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 method Clutter model. With this module, you will be able to estimate volume and basal area.

Class Parameters

Clutter Trainer

ClutterTrainer(df, age1, age2, ba1, ba2, site, vol, iterator=None)

Parameters	Description
df	The dataframe containing the continous 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)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

\ln G_{2} = \ln G_{1} (\frac{t_{1}}{t_{2}}) + α_{0} (1 - \frac{t_{1}}{t_{2}}) + α_{1} (1 - \frac{t_{1}}{t_{2}}) S

Volume prediction

\ln V_{2} = β_{0} + β_{1} \cdot \frac{1}{t_{2}} + β_{2} \cdot S + β_{3} \cdot \ln G_{2}

Notation

$G_{1}$ : Initial basal area (at age $t_{1}$ ) in m²·ha⁻¹
$G_{2}$ : Final basal area (at age $t_{2}$ ) in m²·ha⁻¹
$t_{1}$ : Initial age in months or years
$t_{2}$ : Final age in months or years
$S$ : Site index, dimensionless, $f (site age, t_{i})$
$α_{0}, α_{1}$ : Coefficients to be estimated
$V_{2}$ : Final volume (at age $t_{2}$ ) in m³·ha⁻¹
$β_{i}$ : Coefficients to be estimated

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()Returns the loaded coefficients from coefs_file.  

  ClutterTrainer.predict(age2)Returns the prediction made for age2.  

  ClutterTrainer.predict_range(age_range=(2, 10),show_plots=False)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
fptools.forecast import ClutterTrainer, ClutterPredictorImport ClutterTrainer and ClutterPredictor class.

import pandas as pdImport pandas for data manipulation.

clutter_forecast_example.py
df = pd.read_csv(r'C:\Your\path\clutter_data.csv')Load your CSV file.  

c_train = ClutterTrainer(df,"age-column1","age-column2", "basal-area1",
                         "basal-area1","site-column","volume-target",
                         iterator="Genetic Material")Create the variable c_train containing the ClutterTrainer class.  

c_train_metrics = c_train.fit_model(save_dir = r"C:\Your\path\output")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.  

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

ba_vol_predicted = c_predictor.predict(4)Make the prediction for this plantation when it reaches 4 years old and save the results in ba_vol_predicted.  

coef = predictor.get_coefs()Obtain the model coefficients and save them in the variable coef.  

ba_vol_range_predicted = predictor.predict_range((2,12), show_plots=True)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

References

CLUTTER, J. L.; FORTSON, J. C.; PIENAAR, L. V.; BRISTER, G. H.; BAILEY, R. L. (1983). Timber management: a quantitative approach. New York: John Wiley & Sons, 333p.

ARCE, JULIO EDUARDO; DOBNER JR., MARIO. (2024). Manejo e planejamento de florestas plantadas: com ênfase nos gêneros Pinus e Eucalyptus. Curitiba, PR: Ed. dos Autores, 419p.