Maximizing the Diversity of Ensemble Random Forests for Tree Genera Classification Using High Density LiDAR Data

Recent research into improving the effectiveness of forest inventory management using airborne LiDAR data has focused on developing advanced theories in data analytics. Furthermore, supervised learning as a predictive model for classifying tree genera (and species, where possible) has been gaining p...

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Bibliographic Details

Published in:	Remote Sensing
Main Authors:	Connie Ko, Gunho Sohn, Tarmo K. Remmel, John R. Miller
Format:	Article
Language:	English
Published:	MDPI AG 2016-08-01
Subjects:	tree genera classification LiDAR random forests ensemble classification diversity maximization
Online Access:	http://www.mdpi.com/2072-4292/8/8/646