data_climate

This is simulated monthly climate data, and consists of precipitation sum and mean temperature

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_climate function

A data frame with 16695 rows and 5 variables:<dl>
 <dt>plotID</dt>
<dd>a unique identifier for plot</dd> <dt>year</dt>
<dd>year</dd> <dt>month</dt>
<dd>month</dd> <dt>t_avg</dt>
<dd>monthly mean temperature</dd> <dt>p_sum</dt>
<dd>monthly precipitation sum</dd>
</dl>

Format

An example of climate data — data_climate

A data frame with 16695 rows and 5 variables:<dl>
 <dt>plotID</dt>
<dd>a unique identifier for plot</dd>

 <dt>year</dt>
<dd>year</dd>

 <dt>month</dt>
<dd>month</dd>

 <dt>t_avg</dt>
<dd>monthly mean temperature</dd>

 <dt>p_sum</dt>
<dd>monthly precipitation sum</dd>


</dl>

data_climate: An example of climate data

Description

Usage

Arguments

Format