1. Marine Geospatial Ecology Tools
For more information:
Jason Roberts, Ben Best, Daniel Dunn, and Pat Halpin, Duke University Marine Geospatial Ecology Laboratory, Durham, NC
The Marine Geospatial Ecology Tools (MGET)
A more complex tool: Identify SST Fronts in a GOES 10/12 Image
Example application: Sea turtle habitat in the ETP
Tools under development
In 2006, we developed 81 tools in a prototype tools package. We elected not to release this package because it lacked an installer, documentation, and was too tightly coupled to ArcGIS. In late 2006, we developed a new tool framework suitable for public distribution and began rewriting the tools for it. We anticipate completion in July 2007.
Pre-release builds are available for download at our website. Many of the tools listed below are still being rewritten for the new framework. If you need one now but it’s still being rewritten, we can give you the old implementation.
MGET is targeted at coastal and marine researchers and GIS analysts who work with spatially-explicit oceanographic and ecological data in scientific or managerial workflows. The initial MGET releases focus on tools useful in habitat studies, including tools for processing and sampling remotely-sensed oceanographic data and mapping and filtering ARGOS satellite telemetry. Many tools have both single-input and multi-input (batch processing) implementations.
In collaboration with the Inter-American Tropical Tuna Commission (IATTC), we are investigating the habitats of pelagic sea turtles in the Eastern Tropical Pacific (ETP). In this simplified example, we sampled the NOAA NODC 4km AVHRR v5.0 SST images and pixel quality flags at purse seine set points where fisheries observers recorded the presence of sea turtles. To explore the turtles’ possible SST preferences, we plotted SST histograms for high-quality pixels.
To test theories that animals associate with sea surface temperature (SST) fronts, ecologists need automated methods for identifying them in SST images. The tool presented here implements the 1992 Cayula-Cornillon algorithm. It is agnostic about which SST data are used. This example uses GOES data from NASA JPL.
Key features
ArcGIS geoprocessing model
Tools currently under development (partial listing)
Cayula-Cornillon edge detection algorithm
For each purse seine set point, given its date, generate the file names of the daily SST and pixel quality images
Free, open-source software written mainly in Python, R and MATLAB
Distributed as a self-installing setup program, for easy installation
All tools include full user documentation
For easy execution from many environments, each tool is exposed from:
A Python class
A dual-interface Microsoft COM class (on Windows)
An ArcGIS geoprocessing toolbox
Verbose logging system eases troubleshooting of difficult failures
All tools written to maximize reliability, interoperability and performance
The algorithm passes a moving window over the SST image, flagging windows that exhibit bimodal, spatially-cohesive distributions of pixel temperatures, and tracing the SST values that optimally separate the two populations.
Mexico
120 km
28.0 °C
25.8 °C
Front
SeaWiFS Chl-a
Step 1
Tool parameters
Step 2
Test 1: Bimodal distribution of pixel temperatures in the window
Aviso SSH, currents
A simple tool: HDF SDS to ArcGIS Raster
Many oceanographic remote-sensing data are distributed in HDF format, but ArcGIS 9.x cannot read them. This tool converts a Scientific Data Set (SDS) contained in an HDF file to an ArcGIS raster.
From Python as a native module
Invoking the tool from ArcGIS geoprocessing models
from GeoEco.DataManagement.HDFs import HDF
HDF.SDSToArcGISRaster(u'c:\\temp16\\ s04m1pfv50-sst-16b.hdf',
u'c:\\temp16\\sst199001',
u'sst', -180, -90, , 0)
From VBScript and other languages via Microsoft COM
Set hdf = WScript.CreateObject("GeoEco.HDF")
hdf.SDSToArcGISRaster "c:\\temp16\\ s04m1pfv50-sst-16b.hdf", _
"c:\\temp16\\sst199001", _
"sst", -180, -90, , 0
Nearly all modern languages can call Microsoft COM components, including VBScript, VB, VB.Net, Java, JScript, C++, C#, R, S-Plus, and MATLAB.
Optimal break 27.0 °C
Frequency
Step 3
From Cayula and Cornillon (1992)
Pixel SST
Batch-convert SST and pixel quality images to ArcGIS raster format
Batch-sample the rasters at purse seine set points
Test 2: Spatial cohesion of the two temperature populations
GOES 10/12 SST
Strong cohesion  front present
Weak cohesion  no front
For more information about the modeling tools, attend these GeoTools talks:
E03. Habitat and Connectivity ArcGIS Toolboxes (HabitatToolbox And ConnectivityToolbox) for Multivariate Regression and Graph-Theoretic Marine Applications – Benjamin Best and Pat Halpin – Wednesday March 8, 10:30-12:00, Kensington A
H01. Benthic Complexity Modeling with Coarse Grain (90m) Bathymetric Data: Is It Possible? – Daniel Dunn and Pat Halpin – Thursday March 8, 11:00-12:30, Kensington E
Example output
Results
Olive Ridley
Turtles
This exploratory analysis suggests that in the ETP, olive ridley turtles inhabit slightly warmer water than green turtles. Because the sampling design was determined by fishing effort, spatial, temporal and other biases in it must be considered before robust conclusions are drawn.
Invitation to collaborate
Density
Green
Turtles
Are you searching for collaborators to assist in the development of your coastal or marine geoprocessing tool? Are you searching for a release vehicle for a tool you’ve written? Please contact us to see how we can help!
SST (°C)
References
Acknowledgements
Marine Geospatial Ecology Laboratory
Cayula, J-F and P Cornillon Edge detection algorithm for SST images.
J. Atmos. Oceanic Technol. 9:67-80.
The Duke researchers would like to thank M. Hall, N. Vogel, and C. Lennert of the IATTC for sharing the fishery observer data.