Using filtered NDVI time series, scale it to 0-1.
scale_ndvi(DT)data.table with appended 'scaled' column of 0-1 scaled NDVI.
This functions expects the input DT is the output of previous four filtering steps, or filter_ndvi.
Other scale:
scale_doy()
# 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
#> <int> <num> <num> <int> <num> <num> <num> <num>
#> 1: 0 0.1864 3 11 2015 0.3076500 0.3076500 0.3076500
#> 2: 1 0.0541 2 3 2015 0.3163400 0.3163400 0.3163400
#> 3: 2 0.1781 3 11 2015 0.2649875 0.2649875 0.2649875
#> 4: 3 0.1024 2 5 2015 0.2301750 0.2301750 0.2301750
#> 5: 4 0.0898 2 3 2015 0.2177150 0.2177150 0.2177150
#> ---
#> 801: 2 0.1179 2 364 2019 0.2649875 0.2649875 0.2649875
#> 802: 3 0.0789 2 364 2019 0.2301750 0.2301750 0.2301750
#> 803: 4 0.1572 2 364 2019 0.2177150 0.2177150 0.2177150
#> 804: 5 0.0763 2 364 2019 0.3163400 0.3163400 0.3163400
#> 805: 6 0.1197 2 362 2019 0.3149325 0.3149325 0.3149325
#> top
#> <num>
#> 1: 0.8735000
#> 2: 0.8632175
#> 3: 0.8707500
#> 4: 0.8635525
#> 5: 0.8476000
#> ---
#> 801: 0.8707500
#> 802: 0.8635525
#> 803: 0.8476000
#> 804: 0.8632175
#> 805: 0.8632000
scale_ndvi(ndvi)
#> id NDVI SummaryQA DayOfYear yr filtered winter rolled
#> <int> <num> <num> <int> <num> <num> <num> <num>
#> 1: 0 0.1864 3 11 2015 0.3076500 0.3076500 0.3076500
#> 2: 1 0.0541 2 3 2015 0.3163400 0.3163400 0.3163400
#> 3: 2 0.1781 3 11 2015 0.2649875 0.2649875 0.2649875
#> 4: 3 0.1024 2 5 2015 0.2301750 0.2301750 0.2301750
#> 5: 4 0.0898 2 3 2015 0.2177150 0.2177150 0.2177150
#> ---
#> 801: 2 0.1179 2 364 2019 0.2649875 0.2649875 0.2649875
#> 802: 3 0.0789 2 364 2019 0.2301750 0.2301750 0.2301750
#> 803: 4 0.1572 2 364 2019 0.2177150 0.2177150 0.2177150
#> 804: 5 0.0763 2 364 2019 0.3163400 0.3163400 0.3163400
#> 805: 6 0.1197 2 362 2019 0.3149325 0.3149325 0.3149325
#> top scaled
#> <num> <num>
#> 1: 0.8735000 0
#> 2: 0.8632175 0
#> 3: 0.8707500 0
#> 4: 0.8635525 0
#> 5: 0.8476000 0
#> ---
#> 801: 0.8707500 0
#> 802: 0.8635525 0
#> 803: 0.8476000 0
#> 804: 0.8632175 0
#> 805: 0.8632000 0