ecology  
The 32 m wide corridors created for the study may have themselves represented patches of usable habitat for the mice.  ... 
doi:10.1007/s10980-004-3162-y fatcat:kw25zdjupnfzdjhgewhcixofwq

Potential of Multivariate Quantitative Methods for Delineation and Visualization of Ecoregions

William W. Hargrove, Forrest M. Hoffman
2004 Environmental Management  
The current ranges were successfully predicted by a niche model even when half of the training data were randomly discarded (Hargrove and Hoffman 2000) .  ...  Variables considered include elevation, temperature, precipitation, soil characteristics, and solar inputs (Hargrove and Hoffman 2003) . Darker areas are most similar to the selected ecoregion.  ... 
doi:10.1007/s00267-003-1084-0 pmid:15883870 fatcat:pbkvr44jbrd6vahnzbrk3bcyei

Mapcurves: a quantitative method for comparing categorical maps

William W. Hargrove, Forrest M. Hoffman, Paul F. Hessburg
2006 Journal of Geographical Systems  
25 0.3417 Hargrove/Hoffman 12 State borders 59 0.3412 Hargrove/Hoffman 10 Olson landcover 67 0.3249 Hargrove/Hoffman 10 Bailey aggregated 11 0.3022 Kuchler types 116 MLRAs 221 0.2685  ...  221 0.4088 Bailey aggregated 11 Olson landcover 67 0.3879 Hargrove/Hoffman 10 Kuchler forms 29 0.3858 Hargrove/Hoffman 300 Kuchler forms 29 0.3843 Bailey aggregated 11 Hargrove/Hoffman  ... 
doi:10.1007/s10109-006-0025-x fatcat:zhxjcgdcundqlbezidrafwlo7e

Arctic Vegetation Mapping Using Unsupervised Training Datasets and Convolutional Neural Networks

Zachary L. Langford, Jitendra Kumar, Forrest M. Hoffman, Amy L. Breen, Colleen M. Iversen
2019 Remote Sensing  
A multi-sensor remote sensing-based deep learning approach was developed for generating high-resolution (5 m) vegetation maps for the western Alaskan Arctic on the Seward Peninsula, Alaska.  ...  The fusion of hyperspectral, multispectral, and terrain datasets was performed using unsupervised and supervised classification techniques over a ∼343 km2 area, and a high-resolution (5 m) vegetation classification  ...  Hoffman et al.  ... 
doi:10.3390/rs11010069 fatcat:32nfe6smnnamtoloaw2qm35ydy

Representativeness-based sampling network design for the State of Alaska

Forrest M. Hoffman, Jitendra Kumar, Richard T. Mills, William W. Hargrove
2013 Landscape Ecology  
2 , and subsequently demonstrated its application for sampling network design, environmental niche modeling, and comparison of global model predictions (Hargrove and Hoffman 2004; Hoffman et al. 2005  ...  Maps of Alaska were produced for k = 5, 10, 20, 50, 100, 200, 500, and 1000 ecoregions (Hoffman et al. 2013) .  ... 
doi:10.1007/s10980-013-9902-0 fatcat:nwtkublzprajbmxgmb33hv7zju

Priorities for Pediatric Patient Safety Research

James M. Hoffman, Nicholas J. Keeling, Christopher B. Forrest, Heather L. Tubbs-Cooley, Erin Moore, Emily Oehler, Stephanie Wilson, Elisabeth Schainker, Kathleen E. Walsh
2019 Pediatrics  
Respondents (both health system employees and parents) to the prioritization survey rated safety HOFFMAN et al 4 FIGURE 1 Study methods and number of participants and research topics.  ... 
doi:10.1542/peds.2018-0496 pmid:30674609 pmcid:PMC6361358 fatcat:rubp7ciwm5hzvgp57nt5yieblq

Vectorizing the Community Land Model

Forrest M. Hoffman, Mariana Vertenstein, Hideyuki Kitabata, James B. White
2005 The international journal of high performance computing applications  
AUTHOR BIOGRAPHIES Forrest Hoffman is a researcher at ORNL where he holds joint appointments in the Computer Science and Mathematics and the Environmental Sciences Divisions.  ...  Forrest established the first World Wide Web site at the Laboratory in 1995 and built ORNL's first Beowulf-style parallel computer, called The Stone SouperComputer, in 1997.  ...  Forrest writes a monthly column for Linux Magazine called "Extreme Linux", and presently serves on the Advisory Committee for Advanced Research Computing (ARC) at Georgetown University.  ... 
doi:10.1177/1094342005056113 fatcat:p3wlmbwh4rfu7mrlsw7zgdegbi

Diffusion-dominated mixing in moderate convergence implosions

A. B. Zylstra, N. M. Hoffman, H. W. Herrmann, M. J. Schmitt, Y. H. Kim, K. Meaney, A. Leatherland, S. Gales, C. Forrest, V. Yu. Glebov, M. Schoff, M. Hoppe (+1 others)
2018 Physical review. E  
As a result of the studies described by Kim et al. and Hoffman, Zimmerman et al.  ...  plastic shells, 860µm diameter, with a 0.15 µm deuterated layer.  ... 
doi:10.1103/physreve.97.061201 pmid:30011491 fatcat:jqygtiz47ncuperselrmuw7rhe

Predictability of tropical vegetation greenness using sea surface temperatures

Binyan Yan, Jiafu Mao, Xiaoying Shi, Forrest M Hoffman, Michael Notaro, Tianjun Zhou, Nate McDowell, Robert E Dickinson, Min Xu, Lianhong Gu, Daniel M Ricciuto
2019 Environmental Research Communications  
Much research has examined the sensitivity of tropical terrestrial ecosystems to various environmental drivers. The predictability of tropical vegetation greenness based on sea surface temperatures (SSTs), however, has not been well explored. This study employed fine spatial resolution remotely-sensed Enhanced Vegetation Index (EVI) and SST indices from tropical ocean basins to investigate the predictability of tropical vegetation greenness in response to SSTs and established empirical models
more » ... th optimal parameters for hindcast predictions. Three evaluation metrics were used to assess the model performance, i.e., correlations between historical observed and predicted values, percentage of correctly predicted signs of EVI anomalies, and percentage of correct signs for extreme EVI anomalies. Our findings reveal that the pan-tropical EVI was tightly connected to the SSTs over tropical ocean basins. The strongest impacts of SSTs on EVI were identified mainly over the arid or semi-arid tropical regions. The spatially-averaged correlation between historical observed and predicted EVI time series was 0.30 with its maximum value reaching up to 0.84. Vegetated areas across South America (25.76%), Africa (33.13%), and Southeast Asia (39.94%) were diagnosed to be associated with significant SST-EVI correlations (p<0.01). In general, statistical models correctly predicted the sign of EVI anomalies, with their predictability increasing from ∼60% to nearly 100% when EVI was abnormal (anomalies exceeding one standard deviation). These results provide a basis for the prediction of changes in greenness of tropical terrestrial ecosystems at seasonal to intra-seasonal scales. Moreover, the statistics-based observational relationships have the potential to facilitate the benchmarking of Earth System Models regarding their ability to capture the responses of tropical vegetation growth to long-term signals of oceanic forcings.
doi:10.1088/2515-7620/ab178a fatcat:43piiodnhrhwjarbclh4vagw4a

HBGC123D: a high-performance computer model of coupled hydrogeological and biogeochemical processes

Jin P Gwo, Eduardo F D'Azevedo, Hartmut Frenzel, Melanie Mayes, Gour-Tsyh Yeh, Philip M Jardine, Karen M Salvage, Forrest M Hoffman
2001 Computers & Geosciences  
The size of the domain is 100 m  40 m  7.2 m (Fig. 3) . Initially, a region of high contaminant concentrations (Co 2+ and CoNTA À ) extends from x ¼ 40 to 65 m and y ¼ 20 to 30 m.  ...  At a specific node, a Newton step requires the construction of an m  m Jacobian matrix, where m depends on the number of species.  ...  coefficient; k b =backward reaction rate coefficient; K=stability constant; l=microbial death/decay constant; K S =half-saturation constants for substrate; K A =half-saturation constants for electron acceptor; m  ... 
doi:10.1016/s0098-3004(01)00027-9 fatcat:ouuzwou6ardevahoultaqyxgxq

Uncertainty Quantification of Extratropical Forest Biomass in CMIP5 Models over the Northern Hemisphere

Cheng-En Yang, Jiafu Mao, Forrest M. Hoffman, Daniel M. Ricciuto, Joshua S. Fu, Chris D. Jones, Martin Thurner
2018 Scientific Reports  
In this study, forest carbon density (kg C m −2 ) is converted into carbon mass (petagrams of carbon, Pg C).  ... 
doi:10.1038/s41598-018-29227-7 pmid:30026558 pmcid:PMC6053416 fatcat:q3yy7zupijchxjc4bzi6fqmy2e

Transit times and mean ages for nonautonomous and autonomous compartmental systems [article]

Martin Rasmussen, Alan Hastings, Matthew J. Smith, Folashade B. Agusto, Benito M. Chen-Charpentier, Forrest M. Hoffman, Jiang Jiang, Katherine E.O. Todd-Brown, Ying Wang, Ying-Ping Wang, Yiqi Luo
2016 arXiv   pre-print
Note that at t = 0 (corresponding to the year 1850), we assumed that M 0 = M , where M is the mean age of the equilibrium solution at t = 0 according to Proposition 1.  ...  B m1 (t) B m2 (t) B m3 (t) B mm (t)        (9) for m ≥ 1 with bounded functions B ij : I → R di×dj . Note that m i=1 d i = d.  ... 
arXiv:1603.02497v1 fatcat:3m3k67uikbf4pgxmk6lxofogp4

Interactions between land use change and carbon cycle feedbacks

Natalie M. Mahowald, James T. Randerson, Keith Lindsay, Ernesto Munoz, Scott C. Doney, Peter Lawrence, Sarah Schlunegger, Daniel S. Ward, David Lawrence, Forrest M. Hoffman
2017 Global Biogeochemical Cycles  
M.  ...  crops or pasture (with units of fraction per year) and VegC m [t] is the above ground carbon in vegetation averaged across the grid box (m, for the mean grid box value).  ... 
doi:10.1002/2016gb005374 fatcat:7wzcz7r37zfyhh4kcsotmpghm4
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