Norbert Pfeifer

Determination of terrain models in wooded areas with airborne laser scanner data

Large-scale terrain measurement in wooded areas was an unsolved problem up to now. Laser scanning solves this problem to a large extent. In this article, the characteristics of laser scanning will be compared to photogrammetry with reference to a big pilot project. Laser scanning supplies data with a skew distribution of errors because a portion of the supplied points is not on the terrain but on the treetops. Thus, the usual interpolation and filtering has to be adapted to this new data type. We will report on the implementation of this new method. The results are in accordance with the expectations. The geomorphologic quality of the contours, computed from a terrain model derived from laser scanning, needs to be improved. Solutions are still to be found.

A Comprehensive Automated 3D Approach for Building Extraction, Reconstruction, and Regularization from Airborne Laser Scanning Point Clouds

Three dimensional city models are necessary for supporting numerous management applications. For the determination of city models for visualization purposes, several standardized workflows do exist. They are either based on photogrammetry or on LiDAR or on a combination of both data acquisition techniques. However, the automated determination of reliable and highly accurate city models is still a challenging task, requiring a workflow comprising several processing steps. The most relevant are building detection, building outline generation, building modeling, and finally, building quality analysis. Commercial software tools for building modeling require, generally, a high degree of human interaction and most automated approaches described in literature stress the steps of such a workflow individually. In this article, we propose a comprehensive approach for automated determination of 3D city models from airborne acquired point cloud data. It is based on the assumption that individual buildings can be modeled properly by a composition of a set of planar faces. Hence, it is based on a reliable 3D segmentation algorithm, detecting planar faces in a point cloud. This segmentation is of crucial importance for the outline detection and for the modeling approach. We describe the theoretical background, the segmentation algorithm, the outline detection, and the modeling approach, and we present and discuss several actual projects.

A Comparison of Evaluation Techniques for Building Extraction From Airborne Laser Scanning

In this paper, different methods for the evaluation of building detection algorithms are compared. Whereas pixel-based evaluation gives estimates of the area that is correctly classified, the results are distorted by errors at the building outlines. These distortions are potentially in an order of 30%. Object-based evaluation techniques are less affected by such errors. However, the performance metrics thus delivered are sometimes considered to be less objective, because the definition of a ldquocorrect detectionrdquo is not unique. Based on a critical review of existing performance metrics, selected methods for the evaluation of building detection results are presented. These methods are used to evaluate the results of two different building detection algorithms in two test sites. A comparison of the evaluation techniques shows that they highlight different properties of the building detection results. As a consequence, a comprehensive evaluation strategy involving quality metrics derived by different methods is proposed.

Three-dimensional reconstruction of stems for assessment of taper, sweep and lean based on laser scanning of standing trees

A method and algorithm for reconstructing the three-dimensional (3D) surface of stems based on terrestrial laser scanner data from standing trees is presented. Laser scanning delivers a dense cloud of points, and this raw point data are filtered for deriving a digital terrain model and subsequent fitting of a parametric stem model. The stem model is made up of a sequence of successive cylinders that overlap in space; each cylinder is parameterized by its orientation and radius. The model is estimated iteratively from a given starting point and by adding cylinder segments. Successive segments are added whenever criteria on deviation in orientation and radius relative to the previous cylinder and a fit statistic to the point data are met. The method has proven applicable when applied to a European beech tree and a wild cherry tree from dense forest stands. The use of the resulting 3D reconstruction of tree stems in respect to diameter in breast height and height of crown base calculation, as well as taper, sweep and lean assessment of standing trees, is described. Finally, desirable future improvements to the basic algorithm are discussed.

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

Airborne laser scanning (ALS) is a remote sensing technique well-suited for 3D vegetation mapping and structure characterization because the emitted laser pulses are able to penetrate small gaps in the vegetation canopy. The backscattered echoes from the foliage, woody vegetation, the terrain, and other objects are detected, leading to a cloud of points. Higher echo densities (>20 echoes/m2) and additional classification variables from full-waveform (FWF) ALS data, namely echo amplitude, echo width and information on multiple echoes from one shot, offer new possibilities in classifying the ALS point cloud. Currently FWF sensor information is hardly used for classification purposes. This contribution presents an object-based point cloud analysis (OBPA) approach, combining segmentation and classification of the 3D FWF ALS points designed to detect tall vegetation in urban environments. The definition tall vegetation includes trees and shrubs, but excludes grassland and herbage. In the applied procedure FWF ALS echoes are segmented by a seeded region growing procedure. All echoes sorted descending by their surface roughness are used as seed points. Segments are grown based on echo width homogeneity. Next, segment statistics (mean, standard deviation, and coefficient of variation) are calculated by aggregating echo features such as amplitude and surface roughness. For classification a rule base is derived automatically from a training area using a statistical classification tree. To demonstrate our method we present data of three sites with around 500,000 echoes each. The accuracy of the classified vegetation segments is evaluated for two independent validation sites. In a point-wise error assessment, where the classification is compared with manually classified 3D points, completeness and correctness better than 90% are reached for the validation sites. In comparison to many other algorithms the proposed 3D point classification works on the original measurements directly, i.e. the acquired points. Gridding of the data is not necessary, a process which is inherently coupled to loss of data and precision. The 3D properties provide especially a good separability of buildings and terrain points respectively, if they are occluded by vegetation.

Automatic Roof Plane Detection and Analysis in Airborne Lidar Point Clouds for Solar Potential Assessment

A relative height threshold is defined to separate potential roof points from the point cloud, followed by a segmentation of these points into homogeneous areas fulfilling the defined constraints of roof planes. The normal vector of each laser point is an excellent feature to decompose the point cloud into segments describing planar patches. An object-based error assessment is performed to determine the accuracy of the presented classification. It results in 94.4% completeness and 88.4% correctness. Once all roof planes are detected in the 3D point cloud, solar potential analysis is performed for each point. Shadowing effects of nearby objects are taken into account by calculating the horizon of each point within the point cloud. Effects of cloud cover are also considered by using data from a nearby meteorological station. As a result the annual sum of the direct and diffuse radiation for each roof plane is derived. The presented method uses the full 3D information for both feature extraction and solar potential analysis, which offers a number of new applications in fields where natural processes are influenced by the incoming solar radiation (e.g., evapotranspiration, distribution of permafrost). The presented method detected fully automatically a subset of 809 out of 1,071 roof planes where the arithmetic mean of the annual incoming solar radiation is more than 700 kWh/m2.

Neighborhood Systems for Airborne Laser Data

Analysis of common neighborhood definitions for airborne laser data, triangulation or raster-based, reveals deficiencies in modeling the measured objects. Concepts that originate from 2D data structures are used for modeling complex 3D objects and for handling datasets with different point densities. Realizing these shortcomings, this paper proposes a new neighborhood system for airborne laser data. Based on laser data characteristics the proposed systems consider, among other features, point density, layered and overhanging structures, and local surface trends. Parameters for the proposed systems are derived from theoretical and practical observations. The paper demonstrates the type of neighborhood that is established by the proposed systems, and shows that artifacts that are usually created by the common neighborhoods are avoided here, and that structures within the data that are usually masked are revealed. The paper demonstrates how subsequent applications benefit from the new system. Finally, the estimation of surface normals by the proposed systems is compared to the triangulation; results show a significant improvement in the reliability and quality of the estimation.

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.

Single and two epoch analysis of ICESat full waveform data over forested areas

Analysis of full‐waveform pulses from space‐based laser altimeter systems are expected to improve our ability of measuring forests globally. Moreover, with the increase in the number of waveform data sets, it is now possible to study temporal changes in waveform returns over the same spatial domain. ICESat full waveform data from two epochs, i.e. winter and summer (2003) along near‐coincident ground tracks, are studied. Data analysis methods are discussed, including normalization and matching of near‐coincident waveforms, Gaussian decomposition, and derivation of forest measurement and forest change parameters. We quantify differences between winter and summer waveforms, acquired over broad‐leaved, mixed‐wood, and needle‐leaved forests in Europe. The results indicate that, although maximum tree height barely changes over six months, i.e. <2.2% for all three cover types, the Height of Median Energy (HOME) changed most in broad‐leaved (a 148% change) and least for conifers (a 36% change, winter to summer). Ratios of ground energy to canopy energy of normalized waveforms also changed noticeably over time: 67% in broad‐leaved, 62% in mixed‐wood, and 47% in conifers. Attempts are made to differentiate and classify these three cover types on the basis of these and other canopy metrics. The initial results, with a coefficient κ of agreement between reference and classified data of 0.57, provide a baseline against which improvements in data and methodology can be gauged.

Forest Delineation Based on Airborne LIDAR Data

The delineation of forested areas is a critical task, because the resulting maps are a fundamental input for a broad field of applications and users. Different national and international forest definitions are available for manual or automatic delineation, but unfortunately most definitions lack precise geometrical descriptions for the different criteria. A mandatory criterion in forest definitions is the criterion of crown coverage (CC), which defines the proportion of the forest floor covered by the vertical projection of the tree crowns. For loosely stocked areas, this criterion is especially critical, because the size and shape of the reference area for calculating CC is not clearly defined in most definitions. Thus current forest delineations differ and tend to be non-comparable because of different settings for checking the criterion of CC in the delineation process. This paper evaluates a new approach for the automatic delineation of forested areas, based on airborne laser scanning (ALS) data with a clearly defined method for calculating CC. The new approach, the ‘tree triples’ method, is based on defining CC as a relation between the sum of the crown areas of three neighboring trees and the area of their convex hull. The approach is applied and analyzed for two study areas in Tyrol, Austria. The selected areas show a loosely stocked forest at the upper timberline and a fragmented forest on the hillside. The fully automatic method presented for delineating forested areas from ALS data shows promising results with an overall accuracy of 96%, and provides a beneficial tool for operational applications.