Spatial Data Vector

Remember to download and put into data subdirectory:

• LiDAR rasters and plot locations

Load the following into browser window:

• [Vector Description]

Set-up R Console:

library(ggplot2)

Introduction to Vector Data

• Vector data includes points, lines, and polygons
• Examples include geopolitical boundaries, the location of field observations, and roads

• Vector data comes in a variety of formats
• shapefiles are are one of the most common
• They set of multiple files with the same name, but with different extensions
• We can see this by looking at the data in data/harv
• This data includes data on some field plots at that Harvard Forest NEON site we’ve been working with

• It is stored in the plots_harv files and we can see there are four of them with different extensions

• Work with vector data using the sf package
• We can read this data into R using read_sf
• Let’s load in the plot data we just look at

library(sf)
plots_harv <- read_sf("data/harv/harv_plots.shp")

• When read read the data in we see information about it including
• The data has 7 features
• Each feature is one object, either a point, a line, or a polygon
• The geometry type is “POINT”, which means that the features are points
• The data has 3 fields
• Each field is a piece of information that is associated with each feature
• And there is information on the minimum and maximum spatial values in the dataset
• If we view this object we’ll see that it is a data frame with one row per vector object
• There are three columns
• The first fields is plot_id a numerical plot ID
• The second field is plot_type
• The third field is where the spatial information is stored and which is called geometry
• Since this is point data each object is stored as a pair of x and y coordinates

• This is a “simple feature collection”, which is like a special kind of data frame that can be used by spatial tools

• We can plot this data using a special geom, geom_sf

ggplot() +
  geom_sf(data = plots_harv)

• We can also color vector data based on the values in the fields (or columns)
• For example, our plots have two different types, “Tower” and “Distributed”
• These are stored in the plot_type field
• To color the points based on plot_type we add a mapping

ggplot() +
  geom_sf(data = plots_harv, mapping = aes(color = plot_type))

• Just like in scatter plots this mapping tells ggplot to “color the points based on `plot_type”

Combining multiple spatial layers

• Hard to learn much from just the position of the plots without context
• So let’s load another vector object that shows the boundary of the research site

boundary_harv <- read_sf("data/harv/harv_boundary.shp")

• We can plot them together by adding two geom_sf layers in ggplot

ggplot() +
  geom_sf(data = boundary_harv) +
  geom_sf(data = plots_harv)

• The order of layers is important because they will plot on top of one another
• So if we’d plotted the plots first…

ggplot() +
  geom_sf(data = plots_harv) +
  geom_sf(data = boundary_harv)

• We wouldn’t have been able to see them.
• If we need to see through layers we can do this by setting the transparency using alpha

ggplot() +
  geom_sf(data = plots_harv) +
  geom_sf(data = boundary_harv, alpha = 0.5)

• If we just wanted the outline, which makes sense for site boundaries, we can use

ggplot() +
  geom_sf(data = plots_harv) +
  geom_sf(data = boundary_harv, fill = "transparent")

Do Task 3 of Canopy Height from Space.