Raster Crash Course

Objectives

• Learn how to import, visualize, and transform raster datasets.

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My assumptions

• Given your background in GIS, you have heard the word “raster” before and know what raster data is/contains/typically looks like. If you haven’t, you may want to review this tutorial.

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Pre-lab assignments

• Review the RSpatial chapter on raster data. I won’t cover all of the functions in the raster package so if you’re planning on working with lots of raster data, be sure to review this chapter to learn about other tools stored in the package.
• GO PLAY WITH GOOGLE EARTH!

baseR

• Nothing new this week! Just some base R indexing which you should be familiar with by now.

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Set up

The main tool we’ll be working with in this tutorial is the raster package, so go ahead and install/load that. You can work with rasters using the sp package by loading the raster as a SpatialGridDataFrames, but the raster package is much better, so we’ll focus on that!

library(raster)

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Raster 101

Raindrops on roses and rasters of land use… these are a few of my favorite things. Rasters are fantastic tools to store massive amounts of massively cool data. Think of a raster as a matrix (NO NOT THAT MATRIX)… or a grid of numbers. Each pixel in the raster has number associated with it that tells us something about the data of interest, i.e.

• How red is this pixel?
  
• How near-infrared is this pixel (yes we can answer this)?
  
• How wet is this pixel?
  
• What grows in this pixel?
• How deep is the groundwater under this pixel (see GRACE satellite)?
• How many people live in this pixel (yes, people have done this using night light data)?
  
• How many cars are in that parking lot by your house? How many lawn chairs in your backyard (freaking out yet?)? You get the idea.

Let me pause here and state that you are some of the luckiest people to ever live because you have insanely easy access to incredible, beautiful visualizations of our precious planet.. in near real time, all the time, anywhere. Take a second to ponder that. Then go download Google Earth and start playing.

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Get to know your raster

The data stored in rasters can be continuous (precipitation, elevation, reflectance) or categorical (land use category). In this tutorial we’ll work with both. For a raster to be a raster, we need a few key pieces of information:

• Resolution (in space and time!)
• Extent
• Values (elevation, land use category, etc.)
• Projection information

When you’re working with raster data, especially satellite imagery, think carefully about trade-offs between resolution in space and time. Some datasets are available daily at a low spatial resolution; others are available monthly at a high spatial resolution; and others are high spatial and temporal resolution, but massively large and hard for you to work with on your laptop.

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The raster package

The raster package includes several raster classes:

• A RasterLayer contains a single-layer raster. This object contains the number of columns and rows in the raster, the spatial extent, pixel values, and the projection information (stored in CRS format)
• RasterBricks, and RasterStacks are great for multiband data (i.e. multiple spectral bands, observations through time). The main difference is that a RasterBrick can be linked to only one (though multi-layer) file. RasterStacks can be made with multiple files (band from one file merged with a band from another file) - though these objects have to have the same extent and resolution.

You’ve already learned how to make a raster from scratch, so we won’t cover that here. Instead, we’ll focus on learning how to load and manipulate the most commonly used types of raster data. There are tons of raster extensions out there: .grd, .asc, .sdat, .rst, .nc, .tif, .envi, .bil, .img, .hdf. I’ve encountered all of these at some point… and the raster package can handle just about all of them. You can find the full list in the raster package documentation. And if you still feel confused about the different ways of storing raster data in the raster package, check this out. And finally, if you’re one of the unlucky ones whose data comes in HDF4 or HDF5 data (ahem NASA, get your stuff together!), read this. You’ll definitely want the HDFView tool.

We’ll get to bricks later in the tutorial. For now, let’s work with a single layer of my current favorite raster dataset, the CropScape dataset.. This is a categorical raster that covers most of the country from 2000 to present at a 30 meter - YES T-H-I-R-T-Y meter - resolution. Since the dataset was built by the folks at the U.S. Department of Agriculture, there are more detailed categorizations for agricultural lands than for ecosystems.¹

I went to the CropScape website and downloaded data for Cache Valley in 2016. To do this, click on the little USA map at the top of the page, select County, find Cache, then click Submit. The map should zoom to Cache Valley. Next, click the little folder icon with a green arrow on it. Select 2016, Submit and the data should download to your computer. You’ll have to unzip the files and move both files to a local data folder. So I did all that, and can load my raster with the easiest function ever:

cache <- raster("./data/CDL_2016_49005.tif")
plot(cache)