Classical data pre-processing and analysis chain

• Download the data
• Unpack the data
• Atmospheric correction and orthorectification
• Cloud masking
• Calculation of vegetation indices
• Prepare time-series
• Run algorithm

With bfastSptial we've tried to streamline this process

Enough theory, let's try it!!!

Installation

You need:

• (A recent version of) R
• The devtools package

                
# update existing R packages 
#! the line below can potentially download lot's of R packages!
update.packages(checkBuilt = TRUE, ask = FALSE)                
# install devtools package
install.packages('devtools')
                
            

Then we can install the package using the devtools package

                
# install bfastSpatial from github
devtools::install_github('dutri001/bfastSpatial')
                
            

About the data

• These are Surface reflectance Landsat data downloaded via espa
• The full time-series (2000-2015) of a 5x5 km extent was downloaded
• The product already contains vegetation indices (NDVI, NDMI, etc), processed by the USGS
• For a tutorial on how to order data from USGS and download them, refer to the main bfastSpatial tutorial

At the border between Romania and Hungary

Utilities: Investigate individual pixel time-series

• bfmPixel() (run bfastmonitor on individual pixels)
• Validation via timeSyncR

bfmPixel() example

                
# Plot a cloud free recent NDVI layer
plot(ndviStack, 394)

# Call bfmPixel in interactive mode
bfmPixel(x = ndviStack, start = 2010, monend = 2013, interactive = TRUE, plot = TRUE)

# Click on a pixel
                
            

And what about validation?

We have a tool for that too; try timeSyncR for visual interpretation of time-series