Hawaii Pacific GIS Conference 2012: 3D GIS - Creating Bathymetry Maps with Coarse Data - Bayesian Kriging Using Open Source Tools

Uploaded on


More in: Technology
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Be the first to comment
    Be the first to like this
No Downloads


Total Views
On Slideshare
From Embeds
Number of Embeds



Embeds 0

No embeds

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

    No notes for slide


  • 1. Creating Bathymetry MapsWith Coarse Data -Bayesian Kriging UsingOpen Source ToolsHal KoikeUniversity of Hawaii,Hawaii Fisheries Cooperative Research Unit
  • 2. Why do we need a Bathymetry Map? Marine resource management is pushed toward ecosystem based management (e.g. linking with land development, marine protected area) You need spatial data to fully understand the ecosystem of your interest Species distribution for marine organisms is known to be influenced by depth
  • 3. Outside the United States…Most countries do not have a spatialdata repository where bathymetrydata, land cover data, etc. is readilyavailable to be used for analysis.
  • 4. If $$ is Limited, What are the Options? Stick with what you have Create a pseudo-bathymetry map Some budget friendly data covering the world (bathymetry case)  Navigational chart (low cost)  MODIS (free)  Hyperion (free)
  • 5. Case for Seychelles…What is available
  • 6. What I need
  • 7. SolutionCreate a pseudo bathymetry map usingBayesian Kriging option in GeoR (Rilbeiro jr.,P.J. and Diggle, P.J. 2001)
  • 8. What is GeoR? Created by Paulo J. Ribeiro Jr. and Peter J. Diggle. One of the many packages available through R-CRAN project Operated on R
  • 9. Step 1. Georectification
  • 10. Step 2. Enter the Depth Data
  • 11. What it looks like afterentering all the points
  • 12. Step 3. Import the Point Data to Geo R
  • 13. Step 4. Find your Range
  • 14. Step 5. Run the Bayesian Kriging Simulationx <- seq(241472,403019,2000)y <- seq(9449003,9559751,2000)d1 <- expand.grid(x=x,y=y)ex.bayes <- krige.bayes(YourData,loc=d1,model=model.control( cov.m="matern",kappa=0.5),prior=prior.control(phi. discrete=seq(0,80000,l=10),phi.prior="reciprocal"))
  • 15. Predicted Values
  • 16. Predicted Values
  • 17. Error of Predicted Values (Estimation Variance)
  • 18. Error of Predicted Values (Estimation Variance)
  • 19. Accuracy Check
  • 20. Accuracy Comparison Bathymetry Map Standard DeviationSRTM 30 (1km grid) 76.89Bayesian Kriging (2km grid) 9.00Conventional Kriging (2km grid) 8.30Statistically simulated bathymetry map hadless deviation then remotely sensed data