[GRASS-user] GRASS & earth curvature correction (viewsheds, LOS)

[Markus Metz]

On Thu, May 26, 2011 at 12:26 PM, Benjamin Ducke
<benjamin.ducke at oxfordarch.co.uk> wrote:
>> Hm-hm. Citing from the website:
>> "The problem is that the ratio of change due to air to curvature is
>> not 1:7 (0.13), as the standard refraction coefficient suggests. It is
>> 0.325.
>
> As far as I can tell, this is a mis-understanding. The value "0.325"
> applies to radio waves. Visible light is very close to 1:7.

What if I am interested in radio waves, not visible light, e.g. for
antenna relay positions? IMHO, that refraction coefficient should not
be hard-coded.

>
> I realize the whole discourse is somewhat "clouded". But I don't
> have access to most of the relevant literature for the time
> being, nor do I have the necessary scientific background

Me neither. But any correction should take into account that the
observer is not necessarily a human without optical equipment
(telescope etc), but can also be some technical device, e.g. a sender
or receiver of whatever signals.

my .2c

Markus M


> -- so any fresh input will be much appreciated!
>
> Btw.: using r.ecurv.comp, one can freely specify the
> atmospheric correction factor.
>
>> [...]
>> >
>> >> But given that most DEMs have an inherent vertical error that
>> >> is greater than the influence of these effects,
>> >
>> > can we quantify that? for example what's STRM 95% confidence
>> > accuracy?
>>
>> From Farr et al. 2007:
>>
>> Summary of SRTM performance. All quantities represent 90% errors in
>> meters.
>>
>> Africa Australia Eurasia Islands N. America S. America
>> Absolute Geolocation Error 11.9 7.2 8.8 9 12.6 9
>> Absolute Height Error 5.6 6 6.2 8 9 6.2
>> Relative Height Error 9.8 4.7 8.7 6.2 7 5.5
>> Long Wavelength Height Error 3.1 6 2.6 3.7 4 4.9
>>
>> [sorry for the ugly format, it's tab separated]
>
> What I wold love to see is a method for probabilistic
> viewsheds, which adds random +/- offsets (within the
> vertical error range) to the elevation model cells,
> runs the viewshed computations several times, each time
> with new random offsets, then outputs the average visibility
> of each cell after "n" runs (not sure how best to determine
> "n"). That would be much more realistic than those over-
> confident 0/1 viewsheds.
>
> Such a method could even take into account roughly modelled
> blocks of vegetation or other obstacles for which height
> is hard to quantify precisely.
>
> -- shouldn't be too hard to implement in a little script.
>
> Ben
>
>>
>> >
>> >> I am not sure it's worth spending too much time on (it might
>> >> be for very long distance visibility -- I just don't know).
>> >
>> > it would be good for us to do a rough back of the envelope calc
>> > to justify that before fully forgetting about it.
>> >
>> > I guess for the worst case scenario we could try the views from
>> > Mt. Everest and/or Olympus Mons and see what difference it makes.
>>
>> No need to go into mountains, just increase observer elevation offset,
>> preferably in a moderately flat area to get really far views. Using
>> correction for earth curvature only, max is a bit more than 100 km
>> with 3km observation offset. 200km is impossible without leaving
>> earth's atmosphere.
>>
>> Markus M
>
>
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