10 papers analyzed
These studies suggest that lidar technology is effective for estimating forest biomass, with accuracy influenced by factors such as data integration, point density, and specific algorithms used.
Lidar (Light Detection and Ranging) technology has emerged as a powerful tool for estimating forest biomass. By providing high-resolution, three-dimensional data on forest structure, lidar enables researchers to accurately measure tree height, canopy volume, and other critical variables. This synthesis explores the various methodologies and findings from recent research on using lidar for tree biomass estimation.
Scale-Invariant Models for Biomass Estimation:
Multitemporal Lidar for Biomass Dynamics:
Lidar and Radar Synergies:
Individual Tree Detection (ITD) and Area-Based Approaches (ABA):
Machine Learning Approaches:
Point Density and Landscape Context:
Non-Destructive Biomass Estimation:
Lidar technology, whether used alone or in combination with other remote sensing methods like radar, offers robust and scalable solutions for forest biomass estimation. Scale-invariant models, multitemporal surveys, and machine learning approaches enhance the accuracy and applicability of lidar data. Additionally, the ability to reduce point density without losing significant accuracy and the development of non-destructive methods further solidify lidar's role in forest biomass monitoring and management.
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