Post on 05-Jul-2015
Creating Bathymetry Maps With Coarse Data - Bayesian Kriging Using Open Source Tools Hal Koike University 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 spatial data repository where bathymetry data, land cover data, etc. is readily available 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
Solution
Create a pseudo bathymetry map using Bayesian 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 after entering all the points
Step 3. Import the Point Data to Geo R
Step 4. Find your Range
Step 5. Run the Bayesian Kriging Simulation
x <- 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 Deviation
SRTM 30 (1km grid) 76.89
Bayesian Kriging (2km grid) 9.00
Conventional Kriging (2km grid) 8.30
Statistically simulated bathymetry map had less deviation then remotely sensed data