An article by a CEF PhD student published in the journal Remote Sensing (MDPI) highlights the importance of characterising Management Units (MUs) at tree-level, offering crucial tools for the sustainability of dense forests.
The article “Development of a Methodology Based on ALS Data and Diameter Distribution Simulation to Characterize Management Units at Tree Level” by Jean Magalhães, a PhD student in the ForChange group at the Forest Research Centre (CEF) / Instituto Superior de Agronomia (ISA), School of Agriculture, has been accepted by the journal Remote Sensing (MDPI). This work had the collaboration of CEF researchers Juan Guerra-Hernández, Susete Marques, José G. Borges and Margarida Tomé, as well as the researcher Diogo N. Cosenza from the Forestry Engineering Department of the Federal University of Viçosa.
Characterising Management Units (MUs) with tree-level data is essential for a comprehensive understanding of forest structure and for providing the information needed to support forest management decision-making.
This study proposes a methodology for characterising Management Units (MUs) in dense forests using LiDAR data and the Area-Based Approach (ABA). The Johnson’s SB and Weibull probability density functions (PDFs) were evaluated, selecting the most suitable one to integrate into the ABA and simulate diameter distributions within the MUs. This practical and efficient approach is essential for forest management, as it allows data to be estimated at the level of individual trees, which is essential for the application of individual tree models.
All the conclusions can be found at mdpi.com.