Forest Canopy Gap Fraction From Terrestrial Laser Scanning

Danson, F. Mark; Hetherington, David; Morsdorf, Felix; Koetz, Benjamin; Allgöwer, Britta

doi:10.1109/lgrs.2006.887064

Cited by 204 publications

(160 citation statements)

References 10 publications

(7 reference statements)

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“…Automatical measurements of length and diameter of tree trunks and individual branches, including the changes in their radii , are as well possible as tree lean, sweep and taper (Watt et al 2003;, gap fraction, PAI and LAI (Lovell et al 2003;Chasmer et al 2004;Henning and Radtke 2006;Danson et al 2007;Takeda et al 2008). Most of these applications are still under development, and validation remains a problem ; Van der Zande et al 2006).…”

Section: Lidar and Optical Point Quadrat Methodsmentioning

confidence: 99%

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Review of ground-based methods to measure the distribution of biomass in forest canopies

Seidel

Fleck

Leuschner

et al. 2011

Annals of Forest Science

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& Context Ecological research and an effective forest management need accurate information on the structure of the forest canopy to understand the biochemical, physiological and biogeochemical processes within a forest. & Research question This paper reviews the currently available instruments for measuring the distribution of biomass within forest canopies. We compare the most well-established approaches and present the different measurable parameters. A special focus lies on the resolution of the obtained data. & Results It was found that only 3D laser scanners offer data with the resolution required by ecologists, private landholders, the forest industry and the public to detect trends in tree growth patterns and canopy interactions in all three spatial dimensions. But data validation, data analysis and parameter extraction are still under development, and the price of the instrument is quite high. & Conclusion Research should focus on the parameter extraction from terrestrial laser scanner data as this could allow the calculation of functional attributes for different sections of a canopy on a high spatial resolution. It could also help ecologists characterize the structure of forest stands in a quick and precise way.

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Section: Lidar and Optical Point Quadrat Methodsmentioning

confidence: 99%

“…However, the accuracy of parameters derived from TLS is promising (e.g. Danson et al 2007;Hosoi and Omasa 2007).…”

Section: Accuracy and Resolutionmentioning

confidence: 99%

Review of ground-based methods to measure the distribution of biomass in forest canopies

Seidel

Fleck

Leuschner

et al. 2011

Annals of Forest Science

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“…The ability of LADAR has led to the rapid development of laser scanner-based systems for a wide range of applications such as the modelling of architecture [32], forests [33], and engineering structures [34].…”

Section: Measuring Target Dimensionsmentioning

confidence: 99%

Automatic target recognition on land using three dimensional (3D) laser radar and artificial neural networks

Goztepe¹

2013

sajie

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During combat, measuring the dimensions of targets is extremely important for knowing when to fire on the enemy. The importance of identifying a known target on land emphasizes the importance of techniques devoted to automatic target recognition. Although a number of object-recognition techniques have been developed in the past, none of them have provided the desired specifics for unidentified target recognition. Studies on target recognition are largely based on images that assume that images of a known target can be readily viewed under any circumstance. But this is not true for military operations conducted on various terrains under specific circumstances. Usually it is not possible to capture images of unidentified objects because of weather, inadequate equipment, or concealment. In this study, a new approach that integrates neural networks and laser radar has been developed for automatic target recognition in order to reduce the abovementioned problems. Unlike current studies, the proposed model uses the geometric dimensions of unidentified targets in order to detect and recognise them under severe weather conditions. OPSOMMINGDie bepaling van teikenafmetings is van besondere belanggedurende gevegte sodat vuurtydstipte sodoende vasgelê kan word. In hierdie opsig word outomatiese uitkenning van teikentipe dus ook belangrik. Laasgenoemde tegnieke het desnieteenstaande nie besonder presteer met die uitkenning van vreemdeteikentipes nie. Terreintoestande, weersomstandighede, swak waarnemingstoerusting en kamoeflering speel in die verband ook 'n rol. Nuwerwetse toerusting wat gebruikmaak van neurale netwerke laserradar word voorgehou as 'n oplossing vir die vraagstuk onder uiteenlopende omgewingstoestande.

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“…The results obtained by individual authors depended mainly on a specific type of the scanner and its integration with other devices like ALS (CHASMER et al, 2006, as well as on the parameters of the tree stand itself (DANSON et al, 2006). TLS technology allows getting such taxation features and parameters of trees such as: the location of the tree, DBH, the tree (g) and stand (G) basal area, tree height, length and width of the tree crown, length of the branchless part of the trunk, canopy density, tree stem density, taper of the trunk and its curvature, the leaf area index (LAI), the angle of branches, trunk thickness, vertical profile of the variability of tree stands biomass, trunk quality (texture of wood fibres, damages), detection of understory and natural regeneration, etc.…”

Section: Introductionmentioning

confidence: 99%

“…TLS technology allows getting such taxation features and parameters of trees such as: the location of the tree, DBH, the tree (g) and stand (G) basal area, tree height, length and width of the tree crown, length of the branchless part of the trunk, canopy density, tree stem density, taper of the trunk and its curvature, the leaf area index (LAI), the angle of branches, trunk thickness, vertical profile of the variability of tree stands biomass, trunk quality (texture of wood fibres, damages), detection of understory and natural regeneration, etc. (ASCHOFF;SPIECKER, 2004;HOPKINSON et al, 2004;DANSON et al, 2006;BIENERT et al, 2007;MAAS et al, 2008). While the application of TLS technology has a limited range (usually forest inventory circle plots), the use of ALS refers to wide-area projects.…”

Section: Introductionmentioning

confidence: 99%

The integration of the terrestrial and airborne laser scanning technologies in the semi-automated process of retrieving selected trees and forest stand parameters / Integração das tecnologias terrestre e aerotransportada de scanner laser no processo semi-automático de recuperação de árvores selecionadas e de parâmetros de povoamentos florestais

Wężyk¹

2012

Ambiência

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The traditional forest inventory based usually on thousands of the circle plots (radius = 12.62m; area 500 sq m) set in regular network. On every inventory plot, the basic tree and forest stand parameters have to be collected using calliper and hypsometer or even human eye (i.e. crown closure) as well. The modern multifunctional forestry models requires more and more: dens and accurate data to deliver as fast as possible the precise information on the amount of wood stock or other selected forest stand and tree parameters. One of the promising remote sensing technologies is LiDAR collecting the 3D point cloud data. The TLS technology is very precise and fast but is limited to relatively small areas like forest inventory plot. The ALS is more focused on wide-area data collection. Both technologies are complementary, therefore it is a need for the fusion of those two sources of information to enhance the accuracy of tree parameters and enlarge the results for the wide forest areas with statistical models. Paper presents a method of the TLS and ALS point cloud registration and transformation to one coordinate system. The goal of the data fusion was the semi-automatic extraction of the trees selected parameter (height, DBH, basal area, crown closure, base of crown, 2D and 3D tree crown surface) of the TR2 transect in Niepolomice Forest (Krakow, Poland). The results showed that the big potential of the enhancement of height and crown closure or base of the crown exists.Key words: TLS; ALS; LiDAR point clouds integration; forest taxation parameters. ResumoO inventário florestal tradicional baseia-se normalmente em milhares de parcelas circulares (raio = 12,62 m, área 500 m²) dispostas em uma malha regular. Em cada parcela do inventário, os parâmetros básicos da árvore e do povoamento devem ser dados densos e acurados para gerar o mais rápido possível a informação precisa da quantidade de estoque de madeira ou outro parâmetro selecionado do povoamento e da árvore. Uma das tecnologias promissoras de sensoriamento remoto é o LiDAR coletando os dados da nuvem de pontos 3D. A tecnologia TLS é muito precisa e rápida mas limitada a áreas relativamente pequenas como as parcelas de inventários florestais. A ALS é mais focada na coleta de dados em grandes áreas. Ambas as tecnologias são complementares portanto, é necessário para a fusão das duas fontes de informação aumentar a acurácia dos parâmetros de árvore e ampliar os resultados para grandes áreas florestais com modelos estatísticos. O artigo apresenta um método de registro e transformação do TLS e nuvem de pontos do ALS para um sistema de coordenadas. O objetivo da fusão dos dados foi a extração semi automática de parâmetros selecionados de árvores (altura, DAP, área basal, densidade de copa, base da copa, área 2D e 3D da copa da árvore) do transecto TR2 da Floresta Niepolomice (Krakow, Polônia). Os resultados mostraram que o grande potencial do aprimoramento da altura e do densidade de copa ou da base da copa existe.Palavras-chave: TLS; ALS; integração da nuvem d...

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Forest Canopy Gap Fraction From Terrestrial Laser Scanning

Cited by 204 publications

References 10 publications

Review of ground-based methods to measure the distribution of biomass in forest canopies

Review of ground-based methods to measure the distribution of biomass in forest canopies

Automatic target recognition on land using three dimensional (3D) laser radar and artificial neural networks

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