Unmanned Aerial Vehicles (UAVs) are an exciting new remote sensing tool capable of acquiring high resolution spatial data. Remote sensing with UAVs has the potential to provide imagery at an unprecedented spatial and temporal resolution. The small footprint of UAV imagery, however, makes it necessary to develop automated techniques to geometrically rectify and mosaic the imagery such that larger areas can be monitored. In this paper, we present a technique for geometric correction and mosaicking of UAV photography using feature matching and Structure from Motion (SfM) photogrammetric techniques. Images are processed to create three dimensional point clouds, initially in an arbitrary model space. The point clouds are transformed into a real-world coordinate system using either a direct georeferencing technique that uses estimated camera positions or via a Ground Control Point (GCP) technique that uses automatically identified GCPs within the point cloud. The point cloud is then used to generate a Digital Terrain Model (DTM) required for rectification of the images. Subsequent georeferenced images are then joined together to form a mosaic of the study area. The absolute spatial accuracy of the direct technique was found to be 65-120 cm whilst the GCP technique achieves an accuracy of approximately 10-15 cm.

We present the development of a low-cost Unmanned Aerial Vehicle-Light Detecting and Ranging (UAV-LiDAR) system and an accompanying workflow to produce 3D point clouds. UAV systems provide an unrivalled combination of high temporal and spatial resolution datasets. The TerraLuma UAV-LiDAR system has been developed to take advantage of these properties and in doing so overcome some of the current limitations of the use of this technology within the forestry industry. A modified processing workflow including a novel trajectory determination algorithm fusing observations from a GPS receiver, an Inertial Measurement Unit (IMU) and a High Definition (HD) video camera is presented. The advantages of this workflow are demonstrated using a rigorous assessment of the spatial accuracy of the final point clouds. It is shown that due to the inclusion of video the horizontal accuracy of the final point cloud improves from 0.61 m to 0.34 m (RMS error assessed against ground control). The effect of the very high density point clouds (up to 62 points per m 2 ) produced by the UAV-LiDAR system on the measurement of tree location, height and crown width are also assessed by performing repeat surveys over individual isolated trees. The standard deviation of tree height is shown to reduce from 0.26 m, when using data with a density of 8 points per m 2 , to 0.15 m when the higher density data was used.Improvements in the uncertainty of the measurement of tree location, 0.80 m to 0.53 m, and crown width, 0.69 m to 0.61 m are also shown.Remote Sens. 2012, 4 1520

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[1] Hydrological processes cause variations in gravitational potential and surface deformations, both of which are detectable using space geodetic techniques. We computed elastic deformation using continental water load estimates derived from the Gravity Recovery and Climate Experiment and compared to 3D deformation estimated from GPS observations. The agreement is very good in areas where large hydrologic signals occur over broad spatial scales, with correlation in horizontal components as high as 0.9. Agreement is also observed at smaller scales, including across Europe. This suggests that: a) both techniques are perhaps more accurate than previously thought and b) a large percentage of the non-linear variations seen in our GPS time series are most likely related to geophysical processes rather than analysis error. Low correlation at some sites suggests that local processes or site specific analysis errors dominate the GPS deformation estimates rather than the broad-scale hydrologic signals detected by GRACE.

[1] Improvements in the analyses of Global Positioning System (GPS) observations yield resolvable millimeter to submillimeter differences in coordinate estimates, thus providing sufficient resolution to distinguish subtle differences in analysis methodologies. Here we investigate the effects on site coordinates of using different approaches to modeling atmospheric loading deformation (ATML) and handling of tropospheric delays. The rigorous approach of using the time-varying Vienna Mapping Function 1 yields solutions with lower noise at a range of frequencies compared with solutions generated using empirical mapping functions. This is particularly evident when ATML is accounted for. Some improvement also arises from using improved a priori zenith hydrostatic delays (ZHD), with the combined effect being site-specific. Importantly, inadequacies in both mapping functions and a priori ZHDs not only introduce time-correlated noise but significant periodic terms at solar annual and semiannual periods. We find no significant difference between solutions where nontidal ATML is applied at the observation level rather than as a daily averaged value, but failing to model diurnal and semidiurnal tidal ATML at the observation level can introduce anomalous propagated signals with periods that closely match the GPS draconitic annual ($351.4 days) and semiannual period ($175.7 days). Exacerbated by not fixing ambiguities, these signals are evident in both stacked and single-site power spectra, with each tide contributing roughly equally to the dominant semiannual peak. The amplitude of the propagated signal reaches a maximum of 0.8 mm with a clear latitudinal dependence that is not correlated directly with locations of maximum tidal amplitude.

[1] Within analyses of Global Positioning System (GPS) observations, unmodeled subdaily signals propagate into long-period signals via a number of different mechanisms. In this paper, we investigate the effects of time-variable satellite geometry and the propagation of a time-constant unmodeled multipath signal. Multipath reflectors at H = 0.1 m, 0.2 m, and 1.5 m below the antenna are modeled, and their effects on GPS coordinate time series are examined. Simulated time series at 20 global IGS sites for 2000.0-2008.0 were derived using the satellite geometry as defined by daily broadcast orbits. We observe the introduction of time-variable biases in the time series of up to several millimeters. The frequency and magnitude of the signal is dependent on site location and multipath source. When adopting realistic GPS observation geometries obtained from real data (e.g., including the influence of local obstructions and hardware specific tracking), we observe generally larger levels of coordinate variation. In these cases, we observe spurious signals across the frequency domain, including very high frequency abrupt changes (offsets) in addition to secular trends. Velocity biases of more than 0.5 mm/yr are evident at some sites. The propagated signal has noise characteristics that fall between flicker and random walk and shows spectral peaks at harmonics of the draconitic year for a GPS satellite (∼351 days). When a perfectly repeating synthetic constellation is used, the simulations show near-negligible time correlated noise highlighting that subtle variations in the GPS constellation can propagate multipath signals differently over time, producing significant temporal variations in time series.