Reproject a sf object to UTM coordinates

Description

Reproject a sf object to UTM coordinates

Usage

pa_2utm(df, verbose = FALSE)

Arguments

Details

This function will attempt to automatically determine the adequate UTM zone and reproject a sf object,

Value

a sf object

Author(s)

Caio dos Santos and Fernando Miguez

Examples

## for examples, see vignette pacu

Adjust the effective area of each observation based on vehicular polygon overlap

Description

Adjust the effective area of each observation based on vehicular polygon overlap

Usage

pa_adjust_obs_effective_area(
  polygons,
  obs.vector,
  var.label = "yield",
  overlap.threshold = 0,
  cores = 1L,
  verbose = FALSE
)

Arguments

Details

This function will make use of the vehicular polygons to evaluate the overlap between polygons and adjust the variable in obs.vector to the effective area in the polygon. This is primarely intended for yield.

Value

an sf object

Impose a regular grid over yield polygons

Description

Impose a regular grid over yield polygons

Usage

pa_apportion_mass(
  polygons,
  mass.vector,
  cell.size = NULL,
  sum = FALSE,
  remove.empty.cells = TRUE,
  cores = 1L,
  verbose = FALSE
)

Arguments

Details

This function will impose a regular grid over the yield polygons and compute the weighted average of the mass value represented by each polygon. The averages are weighted according to the polygon area.

Value

sf object

Author(s)

Caio dos Santos and Fernando Miguez

Examples

## for examples, see vignette pacu

Browse satellite products from the Copernicus Data Space Ecosystem

Description

Browse satellite products from the Copernicus Data Space Ecosystem

Usage

pa_browse_dataspace(
  aoi,
  start.date,
  end.date,
  max.cloud.cover = 100,
  collection.name = c("SENTINEL-2"),
  product.name = c("MSIL2A"),
  max.results = 1000
)

Arguments

Details

'pa_browse_dataspace()' will use HTTP requests to communicate with the Data Space API and search for available satellite products matching the filters established by the function parameters.

Value

a list of entries available for download

Author(s)

Caio dos Santos and Fernando Miguez

Examples

## Not run: 
extd.dir <- system.file("extdata", package = "pacu")
area.of.interest <- sf::st_read(file.path(extd.dir, 'cobs_a_aoi.shp'), quiet = TRUE)
available.images <- pa_browse_dataspace(aoi = area.of.interest,
                                        max.cloud.cover = 10,
                                        start.date = '2023-01-01',
                                        end.date = '2023-12-31')

## End(Not run)

Predict cardinal dates from satellite image data

Description

Predict cardinal dates from satellite image data

Usage

pa_cardinal_dates(x, ...)

## S3 method for class 'numeric'
pa_cardinal_dates(
  x,
  y,
  baseline.months = c(1:3, 12),
  model = c("none", "card3", "scard3", "agauss", "harmonic"),
  prior.means,
  prior.vars,
  bias.correction,
  ...
)

## S3 method for class 'Date'
pa_cardinal_dates(
  x,
  y,
  baseline.months = c(1:3, 12),
  model = c("none", "card3", "scard3", "agauss", "harmonic"),
  prior.means,
  prior.vars,
  bias.correction,
  ...
)

## S3 method for class 'veg.index'
pa_cardinal_dates(
  x,
  y = NULL,
  baseline.months = c(1:3, 12),
  model = c("none", "card3", "scard3", "agauss", "harmonic"),
  prior.means,
  prior.vars,
  bias.correction,
  ...
)

Arguments

Value

when x is a vector, returns a vector of length 3 with the predicted cardinal dates. When x is a veg.index object, returns a stars object with spatially distributed cardinal dates

Examples

## Not run: 
x <- seq(1, 365, 6)
y <- nlraa::scard3(x, 120, 210, 300)
pa_cardinal_dates.vector(
  x = x,
  y = y,
  model = 'scard3',
  prior.means = c(130, 190, 297),
  prior.vars = c(11, 13, 18),
  bias.correction = c(10, 10, 10)
)

## End(Not run)

Check the yield data before processing with the pa_yield function

Description

This function will check for red flags so the user can know of potential problems before using the pa_yield functions

Usage

pa_check_yield(input, algorithm = c("all", "simple", "ritas"))

Arguments

Details

This function will check the input yield data for any potential problems before the user runs the 'pa_yield()' function. Ideally, this function warn the user of potential problems.

Value

object of class check.yield

Author(s)

Caio dos Santos and Fernando Miguez

Examples

extd.dir <- system.file("extdata", package = "pacu")
raw.yield <- sf::read_sf(file.path(extd.dir, '2012-basswood.shp'),
                         quiet = TRUE)
chk <- pa_check_yield(raw.yield)
chk

Compute vegetation indices from a zipped Sentinel 2 file

Description

Compute vegetation indices from a zipped Sentinel 2 file.

Usage

pa_compute_vi(
  satellite.images,
  vi = c("ndvi", "ndre", "gcvi", "reci", "evi", "bsi", "other"),
  aoi = NULL,
  formula = NULL,
  check.clouds = FALSE,
  buffer.clouds = 100,
  downscale.to = NULL,
  pixel.res = c("default", "10m", "20m", "60m"),
  img.formats = c("jp2", "tif"),
  fun = function(x) mean(x, na.rm = TRUE),
  verbose = TRUE
)

Arguments

Details

This is script that unzips the Sentinel 2 zipped file into a temporary folder, searches for the index-relevant bands, and then computes the index. If no ‘aoi’ is provided, the script will compute the vegetation index for the area covered by the image. The pre-specified vegetation indices are computed as follows:

BSI = \frac{(SWIR + RED) - (NIR + BLUE)}{(SWIR + RED) + (NIR + BLUE)}

EVI = \frac{2.5 \times (NIR - RED)}{(NIR + (6 \times RED) - (7.5 \times BLUE) - 1) }

GCVI = \frac{(NIR)}{(GREEN)} - 1

NDRE = \frac{(NIR - RED edge)}{(NIR + RED edge)}

NDVI = \frac{(NIR - RED)}{(NIR + RED)}

RECI = \frac{(NIR)}{(RED edge)} - 1

The user can also specify custom vegetation indices using the formula argument. The formula should be two-sided, with the left side naming the vegetation index and the right side defining the mathematical operations used to calculate the vegetation index. The bands should be specified as B01, B02, ..., B12.

An important detail of this function is that, if there are duplicated dates, the function will consolidate the data into a single raster layer. The default behavior is to average the layers that belong to the same date. This can be changed with the 'fun' argument.

Value

an object of class veg.index and stars

Author(s)

Caio dos Santos and Fernando Miguez

Examples

extd.dir <- system.file("extdata", package = "pacu")
## List of zipped Sentinel files in a directory
s2a.files <- list.files(extd.dir, '\\.zip', full.names = TRUE)
area.of.interest <- sf::st_read(file.path(extd.dir, 'cobs_a_aoi.shp'))

## computing ndvi
ndvi <- pa_compute_vi(satellite.images = s2a.files,
                            vi = 'ndvi',
                            aoi = area.of.interest,
                            check.clouds = TRUE)

## computing ndre
ndre <- pa_compute_vi(satellite.images = s2a.files,
                            vi = 'ndre',
                            aoi = area.of.interest,
                            check.clouds = TRUE)

## specifying a differente vegetation index, in this case, the 
## excess green index
egi <- pa_compute_vi(satellite.images = s2a.files,
                            vi = 'other',
                            formula = EGI ~ (2 * B03) - B02 - B04,
                            aoi = area.of.interest,
                            check.clouds = TRUE)

Download satellite products from the Copernicus Data Space Ecosystem

Description

Download satellite products from the Copernicus Data Space Ecosystem to find satellite products

Usage

pa_download_dataspace(x, dir.path = NULL, aoi = NULL, verbose = TRUE)

Arguments

Details

'pa_download_dataspace()' uses the object from 'pa_browse_dataspace()' to download the data from Copernicus Data Space. The aoi argument is optional but was designed to save storage space.

Value

a list of objects that could not be downloaded

Author(s)

Caio dos Santos and Fernando Miguez

Examples

## Not run: 
extd.dir <- system.file("extdata", package = "pacu")
area.of.interest <- sf::st_read(file.path(extd.dir, 'cobs_a_aoi.shp'), quiet = TRUE)
available.images <- pa_browse_dataspace(aoi = area.of.interest,
                                        max.cloud.cover = 10,
                                        start.date = '2023-01-01',
                                        end.date = '2023-12-31')
dwonloaded.images <- pa_download_dataspace(x = available.images)


## End(Not run)

Retrieve an RGB image from a zipped Sentinel 2 file

Description

Retrieve an RGB image from a zipped Sentinel 2 file

Usage

pa_get_rgb(
  satellite.images,
  aoi = NULL,
  pixel.res = "10m",
  img.formats = c("jp2", "tif"),
  rgb.bands = c("B04", "B02", "B03"),
  fun = function(x) mean(x, na.rm = TRUE),
  verbose = TRUE
)

Arguments

Details

This is script that unzips the Sentinel 2 zipped file into a temporary folder, searches for the RGB, and constructs a multi-band raster containing the RGB bands. If no ‘aoi’ is provided, the script will construct the RGB image for the area covered by the image. An important detail of this function is that, if there are duplicated dates, the function will consolidate the data into a single raster layer. The default behavior is to average the layers that belong to the same date. This can be changed with the 'fun' argument.

Value

an object of class rgb and stars

Author(s)

Caio dos Santos and Fernando Miguez

Examples

extd.dir <- system.file("extdata", package = "pacu")
## List of zipped Sentinel files in a directory
s2a.files <- list.files(extd.dir, '\\.zip', full.names = TRUE)
area.of.interest <- sf::st_read(file.path(extd.dir, 'cobs_a_aoi.shp'))
rgb.rast <- pa_get_rgb(satellite.images = s2a.files,
                            aoi = area.of.interest)
pa_plot(rgb.rast)

Request vegetation index statistics from the Data Space Statistics API

Description

Request vegetation index statistics from the Data Space Statistics API

Usage

pa_get_vi_stats(
  aoi,
  start.date,
  end.date,
  collection = c("sentinel-2-l2a"),
  vegetation.index = c("bsi", "evi", "gcvi", "ndre", "ndvi", "reci"),
  agg.time = c("P1D", "P5D", "P10D"),
  by.feature = FALSE
)

Arguments

Details

'pa_get_vi_sentinel2()' will use HTTP requests to communicate with the Data Space Statistics API and request areal statistics for the specified vegetation index

Value

returns an object of class veg.index and stars

Author(s)

Caio dos Santos and Fernando Miguez

Examples

## Not run: 
extd.dir <- system.file("extdata", package = "pacu")
area.of.interest <- sf::st_read(file.path(extd.dir, 'cobs_a_aoi.shp'), quiet = TRUE)
ndvi <- pa_get_vi_stats(aoi = area.of.interest,
                        start.date = '2021-01-01',
                        end.date = '2021-12-31',
                        vegetation.index = 'ndvi')

## End(Not run)

Downloads a met file using the apsimx package

Description

This function retrieves weather data from NASA Power and the Iowa Environmental Mesonet using the apsimx package/

Usage

pa_get_weather_sf(
  aoi,
  source = c("none", "iem", "power"),
  start.date = "1990-01-01",
  end.date = "2021-12-31"
)

Arguments

Value

an object of class met

Author(s)

Caio dos Santos and Fernando Miguez

Examples

## Not run: 
extd.dir <- system.file("extdata", package = "pacu")
area.of.interest <- sf::st_read(file.path(extd.dir, 'cobs_a_aoi.shp'))
weather.met <- pa_get_weather_sf(aoi = area.of.interest,
                               start.date = '1990-01-01',
                               end.date = '2020-12-31',
                               source = 'power')

## End(Not run)

Register the Data Space credentials to the R environment

Description

Register the Data Space credentials to the R environment

Usage

pa_initialize_dataspace(username, password, verbose = TRUE)

Arguments

Details

'pa_initialize_dataspace()' registers the username and password to the machine's R environment. All the other functions that rely on authentication will search for the username and password in the R environment. Do not share your R environment with others, as they will be able to read your username and password. You can register at https://dataspace.copernicus.eu/.

Value

No return value, called for side effects

Author(s)

Caio dos Santos and Fernando Miguez

Examples

## Not run: 
pa_initialize_dataspace('my-username', 'my-password')

## End(Not run)

Register the Oauth2.0 credentials to the R environment

Description

Register the Oauth2.0 credentials to the R environment

Usage

pa_initialize_oauth(client_id, client_secret)

Arguments

Details

initialize_oauth registers the client id and secret to the machine's R environment All the other functions that rely on authentication will search for the clients id ans secret in the R environment. Do not share your R environment with others, as they will be able to read your client id and secret. You can register at https://dataspace.copernicus.eu/news. Please see this section for how to create your Oauth2.0 client: https://documentation.dataspace.copernicus.eu/APIs/SentinelHub/Overview/Authentication.html.

Value

No return value, called for side effects

Author(s)

Caio dos Santos and Fernando Miguez

Examples

## Not run: 
pa_initialize_oauth('my-client-id', 'my-client-secret')

## End(Not run)

Make vehicular polygons for yield monitor observations

Description

Make vehicular polygons for yield monitor observations

Usage

pa_make_vehicle_polygons(
  points,
  swath,
  distance,
  angle = NULL,
  cores = 1L,
  verbose = FALSE
)

Arguments

Details

This function will create vehicular polygons based on the distance between points, angle of the vehicle's trajectory, and swath.

Value

an sf object

Author(s)

Caio dos Santos and Fernando Miguez

Examples

## for examples, see vignette pacu

Create a plot from a pacu object

Description

Create a plot from a pacu object

Usage

pa_plot(x, ...)

## S3 method for class 'yield'
pa_plot(
  x,
  ...,
  plot.type = c("yieldmap", "variogram", "steps"),
  palette = "Temps",
  main = "",
  plot.var = NULL,
  interactive = FALSE,
  border.col = "black",
  style = c("quantile", "pretty", "equal"),
  scale = 1,
  nbreaks = 5,
  breaks = NULL,
  frame = TRUE,
  extent = sf::st_bbox(x[["yield"]]),
  legend.outside = FALSE,
  ask = TRUE
)

## S3 method for class 'trial'
pa_plot(
  x,
  ...,
  plot.type = c("trial"),
  palette = "Temps",
  main = "",
  plot.var = NULL,
  interactive = FALSE,
  border.col = "black",
  style = c("quantile", "pretty", "equal"),
  scale = 1,
  nbreaks = 5,
  breaks = NULL,
  frame = TRUE,
  extent = sf::st_bbox(x[["trial"]]),
  legend.outside = FALSE
)

## S3 method for class 'veg.index'
pa_plot(
  x,
  ...,
  palette = ifelse(plot.type == "timeseries", "Dark 2", "Temps"),
  plot.type = c("spatial", "timeseries"),
  main = "",
  plot.var = NULL,
  by = "year",
  xlab = NULL,
  ylab = NULL,
  style = c("quantile", "pretty", "equal"),
  nbreaks = 5,
  border.col = "black",
  frame = TRUE,
  legend.outside = FALSE,
  legend.title = NULL,
  pch = 16
)

## S3 method for class 'rgb'
pa_plot(
  x,
  ...,
  main = "",
  interactive = FALSE,
  saturation = 1,
  alpha = 1,
  interpolate = FALSE
)

## S3 method for class 'met'
pa_plot(
  x,
  ...,
  plot.type = c("climate_normals", "monthly_distributions"),
  unit.system = c("international", "standard"),
  start = 1,
  end = 365,
  months = 1:12,
  vars = c("maxt", "mint", "crain", "cradn"),
  tgt.year = "last"
)

Arguments

Value

No return value, called for side effects

Author(s)

Caio dos Santos and Fernando Miguez

Examples

## Not run: 
## for examples, please see the pacu vignette

## End(Not run)

Create an interpolated yield object from raw data

Description

Create an interpolated yield object from raw data

Usage

pa_yield(
  input,
  data.columns = NULL,
  data.units = NULL,
  grid = NULL,
  algorithm = c("none", "simple", "ritas"),
  formula = NULL,
  overlap.threshold = 0.5,
  var.label = "yield",
  boundary = NULL,
  clean = FALSE,
  clean.sd = 3,
  clean.edge.distance = 0,
  smooth.method = c("none", "krige", "idw"),
  fun = c("none", "log"),
  lbs.per.bushel = NULL,
  moisture.adj = NULL,
  lag.adj = 0,
  unit.system = c("none", "metric", "standard"),
  remove.crossed.polygons = FALSE,
  steps = FALSE,
  cores = 1L,
  verbose = TRUE,
  ...
)

Arguments

Details

This function will follow the steps in the selected algorithm to produce a yield map from the raw data.

Value

an object of class yield

Author(s)

Caio dos Santos and Fernando Miguez

Examples

## Not run: 
extd.dir <- system.file("extdata", package = "pacu")
raw.yield <- sf::read_sf(file.path(extd.dir, '2012-basswood.shp'),
                         quiet = TRUE)
## the simple algorithm
pa_yield(input = raw.yield,
algorithm = 'simple',
unit.system = 'metric',
lbs.per.bushel = 56) ## 56 lb/bushel of maize

## the ritas algorithm
pa_yield(input = raw.yield,
algorithm = 'ritas',
unit.system = 'metric',
lbs.per.bushel = 56)

## End(Not run)

Environment which stores PACU options

Description

Environment which can store the path to the executable, warning settings and where examples are located. Creating an environment avoids the use of global variables or other similar practices which would have possible undesirable consequences.

Usage

pacu.options

Format

An object of class environment of length 6.

Details

Environment which stores PACU options

Value

This is an environment, not a function, so nothing is returned.

Examples

names(pacu.options)
## to suppress messages
pacu_options(suppress.messages = TRUE)

Setting some options for the package

Description

Set settings regarding messages and default behaviors of the package

Usage

pacu_options(
  suppress.warnings = FALSE,
  suppress.messages = FALSE,
  apportion.size.multiplier = 1,
  minimum.coverage.fraction = 0.5
)

Arguments

Details

Set pacu options

Value

as a side effect it modifies the ‘pacu.options’ environment.

Examples

## Not run: 
names(pacu.options)
pacu_options(suppress.warnings = FALSE)
pacu.options$suppress.warnings

## End(Not run)

Print a pacu object

Description

These functions print meaningful information from pacu objects.

Usage

## S3 method for class 'yield'
print(x, ...)

## S3 method for class 'dslist'
print(x, ...)

## S3 method for class 'check.yield'
print(x, ...)

Arguments

Value

No return value, called for side effects

Produce result summaries of the various pacu objects

Description

Produce summaries for the different pacu objects

Usage

## S3 method for class 'dslist'
summary(object, ...)

## S3 method for class 'yield'
summary(object, ..., by = NULL)

## S3 method for class 'veg.index'
summary(object, ..., by, fun)

Arguments

Value

when object is of class dslist, no return value. Called for side effects.

when object is of class yield, returns an object of class data.frame

when object is of class veg.index, returns an object of class stars