Docs for ‘QGIS testing’. Visit http://docs.qgis.org/2.18 for QGIS 2.18 docs and translations.

Grid analysis

Accumulated cost (anisotropic)

Description

<put algorithm description here>

Parameters

Cost Grid [raster]
<put parameter description here>
Direction of max cost [raster]
<put parameter description here>
Destination Points [raster]
<put parameter description here>
k factor [number]

<put parameter description here>

Default: 1

Threshold for different route [number]

<put parameter description here>

Default: 0

Outputs

Accumulated Cost [raster]
<put output description here>

Console usage

processing.runalg('saga:accumulatedcostanisotropic', cost, direction, points, k, threshold, acccost)

See also

Accumulated cost (isotropic)

Description

<put algorithm description here>

Parameters

Cost Grid [raster]
<put parameter description here>
Destination Points [raster]
<put parameter description here>
Threshold for different route [number]

<put parameter description here>

Default: 0.0

Outputs

Accumulated Cost [raster]
<put output description here>
Closest Point [raster]
<put output description here>

Console usage

processing.runalg('saga:accumulatedcostisotropic', cost, points, threshold, acccost, closestpt)

See also

Aggregation index

Description

<put algorithm description here>

Parameters

Input Grid [raster]
<put parameter description here>
Max. Number of Classes [number]

<put parameter description here>

Default: 5

Outputs

Result [table]
<put output description here>

Console usage

processing.runalg('saga:aggregationindex', input, maxnumclass, result)

See also

Analytical hierarchy process

Description

<put algorithm description here>

Parameters

Input Grids [multipleinput: rasters]
<put parameter description here>
Pairwise Comparisons Table [table]
<put parameter description here>

Outputs

Output Grid [raster]
<put output description here>

Console usage

processing.runalg('saga:analyticalhierarchyprocess', grids, table, output)

See also

Cross-classification and tabulation

Description

<put algorithm description here>

Parameters

Input Grid 1 [raster]
<put parameter description here>
Input Grid 2 [raster]
<put parameter description here>
Max. Number of Classes [number]

<put parameter description here>

Default: 5

Outputs

Cross-Classification Grid [raster]
<put output description here>
Cross-Tabulation Table [table]
<put output description here>

Console usage

processing.runalg('saga:crossclassificationandtabulation', input, input2, maxnumclass, resultgrid, resulttable)

See also

Fragmentation (alternative)

Description

<put algorithm description here>

Parameters

Classification [raster]
<put parameter description here>
Class Identifier [number]

<put parameter description here>

Default: 1

Neighborhood Min [number]

<put parameter description here>

Default: 1

Neighborhood Max [number]

<put parameter description here>

Default: 1

Level Aggregation [selection]

<put parameter description here>

Options:

  • 0 — [0] average
  • 1 — [1] multiplicative

Default: 0

Add Border [boolean]

<put parameter description here>

Default: True

Connectivity Weighting [number]

<put parameter description here>

Default: 1.1

Minimum Density [Percent] [number]

<put parameter description here>

Default: 10

Minimum Density for Interior Forest [Percent] [number]

<put parameter description here>

Default: 99

Search Distance Increment [number]

<put parameter description here>

Default: 0.0

Density from Neighbourhood [boolean]

<put parameter description here>

Default: True

Outputs

Density [Percent] [raster]
<put output description here>
Connectivity [Percent] [raster]
<put output description here>
Fragmentation [raster]
<put output description here>
Summary [table]
<put output description here>

Console usage

processing.runalg('saga:fragmentationalternative', classes, class, neighborhood_min, neighborhood_max, aggregation, border, weight, density_min, density_int, level_grow, density_mean, density, connectivity, fragmentation, fragstats)

See also

Fragmentation classes from density and connectivity

Description

<put algorithm description here>

Parameters

Density [Percent] [raster]
<put parameter description here>
Connectivity [Percent] [raster]
<put parameter description here>
Add Border [boolean]

<put parameter description here>

Default: True

Connectivity Weighting [number]

<put parameter description here>

Default: 0

Minimum Density [Percent] [number]

<put parameter description here>

Default: 10

Minimum Density for Interior Forest [Percent] [number]

<put parameter description here>

Default: 99

Outputs

Fragmentation [raster]
<put output description here>

Console usage

processing.runalg('saga:fragmentationclassesfromdensityandconnectivity', density, connectivity, border, weight, density_min, density_int, fragmentation)

See also

Fragmentation (standard)

Description

<put algorithm description here>

Parameters

Classification [raster]
<put parameter description here>
Class Identifier [number]

<put parameter description here>

Default: 1

Neighborhood Min [number]

<put parameter description here>

Default: 1

Neighborhood Max [number]

<put parameter description here>

Default: 3

Level Aggregation [selection]

<put parameter description here>

Options:

  • 0 — [0] average
  • 1 — [1] multiplicative

Default: 0

Add Border [boolean]

<put parameter description here>

Default: True

Connectivity Weighting [number]

<put parameter description here>

Default: 1.1

Minimum Density [Percent] [number]

<put parameter description here>

Default: 10

Minimum Density for Interior Forest [Percent] [number]

<put parameter description here>

Default: 99

Neighborhood Type [selection]

<put parameter description here>

Options:

  • 0 — [0] square
  • 1 — [1] circle

Default: 0

Include diagonal neighbour relations [boolean]

<put parameter description here>

Default: True

Outputs

Density [Percent] [raster]
<put output description here>
Connectivity [Percent] [raster]
<put output description here>
Fragmentation [raster]
<put output description here>
Summary [table]
<put output description here>

Console usage

processing.runalg('saga:fragmentationstandard', classes, class, neighborhood_min, neighborhood_max, aggregation, border, weight, density_min, density_int, circular, diagonal, density, connectivity, fragmentation, fragstats)

See also

Layer of extreme value

Description

<put algorithm description here>

Parameters

Grids [multipleinput: rasters]
<put parameter description here>
Method [selection]

<put parameter description here>

Options:

  • 0 — [0] Maximum
  • 1 — [1] Minimum

Default: 0

Outputs

Result [raster]
<put output description here>

Console usage

processing.runalg('saga:layerofextremevalue', grids, criteria, result)

See also

Least cost paths

Description

<put algorithm description here>

Parameters

Source Point(s) [vector: point]
<put parameter description here>
Accumulated cost [raster]
<put parameter description here>
Values [multipleinput: rasters]

Optional.

<put parameter description here>

Outputs

Profile (points) [vector]
<put output description here>
Profile (lines) [vector]
<put output description here>

Console usage

processing.runalg('saga:leastcostpaths', source, dem, values, points, line)

See also

Ordered Weighted Averaging

Description

<put algorithm description here>

Parameters

Input Grids [multipleinput: rasters]
<put parameter description here>
Weights [fixedtable]
<put parameter description here>

Outputs

Output Grid [raster]
<put output description here>

Console usage

processing.runalg('saga:orderedweightedaveraging', grids, weights, output)

See also

Pattern analysis

Description

<put algorithm description here>

Parameters

Input Grid [raster]
<put parameter description here>
Size of Analysis Window [selection]

<put parameter description here>

Options:

  • 0 — [0] 3 X 3
  • 1 — [1] 5 X 5
  • 2 — [2] 7 X 7

Default: 0

Max. Number of Classes [number]

<put parameter description here>

Default: 0

Outputs

Relative Richness [raster]
<put output description here>
Diversity [raster]
<put output description here>
Dominance [raster]
<put output description here>
Fragmentation [raster]
<put output description here>
Number of Different Classes [raster]
<put output description here>
Center Versus Neighbours [raster]
<put output description here>

Console usage

processing.runalg('saga:patternanalysis', input, winsize, maxnumclass, relative, diversity, dominance, fragmentation, ndc, cvn)

See also

Soil texture classification

Description

<put algorithm description here>

Parameters

Sand [raster]

Optional.

<put parameter description here>

Silt [raster]

Optional.

<put parameter description here>

Clay [raster]

Optional.

<put parameter description here>

Outputs

Soil Texture [raster]
<put output description here>
Sum [raster]
<put output description here>

Console usage

processing.runalg('saga:soiltextureclassification', sand, silt, clay, texture, sum)

See also