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7

Choosing the right algorithm for hydrologically correcting a DEM really depends on your particular application. DEM size is certainly an issue, as you discovered. If you have a massive DEM, there are only a few algorithms that will work for your application. Another important consideration is whether or not all of the topographic depressions in your DEM are ...


5

Try SAGA module Shapes - Grid -> Grid statistics for polygons - directly or via QGIS: One of statistics is Maximum - so the result will meet your expectations. Resulting table will be your input table with new columns named after statistics used in module (as far as I remeber SAGA doesn't obey the 8 character fieldnames limitation for shp - but you can ...


4

First off, let me say how much I appreciate this question. I have seen so many example of inappropriate colour palettes applied to digital elevation models that it's good to see that people are thinking about this. There are some really good answers here too, but here is my opinion. I doubt that there is a universally good palette but rather a group of ...


3

You may check out the 'Trace Downslope Flowpaths' tool in Whitebox GAT. It will identify the cells in a DEM that receive flow from upslope target cells. It is however based on the D8 flow algorithm and therefore cannot model flow dispersion, which would yield downslope 'areas' as you are referring to them as. Nonetheless, there is some debate in the ...


3

Landsat and Modis are optical sensors, which means that they provide digital numbers of reflected materials that are within the electromagnetic spectrum. These values correspond to the wave length of the corresponding satellite band. To get elevation from just the raw values would be impossible. The only potential means to collect elevation information would ...


3

I wrote a guide posted here on the Landscape Laboratory blog (which requires use of the Mac command line): The data comes as a zip file containing a folder for each major OS grid square, further broken down in many (2,800+) zip files each containing a single 10 x 10km .asc elevation file. For example: ...


3

If you have a raster DEM already, then there is a tool that I developed in Whitebox Geospatial Analysis Tools called Remove Off-Terrain Objects, contained within the LiDAR toolbox, that works well for creating bare-earth DEMs, particularly in urban and agricultural settings. It works less well where either the terrain is steeply sloped or the forest cover is ...


2

The the accuracy refers to the absence of bias. So you can estimate your accuracy as the mean of the difference between yout two datasets. It is often difficult, and sometimes impossible, to distinguish horizontal from vertical bias. However, it is possible if you are in a rugged terrain. Alternatively, you can directly work in 3D for your displacement, but ...


2

You don't want to "convert" vector contours to raster format: that would merely create a raster whose values designate which contour crosses each cell (and ought to have null values in cells not crossed by contours). To convert a set of contours to a DEM you need to interpolate the values from the contours into the regions between and around the contours. ...


2

Sorbus has given an excellent answer (+1). The VRT solution in Sorbus' answer is definitely the way to go, and QGIS will also read the compressed vector contour files too (after the initial decompression). However, sometimes you may need access to the uncompressed data (e.g. if you are using something other than QGIS which doesn't support reading zipped ...


2

Basically an edge is a rapid change of slope and therefore equivalent to large positive or negative values of the curvature of the DEM. As far as I've found in Google you can calculate curvature in ENVI: http://www.exelisvis.de/docs/ExtractingTopographicFeatures.html Afterwards you should filter the result for large pos/neg values. ENVI also offers to ...


1

I believe the gridded effect you are seeing is down to one of two things: How you are displaying the raster and not necessarily anything to do with the base dataset. How you have projected your data Have a read of this article and this blog page. Bilinear Interpolation seems to be the best choice but is not the default setting.


1

You can define the origin as the Xmin, Ymin (or Left & Bottom) of the extent environment setting. Click the Environment button at the bottom of the tool's interface, click Processing Extent and fill in the extent properties. You have to indicate Xmax & Ymax (Right, Top) as well:


1

The short of it is--there's no way to measure infinite points on your DEM and determine its HV accuracy, but a decent sampling should tell you if you're in the ballpark based on whatever accuracy requirements you're striving for. All elevation data works this way. radouxju's answer is on the money as far as the statistics goes. The best way to measure ...


1

I don't believe that that tool is an interpolator, but rather a data format conversion tool. That is, it assumes that your data contained in an xyz format is already structured on a regular grid. You will need to import your irregularly spaced LiDAR XYZ points and then use an interpolation routine (e.g. IDW, splining, or kriging) to interpolate the data onto ...


1

NASA hosts a paper that can be found here which gives detailed answer to your title question. In particular page 2, paragraph 3 and page 21 starting at paragraph 2. The short answer is no, SRTM data is not necessarily bare-earth measurement and may be tree canopy. However, radar can potentially partially penetrate tree canopies, so the given height might not ...


1

I'm not an SRTM expert, not even an SRTM novice. http://www.opendem.info/technology.html provides a nice methodology for correcting for tree canopy height. So, no, it does not appear that the SRTM data is corrected. However, there are ways to do such a thing. Good Luck.


1

It appears that your LIDAR data is in the form of a gridded digital elevation model (DEM) that loads into QGIS without difficulty. I'd suggest that you attempt to render the DEM as shaded relief using the following method (for reasons I'll explain later). So, load the DEM into QGIS and then click on 'Processing' in the menu at the top of the screen and ...


1

Here are some general concepts and terms relevant to your case. They are not QGIS-specific because I don't know QGIS. In "raw" form, lidar data are a dense, irregularly distributed set of X,Y,Z points called a point cloud. In "reduced" form, they're often in a regularly distributed grid (or raster) of Z points (X,Y being implied by location within the ...



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