loadMontanaData: Load Montana Data from GitHub Repository

In Rfssa: Functional Singular Spectrum Analysis

Load Montana Data from GitHub Repository

Description

This function retrieves the Montana dataset from a GitHub repository hosted at https://github.com/haghbinh/dataset/Rfssa_dataset. Hosting datasets on GitHub rather than including them in the Rfssa R package conserves storage space. The Montana dataset contains intraday hourly temperature curves measured in degrees Celsius and normalized difference vegetation index (NDVI) image data. Both types of data are recorded near Saint Mary, Montana, USA. The NDVI images cover a region located between longitudes of 113.30 degrees West and 113.56 degrees West and latitudes of 48.71 degrees North and 48.78 degrees North. For each recorded intraday temperature curve, an NDVI image was captured on the same day every 16 days, starting from January 1, 2008, and ending on September 30, 2013. The dataset is valuable for environmental analysis, especially in the context of studying the impact of temperature changes on vegetation. Combining both temperature and NDVI data can reveal more informative patterns and insights. The returned object is a raw dataset in 'list' format; This raw data can then be further processed and converted into a 'funts' object named 'Montana'. See funts for more details on working with functional time series of class 'funts'.

Usage

loadMontanaData()

Format

A list containing two components:

Temperature Data

A 24 by 133 matrix of discrete samplings of intraday hourly temperature curves.

NDVI Images

An array with dimensions 33 by 33 by 133, where each 33 by 33 slice represents an NDVI image.

References

1. Diamond, H. J., Karl, T., Palecki, M. A., Baker, C. B., Bell, J. E., Leeper, R. D., Easterling, D. R., Lawrimore, J. H., Meyers, T. P., Helfert, M. R., Goodge, G., and Thorne, P.W. (2013). U.S. climate reference network after one decade of operations: status and assessment. [Read More](https://www.ncdc.noaa.gov/crn/qcdatasets.html). Last accessed April 2020.

2. Tuck, S. L., Phillips, H. R., Hintzen, R. E., Scharlemann, J. P., Purvis, A., and Hudson, L. N. (2014). MODISTools – downloading and processing MODIS remotely sensed data in R. Ecology and Evolution, 4(24):4658–4668.

See Also

funts

Examples

require(fda)
# Load Montana data
montana_data <- loadMontanaData()

# Extract variables
Temp <- montana_data$Temp
NDVI <- montana_data$NDVI

# Create a list for Montana data
Montana_Data <- list(Temp / sd(Temp), NDVI)

# Define basis functions
bs1 <- create.bspline.basis(c(0, 23), 11)
bs2 <- create.bspline.basis(c(1, 33), 13)
bs2d <- list(bs2, bs2)
bsmv <- list(bs1, bs2d)

# Convert to funts object
Y <- funts(X = Montana_Data, basisobj = bsmv,
           start = as.Date("2008-01-01"),
           end = as.Date("2013-09-30"),
           vnames = c("Normalized Temperature (\u00B0C)" , "NDVI"),
           dnames = list("Time", c("Latitude", "Longitude")),
           tname = "Date"
)

Rfssa documentation built on May 29, 2024, 8:58 a.m.