Identification of Significative LiDAR Metrics and Comparison of Machine Learning Approaches for Estimating Stand and Diversity Variables in Heterogeneous Brazilian Atlantic Forest

Identification of Significative LiDAR Metrics and Comparison of Machine Learning Approaches for Estimating Stand and Diversity Variables in Heterogeneous Brazilian Atlantic Forest

Data collection and estimation of variables that describe the structure of tropical forests, diversity, and richness of tree species are challenging tasks. Light detection and ranging (LiDAR) is a powerful technique due to its ability to penetrate small openings and cracks in the forest canopy, enab...

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Bibliographic Details
Main Authors: Rorai Pereira Martins-Neto, Antonio Maria Garcia Tommaselli, Nilton Nobuhiro Imai, Hassan Camil David, Milto Miltiadou, Eija Honkavaara
Format: Article
Language:English
Published: MDPI AG 2021-06-01
Series:Remote Sensing
Subjects:
tropical forests
airborne laser scanning
forest structure
forest attributes
artificial intelligence
machine learning
Online Access:https://www.mdpi.com/2072-4292/13/13/2444

Internet

https://www.mdpi.com/2072-4292/13/13/2444

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