Applying data science and machine learning to create a new methodology of Eucalyptus productivity zoning
Year: 2023
Type: article
Abstract: Foresters and resource managers often face difficulties in surveying reliable Eucalyptus productivity levels for the purpose of purchasing and prospecting lands.Data science and machine learning (ML) provide powerful approaches to making the best use of the large datasets available today.Based on more than 12,000 forest inventory plots of Eucalyptus plantations and climate, soil and altitude variable associated to then, a supervised decision tree (DT) approach was adjusted to model the stands productivity.The high quality of the model obtained made it possible to spatialize the DT rules providing a reliable cartographic visualization of the Eucalyptus productivity for a major region of forest-based industries in Brazil.Our study provided deeper analysis and reproducible tools to manage forest assets sustainably.
Source: Série Técnica/Série Técnica IPEF
Institution: Universidade Estadual Paulista (Unesp)
Language: English
Subfield: Forestry
Domain: Life Sciences
SDG: Life on land
Open Access status: bronze
OpenAlex ID: W4387272003