Outlining patches dominated by different plants in wetland vegetation provides information on species succession, microhabitat patterns, wetland health and ecosystem services. Aerial photogrammetry and hyperspectral imaging are the usual data acquisition methods but the application of airborne laser scanning (ALS) as a standalone tool also holds promises for this field since it can be used to quantify 3-dimensional vegetation structure. Lake Balaton is a large shallow lake in western Hungary with shore wetlands that have been in decline since the 1970s. In August 2010, an ALS survey of the shores of Lake Balaton was completed with 1 pt/m² discrete echo recording. The resulting ALS dataset was processed to several output rasters describing vegetation and terrain properties, creating a sufficient number of independent variables for each raster cell to allow for basic multivariate classification. An expert-generated decision tree algorithm was applied to outline wetland areas, and within these, patches dominated by Typha sp. Carex sp., and Phragmites australis. Reed health was mapped into four categories: healthy, stressed, ruderal and die-back. The output map was tested against a set of 775 geo-tagged ground photographs and had a user’s accuracy of > 97% for detecting non-wetland features (trees, artificial surfaces and low density Scirpus stands), > 72% for dominant genus detection and > 80% for most reed health categories (with 62% for one category). Overall classification accuracy was 82.5%, Cohen’s Kappa 0.80, which is similar to some hyperspectral or multispectral-ALS fusion studies. Compared to hyperspectral imaging, the processing chain of ALS can be automated in a similar way but relies directly on differences in vegetation structure and actively sensed reflectance and is thus probably more robust. The data acquisition parameters are similar to the national surveys of several European countries, suggesting that these existing datasets could be used for vegetation mapping and monitoring.

Airborne laser scanning (ALS) is a widely used technique for the sampling of the earth's surface. Nowadays a wide range of ALS sensor systems with different technical specifications can be found. One parameter is the laser wavelength which leads to a sensitivity for the wavelength dependent backscatter characteristic of sensed surfaces. Current ALS sensors usually record next to the geometric information additional information on the recorded signal strength of each echo. In order to utilize this information for the study of the backscatter characteristic of the sensed surface, radiometric calibration is essential. This paper focuses on the radiometric calibration of multi-wavelength ALS data and is based on previous work on the topic of radiometric calibration of monochromatic (single-wavelength) ALS data. After a short introduction the theory and whole workflow for calibrating ALS data radiometrically based on in-situ reference surfaces is presented. Furthermore, it is demonstrated that this approach for the monochromatic calibration can be used for each channel of multi-wavelength ALS data. The resulting active multi-channel radiometric image does not have any shadows and from a geometric viewpoint the position of the objects on top of the terrain surface is not altered (the result is a multi-channel true orthophoto). Within this paper the approach is demonstrated by three different single-wavelength ALS data acquisition campaigns (532nm, 1064nm and 1550nm) covering the area of the city Horn (Austria). The results and practical issues are discussed.

The methods for reconstructing objects from dense point clouds, typically acquired by terrestrial laser scanning (TLS), are currently unsatisfying, especially from a geodetic perspective, for a number of reasons. Digitization in the point cloud lacks redundancy and no stochastic measures are provided generally. Reconstruction from primitives, e.g., planes, spheres, and cylinders, enforces strong requirements on (large) object parts, which are strictly valid only for very special cases. In this article we propose a new reconstruction technique for terrestrial laser scanning point clouds addressing those issues. In line with photogrammetric restitution, lines, not necessarily straight, are reconstructed in a process providing precision, i.e. internal quality measures. Methods for reconstructing different types of lines will be presented, in particular break lines, form lines, step edges, and boundary lines. The process features a high degree of automation, reducing the manual interaction to supplying one approximate point or one short segment per relevant line. The method will be presented with two examples.

The combination of laser scanning (LS, active, direct 3D measurement of the object surface) and photogrammetry (high geometric and radiometric resolution) is widely applied for object reconstruction (e.g. architecture, topography, monitoring, archaeology). Usually the results are a coloured point cloud or a textured mesh. The geometry is typically generated from the laser scanning point cloud and the radiometric information is the result of image acquisition. In the last years, next to significant developments in static (terrestrial LS) and kinematic LS (airborne and mobile LS) hardware and software, research in computer vision and photogrammetry lead to advanced automated procedures in image orientation and image matching. These methods allow a highly automated generation of 3D geometry just based on image data. Founded on advanced feature detector techniques (like SIFT (Scale Invariant Feature Transform)) very robust techniques for image orientation were established (cf. Bundler). In a subsequent step, dense multi-view stereo reconstruction algorithms allow the generation of very dense 3D point clouds that represent the scene geometry (cf. Patch-based Multi-View Stereo (PMVS2)). Within this paper the usage of mobile laser scanning (MLS) and simultaneously acquired image data for an advanced integrated scene reconstruction is studied. For the analysis the geometry of a scene is generated by both techniques independently. Then, the paper focuses on the quality assessment of both techniques. This includes a quality analysis of the individual surface models and a comparison of the direct georeferencing of the images using positional and orientation data of the on board GNSS-INS system and the indirect georeferencing of the imagery by automatic image orientation. For the practical evaluation a dataset from an archaeological monument is utilised. Based on the gained knowledge a discussion of the results is provided and a future strategy for the integration of both techniques is proposed.