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Commission VII, WG VII/7 ABSTRACT: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.

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/m 2 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 OPEN ACCESSRemote Sens. 2012, 4 1618 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.

Small-footprint airborne laser scanners with waveform-digitizing capabilities are becoming increasingly available. Waveform-digitizing laser scanners seize the physical measurement process in its entire complexity. This leads the way to the possibility of deriving the backscatter cross section which is a measure of the electromagnetic energy intercepted and reradiated by objects. The cross section can be obtained by firstly decomposing the echo waveform in several distinct echoes, whereas for each echo its range, amplitude and width are known. Then the radar equation can be used for calibrating the waveform measurements using external reference targets with known backscatter cross sections. The final outcome is a 3D point cloud where each point represents one scatterer with a given cross section and echo width. Using these physical attributes and various geometric criteria the point cloud can be segmented or classified. In this paper this procedure is demonstrated based on waveform measurements acquired by the RIEGL LMS-Q560 sensor. The cross section of the homogenous reference targets is estimated with a RIEGL reflectometer and Spectralon® targets.

<p><strong>Abstract.</strong> Single photon sensitive LiDAR sensors are currently competing with conventional multi-photon laser scanning systems. The advantage of the prior is the potentially higher area coverage performance, which comes at the price of an increased outlier rate and a lower ranging accuracy. In this contribution, the principles of both technologies are reviewed with special emphasis on their respective properties. In addition, a comparison of Single Photon LiDAR (SPL) and FullWaveform LiDAR data acquired in July and September 2018 in the City of Vienna are presented. From data analysis we concluded that (i) less flight strips are needed to cover the same area with comparable point density with SPL, (ii) the sharpness of the resulting 3D point cloud is higher for the waveform LiDAR dataset, (iii) SPL exhibits moderate vegetation penetration under leaf-on conditions, and (iv) the dispersion of the SPL point cloud assessed in smooth horizontal surface parts competes with waveform LiDAR but is higher by a factor of 2&amp;ndash;3 for inclined and grassy surfaces, respectively. Still, SPL yielded satisfactory precision measures mostly below 10&amp;thinsp;cm.</p>