Getting started with irg

  collapse = TRUE,
  comment = '#>',
  eval = FALSE
options(scipen = 9999)

The irg package opts for a tabular calculation of the instantaneous rate of green-up (IRG) as opposed to a raster based approach. Sampling MODIS imagery is left up to the user and a prerequisite for all functions. The main input (DT) for all functions is a data.table of an NDVI time series. The sampling unit (id) is flexible (a decision for the user) though we would anticipate points or polygons, or maybe a pixel. All functions leverage the speed of data.table to efficiently filter, scale and model NDVI time series, and calculate IRG.


Install the latest version with remotes.



irg depends on two packages (and stats):

No external dependencies.

Input data

irg requires an NDVI time series in a data.table.

Though names can be different and specified at input, the default names and required columns are:

SummaryQA details:

Let's take a look at the example data.


ndvi <- fread(system.file('extdata', 'ndvi.csv', package = 'irg'))

# or look at the help page


If your data is a data.frame, convert it by reference:

# Pretend
DF <-

# Convert by reference

Sampling NDVI

Though irg is not involved in the sampling step, it is important that the input data matches the package's expectations.

We used the incredible Google Earth Engine to sample MODIS NDVI (MOD13Q1.006). There are also R packages specific to MODIS (MODIStsp) and general purpose raster operations (raster), and others (let us know)...

Temporal extent

Filtering steps in irg use a baseline 'winterNDVI' and upper quantile as described by Bischoff et al. (2012). These steps require multiple years of sampled NDVI for each id. In addition, make sure to include all samples throughout the year, leaving the filtering for irg.


g <- grViz(
    digraph irg_functions  {
    graph [rankdir=LR, compound=TRUE, fontsize = 28]

    node[shape=none, fontsize=28]

    subgraph cluster_filt{
    label= '1)'; labeljust='l';
    Filtering -> filter_ndvi [dir=none]
    filter_ndvi -> filter_qa [dir=none]
    filter_ndvi -> filter_winter [dir=none]
  filter_ndvi -> filter_roll [dir=none]
  filter_ndvi -> filter_top [dir=none]

    filter_top -> filter_roll -> filter_winter -> filter_qa [dir=back]
    {rank=same; filter_qa; filter_winter; filter_roll; filter_top}

    subgraph cluster_scal{
  label= '2)' labeljust='l';

    Scaling -> scale_doy [dir=none]
    Scaling -> scale_ndvi [dir=none]

    subgraph cluster_mod{
  label= '3)' labeljust='l';

    Modeling -> model_start [dir=none]
    Modeling -> model_params [dir=none]
    Modeling -> model_ndvi [dir=none]

    model_ndvi -> model_params -> model_start [dir=back]
    {rank=same; model_ndvi; model_params; model_start}

    subgraph cluster_irg{
  label= '4)' labeljust='l';

    IRG -> calc_irg [dir=none]

  Filtering -> Scaling -> Modeling -> IRG

    # irg -> Filtering
    # irg -> Scaling
    # irg -> Modeling
    # irg -> IRG

    ", width = 700, height = 600)
fs <-
    data.table(functions = as.character(lsf.str('package:irg')))[, 
             arguments := paste(unlist(formalArgs(functions)), 
                                                 collapse = ', ' ), 
             by = functions]

There are r nrow(fs[grepl('filter', functions)]) filtering functions, r nrow(fs[grepl('scale', functions)]) scaling functions, r nrow(fs[grepl('model', functions)]) modeling functions and r nrow(fs[grepl('irg', functions)]) IRG functions.

The irg::irg function is a wrapper for all steps - filtering, scaling, modeling and calculating IRG in one step. At this point, only defaults. Here's 5 rows from the result.

For options, head to the steps below.

out <- irg(ndvi)
knitr::kable(out[between(t, 0.4, 0.5)][1:5, .(id, yr, t, fitted, irg)])


There are r nrow(fs[grepl('filter', functions)]) filtering functions.

# fs[grepl('qa', functions), order := 1]
# fs[grepl('winter', functions), order := 2]
# fs[grepl('roll', functions), order := 3]
# fs[grepl('top', functions), order := 4]
knitr::kable(fs[grepl('filter', functions), .(functions, arguments)])
# Load data.table

# Read in example data
ndvi <- fread(system.file('extdata', 'ndvi.csv', package = 'irg'))

# Filter NDVI time series
filter_qa(ndvi, qa = 'SummaryQA', good = c(0, 1))

filter_winter(ndvi, probs = 0.025, limits = c(60L, 300L),
                            doy = 'DayOfYear', id = 'id')

filter_roll(ndvi, window = 3L, id = 'id', method = 'median')

filter_top(ndvi, probs = 0.925, id = 'id')


Two scaling functions are use to scale the day of year column and filtered NDVI time series between 0-1.

# Scale variables
scale_doy(ndvi, doy = 'DayOfYear')


Three functions are used to model the NDVI times series to a double logistic curve, as described by Bischoff et al. (2012).

$$fitted = \frac{1}{1 + e^ \frac{xmidS - t}{scalS}} - \frac{1}{1 + e^ \frac{xmidA - t}{scalA}}$$

Two options from this point are available: fitting NDVI and calculating IRG for observed data only, or for the full year.

To calculate for every day of every year, specify returns = 'models' in model_params, observed = FALSE in model_ndvi and assign the output of model_ndvi.

# Guess starting parameters
model_start(ndvi, id = 'id', year = 'yr')

# Double logistic model parameters given starting parameters for nls
mods <- model_params(
  returns = 'models',
  id = 'id', year = 'yr',
  xmidS = 'xmidS_start', xmidA = 'xmidA_start',
  scalS = 0.05,
  scalA = 0.01

# Fit double log to NDVI
fit <- model_ndvi(mods, observed = FALSE)

Alternatively, to calculate for the observed data only, specify returns = 'columns' in model_params and observed = TRUE in model_ndvi.

# Guess starting parameters
model_start(ndvi, id = 'id', year = 'yr')

# Double logistic model parameters given starting parameters for nls
  returns = 'columns',
  id = 'id', year = 'yr',
  xmidS = 'xmidS_start', xmidA = 'xmidA_start',
  scalS = 0.05,
  scalA = 0.01

# Fit double log to NDVI
 model_ndvi(ndvi, observed = TRUE)


$$IRG = \frac{e ^ \frac{t + xmidS}{scalS}}{2 scalS e ^ \frac{t + xmidS}{scalS} + scalS e ^ \frac{2t}{scalS} + scalS e ^ \frac{2midS}{scalS}}$$ Finally, calculate IRG:

# Calculate IRG for each day of the year

# or for observed data

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irg documentation built on May 2, 2019, 9:38 a.m.