Machine Learning Forest Simulator
add_stand_variables_halfPeriod
add_stand_variables
BAI_prediction_halfPeriod
BAI_prediction
calculate_BAL_halfPeriod
calculate_BAL
crownHeight_prediction_halfPeriod
crownHeight_prediction
height_prediction_halfPeriod
height_prediction
MLFS
predict_ingrowth
predict_mortality
A sub model to simulate harvesting within the MLFS
transform_data
volume_form_factors_halfPeriod
volume_form_factors
volume_functions_halfPeriod
volume_functions
volume_merchantable_halfPeriod
volume_merchantable
volume_tariffs_halfPeriod
volume_tariffs
volume_whole_tree_halfPeriod
volume_whole_tree
Climate-sensitive, single-tree forest simulator based on data-driven machine learning. It simulates the main forest processes— radial growth, height growth, mortality, crown recession, regeneration, and harvesting—so users can assess stand development under climate and management scenarios. The height model is described by Skudnik and Jevšenak (2022) <doi:10.1016/j.foreco.2022.120017>, the basal-area increment model by Jevšenak and Skudnik (2021) <doi:10.1016/j.foreco.2020.118601>, and an overview of the MLFS package, workflow, and applications is provided by Jevšenak, Arnič, Krajnc, and Skudnik (2023), Ecological Informatics <doi:10.1016/j.ecoinf.2023.102115>.