Gottfried Mandlburger

OPALS – A framework for Airborne Laser Scanning data analysis

Abstract A framework for Orientation and Processing of Airborne Laser Scanning point clouds, OPALS, is presented. It is designed to provide tools for all steps starting from full waveform decomposition, sensor calibration, quality control, and terrain model derivation, to vegetation and building modeling. The design rationales are discussed. The structure of the software framework enables the automatic and simultaneous building of command line executables, Python modules, and C++ classes from a single algorithm-centric repository. It makes extensive use of (industry-) standards as well as cross-platform libraries. The framework provides data handling, logging, and error handling. Random, high-performance run-time access to the originally acquired point cloud is provided by the OPALS data manager, allowing storage of billions of 3D-points and their additional attributes. As an example geo-referencing of laser scanning strips is presented.

Quantification of Overnight Movement of Birch (Betula pendula) Branches and Foliage with Short Interval Terrestrial Laser Scanning

The goal of the study was to determine circadian movements of silver birch (Petula Bendula) branches and foliage detected with terrestrial laser scanning (TLS). The study consisted of two geographically separate experiments conducted in Finland and in Austria. Both experiments were carried out at the same time of the year and under similar outdoor conditions. Experiments consisted of 14 (Finland) and 77 (Austria) individual laser scans taken between sunset and sunrise. The resulting point clouds were used in creating a time series of branch movements. In the Finnish data, the vertical movement of the whole tree crown was monitored due to low volumetric point density. In the Austrian data, movements of manually selected representative points on branches were monitored. The movements were monitored from dusk until morning hours in order to avoid daytime wind effects. The results indicated that height deciles of the Finnish birch crown had vertical movements between -10.0 and 5.0 cm compared to the situation at sunset. In the Austrian data, the maximum detected representative point movement was 10.0 cm. The temporal development of the movements followed a highly similar pattern in both experiments, with the maximum movements occurring about an hour and a half before (Austria) or around (Finland) sunrise. The results demonstrate the potential of terrestrial laser scanning measurements in support of chronobiology.

Complementing airborne laser bathymetry with UAV-based lidar for capturing alluvial landscapes

In this paper we report on a flight experiment employing airborne laser bathymetry (ALB) and unmanned aerial vehicle (UAV) based laser scanning (ULS) for capturing very high resolution topography of shallow water areas and the surrounding littoral zone at the pre-alpine Pielach River in Austria. The aim of the research is to assess how information gained from non-bathymetric, ultra-high resolution ULS can support the ALB data. We focus first on the characterization of the water surface of a lowland river and provide validation results using the data of a topographic airborne laser scanning (ALS) sensor and a low flying ULS system. By repeat ULS survey of a the meandering river reach we are able to quantify short-term water level changes due to surface waves in high resolution. Based on a hydrodynamic-numerical (HN) model we assess the accuracy of the water surface derived from a water penetrating ALB sensor. In the second part of the paper we investigate the ability of ALB, ALS, and ULS to describe the complex topography and vegetation structure of the alluvial area. This is carried out by comparing the Digital Terrain Models (DTM) derived from different sensor configurations. Finally we demonstrate the potential of ULS for estimating single tree positions and stem diameters for detailed floodplain roughness characterization in HN simulations. The key findings are: (i) NIR scan data from ALS or ULS provide more precise water level height estimates (no bias, 1σ: 2 cm) compared to ALB (bias: 3 cm, 1σ: 4 cm), (ii) within the studied reach short-term water level dynamics irrelevant for ALB data acquisition considering a 60 cm footprint diameter, and (iii) stem diameters can be estimated based on ULS point clouds but not from ALS and ALB.

Experimental evaluation of ALS point cloud ground extraction tools over different terrain slope and land-cover types

The article presents an evaluation of different terrain point extraction algorithms for airborne laser scanning (ALS) point clouds. The research area covers eight test sites with varying point densities in the range 3–15 points m−2 and different surface topography as well as land-cover characteristics. In this article, existing implementations of algorithms were considered. Approaches that are based on mathematical morphology, progressive densification, robust surface interpolation, and segmentation are compared. The results are described based on qualitative and quantitative analyses. A quantification of the qualitative analyses is presented and applied to the data sets in this example. The achieved results show that the analysed algorithms give classification accuracy depending on the landscape and land cover. Although the results for flat and mountainous areas as well as for sparse and dense vegetation are in line with previous tests, this analysis provides an overview of situations in which the quantitative evaluation is not enough to correctly assess the classification results.

A Case Study of UAS Borne Laser Scanning for Measurement of Tree Stem Diameter

Diameter at breast height (DBH) is one of the most important parameter in forestry. With increasing use of terrestrial and airborne laser scanning in forestry, new exceeding possibilities to directly derive DBH emerge. In particular, high resolution point clouds from laser scanners on board unmanned aerial systems (UAS) are becoming available over forest areas. In this case study, DBH estimation from a UAS point cloud based on modeling the relevant part of the tree stem with a cylinder, is analyzed with respect to accuracy and completeness. As reference, manually measured DBHs and DBHs from terrestrial laser scanning point clouds are used for comparison. We demonstrate that accuracy and completeness of the cylinder fit are depending on the stem diameter. Stems with DBH > 20 cm feature almost 100% successful reconstruction with relative differences to the reference DBH of 9% (DBH 20–30 cm) down to 1.8% for DBH > 40 cm.

Airborne laser scanning – high quality digital terrain modelling

After a short introduction into the Airborne Laser Scanning (ALS), the sampling process and a brief comparison of ALS with other data acquisition methods, the paper focuses on the generation of high quality digital terrain models from ALS data. In the first part the hierarchic robust interpolation method for the automatic filtering and classification, respectively, of an ALS point cloud into terrain and off-terrain (points on buildings or on the vegetation, etc.) is presented. Then, an approach for the modelling of linear features, such as break lines, based on the ALS data is described. Based on the feature lines and the classified terrain points a high quality DTM can be determined. For many subsequent processes the data reduction of the detailed DTM is essential in order to handle the large amount of data. Therefore, the paper presents a data reduction method that guaranties a certain DTM representation quality. Next to the presented algorithms, for each of the mentioned topics, references to other published methods are provided. Furthermore, beside the description of the algorithms, practical examples demonstrate the results of the algorithms. At the end of the paper, an outlook section discusses the advanced possibilities of DTM determination based on full-waveform ALS data.

Roughness Spectra Derived from Multi-Scale LiDAR Point Clouds of a Gravel Surface: A Comparison and Sensitivity Analysis

The roughness spectrum (i.e., the power spectral density) is a derivative of digital terrain models (DTMs) that is used as a surface roughness descriptor in many geomorphological and physical models. Although light detection and ranging (LiDAR) has become one of the main data sources for DTM calculation, it is still unknown how roughness spectra are affected when calculated from different LiDAR point clouds, or when they are processed differently. In this paper, we used three different LiDAR point clouds of a 1 m × 10 m gravel plot to derive and analyze the roughness spectra from the interpolated DTMs. The LiDAR point clouds were acquired using terrestrial laser scanning (TLS), and laser scanning from both an unmanned aerial vehicle (ULS) and an airplane (ALS). The corresponding roughness spectra are derived first as ensemble averaged periodograms and then the spectral differences are analyzed with a dB threshold that is based on the 95% confidence intervals of the periodograms. The aim is to determine scales (spatial wavelengths) over which the analyzed spectra can be used interchangeably. The results show that one TLS scan can measure the roughness spectra for wavelengths larger than 1 cm (i.e., two times its footprint size) and up to 10 m, with spectral differences less than 0.65 dB. For the same dB threshold, the ULS and TLS spectra can be used interchangeably for wavelengths larger than about 1.2 dm (i.e., five times the ULS footprint size). However, the interpolation parameters should be optimized to make the ULS spectrum more accurate at wavelengths smaller than 1 m. The plot size was, however, too small to draw particular conclusions about ALS spectra. These results show that novel ULS data has a high potential to replace TLS for roughness spectrum calculation in many applications.