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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

A data frame with species parameters (see SpParamsMED), required for most forest stand variables.

...

Additional arguments (not used).

variable

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

r

An object of class SpatRaster, defining the raster topology.

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.

Soil:

  • "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).

Watershed:

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

  • "bedrock_porosity": Bedrock porosity.

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

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

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

  • "aquifer": Aquifer volume (mm).

  • "snowpack": Snowpack water equivalent (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).

  • "foliar_biomass": Foliar biomass (kg/m2).

  • "fuel_loading": Fine live fuel loading (kg/m2).

  • "shrub_volume": Shrub volume (m3/m2).

See also

forest, soil, summary.forest, shinyplot_land

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