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###############################################################################
# This script shows an example of how to use the "gfcanalysis" R package put
# together by Alex Zvoleff (azvoleff@conservation.org) for working with the
# Hansen et al. 2013 Global Forest Change dataset. Contact Alex if you notice
# any issues or have problems using the package.
#
# See the help files for the functions below for more information. For example,
# type "?download_tiles" in R to see the help file for the "download_tiles"
# function.
#
# NOTE: the gfcanalysis package must be installed before this script will run.
# Run the "install_gfcanalysis.R" script to install/update the gfcanalysis
# package.
###############################################################################
# Load the gfcanalysis package
library(gfcanalysis)
library(sf)
# Indicate where we want to save GFC tiles downloaded from Google. For any
# given AOI, the script will first check to see if these tiles are available
# locally (in the below folder) before downloading them from the server - so I
# recommend storing ALL of your GFC tiles in the same folder. For this example
# we will save files in the current working directory folder.
data_folder <- '.'
###############################################################################
# Download data from Google server for a given AOI
###############################################################################
# Load a demo AOI from the P drive - notice that first we specify the folder
# the shapefile is in, and then the name of the shapefile without the '.shp'
aoi <- read_sf(system.file('extdata', package='gfcanalysis'), 'ZOI_NAK_2012')
# Calculate the google server URLs for the tiles needed to cover the AOI
tiles <- calc_gfc_tiles(aoi)
# Check to see if these tiles are already present locally, and download them if
# they are not.
download_tiles(tiles, data_folder)
# Extract the GFC data for this AOI from the downloaded GFC tiles, mosaicing
# multiple tiles as necessary (if needed to cover the AOI), and saving the
# output data to a GeoTIFF (can also save in ENVI format, Erdas format, etc.).
gfc_data <- extract_gfc(aoi, data_folder, filename='gfc_NAK_extract.tif')
###############################################################################
# Performing thresholding and calculate basic statistics
###############################################################################
# Calculate and save a thresholded version of the GFC product
gfc_thresholded <- threshold_gfc(gfc_data, forest_threshold=90,
filename="gfc_NAK_extract_thresholded.tif")
# Calculate annual statistics on forest loss/gain
gfc_stats <- gfc_stats(aoi, gfc_thresholded)
# Save statistics to CSV files for use in Excel, etc.
write.csv(gfc_stats$loss_table, file='gfc_NAK_extract_losstable.csv', row.names=FALSE)
write.csv(gfc_stats$gain_table, file='gfc_NAK_extract_gaintable.csv', row.names=FALSE)
###############################################################################
# Make visualization of forest change
###############################################################################
# Calculate and save a thresholded annual layer stack from the GFC product
# (useful for simple visualizations, etc.)
gfc_thresholded_annual <- annual_stack(gfc_thresholded)
writeRaster(gfc_thresholded_annual, filename='gfc_NAK_extract_thresholded_annual.tif')
# Save a simple visualization of the thresholded annual layer stack (this is
# just an example, and is using the data in WGS84. The data should be projected
# for this).
animate_annual(aoi, gfc_thresholded_annual)
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