Curvature is a complex terrain derivative to compute, the equation that you use depends on the resolution of your input data, as you have to ensure that the curvature results you compute can be distinguished from noise in the data.
A lot of research has been done recently on curvature calculations on high resolution LiDAR data which showed that a scaling ...
You can use fractals for this: .
The upper row was generated with the fractal dimension d=2.0005 (left: elevation map, right: aspect map), the lower row with fractal dimension d=2.90 (left: elevation map, right: aspect map). I used r.surf.fractal of GRASS GIS. Then simply export the artificial DEM with r.out.gdal (or the GUI) to GeoTIFF.
It is all very dependent on your needs. You know that TIN is a vector-based representation whereas DEM is represented as a raster from grid of squares. Actually TIN is a type of DEM and derived from the raster DEM.
The TIN representation has information about altitude, slope and aspect and you can use them to extract the areas you require.
There is an ...
The direction of a Slope is known as its Aspect. It's usually defined as the direction the slope "faces", to me that's a little ambiguous and it's more intuitive to think of it as the "downhill" direction. Slope (the percentage you have already calculated) and Aspect will usually be two separate rasters. Depending on the tool you used to generate the Slope ...
ESRI's version of Raster Analysis for calculating curvature might be helpful to develop a plugin for QGIS.
For each cell, a fourth-order polynomial of the form:
Z = Ax²y² + Bx²y + Cxy² + Dx² + Ey² + Fxy + Gx + Hy + I
is fit to a surface composed of a 3x3 window. The coefficients a, b, c, and so on, are calculated from this surface.
The relationships ...
There is a difference, and I recommend the typology presented by Lindsay (2015) be used.
Lindsay (2015) presents a typology which defines a pit as a single cell in a DEM whose elevation is below that of the surrounding cells and a depression as a region of cells which drain inwards to a pit. This is consistent with the definitions used by O'Callaghan and ...
There are several open data initiatives on elevation (terrain) data.
A website with several alternatives (I have not checked them all) is available on this website:
For 90 meter accuracy dataset I would try the Shuttle Radar Topography mission (wikipedia article). I have used it on several occasions. An example of what ...
The paper "Multiscale Analysis of Topographic Surface Roughness in the Midland Valley, Scotland" by Grohmann et al., 2011 describes the differences between a six methods of calculating surface roughness measurements from 2D digital topography. His paper was helpful since he provides a quantitative comparison of each method using a single test region at ...
You should not be seeing negative values in the CTI. Since you did not provide a reproducible example I cannot speculate as to why you are getting incorrect results. The expected range is not limited 1-10. The range will be defined by flow accumulation which is influenced by the size of the basins that are accumulating flow. The index does not rely on washed ...
Try or read this page for some good information. and second link show you the way of random digital elevatin model.
Numerical and Scientific Python and Data Visualisation
creating elevation/height field gdal numpy python
Here is an R solution using a Gaussian Kernel to add autocorrelation to a random raster. Although, I have to say that the GRASS r.surf.fractal function, suggested by @markusN, seems like the best approach.
# Create 100x100 random raster with a Z range of 500-1500
r <- raster(ncols=100, nrows=100, xmn=0)
r <- runif(ncell(r), min=...
The curvature could be calculated using SAGA's module 'Terrain analysis - Morphometry ---> Slope, Aspect, Curvature'
The calculation could be done based on one of these algorithms:
Maximum Slope (Travis et al. 1975)
Maximum Triangle Slope (Tarboton 1997)
Least Squares Fitted Plane (Horn 1981, Costa-Cabral & Burgess 1996)
Fit 2.Degree Polynom (Bauer, ...
ArcInfo Macro Language (AML) is old in ESRI Terms though it is possible to run .amls in ArcGIS 10.0
if you have the right requirements:
It's possible to use ARC Macro Language (AML) files in the ArcGIS Desktop environment by creating a new geoprocessing script tool. If you have an ArcInfo license and ArcInfo Workstation installed, you can add a custom ...
You don't need to convert your layer in 3D layer to make interpolations, you only need the z attribute.
You can choose between working with the contour lines or with points:
1) with the contour lines:
you can use the QGIS interpolation plugin to generate a TIN or IDW, but it's better with points
you can use GRASS GIS r.surf.contour in the processing ...
Assuming that the "USGS-provided lidar tiles" are DEM rasters (where they already isolated the ground points, incorporated breaklines & hydro flattened), the most "accurate" contours would be derived by not doing any smoothing and just generating contours directly from the original USGS rasters. These contours are going to not look good for cartographic ...
I was able to replicate your problem, but found a solution. You have to remove the datasets that are used by Terrain dataset first before you can delete it. A bit weird but that seems the way it must work...
So the following code completely removed the FeatureDataset containing the Terrain datasets and the FeatureClass used to construct it. The order that ...
If you search for Terrain analysis in the Processing toolbox you will find what you want. The tool moved from Raster -> Terrain analysis to Processing Toolbox -> Raster Terrain analysis:
I am using QGIS 3.4
In this Wiki we maintain a list of free data sets:
For several also the import commands for GRASS GIS are stated (in many cases read with GDAL, hence the GDAL tools should work as well).
You've got a projection issue. Chances are you've not specified the projection of the DEM correctly.
There are typically two indicators to this and you have both:
You have spikes.
You have a cell size that looks like: 0.000278 (it should be a whole number).
So make sure you've set the correct projection for the DEM as well as the drape (if memory serves, ...
Have you tried to perform any histogram stretching? If you are using a raw satellite image it will be less visually appealing without any stretching. If you are thinking of using osgearth I would assume that you are using the satellite image for a 3D application? I have worked in the 3D visualization industry for the better part of a decade and all raw ...
I've done maps like this before in ArcGIS years ago. I would clip the elevation raster (usually NED from USGS) to the vector polygon of the study area. Next, create a hillshade of the elevation raster, drape that over the source NED raster, then play around with the transparency/contrast/color ramp of the hillshade to get it to look like I wanted - you might ...
You could try using Perlin noise to create some random fractal terrain. This answer on Stackoverflow explains a way you could get started in Python. The trick would be to zoom in on a very small area of the noisy grid so it's not too irregular looking.
You don't need to create a Tool to run the AML. If you have ArcInfo installed follow these steps.
copy the aml to that same folder as your DEM
open a cmd window
cd to the folder where your DEM is located
enter &r tri.aml yourdem outputdem
You could just use a different version of google-maps-api, for example
The same example with your Google-API address (https://maps.googleapis.com/maps/api/js
) is also not working:
In the meantime this ...
Run the aspect tool r.slope.aspect.
Try the < min_slope option set to a very low number. The unclassified areas are your flat area. Play with the setting to get it just how you like. You can then use r.reclass to reclass the aspects created so something like -1 and the gap areas as 1. Now multiply this my the original raster and all your areas are ...
In the SAGA-GIS Tool Library Documentation (v2.1.3) there is a reference to the method which is used for this calculation:
Koethe, R. & Lehmeier, F. (1996): SARA - System zur Automatischen
Relief-Analyse. User Manual, 2. Edition [Dept. of Geography,
University of Goettingen, unpublished]
Unfortunately, I wasn't able to get this document, but I ...