[Qgis-user] Fwd: qgis - v.vol.rst from grass

Nicolas Cadieux nicolas.cadieux at archeotec.ca
Sun Oct 28 13:51:13 PDT 2018 

Hi,

The last I played with Kriging, you could only take into account the x,y, plus on variable that you call “m”.  It does not take into account any other variable like altitude (z).  As a good Kriging interpolation relies on the distribution of m on the x and y plane and that each m variable may behave differently on the variogram, I don’t see how you could do this in one shot using Kriging. You can do this with more simple algorithms like IDW, but not Kriging.  

If there is a multi variable version of Kriging that I don’t know, point it to me.  I would like to read on it.

Nicolas


> Le 28 oct. 2018 à 15:06, Benjamin Ducke <benducke at fastmail.fm> a écrit :
> 
> Hi,
> 
>> On 28/10/2018 18:46, Nicolas Cadieux wrote:
>> Hi, 
>> Some form of Kriging is probably best. Â But it requires a very good
>> understanding of each data set and its distribution on the variogramme.
>> Â Make sure you study each variable separately for each layer. 
> 
> I don't think "separately" would be a good idea.
> 
> Kriging (like all spatial interpolators) is
> fundamentally based on a concept of spatial
> distance: measurements that are closer to the
> location at which to interpolate have more
> influence on the result than measurements
> that are further away. In your case, "closer"
> means "closer in 3D space". If you slice up
> your data and consider each slice separately,
> then the interpolation will be blind for
> measurements that lie closely on the Z axis
> and should actually weigh in heavily. The
> resulting 2D layers will then be heavily biased
> towards trends on the X-Y plane.
> 
> I am not aware of any open source software
> that can perform kriging of "m" at "X/Y/Z"
> locations, but I would love to learn of one.
> 
> But I think you really need to consider using
> GRASS GIS with its native interface and
> running "v.vol.rst " on your data. That's a
> spline-based interpolation method that has
> been shown to give good results for continuous
> environmental data:
> 
> https://grass.osgeo.org/grass72/manuals/v.vol.rst.html
> 
> The result will be a 3D raster that QGIS
> cannot handle. But you can use the native
> GRASS modules to slice your data by Z range
> and export into GeoTIFFs that you can then
> stack on top of each other in QGIS.
> 
> There is some documentation about 3D raster
> processing in GRASS GIS here:
> 
> https://grasswiki.osgeo.org/wiki/Voxel
> https://grasswiki.osgeo.org/wiki/Help_with_3D
> 
> But you will probably be better advised to
> contact the GRASS user list for more help
> on this:
> 
> https://grass.osgeo.org/support/mailing-lists/
> 
> Best,
> 
> Ben
> 
>> If you
>> need to query the data first and save the result to a new file, that
>> will be two steps. Â Having only the data you need will reduce the amount
>> of manipulation errors. Â Keeping track of data manipulation is easy if
>> you have a good file convention.  Ex data.shp becomes data_query_z<250.shp. 
>> Good luck,
>> Nicolas
>> 
>> Le 28 oct. 2018 Ã  13:29, Francois Chartier <fra.chartier at gmail.com
>> <mailto:fra.chartier at gmail.com>> a ÃHi,
> 
>> On 28/10/2018 18:46, Nicolas Cadieux wrote:
>> Hi, 
>> Some form of Kriging is probably best. Â But it requires a very good
>> understanding of each data set and its distribution on the variogramme.
>> Â Make sure you study each variable separately for each layer. 
> 
> I don't think "separately" would be a good idea.
> 
> Kriging (like all spatial interpolators) is
> fundamentally based on a concept of spatial
> distance: measurements that are closer to the
> location at which to interpolate have more
> influence on the result than measurements
> that are further away. In your case, "closer"
> means "closer in 3D space". If you slice up
> your data and consider each slice separately,
> then the interpolation will be blind for
> measurements that lie closely on the Z axis
> and should actually weigh in heavily. The
> resulting 2D layers will then be heavily biased
> towards trends on the X-Y plane.
> 
> I am not aware of any open source software
> that can perform kriging of "m" at "X/Y/Z"
> locations, but I would love to learn of one.
> 
> But I think you really need to consider using
> GRASS GIS with its native interface and
> running "v.vol.rst " on your data. That's a
> spline-based interpolation method that has
> been shown to give good results for continuous
> environmental data:
> 
> https://grass.osgeo.org/grass72/manuals/v.vol.rst.html
> 
> The result will be a 3D raster that QGIS
> cannot handle. But you can use the native
> GRASS modules to slice your data by Z range
> and export into GeoTIFFs that you can then
> stack on top of each other in QGIS.
> 
> There is some documentation about 3D raster
> processing in GRASS GIS here:
> 
> https://grasswiki.osgeo.org/wiki/Voxel
> https://grasswiki.osgeo.org/wiki/Help_with_3D
> 
> But you will probably be better advised to
> contact the GRASS user list for more help
> on this:
> 
> https://grass.osgeo.org/support/mailing-lists/
> 
> Best,
> 
> Ben
> 
>> If you
>> need to query the data first and save the result to a new file, that
>> will be two steps. Â Having only the data you need will reduce the amount
>> of manipulation errors. Â Keeping track of data manipulation is easy if
>> you have a good file convention.  Ex data.shp becomes data_query_z<250.shp. 
>> Good luck,
>> Nicolas
>> 
>> Le 28 oct. 2018 Ã  13:29, Francois Chartier <fra.chartier at gmail.com
>> <mailto:fra.chartier at gmail.com>> a écrit :
>> 
>>> I am trying to interpolate particle size of soils, which i will then
>>> transform into a permeability. 
>>> the option to stack multiple raster is the path i am going to use, and
>>> i think kriging is the probably the most suited for soil properties.
>>> Inverse distance should be good and lighter in terms of computation. 
>>> i have to figure out to run the interpolation based on a query
>>> result.  i am not sure if this has to be a 2 step process or 1 step. 
>>> 
>>> On Sun, Oct 28, 2018, 11:34 Nicolas Cadieux,
>>> <nicolas.cadieux at archeotec.ca <mailto:nicolas.cadieux at archeotec.ca>>
>>> wrote:
>>> 
>>>    Hi,
>>> 
>>>    This is interesting.  Basically the end result is a multi band
>>>    raster, which can also be done by stacking multiple band rasters. 
>>>    I don’t know anything about this v.vol.rst algorithm but it sound
>>>    interesting. I do do see a problem though. Your data sets may not
>>>    all like to be interpolated with a single algorithm. So unless you
>>>    can modify the interpolation algorithm for each  band, you will
>>>    probably end up with a bad raster data set.  The chosen algorithm
>>>    must work like the phenomenon you are trying to study.
>>> 
>>>    As an example, using a simple TIN to predict the weather will most
>>>    likely fail as weather pattern don’t work like a TIN. Inverse
>>>    distance weight would probably be better. 
>>> 
>>>    Nicolas
>>> 
>>>> Le 28 oct. 2018 Ã  11:00, Francois Chartier
>>>    <fra.chartier at gmail.com <mailto:fra.chartier at gmail.com>> a écrit :
>>>> 
>>>> Hi
>>>> 
>>>> Is the plugin v vol rst available from within qgis?  i didnt see
>>>    it in the list of vector plugin from grass.
>>>> i am looking for a 3d interpolator and i believe only grass can
>>>    do this in open source. 
>>>> Are there other plugins that can do 3d interpolation? 
>>>> by 3d i am not referring to interpolation of elevation, but by
>>>    interpolation of 3d dataset with xyz + attribute (ex: soil
>>>    moisture), with data points vertically stacked, and would need to
>>>    interpolate verically and horizontally. 
>>>> 
>>>> thks
>>>> f
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> 
> 
> 
> -- 
> Dr. Benjamin Ducke
> Deutsches Archäologisches Institut (DAI)
> Zentrale Berlin, IT-Referat
> 
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