3

For QGIS I was able to add the plugin 'quickmapservices' which gives me access to different basemaps: OSM, Nasa, etc. I was able to add the OSM Cyclemap to the base layer using the 'quickmapservice' plugin. I then needed an account for Thunderforest to access the API for OSM Cyclemap. Once I created an account I added a 'new connection' to the XYZ tile and ...


3

I hate the be the bearer of bad news but, Riley et al., (1999) is wrong. The ruggedness index itself is fine but, the example in the paper is incorrect. Obviously, the reviewers did not work through the math. I wrote the original TRI AML (Workstation Arc/Info) and caught this error shortly after the paper was published. Functionally, TRI is representing a ...


2

https://www.naturalearthdata.com/downloads/50m-raster-data/50m-natural-earth-2/ https://www.naturalearthdata.com/downloads/10m-raster-data/10m-natural-earth-2/ Natural Earth 2 – This data derived from Natural Earth 1 portrays the world environment in an idealized manner with little human influence. The softly blended colors of Natural Earth 2 are ideal for ...


2

Forêt Ouverte has topographic (LiDAR-derived) data available to download for free. The spatial resolution of the DEMs is 1 meter, and can be downloaded by tile from the ftp link provided in Catalogue > LiDAR > Index et téléchargement > Téléchargement - LiDAR (pleine résolution). This is an ongoing southern Quebec LiDAR survey, eventually the whole ...


2

The page is in French, but you can find a lot of geographic data on the Ministère de l'Énergie et des Ressources naturelles's site: Répertoire des services Web et données géographiques Borders can be found here: Carte générale du Québec A small scale topographic map can be found here: Carte du relief Toggle the sections under "Produits offerts" to ...


2

Using gdalinfo and jq you can get what you need in a succinct fashion. This assumes that band that has your min/max is the first one (change the index if not). gdalinfo -json -mm input.tif | jq .bands[0].computedMin gdalinfo -json -mm input.tif | jq .bands[0].computedMax


2

I agree with mikeLdub, 30m is the best you will get for a global DEM. It stops however at 60°N. If you need elevation data of the Arctic region, check ArcticDEM: https://www.pgc.umn.edu/data/arcticdem/ It is a very high resolution (2meters) elevation dataset.


2

Try GEBCO for bathymetry: https://www.gebco.net/data_and_products/gridded_bathymetry_data/ And these handy tile grabbers for SRTM data from Derek Watkins: 30m resolution: https://dwtkns.com/srtm30m/ 90m resolution: https://dwtkns.com/srtm/ Edit: looks like the 90m version has broken as of Jan 2021. He now links to CGIAR: https://cgiarcsi.community/data/srtm-...


2

I understood your description that in the first case the blue area is totally overlapping the yellow area and in the second case the yellow area has a hole that is totally filled with the blue area. If you want to express those cases in WKT as one geometry you must use GeometryCollection. Case 1: Big polygon without holes + small polygon GEOMETRYCOLLECTION (...


1

You might want to check out the concept of rugosity, specifically the Arc-chord ratio (Du Preez, 2015). Basically, you take the area of a triangulation of the surface and the plane-of-best-fit to the edges of the surface. Note that the fractal dimension is important: as the surface resolution increases, the surface area of its triangulation approaches ...


1

I don't know if there are datasets specifically of elevation change, but conceptually it wouldn't be hard to create this yourself. You could run Zonal Statistics as Table on a DEM, using county boundaries for your zone dataset. If you output all the statistics not only would you get range, but you could also get min, max, and std (plus many others) which ...


1

The tool you're looking for is GRASS' r.walk, which uses a raster DTM to create a cost surface. R.walk calculates higher travel times over stepper slopes and accounts for distances between pixels (no need to build this grid or estimate slopes manually, as you've done with your vertexes). Once you have a cost surface, r.drain will connect points back to the ...


1

For all still searching the same: There is a dataset available for all European mountain ranges on https://www.eea.europa.eu/data-and-maps/data/european-mountain-areas


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