Calculate the instantaneous rate of green-up.
calc_irg(DT, id = "id", year = "yr", scaled = TRUE)data.table of model parameters (output from model_params).
id column. default is 'id'. See details.
year column name. default is 'yr'.
boolean indicating if irg should be rescaled between 0-1 within id and year. If TRUE, provide id and year. Default is TRUE.
Extended data.table 'irg' column of instantaneous rate of green-up calculated for each day of the year, for each individual and year.
The DT argument expects a data.table of model estimated parameters for double logistic function of NDVI for each year and individual. Since it is the rate of green-up, model parameters required are only xmidS and scalS.
The scaled argument is used to optionally rescale the IRG result to 0-1, for each year and individual.
The id argument is used to split between sampling units. This may be a point id, polygon id, pixel id, etc. depending on your analysis. This should match the id provided to filtering functions. The formula used is described in Bischoff et al. (2012):
$$IRG = (exp((t + xmidS) / scalS)) / (2 * scalS * (exp(1) ^ ((t + xmidS) / scalS)) + (scalS * (exp(1) ^ ((2 * t) / scalS))) + (scalS * exp(1) ^ ((2 * xmidS) / scalS)))$$
(See the "Getting started with irg vignette" for a better formatted formula.)
Other irg:
irg()
# Load data.table
library(data.table)
# Read in example data
ndvi <- fread(system.file("extdata", "sampled-ndvi-MODIS-MOD13Q1.csv", package = "irg"))
# Filter and scale NDVI time series
filter_ndvi(ndvi)
#> id NDVI SummaryQA DayOfYear yr filtered winter rolled top
#> 1: 0 0.1864 3 11 2015 0.3076500 0.3076500 0.3076500 0.8735000
#> 2: 1 0.0541 2 3 2015 0.3163400 0.3163400 0.3163400 0.8632175
#> 3: 2 0.1781 3 11 2015 0.2649875 0.2649875 0.2649875 0.8707500
#> 4: 3 0.1024 2 5 2015 0.2301750 0.2301750 0.2301750 0.8635525
#> 5: 4 0.0898 2 3 2015 0.2177150 0.2177150 0.2177150 0.8476000
#> ---
#> 801: 2 0.1179 2 364 2019 0.2649875 0.2649875 0.2649875 0.8707500
#> 802: 3 0.0789 2 364 2019 0.2301750 0.2301750 0.2301750 0.8635525
#> 803: 4 0.1572 2 364 2019 0.2177150 0.2177150 0.2177150 0.8476000
#> 804: 5 0.0763 2 364 2019 0.3163400 0.3163400 0.3163400 0.8632175
#> 805: 6 0.1197 2 362 2019 0.3149325 0.3149325 0.3149325 0.8632000
scale_doy(ndvi)
#> id NDVI SummaryQA DayOfYear yr filtered winter rolled top
#> 1: 0 0.1864 3 11 2015 0.3076500 0.3076500 0.3076500 0.8735000
#> 2: 1 0.0541 2 3 2015 0.3163400 0.3163400 0.3163400 0.8632175
#> 3: 2 0.1781 3 11 2015 0.2649875 0.2649875 0.2649875 0.8707500
#> 4: 3 0.1024 2 5 2015 0.2301750 0.2301750 0.2301750 0.8635525
#> 5: 4 0.0898 2 3 2015 0.2177150 0.2177150 0.2177150 0.8476000
#> ---
#> 801: 2 0.1179 2 364 2019 0.2649875 0.2649875 0.2649875 0.8707500
#> 802: 3 0.0789 2 364 2019 0.2301750 0.2301750 0.2301750 0.8635525
#> 803: 4 0.1572 2 364 2019 0.2177150 0.2177150 0.2177150 0.8476000
#> 804: 5 0.0763 2 364 2019 0.3163400 0.3163400 0.3163400 0.8632175
#> 805: 6 0.1197 2 362 2019 0.3149325 0.3149325 0.3149325 0.8632000
#> t
#> 1: 0.027397260
#> 2: 0.005479452
#> 3: 0.027397260
#> 4: 0.010958904
#> 5: 0.005479452
#> ---
#> 801: 0.994520548
#> 802: 0.994520548
#> 803: 0.994520548
#> 804: 0.994520548
#> 805: 0.989041096
scale_ndvi(ndvi)
#> id NDVI SummaryQA DayOfYear yr filtered winter rolled top
#> 1: 0 0.1864 3 11 2015 0.3076500 0.3076500 0.3076500 0.8735000
#> 2: 1 0.0541 2 3 2015 0.3163400 0.3163400 0.3163400 0.8632175
#> 3: 2 0.1781 3 11 2015 0.2649875 0.2649875 0.2649875 0.8707500
#> 4: 3 0.1024 2 5 2015 0.2301750 0.2301750 0.2301750 0.8635525
#> 5: 4 0.0898 2 3 2015 0.2177150 0.2177150 0.2177150 0.8476000
#> ---
#> 801: 2 0.1179 2 364 2019 0.2649875 0.2649875 0.2649875 0.8707500
#> 802: 3 0.0789 2 364 2019 0.2301750 0.2301750 0.2301750 0.8635525
#> 803: 4 0.1572 2 364 2019 0.2177150 0.2177150 0.2177150 0.8476000
#> 804: 5 0.0763 2 364 2019 0.3163400 0.3163400 0.3163400 0.8632175
#> 805: 6 0.1197 2 362 2019 0.3149325 0.3149325 0.3149325 0.8632000
#> t scaled
#> 1: 0.027397260 0
#> 2: 0.005479452 0
#> 3: 0.027397260 0
#> 4: 0.010958904 0
#> 5: 0.005479452 0
#> ---
#> 801: 0.994520548 0
#> 802: 0.994520548 0
#> 803: 0.994520548 0
#> 804: 0.994520548 0
#> 805: 0.989041096 0
# Guess starting parameters
model_start(ndvi)
#> id NDVI SummaryQA DayOfYear yr filtered winter rolled top
#> 1: 0 0.0791 2 61 2016 NA 0.3076500 NA 0.8735000
#> 2: 1 0.0526 2 61 2016 NA 0.3163400 NA 0.8632175
#> 3: 2 0.0707 2 61 2016 NA 0.2649875 NA 0.8707500
#> 4: 3 0.0456 2 61 2016 NA 0.2301750 NA 0.8635525
#> 5: 5 0.0526 2 61 2016 NA 0.3163400 NA 0.8632175
#> ---
#> 801: 1 0.8755 1 225 2015 0.8755 0.3163400 0.8671 0.8632175
#> 802: 2 0.8740 1 225 2015 0.8740 0.2649875 0.8732 0.8707500
#> 803: 3 0.8643 1 225 2015 0.8643 0.2301750 0.8643 0.8635525
#> 804: 5 0.8755 1 225 2015 0.8755 0.3163400 0.8671 0.8632175
#> 805: 6 0.8729 1 225 2015 0.8729 0.3149325 0.8688 0.8632000
#> t scaled xmidS_start xmidA_start
#> 1: 0.1643836 NA 0.3452055 0.7095890
#> 2: 0.1643836 NA 0.3452055 0.7095890
#> 3: 0.1643836 NA 0.3452055 0.7095890
#> 4: 0.1643836 NA 0.3452055 0.7095890
#> 5: 0.1643836 NA 0.3452055 0.7095890
#> ---
#> 801: 0.6136986 1 0.4000000 0.7123288
#> 802: 0.6136986 1 0.3808219 0.7123288
#> 803: 0.6136986 1 0.3808219 0.7808219
#> 804: 0.6136986 1 0.4000000 0.7123288
#> 805: 0.6136986 1 0.3808219 0.7808219
# Double logistic model parameters given starting parameters for nls
mods <- model_params(
ndvi,
return = 'models',
xmidS = 'xmidS_start',
xmidA = 'xmidA_start',
scalS = 0.05,
scalA = 0.01
)
# Fit double logistic curve to NDVI time series
fit <- model_ndvi(mods, observed = FALSE)
# Calculate IRG for each day of the year
calc_irg(fit)
#> id yr xmidS xmidA scalS scalA t
#> 1: 0 2016 0.3373884 0.7165697 0.02264830 0.03686743 0.000000000
#> 2: 0 2016 0.3373884 0.7165697 0.02264830 0.03686743 0.002739726
#> 3: 0 2016 0.3373884 0.7165697 0.02264830 0.03686743 0.005479452
#> 4: 0 2016 0.3373884 0.7165697 0.02264830 0.03686743 0.008219178
#> 5: 0 2016 0.3373884 0.7165697 0.02264830 0.03686743 0.010958904
#> ---
#> 12806: 5 2019 0.3929376 0.7228242 0.01317131 0.04943371 0.989041096
#> 12807: 5 2019 0.3929376 0.7228242 0.01317131 0.04943371 0.991780822
#> 12808: 5 2019 0.3929376 0.7228242 0.01317131 0.04943371 0.994520548
#> 12809: 5 2019 0.3929376 0.7228242 0.01317131 0.04943371 0.997260274
#> 12810: 5 2019 0.3929376 0.7228242 0.01317131 0.04943371 1.000000000
#> fitted irg
#> 1: 3.355173e-07 1.356661e-06
#> 2: 3.788475e-07 1.531112e-06
#> 3: 4.277638e-07 1.727997e-06
#> 4: 4.829857e-07 1.950198e-06
#> 5: 5.453252e-07 2.200972e-06
#> ---
#> 12806: 4.562408e-03 5.007750e-20
#> 12807: 4.317491e-03 3.342750e-20
#> 12808: 4.085667e-03 1.990435e-20
#> 12809: 3.866242e-03 8.920830e-21
#> 12810: 3.658559e-03 0.000000e+00