We’ll need rayshader (of course) for 3D plotting, raster for loading and manipulating the data, scales to rescale the color channels to adjust image contrast, and sp to transform some point coordinates between coordinate systems. To download: click the tiles, click download, and unzip the files (they should have the file type “hgt”).įollow let’s load the packages we need. We’ll zoom into tiles N37W113.hgt and N37W114.hgt, since the park overlaps both of them. Let’s start by grabbing elevation data from Derek Watkin’s SRTM tile downloader. I took a trip with my Dad 1 Hi dad! out to Zion after I finished my PhD, and the gorgeous landscape made up of deep valleys and sheer cliffs makes for an excellent 3D visualization. We are going to be visualizing Zion National Park, UT, which has beautiful, dramatic topography. Specifically, there’s simple tile selectors out there to download the data by just clicking on a map, which is a far easier process than many other online data repositories. It’s a global 30 meter resolution dataset (meaning, 30m between each point), and although there are more precise and higher resolution datasets out there, few have the global coverage of the SRTM–and it’s dead easy to get the data. A good place to start for reasonable resolution elevation data is the Shuttle Radar Topography Mission (SRTM) dataset. Let’s get started!įirst, we'll download elevation data. In my opinion, the hardest part is all the manual work in downloading the data! Once that's done, the code required is short and straightforward, and rayshader makes visualizing elevation data in 3D a breeze. So I'm going to walk you through how to obtain the data required to make these types of maps, as well as the R code used to generate them. Not only that, but you have to also know where and how to get this data-not obvious to someone who doesn't do this regularly. Additionally, the process behind making this type of map can be intimidating for non-GIS experts, as it exposes you to all complexities of the GIS field: combining different datasets from separate sources-often each with their own distinct coordinate systems-into a single map. Ever since I released rayshader to the public, there's been one question that comes up time and time again: "How do I use rayshader to overlay satellite imagery onto a 3D surface?" And I can see why: 3D maps with real satellite images are super cool, and there are few detailed tutorials out there on how it's done in R.
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