Description of the subject. Understanding the current situation and evolution of forests is essential for a sustainable management plan that maintains forests' ecological and socio-economic functions. Remote sensing is a helpful tool in developing this knowledge. Objectives. This paper investigates the new opportunities offered by using Sentinel-2 (S2) imagery for forest mapping in Belgian Ardenne ecoregion. The first classification objective was to create a forest map at the regional scale. The second objective was the discrimination of 11 forest classes (Fagus sylvatica L., Betula sp., Quercus sp., other broad-leaved stands, Pseudotsuga menziesii (Mirb.) Franco, Larix sp., Pinus sylvestris L., Picea abies (L.) H.Karst., young needle-leaved stands, other needle-leaved stands, and recent clear-cuts). Method. Two S2 scenes were used and a series of spectral indices were computed for each. We applied supervised pixel-based classifications with a Random Forest classifier. The classification models were processed with a pure S2 dataset and with additional 3D data to compare obtained precisions. Results. 3D data slightly improved the precision of each objective, but the overall improvement in accuracy was only significant for objective 1. The produced forest map had an overall accuracy of 93.3%. However, the model testing tree species discrimination was also encouraging, with an overall accuracy of 88.9%. Conclusions. Because of the simple analyses done in this study, results need to be interpreted with caution. However, this paper confirms the great potential of S2 imagery, particularly SWIR and red-edge bands, which are the most important S2 bands in our study.

The use of unmanned aerial systems (UASs) has rapidly grown in many civil applications since the early 2010s. Nowadays, a large variety of reliable low-cost UAS sensors and controllers are available. However, contrary to ultralight aircrafts (ULAs), UASs have a too small operational range to efficiently cover large areas. Flight regulations prevailing in many countries further reduced this operational range; in particular, the “within visual line of sight” rule. This study presents a new system for image acquisition and high-quality photogrammetry of large scale areas (>10 km²). It was developed by upscaling the UAS paradigm, i.e., low-cost sensors and controllers, little (or no) on-board active stabilization, and adequate structure from motion photogrammetry, to an ULA platform. Because the system is low-cost (good quality-price ratio of UAS technologies), multi-sensors (large variety of available UAS sensors) and versatile (high ULA operational flexibility and more lenient regulation than for other platforms), the possible applications are numerous in miscellaneous research domains. The system was described in detail and illustrated from the flight and images acquisition to the photogrammetric routine. The system was successfully used to acquire high resolution and high quality RGB and multispectral images, and produced precisely georeferenced digital elevation model (DEM) and orthophotomosaics for a forested area of 1200 ha. The system can potentially carry any type of sensors. The system compatibility with any sensor can be tested, in terms of image quality and flight plan, with the proposed method. This study also highlighted a major technical limitation of the low-cost thermal infrared cameras: the too high integration time with respect to the flight speed of most UASs and ULAs. By providing the complete information required for reproducing the system, the authors seek to encourage its implementation in different geographical locations and scientific contexts, as well as, its combination with other sensors, in particular, laser imaging detection and ranging (LiDAR) and hyperspectral.