Part III of a four-part article:
- Part I, Get and Understand the Data: Working with Digital Elevation Models in Python (this article)
- Part II, Understand Povray: Height Fields in Povray
- Part III, Povray on real DEM data: Ray-Tracing Digital Elevation Data in 3D with Povray
- Part IV, Make a 3D Panorama: Making a Panorama from Raytraced DEM Images
One of my hiking buddies uses a phone app called Peak Finder. It's a neat program that lets you spin around and identify the mountain peaks you see.
Alas, I can't use it, because it won't work without a compass, and [expletive deleted] Samsung disables the compass in their phones, even though the hardware is there. I've often wondered if I could write a program that would do something similar. I could use the images in planetarium shows, and could even include additions like predicting exactly when and where the moon would rise on a given date.
Before plotting any mountains, first you need some elevation data, called a Digital Elevation Model or DEM.
Get the DEM data
Digital Elevation Models are available from a variety of sources in a variety of formats. But the downloaders and formats aren't as well documented as they could be, so it can be a confusing mess.
USGS steers you to the somewhat flaky and confusing National Map Download Client. Under Data in the left sidebar, click on Elevation Products (3DEP), select the accuracy you need, then zoom and pan the map until it shows what you need.
Current Extent doesn't seem to work consistently, so use Box/Point and sweep out a rectangle. Then click on Find products. Each "product" should have a download link next to it, or if not, you can put it in your cart and View Cart.
Except that National Map tiles often don't load, so you can end up with a mostly-empty map (as shown here) where you have no idea what area you're choosing. Once this starts happening, switching to a different set of tiles probably won't help; all you can do is wait a few hours and hope it gets better..
Or get your DEM data somewhere else. Even if you stick with the USGS, they have a different set of DEM data, called SRTM (it comes from the Shuttle Radar Topography Mission) which is downloaded from a completely different place, SRTM DEM data, Earth Explorer. It's marginally easier to use than the National Map and less flaky about tile loading, and it gives you GeoTIFF files instead of zip files containing various ArcGIS formats. Sounds good so far; but once you've wasted time defining the area you want, suddenly it reveals that you can't download anything unless you first make an account, and you have to go through a long registration process that demands name, address and phone number (!) before you can actually download anything.
Of course neither of these sources lets you just download data for a given set of coordinates; you have to go through the interactive website any time you want anything. So even if you don't mind giving the USGS your address and phone number, if you want something you can run from a program, you need to go elsewhere.
Unencumbered DEM Sources
Fortunately there are several other sources for elevation data. Be sure to read through the comments, which list better sources than in the main article.
The best I found is OpenTypography's SRTM API, which lets you download arbitrary areas specified by latitude/longitude bounding boxes.
Verify the Data: gdaldem
Okay, you've got some DEM data. Did you get the area you meant to get? Is there any data there? DEM data often comes packaged as an image, primarily GeoTIFF. You might think you could simply view that in an image viewer -- after all, those nice preview images they show you on those interactive downloaders show the terrain nicely. But the actual DEM data is scaled so that even high mountains don't show up; you probably won't be able to see anything but blackness.
One way of viewing a DEM file as an image is to load it into GIMP.
Bring up Colors->Levels, go to the input slider (the upper
of the two sliders) and slide the rightmost triangle leftward until it's
near the right edge of the histogram. Don't save it that way (that will
mess up the absolute elevations in the file); it's just
a quick way of viewing the data.
gdaldem hillshade n35_w107_1arc_v3.tif hillshade.png
Then view hillshade.png in your favorite image viewer and see
if it looks like you expect. Having read quite a few elaborate
tutorials on hillshade generation over the years, I was blown away at
how easy it is with gdaldem.
Here are some other operations you can do on DEM data.
Translate the Data to Another Format
gdal has lots more useful stuff beyond gdaldem. For instance, my ultimate goal, ray tracing, will need a PNG:
gdal_translate -ot UInt16 -of PNG srtm_54_07.tif srtm_54_07.png
gdal_translate can recognize most DEM formats. If you have a complicated multi-file format like ARCGIS, try using the name of the directory where the files live.
Get Vertical Limits, for Scaling
What's the highest point in your data, and at what coordinates does that peak occur? You can find the highest and lowest points easily with Python's gdal package if you convert the gdal.Dataset into a numpy array:
import gdal import numpy as np demdata = gdal.Open(filename) demarray = np.array(demdata.GetRasterBand(1).ReadAsArray()) print(demarray.min(), demarray.max())
That gives you the highest and lowest elevations. But where are they in the data? That's not super intuitive in numpy; the best way I've found is:
indices = np.where(demarray == demarray.max()) ymax, xmax = indices, indices print("The highest point is", demarray[ymax][xmax]) print(" at pixel location", xmax, ymax)
Translate Between Lat/Lon and Pixel Coordinates
But now that you have the pixel coordinates of the high point, how do you map that back to latitude and longitude? That's trickier, but here's one way, using the affine package:
import affine affine_transform = affine.Affine.from_gdal(*demdata.GetGeoTransform()) lon, lat = affine_transform * (xmax, ymax)
What about the other way? You have latitude and longitude and you want to know what pixel location that corresponds to? Define an inverse transformation:
inverse_transform = ~affine_transform px, py = [ round(f) for f in inverse_transform * (lon, lat) ]
Those transforms will become important once we get to Part III. But first, Part II, Understand Povray: Height Fields in Povray
[ 18:15 Jul 07, 2019 More mapping | permalink to this entry | ]