fin.csv {climodr} | R Documentation |
Final aggregation for CSV-Data
Description
Extract the raster values of all raster layers from a scene at the station coordinates at each time stamp. The extracted data will be attached to the station data so there is a .csv-file with coordinates, sensor data (response values) and extracted raster data (predictor values). The data is ready to be used for modelling.
Usage
fin.csv(
envrmt = .GlobalEnv$envrmt,
method = "monthly",
crs = NULL,
save_output = TRUE,
...
)
Arguments
envrmt |
variable name of your envrmt list created using climodr's 'envi.create' function. Default = envrmt. |
method |
character. Either "daily", monthly" or "annual". Also depends on the available data. |
crs |
character. If null, coordinate reference system from project files will be taken. Otherwise data will be reprojected into this crs. |
save_output |
logical. If cleaned data should be saved permanently in the Environment put save_output = TRUE. Otherwise the output will be saved in the temporary directory. Default: FALSE. |
... |
arguments passed down from other functions. |
Value
List
See Also
'prep.csv', 'proc.csv', 'spat.csv', 'calc.indices'
Examples
#create climodr environment and allow terra-functions to use 70% of RAM
envrmt <- envi.create(proj_path = tempdir(),
memfrac = 0.7)
# Load the climodr example data into the current climodr environment
clim.sample(envrmt = envrmt)
#prepare csv-files
prep.csv(envrmt = envrmt,
method = "proc",
save_output = TRUE)
#process csv-files
csv_data <- proc.csv(envrmt = envrmt,
method = "monthly",
rbind = TRUE,
save_output = TRUE)
# Crop all raster bands
crop.all(envrmt = envrmt,
method = "MB_Timeseries",
overwrite = TRUE)
# Calculate Indices from cropped raster bands
calc.indices(envrmt = envrmt,
vi = "all",
bands = c("blue", "green", "red",
"nir", "nirb",
"re1", "re2", "re3",
"swir1", "swir2"),
overwrite = TRUE)
#extract station coordinates
csv_spat <- spat.csv(envrmt = envrmt,
method = "monthly",
des_file = "plot_description.csv",
save_output = TRUE)
#extract predictor values from raster files
csv_fin <- fin.csv(envrmt = envrmt,
method = "monthly",
save_output = TRUE)
head(csv_fin)