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library(pacman)
p_load(sp,raster,rgeos,rgdal,rasterVis,raster,fs,sf,tidyverse)

# read data
manchester_boundary <- st_read("data/manchester_boundary_download/Manchester_boundary.shp")
listlandsat <- dir("data/LC08_L1TP_203023_20190513_20190521_01_T1",
    pattern = "[B123456790].TIF",full.names = T) %>% stack()
#check they have the same Coordinate Reference System (CRS)
crs(manchester_boundary)
crs(listlandsat)

# get only Manchester
lsatmask <- listlandsat %>%
  # now crop our temp data to the extent
  crop(.,manchester_boundary)%>%
  mask(.,  manchester_boundary)

# handle 8th band
b8list = dir("data/LC08_L1TP_203023_20190513_20190521_01_T1",
    pattern = "[B8].TIF",full.names = T) %>% 
  raster()

## ngb is a nearest neighbour sampling method
b8correct <- b8list%>%
  # now crop our temp data to the extent
  crop(.,manchester_boundary)%>%
  mask(.,  manchester_boundary)%>%
  resample(., lsatmask$LC08_L1TP_203023_20190513_20190521_01_T1_B1, 
           method = "ngb") 

lsatmask <- lsatmask %>%
  addLayer(., b8correct)

raster::compareRaster(listlandsat$LC08_L1TP_203023_20190513_20190521_01_T1_B1,
                      listlandsat$LC08_L1TP_203023_20190513_20190521_01_T1_B8)




# add mask to the filenames within the raster stack

names(lsatmask) <- names(lsatmask)%>%
  str_c(., 
        "mask", 
        sep="_")