Sensor miniaturisation, improved battery technology and the availability of low-cost yet advanced Unmanned Aerial Vehicles (UAV) have provided new opportunities for environmental remote sensing. The UAV provides a platform for close-range aerial photography. Detailed imagery captured from micro-UAV can produce dense point clouds using multi-view stereopsis (MVS) techniques combining photogrammetry and computer vision. This study applies MVS techniques to imagery acquired from a multi-rotor micro-UAV of a natural coastal site in southeastern Tasmania, Australia. A very dense point cloud (<1-3 cm point spacing) is produced in an arbitrary coordinate system using full resolution imagery, whereas other studies usually downsample the original imagery. The point cloud is sparse in areas of complex vegetation and where surfaces have a homogeneous texture. Ground control points collected with Differential Global Positioning System (DGPS) are identified and used for georeferencing via a Helmert transformation. This study compared georeferenced point clouds to a Total Station survey in order to assess and quantify their geometric accuracy. The results indicate that a georeferenced point cloud accurate to 25-40 mm can be obtained from imagery acquired from ∼50 m. UAV-based image capture provides the spatial and temporal resolution required to map and monitor natural landscapes. This paper assesses the accuracy of the generated point clouds based on field survey points. Based on our key findings we conclude that sub-decimetre terrain change (in this case coastal erosion) can be monitored.Remote Sens. 2012, 4 1574

In unmanned aerial vehicle (UAV) photogrammetric surveys, the camera can be pre-calibrated or can be calibrated "on-the-job" using structure-from-motion and a self-calibrating bundle adjustment. This study investigates the impact on mapping accuracy of UAV photogrammetric survey blocks, the bundle adjustment and the 3D reconstruction process under a range of typical operating scenarios for centimetre-scale natural landform mapping (in this case, a coastal cliff). We demonstrate the sensitivity of the process to calibration procedures and the need for careful accuracy assessment. For this investigation, vertical (nadir or near-nadir) and oblique photography were collected with 80%-90% overlap and with accurately-surveyed (σ ≤ 2 mm) and densely-distributed ground control. This allowed various scenarios to be tested and the impact on mapping accuracy to be assessed. This paper presents the results of that investigation and provides guidelines that will assist with operational decisions regarding camera calibration and ground control for UAV photogrammetry. The results indicate that the use of either a robust pre-calibration or a robust self-calibration results in accurate model creation from vertical-only photography, and additional oblique photography may improve the results. The results indicate that if a dense array of high accuracy ground control points are deployed and the UAV photography includes both vertical and oblique images, then either a pre-calibration or an on-the-job self-calibration will yield reliable models (pre-calibration RMSE XY = 7.1 mm and on-the-job self-calibration RMSE XY = 3.2 mm). When oblique photography was Remote Sens. 2015, 7 11934 excluded from the on-the-job self-calibration solution, the accuracy of the model deteriorated (by 3.3 mm horizontally and 4.7 mm vertically). When the accuracy of the ground control was then degraded to replicate typical operational practice (σ = 22 mm), the accuracy of the model further deteriorated (e.g., on-the-job self-calibration RMSE XY went from 3.2-7.0 mm). Additionally, when the density of the ground control was reduced, the model accuracy also further deteriorated (e.g., on-the-job self-calibration RMSE XY went from 7.0-7.3 mm). However, our results do indicate that loss of accuracy due to sparse ground control can be mitigated by including oblique imagery.

This study is the first to use an Unmanned Aerial Vehicle (UAV) for mapping moss beds in Antarctica. Mosses can be used as indicators for the regional effects of climate change. Mapping and monitoring their extent and health is therefore important. UAV aerial photography provides ultra-high resolution spatial data for this purpose. We developed a technique to extract an extremely dense 3D point cloud from overlapping UAV aerial photography based on structure from motion (SfM) algorithms. The combination of SfM and patchbased multi-view stereo image vision algorithms resulted in a 2 cm resolution digital terrain model (DTM). This detailed topographic information combined with vegetation indices derived from a 6-band multispectral sensor enabled the assessment of moss bed health. This novel UAV system has allowed us to map different environmental characteristics of the moss beds at ultra-high resolution providing us with a better understanding of these fragile Antarctic ecosystems. The paper provides details on the different UAV instruments and the image processing framework resulting in DEMs, vegetation indices, and terrain derivatives. ABSTRACT:This study is the first to use an Unmanned Aerial Vehicle (UAV) for mapping moss beds in Antarctica. Mosses can be used as indicators for the regional effects of climate change. Mapping and monitoring their extent and health is therefore important. UAV aerial photography provides ultra-high resolution spatial data for this purpose. We developed a technique to extract an extremely dense 3D point cloud from overlapping UAV aerial photography based on structure from motion (SfM) algorithms. The combination of SfM and patch-based multi-view stereo image vision algorithms resulted in a 2 cm resolution digital terrain model (DTM). This detailed topographic information combined with vegetation indices derived from a 6-band multispectral sensor enabled the assessment of moss bed health. This novel UAV system has allowed us to map different environmental characteristics of the moss beds at ultra-high resolution providing us with a better understanding of these fragile Antarctic ecosystems. The paper provides details on the different UAV instruments and the image processing framework resulting in DEMs, vegetation indices, and terrain derivatives.

ABSTRACT:Low-cost Unmanned Aerial Vehicles (UAVs) are becoming viable environmental remote sensing tools. Sensor and battery technology is expanding the data capture opportunities. The UAV, as a close range remote sensing platform, can capture high resolution photography on-demand. This imagery can be used to produce dense point clouds using multi-view stereopsis techniques (MVS) combining computer vision and photogrammetry. This study examines point clouds produced using MVS techniques applied to UAV and terrestrial photography. A multi-rotor micro UAV acquired aerial imagery from a altitude of approximately 30-40 m. The point clouds produced are extremely dense (<1-3 cm point spacing) and provide a detailed record of the surface in the study area, a 70 m section of sheltered coastline in southeast Tasmania. Areas with little surface texture were not well captured, similarly, areas with complex geometry such as grass tussocks and woody scrub were not well mapped. The process fails to penetrate vegetation, but extracts very detailed terrain in unvegetated areas. Initially the point clouds are in an arbitrary coordinate system and need to be georeferenced. A Helmert transformation is applied based on matching ground control points (GCPs) identified in the point clouds to GCPs surveying with differential GPS. These point clouds can be used, alongside laser scanning and more traditional techniques, to provide very detailed and precise representations of a range of landscapes at key moments. There are many potential applications for the UAV-MVS technique, including coastal erosion and accretion monitoring, mine surveying and other environmental monitoring applications. For the generated point clouds to be used in spatial applications they need to be converted to surface models that reduce dataset size without loosing too much detail. Triangulated meshes are one option, another is Poisson Surface Reconstruction. This latter option makes use of point normal data and produces a surface representation at greater detail than previously obtainable. This study will visualise and compare the two surface representations by comparing clouds created from terrestrial MVS (T-MVS) and UAV-MVS.