Help for package climodr

Type:

Package

Title:

Climate Modeling with Point Data from Climate Stations

Version:

1.0.0

Author:

Alexander Klug [aut, cre, cph], Luise Wraase [aut], Seda Bekar [ctb], Dirk Zeuss [aut]

Maintainer:

Alexander Klug <kluga@students.uni-marburg.de>

Description:

An automated and streamlined workflow for predictive climate mapping using climate station data. Works within an environment the user provides a destined path to - otherwise it's tempdir(). Quick and relatively easy creation of resilient and reproducible climate models, predictions and climate maps, shortening the usually long and complicated work of predictive modelling. For more information, please find the provided URL. Many methods in this package are new, but the main method is based on a workflow from Meyer (2019) <doi:10.1016/j.ecolmodel.2019.108815> and Meyer (2022) <doi:10.1038/s41467-022-29838-9> , however, it was generalized and adjusted in the context of this package.

License:

GPL (≥ 3)

Encoding:

UTF-8

LazyData:

true

Imports:

caret, CAST, corrplot, doParallel, dplyr, grDevices, lares, magrittr, parallel, rlang, stats, stringr, terra, tidyr, utils

Depends:

R (≥ 2.10)

Suggests:

knitr, rmarkdown, testthat (≥ 3.0.0), pls, randomForest, sp

VignetteBuilder:

knitr

RoxygenNote:

7.3.2

Config/testthat/edition:

URL:

https://envima.github.io/climodr/

BugReports:

https://github.com/envima/climodr/issues

NeedsCompilation:

Packaged:

2025-06-10 08:11:00 UTC; Alexander

Repository:

CRAN

Date/Publication:

2025-06-12 14:20:06 UTC

Station File (G06)

Description

Contains made up climate data for imaginary climate stations for education purposes.

Usage

Station_G06

Format

## 'Station_G06' A data frame with 10 rows and 5 columns

plotID: Plot ID of imaginary climate station
datetime: Timestamp of record for station
Ta_200: Imaginary air temperature at 200 cm above ground

Source

Randomly created climate data at random created stations extracted from a random climate map made by climodr.

Station File (G17)

Description

Contains made up climate data for imaginary climate stations for education purposes.

Usage

Station_G17

Format

## 'Station_G17' A data frame with 10 rows and 5 columns

plotID: Plot ID of imaginary climate station
datetime: Timestamp of record for station
Ta_200: Imaginary air temperature at 200 cm above ground

Source

Randomly created climate data at random created stations extracted from a random climate map made by climodr.

Station File (G20)

Description

Contains made up climate data for imaginary climate stations for education purposes.

Usage

Station_G20

Format

## 'Station_G20' A data frame with 10 rows and 5 columns

plotID: Plot ID of imaginary climate station
datetime: Timestamp of record for station
Ta_200: Imaginary air temperature at 200 cm above ground

Source

Randomly created climate data at random created stations extracted from a random climate map made by climodr.

Station File (G21)

Description

Contains made up climate data for imaginary climate stations for education purposes.

Usage

Station_G21

Format

## 'Station_G21' A data frame with 10 rows and 5 columns

plotID: Plot ID of imaginary climate station
datetime: Timestamp of record for station
Ta_200: Imaginary air temperature at 200 cm above ground

Source

Randomly created climate data at random created stations extracted from a random climate map made by climodr.

Station File (G25)

Description

Contains made up climate data for imaginary climate stations for education purposes.

Usage

Station_G25

Format

## 'Station_G25' A data frame with 10 rows and 5 columns

plotID: Plot ID of imaginary climate station
datetime: Timestamp of record for station
Ta_200: Imaginary air temperature at 200 cm above ground

Source

Randomly created climate data at random created stations extracted from a random climate map made by climodr.

Station File (G48)

Description

Contains made up climate data for imaginary climate stations for education purposes.

Usage

Station_G48

Format

## 'Station_G48' A data frame with 10 rows and 5 columns

plotID: Plot ID of imaginary climate station
datetime: Timestamp of record for station
Ta_200: Imaginary air temperature at 200 cm above ground

Source

Randomly created climate data at random created stations extracted from a random climate map made by climodr.

Station File (W10)

Description

Contains made up climate data for imaginary climate stations for education purposes.

Usage

Station_W10

Format

## 'Station_W10' A data frame with 10 rows and 5 columns

plotID: Plot ID of imaginary climate station
datetime: Timestamp of record for station
Ta_200: Imaginary air temperature at 200 cm above ground

Source

Randomly created climate data at random created stations extracted from a random climate map made by climodr.

Station File (W11)

Description

Contains made up climate data for imaginary climate stations for education purposes.

Usage

Station_W11

Format

## 'Station_W11' A data frame with 10 rows and 5 columns

plotID: Plot ID of imaginary climate station
datetime: Timestamp of record for station
Ta_200: Imaginary air temperature at 200 cm above ground

Source

Randomly created climate data at random created stations extracted from a random climate map made by climodr.

Station File (W19)

Description

Contains made up climate data for imaginary climate stations for education purposes.

Usage

Station_W19

Format

## 'Station_W19' A data frame with 10 rows and 5 columns

plotID: Plot ID of imaginary climate station
datetime: Timestamp of record for station
Ta_200: Imaginary air temperature at 200 cm above ground

Source

Randomly created climate data at random created stations extracted from a random climate map made by climodr.

Station File (W20)

Description

Contains made up climate data for imaginary climate stations for education purposes.

Usage

Station_W20

Format

## 'Station_W20' A data frame with 10 rows and 5 columns

plotID: Plot ID of imaginary climate station
datetime: Timestamp of record for station
Ta_200: Imaginary air temperature at 200 cm above ground

Source

Randomly created climate data at random created stations extracted from a random climate map made by climodr.

Autocorrelation

Description

Tests the final.csv created with 'fin.csv' on autocorrelation to produce reliable models.

Usage

autocorr(
  envrmt = .GlobalEnv$envrmt,
  method = "monthly",
  resp,
  pred,
  plot.corrplot = TRUE,
  corrplot = "coef"
)

Arguments

envrmt

variable name of your envrmt list created using climodr's 'envi.create' function. Default = envrmt.

method

character. Choose the time scale your data is preserved in. Either "annual", "monthly" or "daily".

resp

numerical. Vector or single input of the columns in the final.csv that contain your sensor data ("response variables"). The function will create one file per variable.

pred

numerical. Vector or single input. The columns of your predictor variables, that you want to test for autocorrelation with the response variables.

plot.corrplot

logical. Should correlation matrices be plotted?

corrplot

character. Vector or single input. If plot.corrplot is true, you can choose the design of the correlation plot. You can choose from "coef", "crossout", "blank". Default is "coef".

Value

One .csv file per response variable. These will later be used when 'autocorrelation' is set 'TRUE' during 'calc.model'.

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)

# Test data for autocorrelation after running fin.csv
autocorr(envrmt = envrmt,
         method = "monthly",
         resp = 5,
         pred = c(8:23),
         plot.corrplot = FALSE)

Calculate spectral indices

Description

Calculates a set of spectral indices to have more predictor variables available when further modeling.

Usage

calc.indices(
  envrmt = .GlobalEnv$envrmt,
  vi = "all",
  bands = c("blue", "green", "red", "nir", "nirb", "re1", "re2", "re3", "swir1", "swir2"),
  overwrite = FALSE
)

Arguments

envrmt

variable name of your envrmt list created using climodr's 'envi.create' function. Default = envrmt.

vi

Character. Either "all" or vector containing the preferred spectral indices. See 'Details' for more information.

bands

Character. Vector with lenght(bands) = 10. Contains the names of the bands in the Raster Stack. If bands from the *Usage* example vector dont exist, use "NA" in their position. See 'Details' for more information.

overwrite

logical. Argument passed down from 'terra'-package. Overwrite existing files?

Value

SpatRaster-Stack

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)

# 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)

Modelling

Description

Creates Models for each climate value

Usage

calc.model(
  envrmt = .GlobalEnv$envrmt,
  method = "monthly",
  timespan,
  climresp,
  classifier = c("rf", "pls", "lm", "glm"),
  seed = NULL,
  p = 0.8,
  folds = "all",
  predrows,
  mnote = NULL,
  k = NULL,
  tc_method = "cv",
  metric = "RMSE",
  doParallel = FALSE,
  autocorrelation = FALSE,
  ...
)

Arguments

envrmt

variable name of your envrmt list created using climodr's 'envi.create' function. Default = envrmt.

method

character. Time period of your desired model. Default: "monthly"

timespan

numeric. Vector or single input. Should contain all years to be modeled. The years have to be the same format as in the tabular data.

climresp

numeric. Vector or single input. Should contain all column's in the tabular data that contain response variables.

classifier

vector or character. Model variants to be used. Supported models: Random Forest = "rf", Partial-Least-Squares = "pls", Neural Networks = "nnet", Linear Regression = "lm" or generalized boosted regression = "gbm".

seed

integer. Seed to reproduce the same model over and over.

p

numeric. Between 0 and 1. Percentage of data used for cross validation. Default = 0.8

folds

character. Vector or single input. Either folding over location only "LLO", over time only "LTO", or over both "LLTO". Use "all" to use all possibilitys.

predrows

numeric. Vector or single input. Should contain the rows where all the predictor values are stored in.

mnote

character. Model note for special modifications used. Default: "normal"

k

integer. When 'fold' = "LLO" or "LTO". Set k to the number of unique spatial or temporal units. Leave out to use preset values.

tc_method

character. Method for train control function from caret package. Default = "cv".

metric

character. See 'train'.

doParallel

logical. Parallelization accelerates the modelling process. Warning: Your PC will slow down drastically. Make sure to not run any other heavy processes during this.

autocorrelation

logical. Should autocorrelating data in the predictor variables be excluded from the model run? Only works if 'autocorr' has been executed beforehand.

...

arguments passed down from other functions.

Value

data frame.

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)

# Test data for autocorrelation after running fin.csv
autocorr(envrmt = envrmt,
         method = "monthly",
         resp = 5,
         pred = c(8:23),
         plot.corrplot = FALSE)

# Create 36 different models (12 months x 3 classifiers) for every month in 2017
calc.model(envrmt = envrmt,
           method = "monthly",
           timespan = c(2017),
           climresp = 5,
           classifier = c("rf",
                          "pls",
                          "lm"),
           seed = 707,
           p = 0.8,
           folds = "LLO",
           mnote = "normal",
           predrows = c(8:23),
           tc_method = "cv",
           metric = "RMSE",
           autocorrelation = TRUE,
           doParallel = FALSE)

Load in Example Data

Description

Climodr comes with a full set of example data. But since this package runs primarily with data, that is not linked to the global environment, but saved in local folders build via 'envi.create', one can't just load example data. This function will load all the example data used in the vignette into your climodr environment. This way you can run all the code from the vignette.

Usage

clim.sample(envrmt = .GlobalEnv$envrmt)

Arguments

envrmt

variable name of your envrmt list created using climodr's 'envi.create' function. Default = envrmt.

Value

Multiple files used by the climodr vignette

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)

Create Maps using the 'terra' package graphic parameters

Description

Plot results of climodr into maps. Right now maps are created using the terra package. The maps created are very basic. Will be updated to run with tidyterra in future.

Usage

climplot(
  envrmt = .GlobalEnv$envrmt,
  mnote,
  sensor,
  aoa = FALSE,
  mapcolors = rev(grDevices::terrain.colors(50)),
  scale_position = "bottomleft",
  north_position = "topright"
)

Arguments

envrmt

variable name of your envrmt list created using climodr's 'envi.create' function. Default = envrmt.

mnote

character. The modelnote you want to create maps of.

sensor

character. The sensor you want to create maps for.

aoa

logical. Do you want the area of applicability to be added to your map?

mapcolors

The color pallete you want to use for the map. Default is 'rev(grDevices::terrain.colors(50))'

scale_position

character. Graphical parameter. The relative positiion of the Scale for the map. See 'terra::plot' for more details.

north_position

character. Graphical parameter. The relative positiion of the Scale for the map. See 'terra::plot' for more details.

Value

Maps in PNG-Format to your harddrive.

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)

# Test data for autocorrelation after running fin.csv
autocorr(envrmt = envrmt,
         method = "monthly",
         resp = 5,
         pred = c(8:23),
         plot.corrplot = FALSE)

# Create 36 different models (12 months x 3 classifiers) for every month in 2017
calc.model(envrmt = envrmt,
           method = "monthly",
           timespan = c(2017),
           climresp = 5,
           classifier = c("rf",
                          "pls",
                          "lm"),
           seed = 707,
           p = 0.8,
           folds = "LLO",
           mnote = "normal",
           predrows = c(8:23),
           tc_method = "cv",
           metric = "RMSE",
           autocorrelation = TRUE,
           doParallel = FALSE)

# Make predictions
climpred(envrmt = envrmt,
         method = "monthly",
         mnote = "normal",
         AOA = TRUE)

# Create a Temperature Map from the vignette model
climplot(envrmt = envrmt,
         mnote = "normal",
         sensor = "Ta_200",
         aoa = TRUE,
         mapcolors = rev(heat.colors(50)),
         scale_position = "bottomleft",
         north_position = "topright")

Predict sensor data area wide

Description

Use the models created using 'calc.model' to predict the modeled data onto a full spatial raster scene.

Usage

climpred(envrmt = .GlobalEnv$envrmt, method = "monthly", mnote, AOA = 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.

mnote

Character. Model note to filter models for the fitting model run.

AOA

Logical. Should the Area of Applicability be calculated additional to the models?

Value

Multiple models.rds stored in the /workflow/models folder.

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)

# Test data for autocorrelation after running fin.csv
autocorr(envrmt = envrmt,
         method = "monthly",
         resp = 5,
         pred = c(8:23),
         plot.corrplot = FALSE)

# Create 36 different models (12 months x 3 classifiers) for every month in 2017
calc.model(envrmt = envrmt,
           method = "monthly",
           timespan = c(2017),
           climresp = 5,
           classifier = c("rf",
                          "pls",
                          "lm"),
           seed = 707,
           p = 0.8,
           folds = "LLO",
           mnote = "normal",
           predrows = c(8:23),
           tc_method = "cv",
           metric = "RMSE",
           autocorrelation = TRUE,
           doParallel = FALSE)

# Make predictions
climpred(envrmt = envrmt,
         method = "monthly",
         mnote = "normal",
         AOA = TRUE)

predlist <- list.files(envrmt$path_predictions,
                       pattern = ".tif")
head(predlist)

Cropping tiff data

Description

Crops input data to the extent size and reprojects them into project Coordinate reference system.

Usage

crop.all(
  envrmt = .GlobalEnv$envrmt,
  method = "MB_Timeseries",
  crs = NULL,
  ext = NULL,
  overwrite = FALSE,
  ...
)

Arguments

envrmt

variable name of your envrmt list created using climodr's 'envi.create' function. Default = envrmt.

method

character. Use "MB_Timeseries" for now. More methods are planned and will be added in future.

crs

Coordinate reference system Used to crop all images in folder_path. If crs it will automatically reprojected into this one. Default: crs of smallest Extent.

ext

SpatRaster, SpatVector or SpatExtent. Extent all data is cropped into. Default: Smallest Extent in folder_path.

overwrite

logical. Should existing files with the same filename be overwritten? Default = FALSE

...

arguments passed down from other functions.

Value

SpatRaster-Stack. Also saved to /workflow/rworkflow

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)

# Crop all raster bands
crop.all(envrmt = envrmt,
         method = "MB_Timeseries",
         overwrite = TRUE)

Create climodr environment

Description

Creates an environment climodr will use during the calculation process. A list is returned with all paths to all folders. After creating the environment, all necessary data should be stored into the depending Input sub-folders. There is also an additional temp-folder, where temporary data is stored, which can be deleted after not being used anymore.

Usage

envi.create(proj_path = tempdir(), memfrac = NULL, ...)

Arguments

proj_path

character. Path to project directory. Climodr will work exclusively in this folder and create all project folders in here.

memfrac

numeric. Value between 0 and 0.9. The fraction of RAM that may be used by the terra package

...

arguments passed down from other functions.

Value

list. Contains all paths to each folder in the project directory. Necessary for climodr to operate its functions.

Examples

# create climodr environment and allow terra-functions to use 70% of RAM
envrmt <- envi.create(proj_path = tempdir(),
                      memfrac = 0.7)

Extent file for vignette

Description

A vector file containing the shape and extent of the Area used in the vignette

Usage

ext_vignette

Format

## 'ext_vignette'

class: SpatVector
geometry: polygons
dimensions: 1, 1 (geometries, attributes)
extent: 805737, 812824, 5890352, 5896005 (xmin, xmax, ymin, ymax)
coord. ref.: WGS 84 / UTM zone 32N (EPSG:32632)
values: 1

Spat Vector

Source

Randomly created in (QGIS)[https://www.qgis.org/download/thank-you/].

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

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)

Plot description file

Description

Contains made up coordinates for imaginary climate stations for education purposes.

Usage

plot_description

Format

## 'plot_description' A data frame with 10 rows and 5 columns

plot: imaginary station name and code
general: imaginary category of climate station
region: location name of climate station
lat: Latitude Coordinate of climate station
lon: Longitude Coordinate of climate station
elevation: elevation of climate station

Source

Randomly created coordinates and stations extracted from a random climate map.

Preparing CSV-Data

Description

Crops input data to the extent size and removes NA-Values

Usage

prep.csv(envrmt = .GlobalEnv$envrmt, method = "proc", save_output = TRUE, ...)

Arguments

envrmt

variable name of your envrmt list created using climodr's 'envi.create' function. Default = envrmt.

method

character. "proc" for ready-to-use data in separate .csv-files. "tube" for raw-data from the Tube Data Base. Default "proc"-Method.

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

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)

#check the created csv files
csv_files <- grep("_no_NAs.csv$",
                  list.files(envrmt$path_tworkflow),
                  value=TRUE)
csv_files

Processing CSV-Data

Description

Calculate averaged sensor values aggregated to a given time interval.

Usage

proc.csv(
  envrmt = .GlobalEnv$envrmt,
  method = "monthly",
  rbind = TRUE,
  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.

rbind

logical. Create a single file with all climate stations. If FALSE, every station will be saved in a seperate file.

save_output

logical. If data should be saved permanently in the Environment put save_output = TRUE. Otherwise the output will be saved in the temporary directory. Default: TRUE.

...

arguments passed down from other functions.

Value

List

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)
head(csv_data)

Resolution and Area

Description

This raster contains the area of interest as well as the desired model resolution (100 m * 100 m) and the project extent.

Usage

res_area

Format

## 'res_area' A binary Raster of pixels with value 1 in extent that belong to example area

class: SpatRaster
dimensions: 57, 71, 1 (nrow, ncol, nlyr)
resolution: 100, 100 (x, y)
extent: 805732, 812832, 5890310, 5896010 (xmin, xmax, ymin, ymax)
coord. ref.: WGS 84 / UTM zone 32N (EPSG:32632)
name: res_area
min/max: 0/1

Source

Randomly created binary Spat Raster file with the project resolution of 100 m per pixel. Created in (QGIS)[https://www.qgis.org/download/thank-you/].

Spatial Raster File for Vignette

Description

A spatial Raster file from a random area choose for the Vignette or as dummy data.

Usage

sch_201707

Format

## 'sch_201707' A Spat Raster with 8 spectral bands

class: SpatRaster
dimensions: 86, 151, 10 (nrow, ncol, nlyr)
resolution: 100, 100 (x, y)
extent: 801522.5, 816622.5, 5888973, 5897573 (xmin, xmax, ymin, ymax)
coord. ref.: WGS 84 / UTM zone 32N (EPSG:32632)
names: blue, green, red, nir, nirb, re1, re2, re3, swir1, swir2
min/max: 33.90298/5479.6602

Source

Randomly created Spat Raster file from (Sentinel-2 Data)[https://browser.dataspace.copernicus.eu/?zoom=10&lat=52.96601&lng=13.86818&themeId=DEFAULT-THEME&visualizationUrl=U2FsdGVkX1

Digital Ground Model for Vignette

Description

A Digital Ground Model file from a random area choose for the Vignette or as dummy data.

Usage

sch_dgm

Format

## 'sch_dgm' A Digital Ground Model

class: SpatRaster
dimensions: 86, 151, 10 (nrow, ncol, nlyr)
resolution: 100, 100 (x, y)
extent: 801522.5, 816622.5, 5888973, 5897573 (xmin, xmax, ymin, ymax)
coord. ref.: WGS 84 / UTM zone 32N (EPSG:32632)
names: elevation
min/max: 48.75315/94.67307

Source

Randomly extracted Digital Ground Model.

Spatial aggregation for CSV-Data

Description

Extract station coordinates from meta-data and reproject the coordinates to the project coordinate reference system.

Usage

spat.csv(
  envrmt = .GlobalEnv$envrmt,
  method = "monthly",
  des_file,
  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.

des_file

character. The filename and data type of the meta-data. (Only reads .csv)

crs

character. EPSG of the Coordinate Reference System, if no **res_area.tif** file is provided.

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: TRUE

...

arguments passed down from other functions.

Value

Data Frame

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)

#extract station coordinates
csv_spat <- spat.csv(envrmt = envrmt,
                     method = "monthly",
                     des_file = "plot_description.csv",
                     save_output = TRUE)
head(csv_spat)

Station File (G06)

Description

Usage

Format

Source

Station File (G17)

Description

Usage

Format

Source

Station File (G20)

Description

Usage

Format

Source

Station File (G21)

Description

Usage

Format

Source

Station File (G25)

Description

Usage

Format

Source

Station File (G48)

Description

Usage

Format

Source

Station File (W10)

Description

Usage

Format

Source

Station File (W11)

Description

Usage

Format

Source

Station File (W19)

Description

Usage

Format

Source

Station File (W20)

Description

Usage

Format

Source

Autocorrelation

Description

Usage

Arguments

Value

See Also

Examples

Calculate spectral indices

Description

Usage

Arguments

Value

See Also

Examples

Modelling

Description

Usage

Arguments

Value

See Also

Examples

Load in Example Data

Description

Usage

Arguments

Value

Examples

Create Maps using the 'terra' package graphic parameters

Description

Usage