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2

What you have is a hillshade surface built from a LiDAR point cloud. According to ESRI: A hillshade is a grayscale 3D representation of the surface, with the sun's relative position taken into account for shading the image. So each pixel of your .tif raster has an hypothetical illumination value given a reference position of the sun. What you want is ...


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I believe it is possible but probably not very accurate. Multiple elevations do not have same RGB value if the ramp is continuous and not like having red at both extremes. I would probably start by reducing the number of colors and saving the result into a paletted image by using rgb2pct-py http://www.gdal.org/rgb2pct.html. Then I would vectorize the ...


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I'm not sure of the best answer to this at the moment, but I can explain why you're getting that result. I think this is probably more suited to Kriging. As pierma says, this is available using SAGA (via processing) The Slope tool is doing what it's supposed to. The reason for this output is that you're using a TIN. A TIN is a mesh of triangular faces, ...


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This aspect come from TIN interpolation. You can try others interpolation methods. You'll find more algorithms in the processing toolbox, especially the SAGA tools.


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--- gdal --- use gdal_contour function. documentation here ---- ArcGIS version----- posted before the software was specified Assuming you are using ArcGIS: 1. Convert the GeoTiff into ESRI GRID (esri raster format) using raster to other formats function, or simply import it in arcGIS and use save (export data) and save it as ESRI GRID. 2. use the Contour ...


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What recourse do I have in this situation? Acquire/integrate data with finer resolution. Can the 10m DEM be re-sampled perhaps to more accurately reflect the actual landscape? No, it can't. At least without inputting additional data. Should field surveys be employed in addition to re-sampling? It is an alternative. Alternatively, are you ...


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Your city coordinates are likely to be in WGS84 EPSG:4326. But the DEM grid is not, and Set CRS for Layer is the wrong tool to change the CRS. Use Save As ... to a different filename and CRS. But you first have to now which CRS the DEM data is actually in. If you can't ask the data provider of the DEM about the source projection, take the data from ...


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I have done all modules of grass7.0.3 works but nviz7 NO! Could be a PATH problems. The nviz7 command isn't under grass7.0.3 dir!? I found nvzi7.py in the ./qgis/python/plugins/processing/algs/grass7/nviz7.py dir but how could by call!?


2

Doing this kind of processing is pretty straight-forward, but there are some tricks along the way that will determine how accurate your analysis will be. Your data will be generated from a slope raster of the area, and you usually have to build it yourself. The easiest way is to use a DEM raster to calculate the slope, so you should start with that. Step 1: ...


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You can download a European-wide DTM from the European Environmental Agency Or use other open DTMs of Germany like this As your calculations are in SI units, you should probably use a projected grid like ETRS-LAEA (EPSG:3035) which is the scientific standard in the EU. Germany-specific grids will aslo work, as well as global ones, just make sure that all ...


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You will have to dump that tiff file (or whatever) into a <canvas> and read the values of whatever pixels you want. Be aware of cross-origin issues. Do read: https://developer.mozilla.org/en-US/docs/Web/API/Canvas_API/Tutorial/Using_images https://developer.mozilla.org/en-US/docs/Web/API/Canvas_API/Tutorial/Pixel_manipulation_with_canvas


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I use this model to produce an ideal hiking routes through forests. If you have the DEM and a feature classes of a start and end point, all you need to do is follow my model below. Calculate the slope of DEM, reclassify it, then use weighted overlay to further narrow down the slope criteria for the road, then use the cost distance tool where you input the ...


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I understand you'd like to design a route between 2 points in the mountains with slope being a constraint. The solution I use is neither quick or easy. Place equidistant points over dem. Connect them by lines, using triangulation. Calculate length and slope for each and assign cost of travel through it using both values. Calculate least cost path between ...


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Set environment setting extent=your DEM extent, snap raster = DEM, cell size= one for DEM. Use raster calculator expression: Con("DEM">1500,"DEM) Right click on resulting raster, Source, scroll down to see Mean


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I am not sure if I got you right, did you try to change NoData value to No Color from Symblogy? Symbology -> Check "Display Background Value (RGB)".


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Depends, what was the resolution on the dem when you generated it from your contour lines and how much do you trust thoses? Since you can set yourself the resolution and scr of your data (either when you interpolated it OR by resampling the whole thing afterwards) i'd say it all depends on how much you trust the sources you used to create it. From what i ...


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I am wondering if the expression itself is correct or not. I think the expression should be "dem_3m_Zft1b@1" > 0 not "dem_3m_Zft1b" > 0. I think you need to specify the band number. In case of DEM, it is only one band which I think you need to add @1. In case of a subset DEM called "atest9b@1", the @1 was already included in the raster variable name, ...


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The gdal command line is great for this kind of thing. Using an ASTGTM DEM file, with heights in metres, to generate a shapefile with 20 foot contours (6.096m): gdal_contour -a elev -i 6.096 astdem.tif astdem.shp -a elev: create a field called 'elev' with the contour height in metres; -i 6.096: create contours at 6.096m intervals (20 feet); astdem.tif - ...


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Response From an INSAR professor Dear Eric I heard from XXXXX that you are interested in an (InSAR) DEM, I presume over an area in Kenya? Please note that there are already such products available, e.g. the SRTM DEM and products from the TanDEM-X mission. Both InSAR missions were tuned to obtain the best quality DEM's, as they worked in the single-pass ...


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This Happened because layer offset and factor to convert…units Are different between image and DEM it draped over


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Try this tool box. It has a tool that allows you to create filled contours, from which you can easily work out areas.


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It sounds like you are concerned about the area of a particular polygon/s changing as a result of elevation. That sounds more like surface area. There is an ArcGIS tool that will calculate this called "Surface Volume" that is described here: Surface Volume (3D Analyst). The tool calculates both surface area and volume based on an input raster, TIN, or ...


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This strongly depends of the data structures you have. I think some voxel reperesentation is required for the kind of diagrams you mention. Perhaps GRASS GIS is a worth a consideration: https://grass.osgeo.org/screenshots/3D/


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You can use the Focal statistics tool and choose your 5x5 there or create your own kernel for it. Low-Pass is quite easy as it is just the mean which can be chosen for this tool as well.


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Yes it will, I see nothing in this process that would result in a vertical datum change.


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This is very doable using C++. Some example code is below. You just need to use the correct driver. See here for driver formats. "HFA" is the code for the Erdas imagine (.img) format. GDALDataset *pDS; GDALDriver *pDriver; pDriver = GDALGetDriverManager->getDriverByName("Name of you driver here"); //open the raster in read only mode pDS = ...


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If you do have the 3D Analyst extension, it should work this way: Use the tool 'Interpolate Shape'. This will add a Z coordinate information to your points. Use tool tool 'Add XY coordinates'; this will add the XY and also Z coordinates of each point into the attribute table of the shape.


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gdal_fillnodata Fill raster regions by interpolation from edges. gdal_fillnodata.py [-q] [-md max_distance] [-si smooth_iterations] [-o name=value] [-b band] srcfile [-nomask] [-mask filename] [-of format] [dstfile]


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I think it's the -C that often takes up most of the processing. Just to verify that is the case, can youtry without the -C. -C just adds constraints which are queryable from raster_columns. You may or may not need that but can alsoways add after if you do. Though given -C runs after the load that may not be the issue you are running into. How big are you ...


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I answered a similar question here You can download and clip a portion of the SRTM 30m DEM with one command with the elevation Python command line tool. Install it and perform the self check with: $ pip install elevation Check if you have all the dependencies installed (mainly GDAL tools): $ eio selfcheck Download and clip a ...



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