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Forest Report

Warning

This library is under development, none of the presented solutions are available for download.

Generate a forest report based on estimated heights and volumes using the other modules of ForestPyTools. Obtain volume values and tree types per hectare at various specific levels of the forest stand, along with other metrics. Get the report in xlsx (Excel) or json format.

Class Parameters

ForestReport(df, plot_id, plot_size, tree_type, tree_dbh,
             tree_height,group_levels = None, tree_vol_with_bark = None 
             tree_vol_withou_bark = None, iterator=None)
Parameters Description
df The dataframe containing the processed forest inventory data.
plot_id Name of the column that contains the unique identifier for each plot.
plot_size Numeric value indicating the plot size in square meters or the name of the column that contains the size of each plot in square meters.
tree_type Name of the column that contains the id of the tree types present in the forest inventory. The id must be included in tree_types_df.
tree_dbh Name of the column that contains the diameter at breast height (DBH) values.
tree_height Name of the column that contains the tree height values.
group_levels (Optional) List with the names of grouping levels that the user wants to create. A summary will be generated for each group. Example of groups: Farm, stand, soil type.
tree_vol_with_bark (Optional) Name of the column that contains the tree volume values with bark.
tree_vol_without_bark (Optional) Name of the column that contains the tree volume values without bark.
iterator (Optional) Name of an iterator that will be used to group the data. Example of an iterator: Stratum.

Class Functions

functions and parameters
  ForestReport.update_tree_types(df)#(1)!
  ForestReport.view_tree_types()
  ForestReport.get_report(dir, format="xlsx")#(2)!

  1. df = DataFrame containing the tree types present in the forest inventory. If None, the library's default DataFrame is used.
  2. dir = Directory where the report will be saved.
    format = Format in which the report will be saved. Could be xlsx(Excel) or json.
Parameters Description
.update_tree_types() Updates the tree_types_df -> DataFrame that contains the ids of tree types, names, and description.
.view_tree_types() Displays the tree_types_df that the class is currently using.
.get_report() Saves the forest report in xlsx or json format in the specified dir.

Example of tree types table

ID Name Description Commercial Volume
0 normal Tree without significant distortions or defects 1
1 dead Dead tree 0
2 bifurcated adbh Bifurcated above the DBH 1
3 bifurcated bdbh Bifurcated below the DBH 1
4 burned Burned tree 0

*The column order must be followed.

Column Descriptions

  • id: Represents a unique identifier for each type of tree in the classification. It is used to differentiate records and can serve as a key for reference in other datasets.

  • name: Indicates the name of the tree based on its condition or specific characteristics. This name acts as a short description for easy identification.

  • description: Provides a detailed explanation of the tree's condition, including information about its structure, health status, or possible defects that may affect its commercial quality.

  • commercial_volume: Defines whether the volume of this tree will be considered commercially usable. A value of 1 indicates that the tree has commercial volume and will be used, while 0 means its volume will not be utilized.

Example Usage

forest_report_example.py
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from fptools.forest_report import ForestReport#(1)!
import pandas as pd#(2)!

  1. Import ForestReport class.
  2. Import pandas for data manipulation.

Create a variable for the ForestReport Class

forest_report_example.py
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inventory_data = pd.read_csv(r'C:\Your\path\processed_inventory.csv')#(1)!

tree_df_types = pd.read_csv(r'C:\Your\path\tree_df_types.csv')#(2)!

Report = ForestReport(inventory_data,plot_id = "plot_identifier",plot_size = 'plot_size_column',
                      tree_type='tree_type_tag', group_levels=['Farm', 'Stand'],
                      tree_height="best_predicted_height (m)", tree_dbh="DBH (cm)",
                      tree_vol_with_bark="Tree_volume",tree_vol_without_bark= "Tree_volume_no_bark",
                      iterator="forest stratum")#(3)

Report.view_tree_types()#(4)

Report.update_tree_types(tree_df_types)#(5)

Report.get_report(dir=r"C:\Users\Desktop", format="json")#(6)

  1. Load your procesed inventory data df from a csv file.
  2. Load your tree types df from a csv file.
  3. Create the variable Report containing the ForestReport class and using the inventory_data dataframe.
    Define the column plot_identifier as responsible for identifying each plot.
    Define the column plot_size_column as responsible for providing the plot size in square meters.
    Define the column tree_type_tag as responsible for identifying the ID of each tree present in tree_types_df.
    Define the columns Farm and Stand as sub-groups for which reports will be generated.
    Define the column best_predicted_height (m) as the column containing the tree heights in meters.
    Define the column DBH (cm) as the column containing the tree diameters at breast height in centimeters.
    Define the column Tree_volume as the column containing the total tree volume with bark.
    Define the column Tree_volume_no_bark as the column containing the total tree volume without bark.
    Define the column forest stratum as the main iterator in which the report will be generated.
  4. Show the tree_types_df in use.
  5. Update the tree_types_df using the tree_df_types dataframe.
  6. Save the report in C:\Users\Desktop as a json file.