RFmerge: Merging of satellite datasets with ground observations using...

In RFmerge: Merging of Satellite Datasets with Ground Observations using Random Forests

Description

Merging of satellite datasets with ground observations using Random Forests (RF)

Usage

RFmerge(x, ...)

## Default S3 method:
RFmerge(x, metadata, cov, mask, training, 
        id="id", lat = "lat", lon = "lon", ED = TRUE, 
        seed = NULL, ntree = 2000, na.action = stats::na.omit,
        parallel=c("none", "parallel", "parallelWin"),
        par.nnodes=parallel::detectCores()-1, 
        par.pkgs= c("raster", "randomForest", "zoo"), write2disk=FALSE,
        drty.out, use.pb=TRUE, verbose=TRUE,...)

## S3 method for class 'zoo'
RFmerge(x, metadata, cov, mask, training, 
        id="id", lat = "lat", lon = "lon", ED = TRUE, 
        seed = NULL, ntree = 2000, na.action = stats::na.omit,
        parallel=c("none", "parallel", "parallelWin"),
        par.nnodes=parallel::detectCores()-1, 
        par.pkgs= c("raster", "randomForest", "zoo"), write2disk=FALSE,
        drty.out, use.pb=TRUE, verbose=TRUE, ...)

Arguments

x	data.frame witht the ground-based values that will be used as the dependent variable to train the RF model. Every column must represent one ground-based station and the codes of the stations must be provided as colnames. class(data) must be zoo.
metadata	data.frame with the metadata of the ground-based stations. At least, it MUST have the following 3 columns: -) id: This column stores the unique identifier (ID) of each ground-based observation. Default value is "id". -) lat: This column stores the latitude of ech ground observation. Default value is "lat". -) lon: This column stores the longitude of ech ground observation. Default value is "lon".
cov	List with all the covariates used as independent variables in the Random Forest model. The individual covariates can be a RasterStack or RasterBrick object when they vary in time, or they can be a single RasterLayer object when they do not change in time (e.g., a digital elevation model). All time-varying covariates in cov MUST have the same number of layers (bands). Covariates that do not change in time (e.g., a DEM) are internally trasnformed into RasterStack or RasterBrick objects with the same number of layers as the other time-varying elements in cov
mask	OPTIONAL. If provided, the final merged product maks out all values in cov outside mask. Spatial object (vectorial) with the spatial borders of the study area (e.g., catchment, administrative borders). class(mask) must be a sf object with "POLYGON" or "MULTIPOLYGON" geometry.
training	Numeric indicating the percentage of stations that will be used in the training set. The valid range is from zero to one. If training = 1, all the stations will be used for training purposes.
id	Character, with the name of the column in metadata where the identification code (ID) of each station is stored.
lat	Character, with the name of the column in metadata where the latitude of the stations is stored.
lon	Character, with the name of the column in metadata where the longitude of the stations is stored.
ED	logical, should the Euclidean distances be computed an used as covariates in the random fores model?. The default value is TRUE.
seed	Numeric, indicating a single value, interpreted as an integer, or null.
parallel	character, indicates how to parallelise ‘RFmerge’ (to be precise, only the evaluation of the objective function fn is parallelised). Valid values are: -)none: no parallelisation is made (this is the default value) -)parallel: parallel computations for network clusters or machines with multiple cores or CPUs. A ‘FORK’ cluster is created with the makeForkCluster function. When fn.name="hydromod" the evaluation of the objective function fn is done with the clusterApply function of the parallel package. When fn.name!="hydromod" the evaluation of the objective function fn is done with the parRapply function of the parallel package. -)parallelWin: parallel computations for network clusters or machines with multiple cores or CPUs (this is the only parallel implementation that works on Windows machines). A ‘PSOCK’ cluster is created with the makeCluster function. When fn.name="hydromod" the evaluation of the objective function fn is done with the clusterApply function of the parallel package. When fn.name!="hydromod" the evaluation of the objective function fn is done with the parRapply function of the parallel package.
par.nnodes	OPTIONAL. Used only when parallel!='none' numeric, indicates the number of cores/CPUs to be used in the local multi-core machine, or the number of nodes to be used in the network cluster. By default par.nnodes is set to the amount of cores detected by the function detectCores() (parallel package)
par.pkgs	OPTIONAL. Used only when parallel='parallelWin' list of package names (as characters) that need to be loaded on each node for allowing the objective function fn to be evaluated. By default c("raster", "randomForest", "zoo").
ntree	Numeric indicating the maximum number trees to grow in the Random Forest algorithm. The default value is set to 2000. This should not be set to too small a number, to ensure that every input row gets predicted at least a few times. If this value is too low, the prediction may be biased.
na.action	A function to specify the action to be taken if NAs are found. (NOTE: If given, this argument must be named.)
write2disk	logical, indicates if the output merged raster layers and the training and avaluation datasets (two files each, one with time series and other with metadata) will be written to the disk. By default write2disk=FALSE
drty.out	Character with the full path to the directory where the final merged product will be exported as well as the training and evaluation datasets. Only used when write2disk=TRUE
use.pb	logical, indicates if a progress bar should be used to show the progress of the random forest computations (it might reduce a bit the performance of the computations, but it is useful to track if everything is working well). By default use.pb=TRUE
verbose	logical, indicates if progress messages are to be printed. By default verbose=TRUE
...	further arguments to be passed to the low level function randomForest.default.

Value

It returns a RasterStack object with as many layers as time steps are present in x. Each one of the layers in the output object has the same spatial resolution and spatial extent as the cov argument.

Author(s)

Oscar M. Baez-Villanueva, [e-mail]
Mauricio Zambrano-Bigiarini, mzb.devel@gmail
Juan D. Giraldo-Osorio, [e-mail]

References

Baez-Villanueva, O. M.; Zambrano-Bigiarini, M.; Beck, H.; McNamara, I.; Ribbe, L.; Nauditt, A.; Birkel, C.; Verbist, K.; Giraldo-Osorio, J.D.; Thinh, N.X. (2020). RF-MEP: a novel Random Forest method for merging gridded precipitation products and ground-based measurements, Remote Sensing of Environment, 239, 111610. doi: 10.1016/j.rse.2019.111606. <https://authors.elsevier.com/c/1aKrd7qzSnJWL>.

Hengl, T., Nussbaum, M., Wright, M. N., Heuvelink, G. B., & Gr\"aler, B. (2018). Random forest as a generic framework for predictive modeling of spatial and spatio-temporal variables. PeerJ, 6, e5518.

See Also

raster, stack, brick, resample, rotate, crop.

Examples

library(rgdal)
library(raster)

data(ValparaisoPPts)    
data(ValparaisoPPgis) 
data(ValparaisoSHP)   

chirps.fname   <- system.file("extdata/CHIRPS5km.tif",package="RFmerge")
prsnncdr.fname <- system.file("extdata/PERSIANNcdr5km.tif",package="RFmerge")
dem.fname      <- system.file("extdata/ValparaisoDEM5km.tif",package="RFmerge")

CHIRPS5km        <- brick(chirps.fname)
PERSIANNcdr5km   <- brick(prsnncdr.fname)
ValparaisoDEM5km <- raster(dem.fname)

covariates <- list(chirps=CHIRPS5km, persianncdr=PERSIANNcdr5km, 
                   dem=ValparaisoDEM5km)



# The following code assumes that the region is small enough for neglecting
# the impact of computing Euclidean distances in geographical coordinates.
# If this is not the case, please read the vignette 'Tutorial for merging 
# satellite-based precipitation datasets with ground observations using RFmerge' 


# without using parallelisation
rfmep <- RFmerge(x=ValparaisoPPts, metadata=ValparaisoPPgis, cov=covariates, 
                id="Code", lat="lat", lon="lon", mask=ValparaisoSHP, training=1)


# Detecting if your OS is Windows or GNU/Linux, 
# and setting the 'parallel' argument accordingly:
onWin <- ( (R.version$os=="mingw32") | (R.version$os=="mingw64") )
ifelse(onWin, parallel <- "parallelWin", parallel <- "parallel")

#Using parallelisation, with a maximum number of nodes/cores to be used equal to 2:
par.nnodes <- min(parallel::detectCores()-1, 2)
rfmep <- RFmerge(x=ValparaisoPPts, metadata=ValparaisoPPgis, cov=covariates,
                 id="Code", lat="lat", lon="lon", mask=ValparaisoSHP, 
                 training=0.8, parallel=parallel, par.nnodes=par.nnodes)

RFmerge documentation built on July 2, 2020, 3:57 a.m.