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

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

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

    • Creating Bathymetry MapsWith Coarse Data -Bayesian Kriging UsingOpen Source ToolsHal KoikeUniversity of Hawaii,Hawaii Fisheries Cooperative Research Unit
    • 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
    • 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.
    • 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)
    • Case for Seychelles…What is available
    • What I need
    • SolutionCreate a pseudo bathymetry map usingBayesian Kriging option in GeoR (Rilbeiro jr.,P.J. and Diggle, P.J. 2001)
    • 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
    • Step 1. Georectification
    • Step 2. Enter the Depth Data
    • What it looks like afterentering all the points
    • Step 3. Import the Point Data to Geo R
    • Step 4. Find your Range
    • 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"))
    • Predicted Values
    • Predicted Values
    • Error of Predicted Values (Estimation Variance)
    • Error of Predicted Values (Estimation Variance)
    • Accuracy Check
    • 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