Lots of people have been asking me about terrain derivatives lately, and I’ve been putting off blogging about them for a while because I just didn’t know where to start. Instead of one master post with all the things I decided to split it into some more manageable chunks– for both your sake and mine. Today I’m sharing a little intro, and over the next few weeks I’ll share some software recommendations and how-to guides.
Let’s start with some definitions! A digital elevation model (DEM) is a grid of cells covering an area of interest, and each cell has an elevation. Usually this is stored as raster data. Elevation helps us understand some things about a place, like its temperature relative to other places at that latitude. But if you think about a mesa at 1,200 ft above sea level and the midpoint of a 2,400ft mountain, you realize elevation doesn’t tell the whole story about a landscape.
Terrain derivatives are things you can calculate from your DEM to describe other details about a piece of land. Hillshade helps us understand how much light a piece of land gets throughout a day. Terrain wetness index shows where water is likely to accumulate. Aspect can impact how warm a place gets.
You can use terrain derivatives for all sorts of things, like planning where to put a hiking trail so it won’t get mudded out or understanding which direction a wildfire will spread in. I use terrain derivatives as part of my agronomic research because water, nutrients, light, and temperature are all crucial to understanding crop growth. Right now I’m using terrain derivatives to predict where soybean emergence rates are likely to be low, which can help inform management decisions like seeding rate.
There are many different terrain derivatives and many different ways to calculate them– I’ll get into some next week. Thanks for reading!