Task #360
openProducing, formatting and extracting variables for the GAM regression (OREGON)
30%
Description
GIS data layers developed in the past few months and meteorological station data are used to create a first "pilot" dataset to test the GAM regression in Oregon.
Input variables are: lat, lon, ELEV_SRTM, DISTOC, ASPECT.
Locations of stations data for Oregon were reprojected in NAD83 Oregon Lambert Conic Conformal (EPSG2991). All original raster input layers were reprojected from sinusoidal to EPSG2991 and windowed (i.e. spatially subset) to match the extent of the Oregon case study. Note that a distance to ocean (DISTOC) variable was created from the input land cover data available on E&O server.
The work was done through ArcGIS and IDRISI softwares but a r script is currently under development to translate the necessary steps to automate the proces.
Updated by Benoit Parmentier almost 13 years ago
- Category set to Climate
- Assignee set to Benoit Parmentier
- % Done changed from 0 to 30
Updated by Benoit Parmentier almost 13 years ago
- Subject changed from Producting, formatting and extracting variables for the GAM regression (OREGON) to Producing, formatting and extracting variables for the GAM regression (OREGON)
Updated by Benoit Parmentier over 12 years ago
There is now a code available to extract from the raster covariate layers produced:
commit: e0d23f1c
Given a shape file of station locations, values are extracted using R raster package. This is an initial commit.
Note that the production of the covariate raster layers still needs to be automated in R or Python.
Updated by Benoit Parmentier over 12 years ago
- Status changed from New to In Progress