Title: | Projecting Satellite-Derived Phenology in Space |
Version: | 2.0.1 |
Date: | 2023-10-12 |
Maintainer: | Christian John <cjohn@ucsb.edu> |
Depends: | R (≥ 4.1.0) |
Imports: | phenex, plyr, stringr, terra, doParallel |
Description: | This takes in a series of multi-layer raster files and returns a phenology projection raster, following methodologies described in John (2016) https://etda.libraries.psu.edu/catalog/13521clj5135. |
License: | GPL-3 |
URL: | https://github.com/JepsonNomad/phenomap |
BugReports: | https://github.com/JepsonNomad/phenomap/issues |
RoxygenNote: | 7.1.2 |
NeedsCompilation: | no |
Packaged: | 2023-10-12 21:22:27 UTC; christianjohn |
Author: | Christian John [aut, cre] |
Repository: | CRAN |
Date/Publication: | 2023-10-12 22:20:09 UTC |
Convert a series of raster files to a single phenology raster.
Description
Convert a series of raster files to a single phenology raster.
Usage
mapPheno(
File_List = NA,
PhenoFactor = NA,
phase = NA,
threshold = NA,
year = NA,
NDVI = NA,
VIQ = NA,
DOY = NA,
PR = NA,
SnowExtent = NA,
verbose = FALSE
)
Arguments
File_List |
List of raster files |
PhenoFactor |
Character string; type of dataset to analyze (e.g., "VI", "Snow") |
phase |
Character string; name of phenophase to be measured (e.g., "greenup", "snowmelt", "senescence" or other arguments passed to phenex::phenophase()) |
threshold |
Float threshold GWI value to be projected. Use only for VI option. |
year |
Integer Year (YYYY) |
NDVI |
Integer Band number of NDVI band in raster files |
VIQ |
Integer Band number of VI Quality layer in raster files |
DOY |
Integer Band number of Composite Day of Year layer in raster files |
PR |
Integer Band Number of PR layer in raster files |
SnowExtent |
Integer Band number of Maximum_Snow_Extent in raster files |
verbose |
TRUE or FALSE (Default = FALSE) |
Value
Raster object with extent=extent(terra::rast(File_List)[1]) and CRS = crs(terra::rast(File_List)[1]). Digital numbers are expressed as Day of Year.
Examples
## Not run:
fpath <- system.file("extdata", package="phenomap")
File_List <- paste(fpath, list.files(path = fpath, pattern=c("TinyCrop_")), sep="/")
File_List
PhenoFactor = "VI"
phase = "greenup"
threshold = 0.5
year = 2016
NDVI = 1
VIQ = 3
DOY = 4
PR = 5
verbose = TRUE
Sample.Greenup <- mapPheno(File_List = File_List, PhenoFactor = PhenoFactor,
phase = phase, threshold = threshold, year = year,
NDVI = NDVI, VIQ = VIQ, DOY = DOY, PR = PR,
SnowExtent=SnowExtent,
verbose = verbose)
## End(Not run)
Convert a series of phenology terra::raster files to a single long-term trend terra::raster.
Description
Convert a series of phenology terra::raster files to a single long-term trend terra::raster.
Usage
mapTrend(
File_List,
Year_List,
parallel = FALSE,
n.cores = NULL,
verbose = FALSE
)
Arguments
File_List |
List of phenology terra::raster files (i.e. those produced in 'mapPheno') |
Year_List |
Vector of Integer Year (YYYY) with length > 5 |
parallel |
TRUE or FALSE (Default = FALSE) if TRUE, use parallel backend through plyr::aaply |
n.cores |
Integer number of cores to be used for parallel processing (only use if parallel = TRUE) |
verbose |
TRUE or FALSE (Default = FALSE) |
Value
terra::raster object with extent=ext(rast(File_List)[1]) and CRS = crs(rast(File_List)[1]). Layer 1 is the slope estimate of the linear model relating green-up timing (Day of Year) to time (Year). Layer 2 is the p-value of the slope estimate. Layer 3 is the standard error of the slope estimate. Layer 4 is the r-squared value for the linear model.
Examples
## Not run:
fpath <- system.file("extdata", package="phenomap")
File_List.Trend <- paste(fpath, list.files(path = fpath, pattern=c("Sample_Greenup_")), sep="/")
Year_List <- 2011:2016 # Tell it what years you're using
n.cores <- 4 # Set up parallel computing
phenotrend <- mapTrend(File_List = File_List.Trend,
Year_List = Year_List,
parallel = TRUE,
n.cores = n.cores,
verbose=TRUE)
## End(Not run)