On Mon, Apr 17, 2017 at 2:58 PM, Benjamin Ducke <bendu...@fastmail.fm> wrote:
> > I've added a figure which shows the r.slope.aspect products from the > > surface without and with -p. You can see that the smoothing gives > > significantly different results and probably better ones. I had the same > > experience in past when trying to patch result of r.neighbors with the > > original raster. Perhaps using smaller distance as you say or greater > > power would help, but I haven't tested that yet. > > I find the results for "curvature" most interesting. > In the unsmoothed LiDAR data, that metric is basically useless. > > It is the same for profile and tangential curvature. At certain zoom level, you can see, with a lot of imagination, some pattern, but comparing to the more expected result from the smoothed data, it is just noise. > If I understand correctly, then slope, aspect > and curvature are all computed within a small > neighbourhood of cells. Right, that's what r.slope.aspect does. You can avoid that by using r.param.scale, but for another analysis, it would matter (depending on the implementation/method). > So if LiDAR data is > also noisy by nature, then it is not surprising > that these metrics are all distorted by strongly > fluctuating local means. > > Yes, there is noise. Usually, you either interpolate from points or you do binning at much courser res averaging the noise out. > Please try experimenting with the power parameter, > if you have the time. In theory, this should allow > you to interpolate over larger distances with less > of a low-pass effect (or the other way around, > depending on what you want to achieve). > I have this on my todo list. > > > > > I managed to commit just the figure its caption. If somebody gets to it, > > please extent the section. > > I will update the HTML page, including a more > prominent mentioning of the default smoothing. > I also found some small typos that need fixing. Thanks. I just added a section, high res image and some basic text.
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