This study investigates the potential of unmanned aerial vehicles (UAVs) to measure and monitor structural properties of forests. Two remote sensing techniques, airborne laser scanning (ALS) and structure from motion (SfM) were tested to capture three-dimensional structural information from a small multi-rotor UAV platform. A case study is presented through the analysis of data collected from a 30ˆ50 m plot in a dry sclerophyll eucalypt forest with a spatially varying canopy cover. The study provides an insight into the capabilities of both technologies for assessing absolute terrain height, the horizontal and vertical distribution of forest canopy elements, and information related to individual trees. Results indicate that both techniques are capable of providing information that can be used to describe the terrain surface and canopy properties in areas of relatively low canopy closure. However, the SfM photogrammetric technique underperformed ALS in capturing the terrain surface under increasingly denser canopy cover, resulting in point density of less than 1 ground point per m 2 and mean difference from ALS terrain surface of 0.12 m. This shortcoming caused errors that were propagated into the estimation of canopy properties, including the individual tree height (root mean square error of 0.92 m for ALS and 1.30 m for SfM). Differences were also seen in the estimates of canopy cover derived from the SfM (50%) and ALS (63%) pointclouds. Although ALS is capable of providing more accurate estimates of the vertical structure of forests across the larger range of canopy densities found in this study, SfM was still found to be an adequate low-cost alternative for surveying of forest stands.

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

In this study, we present a flexible, cost-effective, and accurate method to monitor landslides using a small unmanned aerial vehicle (UAV) to collect aerial photography. In the first part, we apply a Structure from Motion (SfM) workflow to derive a 3D model of a landslide in southeast Tasmania from multi-view UAV photography. The geometric accuracy of the 3D model and resulting DEMs and orthophoto mosaics was tested with ground control points coordinated with geodetic GPS receivers. A horizontal accuracy of 7 cm and vertical accuracy of 6 cm was achieved. In the second part, two DEMs and orthophoto mosaics acquired on 16 July 2011 and 10 November 2011 were compared to study landslide dynamics. The COSI-Corr image correlation technique was evaluated to quantify and map terrain displacements. The magnitude and direction of the displacement vectors derived from correlating two hillshaded DEM layers corresponded to a visual interpretation of landslide change. Results show that the algorithm can accurately map displacements of the toes, chunks of soil, and vegetation patches on top of the landslide, but is not capable of mapping the retreat of the main scarp. The conclusion is that UAV-based imagery in combination with 3D scene reconstruction and image correlation algorithms provide flexible and effective tools to map and monitor landslide dynamics.

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Abstract:In this study, we used an Unmanned Aerial Vehicle (UAV) to collect a time series of high-resolution images over four years at seven epochs to assess landslide dynamics. Structure from Motion (SfM) was applied to create Digital Surface Models (DSMs) of the landslide surface with an accuracy of 4-5 cm in the horizontal and 3-4 cm in the vertical direction. The accuracy of the co-registration of subsequent DSMs was checked and corrected based on comparing non-active areas of the landslide, which minimized alignment errors to a mean of 0.07 m. Variables such as landslide area and the leading edge slope were measured and temporal patterns were discovered. Volumetric changes of particular areas of the landslide were measured over the time series. Surface movement of the landslide was tracked and quantified with the COSI-Corr image correlation algorithm but without ground validation. Historical aerial photographs were used to create a baseline DSM, and the total displacement of the landslide was found to be approximately 6630 m 3 .This study has demonstrated a robust and repeatable algorithm that allows a landslide's dynamics to be mapped and monitored with a UAV over a relatively long time series.

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