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Polar Synthesis: Analytical Components Mapping and Visualization Team

Polar Synthesis: Analytical Components Mapping and Visualization Team. Ben Best Patrick Halpin Jason Roberts Ei Fujioka Ben Donnely Jesse Cleary. Polar Synthesis Macroscope Team Duke University NC 26-28 Oct 2008. Partners: Other Visualization Experts.

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Polar Synthesis: Analytical Components Mapping and Visualization Team

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  1. Polar Synthesis:Analytical ComponentsMapping and Visualization Team Ben Best Patrick Halpin Jason Roberts Ei Fujioka Ben Donnely Jesse Cleary

  2. Polar Synthesis Macroscope Team Duke University NC 26-28 Oct 2008

  3. Partners: Other Visualization Experts • Xianhua Liu / David Kidd (NESCent National Evolutionary Synthesis Center) GeoPhyloBuilder Reference: Kidd, D. M. and M. G. Ritchie (2006). "Phylogeographic information systems; Putting the geography into phylogeography." Journal of Biogeography 33: 1851-1865.

  4. Google Earth’s Oceans - Octopuses

  5. ocean surrounded by continent continent surrounded by ocean contrast Antarctica exports oxygen-rich cold bottom water

  6. Geo/Phylo/Hab Workflow 3Dgeophylo extent points Get Observations GeoPhyloBuilder taxa phylogeny Calculate Species Richness diversity grid Fit Model Get Environment variable environment grid(s) Predict Model prediction model

  7. Oceanographic Data Online Pointers: CoMLmaps.org > HowTo > Layers and Resources

  8. OceanWatch LAS http://las.pfeg.noaa.gov/oceanWatch/oceanwatch_safari.php Formats • txt • netcdf • kml • OPeNDAP

  9. OPeNDAP Open-source Project for a Network Data Access Protocol • OPeNDAP form (.html .dds .das): http://oceanwatch.pfeg.noaa.gov/thredds/dodsC/satellite/MB/chla/8day.html • MATLAB command:loaddods('http://las.pfeg.noaa.gov/OceanWatch-FDS/LAS/MB/chla8day?MBCHLA[1033:1033][0:0][3218:3379][4558:4760]') • Python: pydap

  10. Modeling habitat Probability of occurrence predicted from environmental covariates Presence/absence observations Multivariate statistical model Sampled environmental data Binary classification Bathymetry SST Chlorophyll

  11. What is MGET? • A collection of geoprocessing tools for marine ecology • Oceanographic data management and analysis • Habitat modeling, connectivity modeling, statistics • Highly modular; designed to be used in many scenarios • Emphasis on batch processing and interoperability • Free, open source software • Written in Python, R, MATLAB, and C++ • Minimum requirements: Win XP, Python 2.4 • ArcGIS 9.1 or later needed for some tools • ArcGIS and Windows are only non-free requirements

  12. MGET interface in ArcGIS Drill into the toolbox to find the tools Double-click tools to execute directly, or drag to geoprocessing models to create a workflow

  13. Interoperability MGET “tools” are really just Python functions with input and output parameters: defDoSomething(input1, input2, output1) Python programmers can call MGET functions directly. To facilitate interoperability, MGET exposes these functions as COM Automation objects and ArcGIS tools. COM-capable program: C / C++ / C#, Visual Basic, R, MATLAB, Java, etc. MGET COM Automation class DoSomething ArcGIS geoprocessing tool

  14. Integration The Python functions can invoke C++, MATLAB, R, ArcGIS, and COM classes.

  15. Typical observation data Fishery catch and bycatch records Surveys IATTC Olive Ridley Encounters 1990-2005 Argos satellite tracks Figure courtesy of Scott Eckert

  16. Typical workflow MGET includes tools that assist with all of these steps Import species observations into GIS Obtain oceanographic datasets Prepare oceanographic data for use Explore maps of oceano. and observations Analyze/model species habitat or behavior Create derived oceanographic datasets Sample oceanographic data

  17. Typical workflow Import species observations into GIS Obtain oceanographic datasets Prepare oceanographic data for use Explore maps of oceano. and observations Analyze/model species habitat or behavior Create derived oceanographic datasets Sample oceanographic data

  18. Species observations Species presence field: 1 = present, 0 = absent Date field records date of observation Skipping the details of this step to save time Ultimately you must produce a point shapefile or feature class that shows locations where the species was present and where it was absent

  19. Typical habitat modeling workflow Import species observations into GIS Download oceanographic datasets Prepare oceanographic data for use Explore maps of oceano. and observations Analyze/model species habitat or behavior Create derived oceanographic datasets Sample oceanographic data

  20. Options for obtaining data • Download files from data providers using FTP • Nearly all data products are available with FTP • Powerful, free downloaders exist (e.g. SmartFTP) • But must often convert files to ArcGIS-compatible formats • Download using MGET or other tool (e.g. NOAA EDC) • The tool hides details of download, using FTP, OPeNDAP or other protocols, and writes ArcGIS-compatible formats • Not many such tools exist • Order files on CD-ROM or DVD-ROM • Use this if your Internet connection is slow

  21. Tools for specific products Downloads sea surface height data from http://opendap.aviso.oceanobs.com/thredds

  22. Example SSH and currents data with turtle track

  23. Typical workflow Import species observations into GIS Obtain oceanographic datasets Prepare oceanographic data for use Explore maps of oceano. and observations Analyze/model species habitat or behavior Create derived oceanographic datasets Sample oceanographic data

  24. Preparing oceanography for use Most oceanographic datasets are not immediately usable by ArcGIS Common preprocessing steps include: Converting to an ArcGIS-supported format Projecting to a desired projection Clipping to region of interest Performing basic calculations (via map algebra) E.g. converting integers given by the original data provider to floats that represent the real values Building pyramids

  25. Converting data

  26. Sea surface temperature NOAA CoastWatch AVHRR GOES 10/12 from PO.DAAC NOAA NODC 4km AVHRR Pathfinder v5 Also: MODIS Aqua and Terra, GOES 9

  27. Sea surface chlorophyll density SeaWiFS from the NASA GSFC OceanColor Group Also: MODIS Aqua and combined MODIS/SeaWiFS

  28. QuikSCAT ocean winds from PO.DAAC 28-Aug-2005 Also: BYU QuikSCAT Sigma-0 (approximates sea surface rougness) Katrina

  29. Global bathymetries ETOPO2 GEBCO S2004 Map shows S2004 clipped to eastern Pacific ocean

  30. Typical workflow Import species observations into GIS Obtain oceanographic datasets Prepare oceanographic data for use Explore maps of oceano. and observations Analyze/model species habitat or behavior Create derived oceanographic datasets Sample oceanographic data

  31. AVHRR Daytime SST 03-Jan-2005 Identifying SST fronts Mexico Cayula and Cornillion (1992) edge detection algorithm Step 1: Histogram analysis ArcGIS model Bimodal Optimal break 27.0 °C Frequency Temperature Example output 28.0 °C Step 2: Spatial cohesion test Front Mexico 25.8 °C Strong cohesion  front present Weak cohesion  no front ~120 km

  32. Identifying geostrophic eddies Available in MGET 0.8 SSH anomaly Example output Aviso DT-MSLA 27-Jan-1993 Red: Anticyclonic Blue: Cyclonic Negative W at eddy core

  33. Typical workflow Import species observations into GIS Obtain oceanographic datasets Prepare oceanographic data for use Explore maps of oceano. and observations Analyze/model species habitat or behavior Create derived oceanographic datasets Sample oceanographic data

  34. Sampling raster data Chlorophyll-a Density Chl attribute of the points filled with values from the map MGET has sampling tools for various scenarios Sampling is the procedure of overlaying points over a map and storing the map’s value as an attribute of each point.

  35. Typical workflow Import species observations into GIS Obtain oceanographic datasets Prepare oceanographic data for use Explore maps of oceano. and observations Analyze/model species habitat with statistics Create derived oceanographic datasets Sample oceanographic data

  36. MGET statistics tools • Lots of tools, many more planned • Built from Ben Best’s ArcRStats / HabMod projects • Tools require the R statistics program to be installed on your computer

  37. Exploratory analysis Density Histogram tool Scatterplot Matrix tool Turtle present Density Turtle absent Distance to nesting beach (m)

  38. Fitting statistical models ROC plots Term plots

  39. Predicting habitat maps from the model Binary habitat (cutoff = 0.025) Input #3: Rasters for predictor variables Predict GAM tool Input #2: Cutoff value Input #1: The fitted model Bayesian probability that predicted presence ≥ 0.025 Predicted species presence

  40. Analyzing coral reef connectivity Ocean currents data Larval density time series rasters Coral reef ID and % cover maps Tool downloads data for the region and dates you specify Edge list feature class representing dispersal network Original research by Eric A. Treml

  41. Available in MGET 0.7 alpha 10 Calculate Species Diversity

  42. More Information Census of Marine Life Map & Vis www.comlmaps.org info@comlmaps.org Marine Geospatial Ecology Tools code.env.duke.edu/projects/mget

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