Estimates phenological dates of satellite imagery time series. Originally conceived to handle MODIS time series (especifically MOD13Q1), this package can handle Earth Observation time series from any satellite mission.
The following functions allow to access numeric vectors of time series satellite imagery, in particular, MOD13Q1 time series starting at February 18, 2000.
fill_initialgap_MOD13Q1 | Fill first 3 MOD13Q1 observations |
vecFromData | Get numeric vector from an RData file |
vecToMatrix | Set numeric vector as a matrix |
get_metadata_years | Get metadata useful in certain visualizations |
The following functions allow to smooth out and fit a regression model based on Functional Principal Components. Applications of these functions allow to estimate phenological parameters of numeric vectors of Earth Observation time series:
ndvi_derivatives | Derivatives of idealized NDVI curve |
phenopar | Estimate phenological dates |
phenopar_polygon | Estimate phenological dates (parallel processing) |
Plot methods for numeric and sephora objects:
getSpiralPlot | Spiral plot of polygon-based phenological date estimates |
plot.sephora | Plot methods for sephora-class object |
datesToDoY | Maps estimated phenological dates to days of a year |
getDist_phenoParam | Access to vectors of phenological date estimates from a list |
global_min_max | Global critical points of a curve on a closed interval |
local_min_max | Local critical points of a curve on a union of open intervals |
Tecuapetla-Gómez, I. itecuapetla@conabio.gob.mx
The main function of this package, phenopar, allows a numeric
vector containing satellite-based measurements (preferably, vegetation indices for better results).
These observations can be construed as realizations of an underlying periodic stochastic process that has been recorded
from the first day of the year (DoY) of startYear to the last DoY of endYear.
Thus, each numeric vector can be assembled as a matrix whose number of rows and columns equal
to length(startYear:endYear) and frequency, respectively, see get_metadata_years. Moreover,
each row of this matrix can be thought as the realization of the periodic stochastic
process throughout a season. Thus, having multiple measurements of such a process, functional
principal component methods are employed to extract an underlying idealized (vegetation index) curve.
The phenological dates that can be estimated with sephora are:
Green Up (GU).
Start of Season (SoS).
Maturity (Mat).
Senescence (Sen).
End of Season (EoS).
Dormancy (Dor).