natcap.invest.forest_carbon_edge_effect

InVEST Carbon Edge Effect Model.

An implementation of the model described in ‘Degradation in carbon stocks near tropical forest edges’, by Chaplin-Kramer et. al (2015).

natcap.invest.forest_carbon_edge_effect.combine_carbon_maps(*carbon_maps)

Combine the carbon maps and leave nodata where all inputs were nodata.

Parameters:

*carbon_maps (array) – arrays of carbon stocks stored in different pool types.

Returns:

an array consists of all the carbon stocks from

different pool types.

Return type:

result (array)

natcap.invest.forest_carbon_edge_effect.execute(args)

Forest Carbon Edge Effect.

InVEST Carbon Edge Model calculates the carbon due to edge effects in tropical forest pixels.

Parameters:

  • args['workspace_dir'] (string) – a path to the directory that will write output and other temporary files during calculation. (required)

  • args['results_suffix'] (string) – a string to append to any output file name (optional)

  • args['n_nearest_model_points'] (int) – number of nearest neighbor model points to search for

  • args['aoi_vector_path'] (string) – (optional) if present, a path to a shapefile that will be used to aggregate carbon stock results at the end of the run.

  • args['biophysical_table_path'] (string) –

    a path to a CSV table that has at least the fields ‘lucode’ and ‘c_above’. If args['compute_forest_edge_effects'] == True, table must also contain an ‘is_tropical_forest’ field. If args['pools_to_calculate'] == 'all', this table must contain the fields ‘c_below’, ‘c_dead’, and ‘c_soil’.

    • lucode: an integer that corresponds to landcover codes in the raster args['lulc_raster_path']

    • is_tropical_forest: either 0 or 1 indicating whether the landcover type is forest (1) or not (0). If 1, the value in c_above is ignored and instead calculated from the edge regression model.

    • c_above: floating point number indicating tons of above ground carbon per hectare for that landcover type

    • {'c_below', 'c_dead', 'c_soil'}: three other optional carbon pools that will statically map landcover types to the carbon densities in the table.

    Example:

    lucode,is_tropical_forest,c_above,c_soil,c_dead,c_below
0,0,32.8,5,5.2,2.1
1,1,n/a,2.5,0.0,0.0
2,1,n/a,1.8,1.0,0.0
16,0,28.1,4.3,0.0,2.0

    Note the “n/a” in c_above are optional since that field is ignored when is_tropical_forest==1.

  • args['lulc_raster_path'] (string) – path to a integer landcover code raster

  • args['pools_to_calculate'] (string) – if “all” then all carbon pools will be calculted. If any other value only above ground carbon pools will be calculated and expect only a ‘c_above’ header in the biophysical table. If “all” model expects ‘c_above’, ‘c_below’, ‘c_dead’, ‘c_soil’ in header of biophysical_table and will make a translated carbon map for each based off the landcover map.

  • args['compute_forest_edge_effects'] (boolean) – if True, requires biophysical table to have ‘is_tropical_forest’ forest field, and any landcover codes that have a 1 in this column calculate carbon stocks using the Chaplin-Kramer et. al method and ignore ‘c_above’.

  • args['tropical_forest_edge_carbon_model_vector_path'] (string) –

    path to a shapefile that defines the regions for the local carbon edge models. Has at least the fields ‘method’, ‘theta1’, ‘theta2’, ‘theta3’. Where ‘method’ is an int between 1..3 describing the biomass regression model, and the thetas are floating point numbers that have different meanings depending on the ‘method’ parameter. Specifically,

    • method 1 (asymptotic model):

      biomass = theta1 - theta2 * exp(-theta3 * edge_dist_km)

    • method 2 (logarithmic model):

      # NOTE: theta3 is ignored for this method
biomass = theta1 + theta2 * numpy.log(edge_dist_km)

    • method 3 (linear regression):

      biomass = theta1 + theta2 * edge_dist_km

  • args['biomass_to_carbon_conversion_factor'] (string/float) – Number by which to multiply forest biomass to convert to carbon in the edge effect calculation.

  • args['n_workers'] (int) – (optional) The number of worker processes to use for processing this model. If omitted, computation will take place in the current process.

Returns:

None