Evaluation automatischer Einzelbaumerkennung aus luftgestützten Laserscanning-Daten

Evaluation of automated single-tree recognition from airborne laser scanning data In the present study, we investigated whether the detection tool FINT (Find Individual Trees) can identify single trees out of canopy height models (CHM) precisely enough to assess the protective effect of forests, even on steep slopes. For this purpose, reference trees were measured and described in twelve randomly selected sample plots in the Bündner Herrschaft and Schanfigg regions (Canton Graubünden, Switzerland). CHMs of different resolution and smoothing were generated from airborne laser scanning data for each sample plot and subsequently processed with FINT. In addition, we tested whether the use of a model that defines the minimum distance between a tree and its neighbours based on its height (MBA model) improved the quality of the results. The study showed that a finer-resolution CHM combined with stronger smoothing produced results comparable to those obtained with an unsmoothed and lower-resolution CHM. The smallest difference between the numbers of trees measured and detected was achieved with the 1-m resolution CHM, with no smoothing and no MBA model. In conclusion, FINT can provide a basis for assessing the protective effect of a forest with its existing structures, and its results – after evaluation in the field – can be directly integrated into natural hazard simulation models.

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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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Accuracy of Ground Surface Interpolation from Airborne Laser Scanning (ALS) Data in Dense Forest Cover

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi9040224 ◽

2020 ◽

Vol 9 (4) ◽

pp. 224

Author(s):

Mihnea Cățeanu ◽

Arcadie Ciubotaru

Keyword(s):

Thin Plate ◽

Laser Scanning ◽

Ground Surface ◽

Forest Vegetation ◽

Airborne Laser Scanning ◽

Canopy Density ◽

Digital Terrain ◽

Airborne Laser ◽

Dense Forest

A digital model of the ground surface has many potential applications in forestry. Nowadays, Light Detection and Ranging (LiDAR) is one of the main sources for collecting morphological data. Point clouds obtained via laser scanning are used for modelling the ground surface by interpolation, a process which is affected by various errors. Using LiDAR data to collect ground surface data for forestry applications is a challenging scenario because the presence of forest vegetation will hinder the ability of laser pulses to reach the ground. The density of ground observations will be therefore reduced and not homogenous (as it is affected by the variations in canopy density). Furthermore, forest areas are generally present in mountainous areas, in which case the interpolation of the ground surface is more challenging. In this paper, we present a comparative analysis of interpolation accuracy for nine algorithms, which are used for generating Digital Terrain Models from Airborne Laser Scanning (ALS) data, in mountainous terrain covered by dense forest vegetation. For most of the algorithms we find a similar performance in terms of general accuracy, with RMSE values between 0.11 and 0.28 m (when model resolution is set to 0.5 m). Five of the algorithms (Natural Neighbour, Delauney Triangulation, Multilevel B-Spline, Thin-Plate Spline and Thin-Plate Spline by TIN) have vertical errors of less than 0.20 m for over 90 percent of validation points. Meanwhile, for most algorithms, major vertical errors (of over 1 m) are associated with less than 0.05 percent of validation points. Digital Terrain Model (DTM) resolution, ground slope and point cloud density influence the quality of the ground surface model, while for canopy density we find a less significant link with the quality of the interpolated DTMs.

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

Remote Sensing ◽

10.3390/rs8090729 ◽

2016 ◽

Vol 8 (9) ◽

pp. 729 ◽

Cited By ~ 11

Author(s):

Lin Cao ◽

Sha Gao ◽

Pinghao Li ◽

Ting Yun ◽

Xin Shen ◽

...

Keyword(s):

Aboveground Biomass ◽

Laser Scanning ◽

Airborne Laser Scanning ◽

Biomass Estimation ◽

Planted Forest ◽

Full Waveform ◽

Airborne Laser ◽

Individual Trees

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Effects of pulse density on predicting characteristics of individual trees of Scandinavian commercial species using alpha shape metrics based on airborne laser scanning data

Canadian Journal of Remote Sensing ◽

10.5589/m08-052 ◽

2008 ◽

Vol 34 (sup2) ◽

pp. S441-S459 ◽

Cited By ~ 38

Author(s):

Jari Vauhkonen ◽

Timo Tokola ◽

Matti Maltamo ◽

Petteri Packalén

Keyword(s):

Laser Scanning ◽

Airborne Laser Scanning ◽

Alpha Shape ◽

Airborne Laser ◽

Pulse Density ◽

Commercial Species ◽

Individual Trees ◽

Shape Metrics

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Quality of Buildings Extracted from Airborne Laser Scanning Data

Topographic Laser Ranging and Scanning ◽

10.1201/9781420051438.ch19 ◽

2008 ◽

pp. 535-574 ◽

Cited By ~ 1

Author(s):

Eberhard Gülch ◽

Harri Kaartinen ◽

Juha Hyyppä

Keyword(s):

Laser Scanning ◽

Airborne Laser Scanning ◽

Airborne Laser

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Classification of tree species composition using a combination of multispectral imagery and airborne laser scanning data

Central European Forestry Journal ◽

10.1515/forj-2017-0002 ◽

2017 ◽

Vol 63 (1) ◽

pp. 1-9 ◽

Cited By ~ 2

Author(s):

Maroš Sedliak ◽

Ivan Sačkov ◽

Ladislav Kulla

Keyword(s):

Species Composition ◽

Tree Species ◽

Laser Scanning ◽

Airborne Laser Scanning ◽

Tree Species Composition ◽

Object Based Image Analysis ◽

Airborne Laser ◽

Object Based ◽

Individual Trees

AbstractRemote Sensing provides a variety of data and resources useful in mapping of forest. Currently, one of the common applications in forestry is the identification of individual trees and tree species composition, using the object-based image analysis, resulting from the classification of aerial or satellite imagery. In the paper, there is presented an approach to the identification of group of tree species (deciduous - coniferous trees) in diverse structures of close-to-nature mixed forests of beech, fir and spruce managed by selective cutting. There is applied the object-oriented classification based on multispectral images with and without the combination with airborne laser scanning data in the eCognition Developer 9 software. In accordance to the comparison of classification results, the using of the airborne laser scanning data allowed identifying ground of terrain and the overall accuracy of classification increased from 84.14% to 87.42%. Classification accuracy of class “coniferous” increased from 82.93% to 85.73% and accuracy of class “deciduous” increased from 84.79% to 90.16%.

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