data_thinning_weights

Each species should have one weight that is multiplied with the probability of being harvested when thinning is applied

datasets

Climate-sensitive forest simulator based on the principles of
machine learning. It stimulates all key processes in the forest: radial growth,
height growth, mortality, crown recession, regeneration and harvesting. The
method for predicting tree heights was described by Skudnik and Jevšenak
(2022) <doi:10.1016/j.foreco.2022.120017>, while the method for predicting
basal area increments (BAI) was described by Jevšenak and Skudnik (2021)
<doi:10.1016/j.foreco.2020.118601>.

Jernej Jevsenak

MLFS

Machine Learning Forest Simulator

data_thinning_weights function

A data frame with 36 rows and 6 variables:<dl>
 <dt>species</dt>
<dd>species name as used in data_NFI</dd> <dt>step_1</dt>
<dd>thinning weight applied in step 1</dd> <dt>step_2</dt>
<dd>thinning weight applied in step 2</dd> <dt>step_3</dt>
<dd>thinning weight applied in step 3</dd> <dt>step_4</dt>
<dd>thinning weight applied in step 4</dd> <dt>step_5</dt>
<dd>thinning weight applied in step 5 and all subsequent steps</dd>
</dl>

Format

An example of data_thinning_weights — data_thinning_weights

A data frame with 36 rows and 6 variables:<dl>
 <dt>species</dt>
<dd>species name as used in data_NFI</dd>

 <dt>step_1</dt>
<dd>thinning weight applied in step 1</dd>

 <dt>step_2</dt>
<dd>thinning weight applied in step 2</dd>

 <dt>step_3</dt>
<dd>thinning weight applied in step 3</dd>

 <dt>step_4</dt>
<dd>thinning weight applied in step 4</dd>

 <dt>step_5</dt>
<dd>thinning weight applied in step 5 and all subsequent steps</dd>


</dl>

data_thinning_weights: An example of data_thinning_weights

Description

Usage

Arguments

Format