Remember to download and put into data subdirectory:

• LiDAR rasters and plot locations
• Harvard Forest NDVI
• San Joaquin Experimental Range 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("data/NEON-airborne/HARV_dsmCrop.tif")

• 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("data/NEON-airborne/HARV_dtmCrop.tif")
chm_harv <- dsm_harv - dtm_harv

Do Tasks 1-2 of 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_extensions")
      • stores data in a single data.frame
      • access ‘attributes’ similar to GIS software using $
        • file_name$site_id

plots_harv <- readOGR("data/NEON-airborne/plot_locations", "HARV_plots")
plot(chm_harv)
plot(plots_harv, add = TRUE, pch = 4, cex = 1.5)

• Uh oh, nothing happened.

• Coordinate Reference System (crs or projection) is different from raster.

crs(chm_harv)
crs(plots_harv)

• Change projection:
  • reproject raster with projectRaster()
  • reproject vector with spTransform()

plots_harv_utm <- spTransform(plots_harv, crs(chm_harv))
plot(plots_harv_utm, add = TRUE, pch = 4, cex = 1.5)

Extract raster data

• Use vector to extract() values from raster
• These are canopy heights from chm_harv at the coordinates from plots_harv_utm.

plots_chm <- extract(chm_harv, plots_harv_utm)

• Order of values lines up with plots_harv_utm$plot_id.

plots_harv_utm$plot_id
plots_chm <- data.frame(plot_num = plots_harv_utm$plot_id, plot_value = plots_chm)

• To get an average of the values in a nearby region use buffer

plots_chm$plot_buffer_value <- extract(chm_harv, plots_harv_utm, buffer = 10, fun = mean)

Do Tasks 3-4 of Canopy Height from Space.

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

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

• Can visualize up to 16 layers using plot()

plot(ndvi_rasters)
plot(ndvi_rasters, c(1, 3, 5, 7, 9, 11))

• Calculate values across each raster using cellStats()

avg_ndvi <- cellStats(ndvi_rasters, mean)

• Store in data frame

avg_ndvi_df <- data.frame(samp_period = 1:length(avg_ndvi), ndvi = avg_ndvi)

• Get row names into column

library(dplyr)
avg_ndvi_df <- tibble::rownames_to_column(avg_ndvi_df, var = "name")

Do Phenology from Space.

Making your own point data

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

points_csv <- read.csv("data/NEON-airborne/plot_locations/HARV_PlotLocations.csv")
points_crs <- crs("+proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0")
points_spat <- SpatialPointsDataFrame(
	points_csv[c('long', 'lat')], 
	points_csv, 
	proj4string = points_crs)

str(points_spat)
plot(points_spat)

Do Species Occurrences Elevation Histogram.

Map of point data

• Can plot points on Google Map segment with ggmap package

library(ggmap)

• Uses only dataframes, not spatial data
• Generate map segment with csv coordinates

avg_long = mean(points_csv$long)
avg_lat = mean(points_csv$lat)
map = get_map(location = c(lon = avg_long, lat = avg_lat), zoom = 14)

• Plot original csv points

ggmap(map) +
    geom_point(data = points_csv, aes(x = long, y = lat))