During the last years commercial hyperspectral imaging sensors have been miniaturized and their performance has been demonstrated on Unmanned Aerial Vehicles (UAV). However currently the commercial hyperspectral systems still require minimum payload capacity of approximately 3 kg, forcing usage of rather large UAVs. In this article we present a lightweight hyperspectral mapping system (HYMSY) for rotor-based UAVs, the novel processing chain for the system, and its potential for agricultural mapping and monitoring applications. The HYMSY consists of a custom-made pushbroom spectrometer (400–950 nm, 9 nm FWHM, 25 lines/s, 328 px/line), a photogrammetric camera, and a miniature GPS-Inertial Navigation System. The weight of HYMSY in ready-to-fly configuration is only 2.0 kg and it has been constructed mostly from off-the-shelf components. The processing chain uses a photogrammetric algorithm to produce a Digital Surface Model (DSM) and provides high accuracy orientation of the system over the DSM. The pushbroom data is georectified by projecting it onto the DSM with the support of photogrammetric orientations and the GPS-INS data. Since an up-to-date DSM is produced internally, no external data are required and the processing chain is capable to georectify pushbroom data fully automatically. The system has been adopted for several experimental flights related to agricultural and habitat monitoring applications. For a typical flight, an area of 2–10 ha was mapped, producing a RGB orthomosaic at 1–5 cm resolution, a DSM at 5–10 cm resolution, and a hyperspectral datacube at 10–50 cm resolution.

Digital Elevation Models (DEMs) are 3D representations of the Earth’s surface and have numerous applications in geomorphology, hydrology and ecology. Structure-from-Motion (SfM) photogrammetry using photographs obtained by unmanned aerial vehicles (UAVs) have been increasingly used for obtaining high resolution DEMs. These DEMs are interpolated from point clouds representing entire landscapes, including points of terrain, vegetation and infrastructure. Up to date, there has not been any study clearly comparing different algorithms for filtering of vegetation. The objective in this study was, therefore, to assess the performance of various vegetation filter algorithms for SfM-obtained point clouds. The comparison was done for a Mediterranean area in Murcia, Spain with heterogeneous vegetation cover. The filter methods that were compared were: color-based filtering using an excessive greenness vegetation index (VI), Triangulated Irregular Networks (TIN) densification from LAStools, the standard method in Agisoft Photoscan (PS), iterative surface lowering (ISL), and a combination of iterative surface lowering and the VI method (ISL_VI). Results showed that for bare areas there was little to no difference between the filtering methods, which is to be expected because there is little to no vegetation present to filter. For areas with shrubs and trees, the ISL_VI and TIN method performed best. These results show that different filtering techniques have various degrees of success in different use cases. A default filter in commercial software such as Photoscan may not always be the best way to remove unwanted vegetation from a point cloud, but instead alternative methods such as a TIN densification algorithm should be used to obtain a vegetation-less Digital Terrain Model (DTM).

Multi-temporal LiDAR digital terrain models (DTMs) are used for the development and testing of a method for geomorphological change analysis in western Austria. Point data from two airborne LiDAR campaigns of 2003 and 2011 were filtered and interpolated into two 2m DTMs. Seven geomorphological features were mapped by using stratified object-based image analysis (OBIA) using terrain properties derived from the DTMs. Segmentation parameters and classification rules were set and applied to both data sets which allowed analysis of geomorphological change between 2003 and 2011. Volumetric change was calculated and summarized by their landform category. The multi-temporal landform classifications show where landforms changed into other landforms as the result of geomorphological process activity. However, differences in point densities and lack of data points below dense forest hindered accurate geomorphological change detection in these areas. When challenges related to interpolation techniques are tackled, stratified OBIA of multi-temporal LiDAR data sets is a promising tool for geomorphological change detection, and affiliated applications such as monitoring risk and natural hazards, rate of change analyses, and vulnerability assessments.

Soil erosion, rapid geomorphological change and vegetation degradation are major threats to the human and natural environment. Unmanned Aerial Systems (UAS) can be used as tools to provide detailed and accurate estimations of landscape change. The effect of flight strategy on the accuracy of UAS image data products, typically a digital surface model (DSM) and orthophoto, is unknown. Herein different flying altitudes (126-235 m) and area coverage orientations (N-S and SW-NE) are assessed in a semi-arid and medium-relief area where terraced and abandoned agricultural fields are heavily damaged by piping and gully erosion. The assessment was with respect to cell size, vertical and horizontal accuracy, absolute difference of DSM, and registration of recognizable landscape features. The results show increasing cell size (5-9 cm) with increasing altitude, and differences between elevation values (10-20 cm) for different flight directions. Vertical accuracy ranged 4-7 cm but showed no clear relationship with flight strategy, whilst horizontal error was stable (2-4 cm) for the different orthophotos. In all data sets, geomorphological features such as piping channels, rills and gullies and vegetation patches could be labeled by a technician. Finally, the datasets have been released in a public repository.