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Landscape variables

Source: R/extract_variables.R
extract_variables.Rd

Extract or estimate variables from landscape objects (class 'sf').

Usage

extract_variables(x, vars = "land_cover_type", SpParams = NULL, ...)

plot_variable(x, variable = "land_cover_type", SpParams = NULL, r = NULL, ...)

Arguments

x

An object of class sf with the appropriate columns.

vars

A string vector with the name of the variables to extract (see details).

SpParams

An optional data frame with species parameters (see SpParamsMED), required for some forest stand variables.

...

Additional arguments (not used).

variable

A string with the name of the variables to draw (see details).

r

An optional object of class SpatRaster, defining the raster topology. If supplied, values are shown as raster pixels. Otherwise, points are drawn.

Value

Function extract_variables() returns an object of class sf with the desired variables. Function plot_variables() returns a ggplot object.

Details

The following string values are available for vars.

Topography:

  • "elevation": Elevation in m.

  • "slope": Slope in degrees.

  • "aspect": Slope in degrees.

  • "land_cover_type": Land cover type.

Snowpack and soil:

  • "snowpack": Snowpack water equivalent (mm). Requires 'snowpack' to be defined in x.

  • "soil_vol_extract": Total water extractable volume (mm).

  • "soil_vol_sat": Total water volume at saturation (mm).

  • "soil_vol_fc": Total water volume at field capacity (mm).

  • "soil_vol_wp": Total water volume at wilting point (mm).

  • "soil_vol_curr": Current total water volume (mm).

  • "soil_rwc_curr": Current soil relative water content (%).

  • "soil_rew_curr": Current soil relative extractable water (%).

  • "soil_theta_curr": Current soil moisture content (% vol.)

  • "soil_psi_curr": Current soil water potential (MPa).

Hydrogeology:

  • "depth_to_bedrock": Depth to bedrock (m).

  • "bedrock_porosity": Bedrock porosity.

  • "bedrock_conductivity": Bedrock conductivity (m/day).

  • "channel": River channel network. Requires 'channel' to be defined in x.

Aquifer (requires 'aquifer' to be defined in x):

  • "aquifer_elevation": Aquifer elevation over bedrock (m).

  • "depth_to_aquifer": Depth to aquifer (m).

  • "aquifer": Aquifer volume (mm).

Forest stand:

  • "basal_area": Basal area (m2/ha).

  • "tree_density": Tree density (ind/ha).

  • "mean_tree_height": Mean tree height (cm).

  • "dominant_tree_height": Dominant tree height (cm).

  • "dominant_tree_diameter": Dominant tree diameter (cm).

  • "quadratic_mean_tree_diameter": Quadratic mean tree diameter (cm).

  • "hart_becking_index": Hart-Becking index.

  • "leaf_area_index": Leaf area index (m2/m2). Requires SpParams to be supplied.

  • "foliar_biomass": Foliar biomass (kg/m2). Requires SpParams to be supplied.

  • "fuel_loading": Fine live fuel loading (kg/m2). Requires SpParams to be supplied.

  • "shrub_volume": Shrub volume (m3/m2). Requires SpParams to be supplied.

See also

forest, soil, summary.forest, shinyplot.sf

Author

Miquel De Cáceres Ainsa, CREAF.

Examples

# Load data and species parameters from medfate
data(example_ifn)
data(SpParamsMED)
  
# Calculate basal area and leaf area index
# for all forest stands
extract_variables(example_ifn, vars = c("basal_area", "leaf_area_index"),
                  SpParams = SpParamsMED)
#> Simple feature collection with 100 features and 2 fields
#> Geometry type: POINT
#> Dimension:     XY
#> Bounding box:  xmin: 1.817095 ymin: 41.93301 xmax: 2.142956 ymax: 41.99881
#> Geodetic CRS:  WGS 84
#> # A tibble: 100 × 3
#>               geometry basal_area leaf_area_index
#>            <POINT [°]>      <dbl>           <dbl>
#>  1 (2.130641 41.99872)      13.9            5.29 
#>  2 (2.142714 41.99881)      15.7            2.91 
#>  3 (1.828998 41.98704)       0              0.833
#>  4 (1.841068 41.98716)       0              3.03 
#>  5 (1.853138 41.98728)       0              1.79 
#>  6 (1.901418 41.98775)       0              2.76 
#>  7 (1.937629 41.98809)      19.7            5.88 
#>  8  (1.949699 41.9882)       9.91           3.45 
#>  9  (1.96177 41.98831)       0              2.41 
#> 10  (1.97384 41.98842)      19.8            3.75 
#> # ℹ 90 more rows