Remember to download and set-up directory:

• LiDAR rasters and plots
• HARV_NDVI
• SJER_NDVI

Load the following into browser window:

• Projections figure from http://neondataskills.org/R/Introduction-to-Raster-Data-In-R/
• Canopy Height Model figure from http://neondataskills.org/R/Raster-Calculations-In-R/

Set-up R Console:

library(raster)
library(rgdal)

raster structure, import, and plotting

• Spatial data is often organized in a raster.
  • gridded format (like coordinates)
  • each pixel is a value
  • most remote sensing and environmental data
• raster() import

dsm_harv <- raster("HARV_dsmCrop.tif")
dsm_harv

• Metadata is important to describe the context of spatial data.
  • bands
  • projection
  • units
  • min, max, mean
• dsm_harv is a RasterLayerObject and we can get individual pieces of it’s metadata using appropriate functions

nbands(dsm_harv)
crs(dsm_harv)

• Plotting
  • package modifies R basic graphics

plot(dsm_harv)

• Looking at raster values

hist(dsm_harv)

raster math

• The DSM data is a Digital Surface Model: elevation of top physical point
• DTM is Digital Terrain Model: elevation of the ground
• We can create a Canopy Height Model (CHM) by taking the difference between them

dtm_harv <- raster("HARV_dtmCrop.tif")
chm_harv <- dsm_harv - dtm_harv

Do Tasks 1-2 from Exercise 1 - Canopy Height from Space.

Import and reproject shapefiles

• vector data can be added to rasters to provide reference information
  • csv
  • shapefile
    • set of multiple files
      • same name, different extensions
    • readOGR("directory/", "file_name_without_extentsions")
      • stores data in a single data.frame
      • access ‘attributes’ similar to GIS software using $
        • file_name$site_id

plots_harv <- readOGR("plot_locations/", "HARV_plots")
plot(plots_harv, add=TRUE, pch=1, cex=2, lwd=2)

• Uh oh, nothing happened.

plots_harv

• Coordinate Reference System (crs or ‘projection’) is different from raster.
  • reproject raster with projectraster()
  • reproject vector with spTransform()

plots_harv_utm <- spTransform(plots_harv, crs(chm_harv))
plot(plots_harv_utm, add=TRUE, pch=1, cex=2, lwd=2)

Extract raster data

• Use vector to extract() values from raster

extract(chm_harv, plots_harv_utm)

• These are canopy heights from chm_harv at the coordinates from plots_harv_utm.
  • Order of values lines up with plots_harv_utm$site_id.
• To get an average of the values in a nearby region use buffer

extract(chm_harv, plots_harv_utm, buffer = 10, fun = mean)

Assign remainder of Exercise 1 - Canopy Height from Space.

Making your own point data

• Make spatial data from csv file with latitudes and longitudes
• Need to know the proj4string for standard latitude/longitude data
• "+proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0"

plot_latlong_data <- read.csv("data/NEON-airborne/plot_locations/HARV_PlotLocations.csv")
plot_latlong_data
crs_longlat <- crs("+proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0")
plot_latlong_data_spat <- SpatialPointsDataFrame(plot_latlong_data[c('long', 'lat')],
                                                    plot_latlong_data,
                                                    proj4string = crs_longlat)
str(plot_latlong_data_spat)
plot(plot_latlong_data_spatial)

Stacks of rasters

• Sets of rasters in the same location often analyzed together
• Raster stack
• Can be stored in one or multiple files
• To load all layers use stack() on a single multi-band file or multiple files
• We’ll load a time-series of NDVI data from Harvard Forest into a raster stack
  • NDVI is a remotely sensed vegetation index that measures greenness
  • Provides information on plant phenology and productivity

library(raster)
ndvi_files = list.files("data/HARV_NDVI/",
                         full.names = TRUE,
                         pattern = "HARV_NDVI.*.tif")
ndvi_files
ndvi_rasters <- stack(ndvi_files)

• Can visualize up to 16 layers using plot()

plot(ndvi_rasters)
plot(ndvi_rasters, seq(1, nlayers(ndvi_rasters), by = 2))
hist(ndvi_rasters)

• Calculate values across each raster using cellStats()

avg_ndvi <- cellStats(ndvi_rasters, mean)

• Store in data frame

avg_ndvi_df <- data.frame(samp_period = seq_along(avg_ndvi), ndvi = avg_ndvi)

• Get row names into column

library(dplyr)
avg_ndvi_df <- tibble::rownames_to_column(avg_ndvi_df)

Exercise 2 - Phenology from Space.