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GeoComputation 2009
[chapter]
2009
Lecture Notes in Computer Science
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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
doi:10.1007/978-3-642-01973-9_38
fatcat:tafxqi442vhqhj2uiw44gidtdm
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... 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
A Fractal Landscape Realizer for Generating Synthetic Maps
2002
Conservation ecology
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A Practical Map-Analysis Tool for Detecting Potential Dispersal Corridors
2005
Landscape Ecology
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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
2004
Environmental Management
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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
2006
Journal of Geographical Systems
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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
2019
Remote Sensing
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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
2013
Landscape Ecology
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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
2019
Pediatrics
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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
2005
The international journal of high performance computing applications
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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
2018
Physical review. E
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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
2019
Environmental Research Communications
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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
doi:10.1088/2515-7620/ab178a
fatcat:43piiodnhrhwjarbclh4vagw4a
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... 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.
HBGC123D: a high-performance computer model of coupled hydrogeological and biogeochemical processes
2001
Computers & Geosciences
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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
2018
Scientific Reports
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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]
2016
arXiv
pre-print
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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
2017
Global Biogeochemical Cycles
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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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