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Clutter Forecast

Warning

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

Use continuous forest inventory databases to predict forest growth and production. Utilize traditional methods such as the Clutter model or artificial neural networks for greater flexibility. With this module, you will be able to estimate volume, the number of stems, basal area, among other variables of interest.

Class Parameters

Clutter Trainer

ClutterTrainer(df, age1, age2, ba1, ba2, site, vol, iterator=None)
Parameters Description
df The dataframe containing the processed forest inventory data.
age1 Name of the column containing the age of the previously sampled plot.
age2 Name of the column containing the age of the subsequently sampled plot.
ba1 Name of the column containing the basal area of the previously sampled plot.
ba2 Name of the column containing the basal area of the subsequently sampled plot.
site Name of the column containing the site index of the stand.
vol Name of the column containing the volume of the subsequently sampled plot.
iterator (Optional) Name of an iterator that will be used to group the data. Example of an iterator: Genetic material, Stratum.
Example of clutter input
Iterator Plot age1 age2 ba1 ba2 site vol
GM 1 1 2.5 3.5 7.57 8.42 7.83 44.04
GM 1 1 3.5 4.5 8.42 14 8.73 51.42
GM 1 2 2.1 3.1 4.94 5.51 6.98 38.06
GM 1 2 3.1 4.33 5.51 6.45 7.45 39.26
GM 2 1 2 3 7.3 8.25 11.37 74.63
GM 2 1 3 4 8.25 9.13 11.69 68.27
GM 2 1 4 5 9.13 12.79 12.83 72.76
GM 2 1 5 6 12.79 15.63 14 73.87

Class Functions

functions and parameters
  ClutterTrainer.fit_model(save_dir=None)#(1)

  1. save_dir = Directory where the coefficients and parameters of the trained models will be saved.
Parameters Description
.fit_model() Adjust the models for predicting basal area and volume.

Clutter models

  • Basal area prediction

    • \[ \operatorname{lnb_2}^{\text{est}} =b_0 + b_1 \cdot x_1 + b_2 \cdot x_2 + b_3 \cdot x_3 + b_4 \cdot \frac{1}{I_2} + b_5 \cdot S \]

  • Volume prediction

    • \[ \operatorname{lnv_2} = b_0 + b_1 \cdot \frac{1}{I_2} + b_2 \cdot S + b_3 \cdot \operatorname{lnb_2}^{\text{est} } \]
    Notation
    • \( β_n \): Fitted parameters
    • \( I \): Age
    • \[\operatorname{x_1} = \ln(β_1) \cdot \frac{\operatorname{I_1}}{\operatorname{I}_2}\]
    • \[\operatorname{x_2} = 1 - \frac{\operatorname{I}_1}{\operatorname{I}_2}\]
    • \[\operatorname{x_3} = \left( 1 - \frac{\operatorname{I}_1}{\operatorname{I}_2} \right) \cdot \operatorname{S}\]
    • \( S \): Forest site

    Class Parameters

    Clutter Predictor

    ClutterPredictor(coefs_file, age1, site, ba1, iterator=None)
    Parameters Description
    coefs_file Directory of the json file containing the coefficients and parameters of the fitted models.
    age1 Name of the column containing the age of the previously sampled plot.
    ba1 Name of the column containing the basal area of the previously sampled plot.
    site Name of the column containing the site index of the stand.
    iterator (Optional) Name of an iterator that will be used for predictions.
    Class Functions

    functions and parameters
      ClutterTrainer.get_coefs()#(1)
  ClutterTrainer.predict(age2)#(2)
  ClutterTrainer.predict_range(age_range=(2, 10),show_plots=False)#(3)

    1. Returns the loaded coefficients from coefs_file.
    2. Returns the prediction made for age2.
    3. Returns the prediction made for a specified age range in age_range as a tuple.
      If show_plots=True, displays the plots of the performed predictions.

    Example Usage

    clutter_forecast_example.py
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    fptools.forecast import ClutterTrainer, ClutterPredictor #(1)
import pandas as pd #(2)

    1. Import ClutterTrainer and ClutterPredictor class.
    2. Import pandas for data manipulation.

    taper_functions_example.py
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    df = pd.read_csv(r'C:\Your\path\clutter_data.csv') #(1)
c_train = ClutterTrainer(df,"age-column1","age-column2", "basal-area1",
                         "basal-area1","site-column","volume-target",
                         iterator="Genetic Material") #(2)
c_train_metrics = c_train.fit_model(save_dir = r"C:\Your\path\output") #(3)

#
c_predictor = ClutterPredictor(r"C:\Your\path\output\all_coefficients.json",
                             2.35,9.27,9.13, iterator="type-x") #(4)

ba_vol_predicted = c_predictor.predict(4) #(5)
coef = predictor.get_coefs() #(6)
ba_vol_range_predicted = predictor.predict_range((2,12), show_plots=True) #(7)

    1. Load your CSV file.
    2. Create the variable c_train containing the ClutterTrainer class.
    3. Adjust the Clutter model, saving the coefficients and parameters in the folder C:\Your\path\output, and save the training metrics in the variable c_train_metrics.
    4. Create a variable containing the predictor. This predictor will use the saved model C:\Your\path\output\all_coefficients.json to apply an inventory with an age of 2.35, a site index of 9.27, and a basal area of 9.13 to predict future volume production and basal area.
    5. Make the prediction for this plantation when it reaches 4 years old and save the results in ba_vol_predicted.
    6. Obtain the model coefficients and save them in the variable coef.
    7. Make the prediction for this plantation from 2 to 12 years, generating a graph showing the evolution of basal area and volume over this period, along with the confidence level.

    Example of output prediction plot

    First Image
    Second Image
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