An Improved Generalized Hierarchical Estimation Framework with Geostatistics for Mapping Forest Parameters and Its Uncertainty: A Case Study of Forest Canopy Height

An Improved Generalized Hierarchical Estimation Framework with Geostatistics for Mapping Forest Parameters and Its Uncertainty: A Case Study of Forest Canopy Height

Forest canopy height is an essential parameter in estimating forest aboveground biomass (AGB), growing stock volume (GSV), and carbon storage, and it can provide necessary information in forest management activities. Light direction and ranging (LiDAR) is widely used for estimating canopy height. Co...

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Bibliographic Details
Main Authors: Junpeng Zhao, Lei Zhao, Erxue Chen, Zengyuan Li, Kunpeng Xu, Xiangyuan Ding
Format: Article
Language:English
Published: MDPI AG 2022-01-01
Series:Remote Sensing
Subjects:
forest
up-scaling
regression kriging
uncertainty
LiDAR
Online Access:https://www.mdpi.com/2072-4292/14/3/568

Internet

https://www.mdpi.com/2072-4292/14/3/568

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