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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.

A new generation of laser scanners mounted on Unmanned Aerial Vehicles (UAVs) have the potential to provide highquality point clouds of comparatively small areas (a few hectares). The high maneuverability of the UAVs, a typically large field of view of the laser scanners, and a comparatively small measurement range lead to point clouds with very high point density, less occlusions, and low measurement noise. However, due to the limited payload of UAVs, lightweight navigation sensors with a moderate level of accuracy are used to estimate the platform's trajectory. As a consequence, the georeferencing quality of the point clouds is usually sub-optimal; for this, strip adjustment can be performed. The main goal of strip adjustment is to simultaneously optimize the relative and absolute orientation of the strip-wise collected point clouds. This is done by fully re-calibrating the laser scanning system and by correcting systematic measurement errors of the trajectory. In this paper, we extend our previous work on the topic of strip adjustment by the estimation of time-dependent trajectory errors. The errors are thereby modelled by natural cubic splines with constant segment length in time domain. First results confirm the suitability of this flexible correction model by reducing the relative and absolute strip discrepancies to 1.38 cm and 1.65 cm, respectively.

Terrestrial laser scanning can provide high-resolution, two-dimensional sampling of soil surface roughness. While previous studies demonstrated the usefulness of these roughness measurements in geophysical applications, questions about the number of required scans and their resolution were not investigated thoroughly. Here, we suggest a method to generate digital elevation models, while preserving the surface's stochastic properties at high frequencies and additionally providing an estimate of their spatial resolution. We also study the impact of the number and positions of scans on roughness indices' estimates. An experiment over a smooth and isotropic soil plot accompanies the analysis, where scanning results are compared to results from active triangulation. The roughness measurement conditions for ideal sampling are revisited and updated for diffraction-limited sampling valid for close-range laser scanning over smooth and isotropic soil roughness. Our results show that terrestrial laser scanning can be readily used for roughness assessment on scales larger than 5 cm, while for smaller scales, special processing is required to mitigate the effect of the laser beam footprint. Interestingly, classical roughness parametrization (correlation length, root mean square height (RMSh)) was not sensitive to these effects. Furthermore, comparing the classical roughness parametrization between one-and four-scan setups shows that the one-scan data can replace the four-scan setup with a relative loss of accuracy below 1% for ranges up to 3 m and incidence angles no larger than 50• , while two opposite scans can replace it over the whole plot. The incidence angle limit Remote Sens. 2015, 7 2008for the spectral slope is even stronger and is 40• . These findings are valid for scanning over smooth and isotropic soil roughness.

ABSTRACT:Unmanned aerial vehicles (UAV) are a promising platform for close range airborne photogrammetry. Next to the possibility of carrying certain sensor equipment, different on board navigation components may be integrated. These devices are getting, due to recent developments in the field of electronics, smaller and smaller and are easily affordable. Therefore, UAV platforms are nowadays often equipped with several navigation devices in order to support the remote control of a UAV. Furthermore, these devices allow an automated flight mode that allows to systematically sense a certain area or object of interest. However, next to their support for the UAV navigation they allow the direct georeferencing of synchronised sensor data. This paper introduces the direct georeferencing of airborne UAV images with a low cost solution based on a quadrocopter. The system is equipped with a Global Navigation Satellite System (GNSS), an Inertial Measurement Unit (IMU), an air pressure sensor, a magnetometer, and a small compact camera. A challenge using light weight consumer-grade sensors is the acquisition of high quality images with respect to brightness and sharpness. It is demonstrated that an appropriate solution for data synchronisation and data processing allows a direct georeferencing of the acquired images with a precision below 1 m in each coordinate. The precision for roll and pitch is below 1• and for the yaw it is 2.5• . The evaluation is based on image positions estimated based on the on board sensors and compared to an independent bundle block adjustment of the images.