Comparison of Machine Learning Methods for Mapping the Stand Characteristics of Temperate Forests Using Multi-Spectral Sentinel-2 Data

Comparison of Machine Learning Methods for Mapping the Stand Characteristics of Temperate Forests Using Multi-Spectral Sentinel-2 Data

The<b> </b>estimation and mapping of forest stand characteristics are vital because this information is necessary for sustainable forest management. The present study considers the use of a Bayesian additive regression trees (BART) algorithm as a non-parametric classifier using Sentinel-...

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Bibliographic Details
Main Authors: Kourosh Ahmadi, Bahareh Kalantar, Vahideh Saeidi, Elaheh K. G. Harandi, Saeid Janizadeh, Naonori Ueda
Format: Article
Language:English
Published: MDPI AG 2020-09-01
Series:Remote Sensing
Subjects:
machine learning
remote sensing
forest stand characteristics
Bayesian additive regression tree
Online Access:https://www.mdpi.com/2072-4292/12/18/3019

Internet

https://www.mdpi.com/2072-4292/12/18/3019

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