scholarly journals Object-Based Point Cloud Analysis of Full-Waveform Airborne Laser Scanning Data for Urban Vegetation Classification

Sensors ◽  
2008 ◽  
Vol 8 (8) ◽  
pp. 4505-4528 ◽  
Author(s):  
Martin Rutzinger ◽  
Bernhard Höfle ◽  
Markus Hollaus ◽  
Norbert Pfeifer
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Vegetation Classification ◽  
Airborne Laser Scanning ◽  
Urban Vegetation ◽  
Full Waveform ◽  
Airborne Laser ◽  
Object Based ◽  
Point Cloud Analysis ◽  
Cloud Analysis

scholarly journals Regional Modeling of Forest Fuels and Structural Attributes Using Airborne Laser Scanning Data in Oregon

2021 ◽  
Vol 13 (2) ◽  
pp. 261
Author(s):  
Francisco Mauro ◽  
Andrew T. Hudak ◽  
Patrick A. Fekety ◽  
Bryce Frank ◽  
Hailemariam Temesgen ◽  
...  
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Field Measurements ◽  
Point Clouds ◽  
Semiparametric Models ◽  
Airborne Laser Scanning ◽  
Parametric Models ◽  
Fuel Load ◽  
Forest Fuels ◽  
Airborne Laser

Airborne laser scanning (ALS) acquisitions provide piecemeal coverage across the western US, as collections are organized by local managers of individual project areas. In this study, we analyze different factors that can contribute to developing a regional strategy to use information from completed ALS data acquisitions and develop maps of multiple forest attributes in new ALS project areas in a rapid manner. This study is located in Oregon, USA, and analyzes six forest structural attributes for differences between: (1) synthetic (i.e., not-calibrated), and calibrated predictions, (2) parametric linear and semiparametric models, and (3) models developed with predictors computed for point clouds enclosed in the areas where field measurements were taken, i.e., “point-cloud predictors”, and models developed using predictors extracted from pre-rasterized layers, i.e., “rasterized predictors”. Forest structural attributes under consideration are aboveground biomass, downed woody biomass, canopy bulk density, canopy height, canopy base height, and canopy fuel load. Results from our study indicate that semiparametric models perform better than parametric models if no calibration is performed. However, the effect of the calibration is substantial in reducing the bias of parametric models but minimal for the semiparametric models and, once calibrations are performed, differences between parametric and semiparametric models become negligible for all responses. In addition, minimal differences between models using point-cloud predictors and models using rasterized predictors were found. We conclude that the approach that applies semiparametric models and rasterized predictors, which represents the easiest workflow and leads to the most rapid results, is justified with little loss in accuracy or precision even if no calibration is performed.

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

2008 ◽  
Vol 35 (4) ◽  
pp. 882-893 ◽  
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

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

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

<p class="Bodytext">This PhD thesis addresses the development of full-waveform airborne laser scanning (ALS<sub>FW</sub>) 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 ALS<sub>FW</sub> metric values and on the estimate of forest attributes. Additionally, a new software tool to process ALS<sub>FW</sub> data is presented, which includes new metrics related to understory vegetation. On the other hand, occlusion caused by vegetation in the ALS<sub>FW</sub>, discrete airborne laser scanning (ALS<sub>D</sub>) and terrestrial laser scanning (TLS) signal is characterized along the vertical structure. Finally, understory vegetation density is detected and determined by ALS<sub>FW</sub> data, as well as characterized by using the new proposed metrics.</p>

scholarly journals ANALYSIS OF 3D BUILDING MODELS ACCURACY BASED ON THE AIRBORNE LASER SCANNING POINT CLOUDS

2018 ◽  
Vol XLII-2 ◽  
pp. 797-804 ◽  
Author(s):  
W. Ostrowski ◽  
M. Pilarska ◽  
J. Charyton ◽  
K. Bakuła
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Large Scale ◽  
Point Clouds ◽  
3D Models ◽  
Airborne Laser Scanning ◽  
Aerial Images ◽  
Statistical Parameters ◽  
Airborne Laser ◽  
Building Models

Creating 3D building models in large scale is becoming more popular and finds many applications. Nowadays, a wide term “3D building models” can be applied to several types of products: well-known CityGML solid models (available on few Levels of Detail), which are mainly generated from Airborne Laser Scanning (ALS) data, as well as 3D mesh models that can be created from both nadir and oblique aerial images. City authorities and national mapping agencies are interested in obtaining the 3D building models. Apart from the completeness of the models, the accuracy aspect is also important. Final accuracy of a building model depends on various factors (accuracy of the source data, complexity of the roof shapes, etc.). In this paper the methodology of inspection of dataset containing 3D models is presented. The proposed approach check all building in dataset with comparison to ALS point clouds testing both: accuracy and level of details. Using analysis of statistical parameters for normal heights for reference point cloud and tested planes and segmentation of point cloud provides the tool that can indicate which building and which roof plane in do not fulfill requirement of model accuracy and detail correctness. Proposed method was tested on two datasets: solid and mesh model.

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