Batch Processing FedData
+ +Last update: 2025-01-02
+ + Source:vignettes/Batch-Process-Raster-from-FedData.Rmd
+ Batch-Process-Raster-from-FedData.RmdBatch Processing +
+This is an example for batch processing multiple years of NLCD data. +It requires creating a folder with two subfolders where the tif’s can be +stored. The NCLD data is unprojected when it’s downloaded, so you will +need to project it to your preferred coordinate system. It’s a +multi-step process.
+Setup +
+
+dir.create('C:/Users/Lyonst/Desktop/NLCD/unproj',
+ recursive = TRUE)# create a folder and first subfolder for storing unprojected tif's. Use relative paths if you know how to and/or have an R project set up.
+
+my.path<-'C:/Users/Lyonst/Desktop/NLCD' # save the parent folder path as an object, not just the lowest folder
+
+years_to_dl<-c(2010,2015,2020) # object referencing the years of NCLD you'd like to download
+# Older NLCD data was only released about every 3 years, but now USGS is going back and using different processing to generate/estimate/interpolate annual data sets. Upshot is, you now have annual data if you think you need itNow use the vector of years to iteratively download the data from +[FedData].
+
+# pass the vector of years to the map function to iteratively call FedData::get_nlcd_annual
+
+purrr::map(years_to_dl,
+ ~FedData::get_nlcd_annual(template=MDCSpatialData::state.mo,
+ label='MO_noproj',
+ year=.x,
+ product='LndCov',
+ extraction.dir=paste0(my.path,'/unproj')
+ )
+)This writes 3 tif files with “MO_noproj” in the file name. The files +are 2010, 2015, and 2020 annual NLCD landcover. The way FedData works is +it creates a rectangle of min/max X and Y for the template and crops to +that. So our rasters contain a good chunk of KS, IL and a little bit of +IA and AR. We’ll clean that up in the next step
+Reproject +
+To reproject, you’ll need to create an object of the full file paths +of all the unprojected files, setup another subfolder to hold the +reprojected tif’s,
+
+# List the tif files in the folder
+
+unproj.files<-list.files(path=paste0(my.path,'/unproj'),
+ pattern='.tif$', # regex for files that end in .tif
+ full.names = TRUE)
+
+
+#create a vector of new names
+
+rast.names<-purrr::map(unproj.files,
+ ~paste0('NLCD_Y',stringr::str_extract(.x,pattern='20\\d{2}'),'_proj'))
+
+#tif file names will now look like 'NLCD_Y2010_proj.tif' after reprojections
+
+
+
+
+dir.create('C:/Users/Lyonst/Desktop/NLCD/prj') # Create a directory to hold the projected files
+
+
+ # write a function to take the list of unprojected files and names we'd like to have for the new projected files, and reproject, crop/mask and write them
+
+rast.fxn<-function(X,Y){
+
+ terra::project(terra::rast(X),'EPSG:26915')->Z#NAD83 UTM15N code
+ terra::mask(Z,terra::vect(MDCSpatialData::state.mo))->W # assign raster cells outside the state boundary an NA value
+ terra::writeRaster(W,
+ filename=paste0(my.path,'/prj/',rast.names,'.tif'))
+}
+
+# use future to run these in parallel
+future::plan("multisession",workers=3)
+furrr::future_map2(unproj.files,rast.names,~rast.fxn(.x,.y))
+# this takes approximately 10 minutes for 3 NLCD rasters for the state of MO
+
+
+future::plan('sequential') # resetYou can stop here and delete all the entire +…/unproj/ folder if you like. Alternatively, you can +combine all the annual raster files into a multiband raster object. The +advantage is you only have to read in one object to get all your +rasters. The downside is it’s probably more efficient to process the +data by creating lists of files and passing it to [purrr::map] as we +have done above. Still…if you want to try…
+
+my.rast<-list.files(path=paste0(my.path,'/prj'),
+ pattern='.tif$', # regex for files that end in .tif
+ full.names = TRUE)
+
+# read in multiple rasters
+
+tmp<-terra::rast(my.rast)
+
+terra::writeRaster(tmp,file=paste0(my.path,'/prj/all_nlcd.tif'))There are additional functions in terra that help you process the +bands together or separately. Note that the multiband raster only works +if all the bands have the exact same spatial extent. So if you think you +might want to crop or clip different years in different ways, it’s best +to keep the individual years as separate files and objects, even if it +is less efficient with memory.
+