Thomas Vögtle

Terrestrial laser scanning for estimating urban tree volume and carbon content

The acquisition of single-tree-related information is an important task, especially in urban areas where there is an increasing interest in standing carbon stock. Therefore, an easy and robust method was developed to extract the volume, diameter at breast height (DBH) and height of single trees from point clouds of terrestrial laser scanning. For data acquisition, nine trees were scanned from several positions and the resulting high-resolution point clouds (20–60 million points) were analysed by an algorithm based on voxel structure. First, noise reduction was carried out, followed by filling of voxels inside the stem and branches through the intersection of four orthogonal viewing directions. After the elimination of erroneously generated fillings, volume was determined layer-wise for each cross section. For quality assessment, nine deciduous trees were selected, cut, weighed and analysed for wet specific gravity and carbon content in order to provide a control value. The estimated volumes agree with the control value within a range of –5.1% to +14.3%. This is also the case with DBH values; however, heights are systematically underestimated.

An approach for the extraction of settlement areas

This paper describes a new approach to extract settlement areas from satellite images. The main aspect is an integrated knowledge processing (data fusion) of image information and digital topographic databases, which are currently built up in many countries (e.g. ATKIS-DLM200 in Germany). A topographical database offers a geometric as well as a semantic description for every object in the satellite image, but possibly not up-to-date. Therefore, if we suppose that the majority of objects has not changed in real world the system can “learn” the actual manifestation of relevant object features by a robust estimation. The result of this feature extraction is a symbolic description (segments plus attributes) of the image content, in this case all candidates for settlement areas.

Fusion of 3D building models derived from first and last pulse laserscanning data

Abstract The work presented in this article is part of a project of collaborative research center 461: “Strong earthquakes”. Geometric modelling of buildings in urban areas, detecting and interpreting their changes is necessary to obtain fast damage information after earthquakes as important input for a disaster management system. Airborne laserscanning was chosen as data basis for this application due to its advantages like extensive independence of weather and lighting conditions, high accuracy and measurement density. Investigations have shown that building models created by means of laserscanning data have some systematic deviations compared to accurate reference models. Modern laserscanning sensors are able to record first signal response (first pulse) and last response (last pulse) simultaneously. Dependent on these modes the derived building models show system inherent differences: a principle enlargement using first pulse mode and an analogous reduction in last pulse mode. Their amount is dependent on the flights parameters and the resolution of the derived data set, here the enlargement was about +1.2 m and the reduction about −1.2 m. It will be demonstrated that a fusion of two models of a building, derived on the basis of first and last pulse data respectively, leads to an enormous accuracy improvement. At this state the fusion process is—according to the used quality assessment—divided into a positioning and height component. An algorithm for the determination of a centerline between the borderlines of the two building models will be presented. This line is used for adjustment of the position. For adjustment of the height, i.e. the roof structure, vertical distances inside corresponding planes of the two models are used to calculate a new adjusted one. The final building modelling is based on the intersection of these adjusted planes. The resulting building models fit much better to their related reference models. The mean positioning deviation decreases to ≈0.25 m, while the deviations in height are estimated between ≈0.02 m (flat roofs) and 0.10 m (steep sloped roofs). This significantly improved accuracy level lies below the accuracy of original laserscanning data and therefore enables, beside the recognition of building damages, a great variety of applications in urban environment like generation of 3D city models, e.g. for planning purposes.

An Approach for the Detection of Damages in Buildings from Digital Aerial Information

Management procedures in general, and disaster management in particular, depend on information. The central questions are however, which information is necessary in order to draw for a particular decision, when is this information needed and at which resolution it is required. “Resolution” in this context is defined as the “level of detail”. Quality of management is thus directly related to the quality of information.

Two Methods to Estimate the Spot Size of Terrestrial Laser Scanners

This technical note presents the results of a study focused on the evaluation of laser beam divergence and its effect on the spot size of terrestrial laser scanners. For this purpose, experiments were accomplished using the Leica HDS3000 and HDS6000 laser scanners. Two methods for the evaluation of the spot size are compared: a direct method, based on the analysis of the capacity of the laser beam to go through small slots, and an analytical method that estimates the spot diameter based on the edge effect. Based on the performed experiments, the development of a conceptual model of the edge effect at sharp edges is described for each method and validated. The experiments proved that the methods are suitable to analyze the variation of the diameter of the spot as a function of the distance. The estimate of the spot size using slots of varying sizes is a direct and easy to use method, but the computation of the spot size using the sharp edge model is more stable in relation to the point density, and can be ...

The potential of runoff-farming in the Sahel region: Developing a methodology to identify suitable areas

Runoff-farming, a water harvesting technique, is the diversion of rain water from a collecting area to a cropping area, thereby increasing the quantity of water available for crop growth. This technique, applicable in various types, is regarded as necessary to secure and increase food production in semi-arid regions. Simultaneously it minimizes water erosion risk where it is applied. As the potential of runoff-farming has not been investigated, a methodology had to be developed to enable planners to identify suitable areas the most efficient way. Five main information sources are tapped: ground checks by various specialists, maps, data, aerial photographs and satellite images. The interpretation of the collected data yields in the quantification of the runoff water, the water requirements of the crops and the water storage capacity of the soil. All factors have to be validated and integrated into a decision model.