Classification of forest development stages from national low-density lidar datasets: a comparison of machine learning methods

Classification of forest development stages from national low-density lidar datasets: a comparison of machine learning methods

The area-based method has become a widespread approach in airborne laser scanning (ALS), being mainly employed for the estimation of continuous variables describing forest attributes: biomass, volume, density, etc. However, to date, classification methods based on machine learning, which are fairly...

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Bibliographic Details
Main Authors: R. Valbuena, M. Maltamo, P. Packalen
Format: Article
Language:English
Published: Universitat Politécnica de Valencia 2016-02-01
Series:Revista de Teledetección
Subjects:
airborne laser scanning
discriminant analysis
maximum likelihood
minimum volume ellipsoid
naïve Bayes
support vector machine
artificial neural networks
random forest
nearest neighbour.
Online Access:http://polipapers.upv.es/index.php/raet/article/view/4029

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http://polipapers.upv.es/index.php/raet/article/view/4029

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