Filtering ground noise from LiDAR returns produces inferior models of forest aboveground biomass in heterogenous landscapes

Filtering ground noise from LiDAR returns produces inferior models of forest aboveground biomass in heterogenous landscapes

Airborne LiDAR has become an essential data source for large-scale, high-resolution modeling of forest aboveground biomass and carbon stocks, enabling predictions with much higher resolution and accuracy than can be achieved using optical imagery alone. Ground noise filtering – that is, excluding re...

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Bibliographic Details
Main Authors: Michael J Mahoney, Lucas K Johnson, Eddie Bevilacqua, Colin M Beier
Format: Article
Language:English
Published: Taylor & Francis Group 2022-12-01
Series:GIScience & Remote Sensing
Subjects:
aboveground biomass
ground noise
lidar
machine learning
random forest
introduction
Online Access:http://dx.doi.org/10.1080/15481603.2022.2103069

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