Calculate MODIS product covariates in multiple CPU threads
Source:R/calculate_covariates.R
calculate_modis.Rd
calculate_modis
essentially runs calculate_modis_daily
function
in each thread (subprocess). Based on daily resolution, each day's workload
will be distributed to each thread. With product
argument,
the files are processed by a customized function where the unique structure
and/or characteristics of the products are considered.
Usage
calculate_modis(
from = NULL,
locs = NULL,
locs_id = "site_id",
radius = c(0L, 1000L, 10000L, 50000L),
preprocess = process_modis_merge,
name_covariates = NULL,
subdataset = NULL,
fun_summary = "mean",
package_list_add = NULL,
export_list_add = NULL,
max_cells = 3e+07,
geom = FALSE,
...
)
Arguments
- from
character. List of paths to MODIS/VIIRS files.
- locs
sf/SpatVector object. Unique locs where covariates will be calculated.
- locs_id
character(1). Site identifier. Default is
"site_id"
- radius
numeric. Radii to calculate covariates. Default is
c(0, 1000, 10000, 50000)
.- preprocess
function. Function to handle HDF files.
- name_covariates
character. Name header of covariates. e.g.,
"MOD_NDVIF_0_"
. The calculated covariate names will have a form of "{name_covariates}{zero-padded buffer radius in meters}
", e.g., 'MOD_NDVIF_0_50000' where 50 km radius circular buffer was used to calculate mean NDVI value.- subdataset
Indices, names, or search patterns for subdatasets. Find detail usage of the argument in notes.
- fun_summary
character or function. Function to summarize extracted raster values.
- package_list_add
character. A vector with package names to load these in each thread. Note that
sf
,terra
,exactextractr
,doParallel
,parallelly
anddplyr
are the default packages to be loaded.- export_list_add
character. A vector with object names to export to each thread. It should be minimized to spare memory.
- max_cells
integer(1). Maximum number of cells to be read at once. Higher values will expedite processing, but will increase memory usage. Maximum possible value is
2^31 - 1
. Seeexactextractr::exact_extract
for details.- geom
FALSE/"sf"/"terra".. Should the function return with geometry? Default is
FALSE
, options with geometry are "sf" or "terra". The coordinate reference system of thesf
orSpatVector
is that offrom.
- ...
Arguments passed to
preprocess
.
Value
A data.frame or SpatVector with an attribute:
attr(., "dates_dropped")
: Dates with insufficient tiles. Note that the dates mean the dates with insufficient tiles, not the dates without available tiles.
Note
Overall, this function and dependent routines assume that the file
system can handle concurrent access to the (network) disk by multiple
processes. File system characteristics, package versions, and hardware
settings and specification can affect the processing efficiency.
locs
is expected to be convertible to sf
object. sf
, SpatVector
, and
other class objects that could be converted to sf
can be used.
Common arguments in preprocess
functions such as date
and path
are
automatically detected and passed to the function. Please note that
locs
here and path
in preprocess
functions are assumed to have a
standard naming convention of raw files from NASA.
The argument subdataset
should be in a proper format
depending on preprocess
function:
process_modis_merge()
: Regular expression pattern. e.g.,"^LST_"
process_modis_swath()
: Subdataset names. e.g.,c("Cloud_Fraction_Day", "Cloud_Fraction_Night")
process_blackmarble()
: Subdataset number. e.g., for VNP46A2 product, 3L. Dates with less than 80 percent of the expected number of tiles, which are determined by the mode of the number of tiles, are removed. Users will be informed of the dates with insufficient tiles. The result data.frame will have an attribute with the dates with insufficient tiles.
See also
This function leverages the calculation of single-day MODIS covariates:
Also, for preprocessing, please refer to:
Examples
## NOTE: Example is wrapped in `\dontrun{}` as function requires a large
## amount of data which is not included in the package.
if (FALSE) { # \dontrun{
locs <- data.frame(lon = -78.8277, lat = 35.95013, id = "001")
locs <- terra::vect(locs, geom = c("lon", "lat"), crs = "EPSG:4326")
calculate_modis(
from =
list.files("./data", pattern = "VNP46A2.", full.names = TRUE),
locs = locs,
locs_id = "site_id",
radius = c(0L, 1000L),
preprocess = process_modis_merge,
name_covariates = "cloud_fraction_0",
subdataset = "Cloud_Fraction",
fun_summary = "mean"
)
} # }