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Marco
Marco
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 |        Projection: Latitude-Longitude                                  
 |
 |            N: 11:15:44.273502N    S: 10:55:46.447629N   Res: 0:00:02.76634 |
 |            E: 87:04:08.435069W    W: 87:23:22.390832W   Res: 0:00:02.80086 |
 |   Range of data:    min = -4783.09  max = -760.2788                        ||`

  1. Load the DTM and set the region to the input data

    r.import input=C:...\mb.grd output=mb g.region raster=mbr.import input=mb.grd output=mb g.region raster=mb

  2. Digitalize the contourlines before the slide. I did the digitalization in QGIS and I saved the elevation values in field level

    v.import input=C:...\Contour50_WGS84.shp layer=Contour50_WGS84 output=Contour50_WGS84v.import input=Contour50_WGS84.shp layer=Contour50_WGS84 output=Contour50_WGS84

  1. Digitalize the boundaries of the scarp to use it as a mask.

    v.import input=C:...\mascara.shp layer=mascara output=mascarav.import input=mascara.shp layer=mascara output=mascara

  1. Mask the area of interest and fill the null values

    r.mask --overwrite vector=mascara r.fillnulls input=mb output=mbfilled method=rstr.mask --overwrite vector=mascara r.fillnulls input=mb output=mbfilled method=rst

  2. Conversion of contour lines into raster lines and interpolation for the DTM prior the slide

    v.to.rast input=Contour50_WGS84 type=line output=prescarp use=attr attribute_column=level r.surf.contour input=prescarp output=prescarpfilled enter image description here

  3. Calculate the difference between the two rasters

    r.mapcalc expression=scarpdepth = prescarpfilled - mbfilledv.to.rast input=Contour50_WGS84 type=line output=prescarp use=attr attribute_column=level r.surf.contour input=prescarp output=prescarpfilled

enter image description here

  1. Calculate the difference between the two rasters

    r.mapcalc expression=scarpdepth = prescarpfilled - mbfilled

  1. Calculate the average cell size and the sum of the scarpdepth

    Calculate the average cell size and the sum of the scarpdepth

    r.report map=MASK units=me |-----------------------------------------------------------------------------| | Category Information | square| |#|description | meters| |-----------------------------------------------------------------------------| |1|Value 1. . . . . . . . . . . . . . . . . . . . . . . . . . . | 53,830,997| |*|no data. . . . . . . . . . . . . . . . . . . . . . . . . . . |1,235,078,890| |-----------------------------------------------------------------------------| |TOTAL |1,288,909,887| +-----------------------------------------------------------------------------+

    r.univar map=scarpdepth total null and non-null cells: 178396 total null cells: 170947 Of the non-null cells: ---------------------- n: 7449 minimum: -2.60534 maximum: 387.981 range: 390.586 mean: 206.303 mean of absolute values: 206.305 standard deviation: 76.9735 variance: 5924.92 variation coefficient: 37.3109 % sum: 1536751.12633864

r.report map=MASK units=me

|-----------------------------------------------------------------------------| | Category Information | square| |#|description | meters| |-----------------------------------------------------------------------------| |1|Value 1. . . . . . . . . . . . . . . . . . . . . . . . . . . | 53,830,997| |*|no data. . . . . . . . . . . . . . . . . . . . . . . . . . . |1,235,078,890| |-----------------------------------------------------------------------------| |TOTAL |1,288,909Then,887| +-----------------------------------------------------------------------------+

r.univar map=scarpdepth                                                
total null and non-null cells: 178396
total null cells: 170947
Of the non-null cells:
----------------------
n: 7449
minimum: -2.60534
maximum: 387.981
range: 390.586
mean: 206.303
mean of absolute values: 206.305
standard deviation: 76.9735
variance: 5924.92
variation coefficient: 37.3109 %
sum: 1536751.12633864

Here with 7449 cells and an area of 53.8 km^2, the cell size is 7226 m^2/cell

  1. The volume approximately is 7+/-11 km^3 approximately (7,226 m^2 * 1,536,751 m = 11 km^3)
 |        Projection: Latitude-Longitude                                  
 |
 |            N: 11:15:44.273502N    S: 10:55:46.447629N   Res: 0:00:02.76634 |
 |            E: 87:04:08.435069W    W: 87:23:22.390832W   Res: 0:00:02.80086 |
 |   Range of data:    min = -4783.09  max = -760.2788                        |

  1. Load the DTM and set the region to the input data

    r.import input=C:...\mb.grd output=mb g.region raster=mb

  2. Digitalize the contourlines before the slide. I did the digitalization in QGIS and I saved the elevation values in field level

    v.import input=C:...\Contour50_WGS84.shp layer=Contour50_WGS84 output=Contour50_WGS84

  1. Digitalize the boundaries of the scarp to use it as a mask.

    v.import input=C:...\mascara.shp layer=mascara output=mascara

  1. Mask the area of interest and fill the null values

    r.mask --overwrite vector=mascara r.fillnulls input=mb output=mbfilled method=rst

  2. Conversion of contour lines into raster lines and interpolation for the DTM prior the slide

    v.to.rast input=Contour50_WGS84 type=line output=prescarp use=attr attribute_column=level r.surf.contour input=prescarp output=prescarpfilled enter image description here

  3. Calculate the difference between the two rasters

    r.mapcalc expression=scarpdepth = prescarpfilled - mbfilled

  1. Calculate the average cell size and the sum of the scarpdepth

r.report map=MASK units=me

|-----------------------------------------------------------------------------| | Category Information | square| |#|description | meters| |-----------------------------------------------------------------------------| |1|Value 1. . . . . . . . . . . . . . . . . . . . . . . . . . . | 53,830,997| |*|no data. . . . . . . . . . . . . . . . . . . . . . . . . . . |1,235,078,890| |-----------------------------------------------------------------------------| |TOTAL |1,288,909,887| +-----------------------------------------------------------------------------+

r.univar map=scarpdepth                                                
total null and non-null cells: 178396
total null cells: 170947
Of the non-null cells:
----------------------
n: 7449
minimum: -2.60534
maximum: 387.981
range: 390.586
mean: 206.303
mean of absolute values: 206.305
standard deviation: 76.9735
variance: 5924.92
variation coefficient: 37.3109 %
sum: 1536751.12633864

Here with 7449 cells and an area of 53.8 km^2, the cell size is 7226 m^2/cell

  1. The volume approximately is 7,226 m^2 * 1,536,751 m = 11 km^3
 |        Projection: Latitude-Longitude                                  
 |
 |            N: 11:15:44.273502N    S: 10:55:46.447629N   Res: 0:00:02.76634 |
 |            E: 87:04:08.435069W    W: 87:23:22.390832W   Res: 0:00:02.80086 |
 |   Range of data:    min = -4783.09  max = -760.2788                        |`

  1. Load the DTM and set the region to the input data

    r.import input=mb.grd output=mb g.region raster=mb

  2. Digitalize the contourlines before the slide. I did the digitalization in QGIS and I saved the elevation values in field level

    v.import input=Contour50_WGS84.shp layer=Contour50_WGS84 output=Contour50_WGS84

  1. Digitalize the boundaries of the scarp to use it as a mask.

    v.import input=mascara.shp layer=mascara output=mascara

  1. Mask the area of interest and fill the null values

    r.mask --overwrite vector=mascara r.fillnulls input=mb output=mbfilled method=rst

  2. Conversion of contour lines into raster lines and interpolation for the DTM prior the slide

    v.to.rast input=Contour50_WGS84 type=line output=prescarp use=attr attribute_column=level r.surf.contour input=prescarp output=prescarpfilled

enter image description here

  1. Calculate the difference between the two rasters

    r.mapcalc expression=scarpdepth = prescarpfilled - mbfilled

  1. Calculate the average cell size and the sum of the scarpdepth

    r.report map=MASK units=me |-----------------------------------------------------------------------------| | Category Information | square| |#|description | meters| |-----------------------------------------------------------------------------| |1|Value 1. . . . . . . . . . . . . . . . . . . . . . . . . . . | 53,830,997| |*|no data. . . . . . . . . . . . . . . . . . . . . . . . . . . |1,235,078,890| |-----------------------------------------------------------------------------| |TOTAL |1,288,909,887| +-----------------------------------------------------------------------------+

    r.univar map=scarpdepth total null and non-null cells: 178396 total null cells: 170947 Of the non-null cells: ---------------------- n: 7449 minimum: -2.60534 maximum: 387.981 range: 390.586 mean: 206.303 mean of absolute values: 206.305 standard deviation: 76.9735 variance: 5924.92 variation coefficient: 37.3109 % sum: 1536751.12633864

Then, with 7449 cells and an area of 53.8 km^2, the cell size is 7226 m^2/cell

  1. The volume is +/-11 km^3 approximately (7,226 m^2 * 1,536,751 m)
update with an example
Source Link
Marco
Marco
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UPDATE

@Gery provided a working example offshore Nicaragua and I attempted to calculate the volume with my own description above using GRASS and bit QGIS.

 |        Projection: Latitude-Longitude                                  
 |
 |            N: 11:15:44.273502N    S: 10:55:46.447629N   Res: 0:00:02.76634 |
 |            E: 87:04:08.435069W    W: 87:23:22.390832W   Res: 0:00:02.80086 |
 |   Range of data:    min = -4783.09  max = -760.2788                        |

Here an overview of the scarp around 3000 m below sea level

  1. Load the DTM and set the region to the input data

    r.import input=C:...\mb.grd output=mb g.region raster=mb

  2. Digitalize the contourlines before the slide. I did the digitalization in QGIS and I saved the elevation values in field level

    v.import input=C:...\Contour50_WGS84.shp layer=Contour50_WGS84 output=Contour50_WGS84

Contour lines before the slide

  1. Digitalize the boundaries of the scarp to use it as a mask.

    v.import input=C:...\mascara.shp layer=mascara output=mascara

Mask

  1. Mask the area of interest and fill the null values

    r.mask --overwrite vector=mascara r.fillnulls input=mb output=mbfilled method=rst

  2. Conversion of contour lines into raster lines and interpolation for the DTM prior the slide

    v.to.rast input=Contour50_WGS84 type=line output=prescarp use=attr attribute_column=level r.surf.contour input=prescarp output=prescarpfilled enter image description here

  3. Calculate the difference between the two rasters

    r.mapcalc expression=scarpdepth = prescarpfilled - mbfilled

enter image description here

  1. Calculate the average cell size and the sum of the scarpdepth

r.report map=MASK units=me

|-----------------------------------------------------------------------------| | Category Information | square| |#|description | meters| |-----------------------------------------------------------------------------| |1|Value 1. . . . . . . . . . . . . . . . . . . . . . . . . . . | 53,830,997| |*|no data. . . . . . . . . . . . . . . . . . . . . . . . . . . |1,235,078,890| |-----------------------------------------------------------------------------| |TOTAL |1,288,909,887| +-----------------------------------------------------------------------------+

r.univar map=scarpdepth                                                
total null and non-null cells: 178396
total null cells: 170947
Of the non-null cells:
----------------------
n: 7449
minimum: -2.60534
maximum: 387.981
range: 390.586
mean: 206.303
mean of absolute values: 206.305
standard deviation: 76.9735
variance: 5924.92
variation coefficient: 37.3109 %
sum: 1536751.12633864

Here with 7449 cells and an area of 53.8 km^2, the cell size is 7226 m^2/cell

  1. The volume approximately is 7,226 m^2 * 1,536,751 m = 11 km^3

UPDATE

@Gery provided a working example offshore Nicaragua and I attempted to calculate the volume with my own description above using GRASS and bit QGIS.

 |        Projection: Latitude-Longitude                                  
 |
 |            N: 11:15:44.273502N    S: 10:55:46.447629N   Res: 0:00:02.76634 |
 |            E: 87:04:08.435069W    W: 87:23:22.390832W   Res: 0:00:02.80086 |
 |   Range of data:    min = -4783.09  max = -760.2788                        |

Here an overview of the scarp around 3000 m below sea level

  1. Load the DTM and set the region to the input data

    r.import input=C:...\mb.grd output=mb g.region raster=mb

  2. Digitalize the contourlines before the slide. I did the digitalization in QGIS and I saved the elevation values in field level

    v.import input=C:...\Contour50_WGS84.shp layer=Contour50_WGS84 output=Contour50_WGS84

Contour lines before the slide

  1. Digitalize the boundaries of the scarp to use it as a mask.

    v.import input=C:...\mascara.shp layer=mascara output=mascara

Mask

  1. Mask the area of interest and fill the null values

    r.mask --overwrite vector=mascara r.fillnulls input=mb output=mbfilled method=rst

  2. Conversion of contour lines into raster lines and interpolation for the DTM prior the slide

    v.to.rast input=Contour50_WGS84 type=line output=prescarp use=attr attribute_column=level r.surf.contour input=prescarp output=prescarpfilled enter image description here

  3. Calculate the difference between the two rasters

    r.mapcalc expression=scarpdepth = prescarpfilled - mbfilled

enter image description here

  1. Calculate the average cell size and the sum of the scarpdepth

r.report map=MASK units=me

|-----------------------------------------------------------------------------| | Category Information | square| |#|description | meters| |-----------------------------------------------------------------------------| |1|Value 1. . . . . . . . . . . . . . . . . . . . . . . . . . . | 53,830,997| |*|no data. . . . . . . . . . . . . . . . . . . . . . . . . . . |1,235,078,890| |-----------------------------------------------------------------------------| |TOTAL |1,288,909,887| +-----------------------------------------------------------------------------+

r.univar map=scarpdepth                                                
total null and non-null cells: 178396
total null cells: 170947
Of the non-null cells:
----------------------
n: 7449
minimum: -2.60534
maximum: 387.981
range: 390.586
mean: 206.303
mean of absolute values: 206.305
standard deviation: 76.9735
variance: 5924.92
variation coefficient: 37.3109 %
sum: 1536751.12633864

Here with 7449 cells and an area of 53.8 km^2, the cell size is 7226 m^2/cell

  1. The volume approximately is 7,226 m^2 * 1,536,751 m = 11 km^3
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Marco
Marco
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  • 37

I will try to address your question with GRASS.

Your problem has a first task that is a bit challenging already, which is to get the situation before the event you want to quantify. I think the best option is to produce a raster of the original situation. To produce it, I would use some countour lines to draw "by hand" the terrain as it was before the event, then with the help of r.surf.contour fill a terrain model.

Once you have the two rasters, before and after, you ca produce a raster of the difference with r.mapcalc (rasterDiff= rasterBefore - rasterAfter). I guess you will need appropiate masks to box your areas of interest.

You can have the volume by using the r.sum of rasterDiff and multiplying the results by the cell size.