#1. Prepare NDVI tiff file and shapefile 

#2. Download R and RStudio

#3. Open new RProject

#4. Copy NDVI tiff file and shapefile into RProject folder

#5. Open new R Notebook

#6. Install packages

install.packages("rmarkdown")
install.packages("tidyverse", dependencies = TRUE)
install.packages("raster", dependencies = TRUE)
install.packages("sf", dependencies = TRUE)
install.packages("landscapetools", dependencies = TRUE)
install.packages("landscapemetrics", dependencies = TRUE)

#7. Load required packages

library(raster)
library(landscapemetrics)
library(landscapetools)
library(sf)

#8. Reload our saved landscape mosaic

NDVI <- raster('SG/RECLASS2.tif')

#9. Apply mask to crop out unnecessary areas

sgshp <- st_read("SG/SGSHAPE.shp")

sgshp <- st_transform(sgshp, crs = crs(NDVI))

NDVI2 <- mask(NDVI, sgshp)

#10. View mosaic. check for 3 classifications

show_landscape(NDVI2, discrete = TRUE)

#11. Check if NDVI2 is in the right format

check_landscape(NDVI2)

#12. List all available metrics 

list_lsm()

#13. Calculate multiple metrics by name

calculate_lsm(NDVI2, 
what = c("lsm_l_ta", #total area of landscape
"lsm_c_ca", #total area of each class
"lsm_c_pland"), #proportional area of each class
full_name = TRUE) #include info about metrics in results

#14. Optional. To save image,

#At top of chunk:
#png('imagename.png', height =3600, width =3600, res=300)

#show_landscape(NDVI2, discrete = TRUE)

#At bottom of chunk:
#dev.off()

#15. Optional. To save table

#n <- calculate_lsm(NDVI2, 
#what = c("lsm_l_ta", #total area of landscape
#"lsm_c_ca", #total area of each class
#"lsm_c_pland"), #proportional area of each class
#full_name = TRUE) #include info about metrics in results

#write.table(n, file='results.csv', sep = ',')