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For floating-point DEMs, I use -9999 because it's easy to remember, easy to type and, in terms of terrain elevations (in metres), impossible. If you can meet the latter condition, it doesn't really matter what you choose. A lot of climate-related datasets use some variation on the negative-multiple-nines theme, but it's conceivable that some other scientific ...


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I've done some environmental consultation on that area and the best available resolution for a DEM there is probably the ASTER GDEM 30m. Even then, that data isn't processed/cleaned so you might run into problems with clouds/reflective surfaces giving you false values. The thing with Lidar is that it costs quite a bit of money to produce and while some ...


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In step 4 of the tutorial it states "If desired, choose spatial and/or temporal search criteria". In essence, what you should do is draw a box on the map covering the area that you wish to search for data in (while leaving the temporal options alone). This will give you only the tiles within your search box, rather than each and every tile available in the '...


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I used GIMP plugin wavelet noise reduction tool and I get good results and fast: GIMP Plugin Wavelet noise reduction


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I don't know about the way to do it in one step but here is a 3 step way: Use Raster / Raster calculator... to get raster 1 / Null with 1 for your altitude interval and null for the rest like: General: "dem@1" > min_altitude AND "dem@1" < max_altitude Example: "dem@1" > 250 AND "dem@1" < 300 Use Raster / Conversion / Polygonize (Raster to Polygon) ...


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There really was no easy way for me to complete this other than what I suggested in the first place which was to "render the DEMs as all values of 1 and mask out the NoData values and then convert to polygon". Technically I converted the NoData to 0 and all valid pixels to 1 with this Con Statement ... Con(IsNull("myDEM"), 0, 1) and did away with the "0" ...


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I would try to add a low resolution DEM (e.g. SRTM 30m) to fill the gaps where your high resolution DEM is not available. The problem with a crop before orthorectification is that the RPC link image coordinates with ground, so if your image is not georeferenced and you crop it, this relationship will be detroyed.


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In a nutshell, the problem is with the loose-fit of the regression equation. The 2nd-order equation describes a parabolic curve anchored (mid-span) at ~3200 ft elevation and crossing the zero, zero origin. Both of these observations point to a poorly curve-fitted dataset, since your temperatures appear well above zero near the beach. Here is my suggestion:...


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If you have SAGA installed, try to use Fill Sinks as an alternative, and see if this tool can solve your problem. The tool can be accessed from Processing Toolbox -> SAGA -> Terrain Analysis -> Hydrology -> Fill Sinks.


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You can use Saga "Upslope area" geoalgorithm. You must install Saga and configure the path where you installed Saga inside QGIS in Processing->Options or Ctrl+Alt+C. Then expand Providers->Saga, set up the folder and mark Active. If you installed through osgeo4w you just need to restart qgis and you should be done. Then open up the toolbox Ctrl+Alt+...


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You are looking for GDALRasterBand::RasterIO. For efficiency, if you are indexing multiple points, you will want to read data in blocks then index into the resulting array


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You can do something similar using gdal_calc.py, e.g.: gdal_calc.py -A dtm.tif --calc='((A>=100)*(A<=200))*A+((A<100)*0)+((A>200)*0)' --outfile=dtm_reclass.tif --NoDataValue=-32767 This calc expression would: Assign a value of 0 to all pixel values less than 100 ((A<100)*0) Assign a value of 0 to all pixel values greater than 200 ((A>...


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First of all, if you want to work with heights, normalize the LiDAR point cloud. You have a dense point cloud (4 pts/m²) and also high resolution aerial photos, hence, as you said getting the outside roof perimeter is not an issue. Therefore, assuming you'll manage to have a shapefile with the outer roof boundaries, use it to horizontally clip the point ...


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If your extent is a round number of kilometres in each direction then I would use the Create Fishnet tool twice: Once to create a single cell fishnet (LINE not POLY) that matches your DEM extent The second one to place label points in rows and columns at the 1 km interval you desire, ensuring that the outside ones fall on the extent Then you can use the ...


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I'm not sure if you can do this with any of the gdal cli tools, but I wrote something in python which accomplishes it: from osgeo import gdal from osgeo.gdalconst import GDT_Float32 import sys import numpy as np def fix_dem_nodata(raster_input, raster_output, nodata=0, threshold=-900): try: in_data, out_data = None, None # open ...


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So just in case someone else encounters the same problem: this seems to work ! CREATE OR REPLACE FUNCTION drape(my_wkt text) RETURNS geometry AS $$ DECLARE geom3d geometry; BEGIN WITH line AS (SELECT my_wkt::geometry as geom), linemesure AS -- Add a mesure dimension to extract ...



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