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Spatial Data Introduction
R

Remember to download and set-up directory:

  • [LiDAR rasters and plots]({{ site.baseurl }}/data/NEON-airborne.zip)
  • [HARV_NDVI]({{ site.baseurl }}/data/HARV-NDVI.zip)
  • [SJER_NDVI]({{ site.baseurl }}/data/SJER-NDVI.zip)

Load the following into browser window:

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]({{ site.baseurl }}/exercises/Neon-canopy-height-from-space-R).

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]({{ site.baseurl }}/exercises/Neon-canopy-height-from-space-R).

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]({{ site.baseurl }}/exercises/Neon-phenology-from-space-R).