Philippe Grandjean

Assessing the Accuracy of High Resolution Digital Surface Models Computed by PhotoScan ® and MicMac ® in Sub-Optimal Survey Conditions

For monitoring purposes and in the context of geomorphological research, Unmanned Aerial Vehicles (UAV) appear to be a promising solution to provide multi-temporal Digital Surface Models (DSMs) and orthophotographs. There are a variety of photogrammetric software tools available for UAV-based data. The objective of this study is to investigate the level of accuracy that can be achieved using two of these software tools: Agisoft PhotoScan ® Pro and an open-source alternative, IGN

Potential and limitation of UAV for monitoring subsidence in municipal landfills

Multitemporal topographic measurements are essential for monitoring municipal landfills for economic and security reasons. Accurate evaluation of the landfill volumes still to exploit and the extent of subsidence occurring in the closed plots are critical parameters in landfill management. A municipal landfill located in central Italy has been imaged by a UAV helicopter able to fly in full autonomy in industrial areas. Very high resolution images have been processed by a photogrammetric chain in order to obtain Digital Surface Models and orthorectified images. The resolution of these products was 2.8 cm and the vertical precision of the DSM was at worse 0.5 m, resulting into acceptable errors (better than 5%) on available volume estimations. The DSM has been compared to elevation values produced by conventional theodolite and LIDAR measurements. It is shown that the UAV helicopter system is more flexible and more productive than the two other techniques for a similar precision.

Direct Georeferencing of a Pushbroom, Lightweight Hyperspectral System for Mini-UAV Applications

Hyperspectral imagery has proven its potential in many research applications, especially in the field of environmental sciences. Currently, hyperspectral imaging is generally performed by satellite or aircraft platforms, but mini-UAV (Unmanned Aerial Vehicle) platforms (<20 kg) are now emerging. On such platforms, payload restrictions are critical, so sensors must be selected according to stringent specifications. This article presents the integration of a light pushbroom hyperspectral sensor onboard a multirotor UAV, which we have called Hyper-DRELIO (Hyperspectral DRone for Environmental and LIttoral Observations). This article depicts the system design: the UAV platform, the imaging module, the navigation module, and the interfacing between the different elements. Pushbroom sensors offer a better combination of spatial and spectral resolution than full-frame cameras. Nevertheless, data georectification has to be performed line by line, the quality of direct georeferencing being limited by mechanical stability, good timing accuracy, and the resolution and accuracy of the proprioceptive sensors. A georegistration procedure is proposed for geometrical pre-processing of hyperspectral data. The specifications of Hyper-DRELIO surveys are described through two examples of surveys above coastal or inland waters, with different flight altitudes. This system can collect hyperspectral data in VNIR (Visible and Near InfraRed) domain above small study sites (up to about 4 ha) with both high spatial resolution (<10 cm) and high spectral resolution (1.85 nm) and with georectification accuracy on the order of 1 to 2 m.

Examining high-resolution survey methods for monitoring cliff erosion at an operational scale

This paper aims to compare models from terrestrial laser scanning (TLS), terrestrial photogrammetry (TP), and unmanned aerial vehicle photogrammetry (UAVP) surveys to evaluate their potential in cliff erosion monitoring. TLS has commonly been used to monitor cliff-face erosion (monitoring since 2010 in Normandy) because it guarantees results of high precision. Due to some uncertainties and limitations of TLS, TP and UAVP can be seen as alternative methods. First, the texture quality of the photogrammetry models is better than that of TLS which could be useful for analysis and interpretation. Second, a comparison between the TLS model and UAV or TP models shows that the mean error value is mainly from 0.013 to 0.03 m, which meets the precision requirements for monitoring cliff erosion by rock falls and debris falls. However, TP is more sensitive to roughness than UAVP, which increases the data standard deviation. Thus, UAVP appears to be more reliable in our study and provides a larger spatial coverage, enabling a larger cliff-face section to be monitored with a regular resolution. Nevertheless, the method remains dependent on the weather conditions and the number of operators is not reduced. Third, even though UAVP has more advantages than TP, the methods could be interchangeable when no pilot is available, when weather conditions are bad or when high reactivity is needed.