Aboveground Biomass Estimation of Individual Trees in a Coastal Planted Forest Using Full-Waveform Airborne Laser Scanning Data

This study considered airborne laser scanning (ALS) based aboveground biomass (AGB) prediction in mountain forests. The study area consisted of a long transect from southern Norway to northern parts of the country with wide ranges of elevation along a long latitudinal gradient (58°N–69°N). This transect was covered by ALS data and field data from 238 plots. AGB was modeled using different types of predictor variables, namely ALS metrics, variables related to growing conditions (elevation, latitude, and climatic variables), and tree species information. Modelling of AGB in the long transect covering diverse mountainous forest conditions was challenging: the RMSE values were rather large (37%–70%). The effects of growing conditions on model predictions were minor. However, species information was essential to improve accuracy. The analysis revealed that when doing inventories of spruce-dominated areas, all plots should be pooled together when the models are developed, whereas if pine or deciduous species dominate the area in question, separate dominant species-wise models should be constructed.

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Rigorous pulse detection from full-waveform airborne laser scanning data

International Journal of Remote Sensing ◽

10.1080/01431160903380599 ◽

2010 ◽

Vol 31 (5) ◽

pp. 1303-1324 ◽

Cited By ~ 24

Author(s):

Yu-Ching Lin ◽

J. P. Mills ◽

S. Smith-Voysey

Keyword(s):

Laser Scanning ◽

Airborne Laser Scanning ◽

Pulse Detection ◽

Full Waveform ◽

Airborne Laser

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Archaeological prospection of forested areas using full-waveform airborne laser scanning

Journal of Archaeological Science ◽

10.1016/j.jas.2007.06.013 ◽

2008 ◽

Vol 35 (4) ◽

pp. 882-893 ◽

Cited By ~ 134

Author(s):

Michael Doneus ◽

Christian Briese ◽

Martin Fera ◽

Martin Janner

Keyword(s):

Laser Scanning ◽

Airborne Laser Scanning ◽

Archaeological Prospection ◽

Full Waveform ◽

Airborne Laser ◽

Forested Areas

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Processing and analysis of airborne full-waveform laser scanning data for the characterization of forest structure and fuel properties

Revista de Teledetección ◽

10.4995/raet.2020.14551 ◽

2020 ◽

pp. 95

Author(s):

P. Crespo-Peremarch ◽

L. A. Ruiz

Keyword(s):

Forest Structure ◽

Laser Scanning ◽

Software Tool ◽

Understory Vegetation ◽

Airborne Laser Scanning ◽

Vegetation Density ◽

Regression Methods ◽

Full Waveform ◽

Airborne Laser ◽

Pulse Density

This PhD thesis addresses the development of full-waveform airborne laser scanning (ALSFW) processing and analysis methods to characterize the vertical forest structure, in particular the understory vegetation. In this sense, the influence of several factors such as pulse density, voxel parameters (voxel size and assignation value), scan angle at acquisition, radiometric correction and regression methods is analyzed on the extraction of ALSFW metric values and on the estimate of forest attributes. Additionally, a new software tool to process ALSFW data is presented, which includes new metrics related to understory vegetation. On the other hand, occlusion caused by vegetation in the ALSFW, discrete airborne laser scanning (ALSD) and terrestrial laser scanning (TLS) signal is characterized along the vertical structure. Finally, understory vegetation density is detected and determined by ALSFW data, as well as characterized by using the new proposed metrics.

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Spatial Models With Inter-Tree Competition From Airborne Laser Scanning Improve Estimates of Genetic Variance

Frontiers in Plant Science ◽

10.3389/fpls.2020.596315 ◽

2021 ◽

Vol 11 ◽

Author(s):

David Pont ◽

Heidi S. Dungey ◽

Mari Suontama ◽

Grahame T. Stovold

Keyword(s):

Laser Scanning ◽

Management Practices ◽

Spatial Models ◽

Airborne Laser Scanning ◽

Tree Level ◽

Residual Variance ◽

Spatial Effects ◽

Individual Tree ◽

Airborne Laser ◽

Individual Trees

Phenotyping individual trees to quantify interactions among genotype, environment, and management practices is critical to the development of precision forestry and to maximize the opportunity of improved tree breeds. In this study we utilized airborne laser scanning (ALS) data to detect and characterize individual trees in order to generate tree-level phenotypes and tree-to-tree competition metrics. To examine our ability to account for environmental variation and its relative importance on individual-tree traits, we investigated the use of spatial models using ALS-derived competition metrics and conventional autoregressive spatial techniques. Models utilizing competition covariate terms were found to quantify previously unexplained phenotypic variation compared with standard models, substantially reducing residual variance and improving estimates of heritabilities for a set of operationally relevant traits. Models including terms for spatial autocorrelation and competition performed the best and were labelled ACE (autocorrelation-competition-error) models. The best ACE models provided statistically significant reductions in residuals ranging from −65.48% for tree height (H) to −21.03% for wood stiffness (A), and improvements in narrow sense heritabilities from 38.64% for H to 14.01% for A. Individual tree phenotyping using an ACE approach is therefore recommended for analyses of research trials where traits are susceptible to spatial effects.

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