getting-started-with-irg.Rmd
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
.
remotes::install_gitlab('robit.a/irg')
irg
depends on two packages (and stats
):
data.table
for all tabular processingRcppRoll
for fast rolling medians in filter_roll
.No external dependencies.
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.
library(irg)
library(data.table)
ndvi <- fread(system.file('extdata', 'ndvi.csv', package = 'irg'))
# or look at the help page
?ndvi
id | yr | DayOfYear | NDVI | SummaryQA |
---|---|---|---|---|
2941 | 2002 | 133 | -1021 | 2 |
7652 | 2002 | 153 | 4251 | 3 |
7035 | 2002 | 147 | 308 | 3 |
4368 | 2002 | 146 | 4802 | 1 |
5196 | 2002 | 159 | 6397 | 0 |
12377 | 2002 | 149 | 5219 | 1 |
If your data is a data.frame
, convert it by reference:
# Pretend
DF <- as.data.frame(ndvi)
# Convert by reference
setDT(DF)
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)…
There are 5 filtering functions, 2 scaling functions, 3 modeling functions and 2 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)
#> Warning in calc_irg(model_ndvi(DT, observed = FALSE)): NAs found in DT, IRG
#> will be set to NA.
id | yr | t | fitted | irg |
---|---|---|---|---|
7035 | 2003 | 0.4000000 | 0.1504764 | 0.5111129 |
7035 | 2003 | 0.4027397 | 0.1568717 | 0.5288405 |
7035 | 2003 | 0.4054795 | 0.1634865 | 0.5468322 |
7035 | 2003 | 0.4082192 | 0.1703238 | 0.5650611 |
7035 | 2003 | 0.4109589 | 0.1773864 | 0.5834979 |
There are 5 filtering functions.
functions | arguments |
---|---|
filter_ndvi | DT |
filter_qa | DT, qa, good |
filter_roll | DT, window, id, method |
filter_top | DT, probs, id |
filter_winter | DT, probs, limits, doy, id |
# Load data.table
library(data.table)
library(irg)
# 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')
scale_ndvi(ndvi)
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(
ndvi,
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
model_params(
ndvi,
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)