This algorithm allows to detect disturbances in the forests using Landsat data. It can also be run on data from other sensors such as MODIS.
pvts(x, startm, endm, threshold = 5, verbose = FALSE)Vector (class "numeric"), univariate time series (class "ts") without NA's.
The start of the monitoring time.
The end of the monitoring time.
The default threshold is 5 for photosynthetic vegetation or for Normalized Difference Fraction Index (NDFI), while for indices such as NDVI and EVI the threshold is 3. Please see Tarazona et al. (2018) for more details.
This parameter is Logical. It Prints progress messages during execution.
Regarding the example data: if the idea is to detect changes in 2008 (position 19),
then we will smooth the data only up to that position (i.e. ndfi[1:19]). This is to avoid
a smoothing of the monitor position value.
In order to optimize the detection, it is advisable to make a smoothing through the smootH function before detecting changes. The smoothing will allow to remove outliers that were not eliminated during the masking. If the object is a time series, it must have the structure of a "ts", that is, the serie must have a start, end and frequency. See ts for more details. In addition, in case the input is a matrix, the first dimension must be rows*columns of the image, and the second dimension the number of images.
Tarazona, Y., Mantas, V.M., Pereira, A.J.S.C. (2018). Improving tropical deforestation detection through using photosynthetic vegetation time series (PVts-\(\beta\)). Ecological Indicators, 94, 367 379.
library(ForesToolboxRS)
library(raster)
# NDFI from 1990 to 2017, one index for each year.
ndfi <- c(
0.86, 0.93, 0.97, 0.91, 0.95, 0.96, 0.91, 0.88, 0.92, 0.89,
0.90, 0.89, 0.91, 0.92, 0.89, 0.90, 0.92, 0.84, 0.46, 0.20,
0.27, 0.22, 0.52, 0.63, 0.61, 0.67, 0.64, 0.86
)
# Detect change in 2008 (position 19)
# First, applying a smoothing
ndfi_smooth <- ndfi
ndfi_smooth[1:19] <- smootH(ndfi[1:19])
# Now, detect changes in 2008 (position 19)
cd <- pvts(x = ndfi_smooth, startm = 19, endm = 19, threshold = 5)
plot(cd)
# Detect changes in 2008 (time serie)
ndfi_smt_ts <- ts(ndfi_smooth, start = 1990, end = 2017, frequency = 1)
cd <- pvts(x = ndfi_smt_ts, startm = 2008, endm = 2008, threshold = 5)
plot(cd)