The recent development of operational small unmanned aerial systems (UASs) opens the door for their extensive use in forest mapping, as both the spatial and temporal resolution of UAS imagery better suit local-scale investigation than traditional remote sensing tools. This article focuses on the use of combined photogrammetry and “Structure from Motion” approaches in order to model the forest canopy surface from low-altitude aerial images. An original workflow, using the open source and free photogrammetric toolbox, MICMAC (acronym for Multi Image Matches for Auto Correlation Methods), was set up to create a digital canopy surface model of deciduous stands. In combination with a co-registered light detection and ranging (LiDAR) digital terrain model, the elevation of vegetation was determined, and the resulting hybrid photo/LiDAR canopy height model was compared to data from a LiDAR canopy height model and from forest inventory data. Linear regressions predicting dominant height and individual height from plot metrics and crown metrics showed that the photogrammetric canopy height model was of good quality for deciduous stands. Although photogrammetric reconstruction significantly smooths the canopy surface, the use of this workflow has the potential to take full advantage of the flexible revisit period of drones in order to refresh the LiDAR canopy height model and to collect dense multitemporal canopy height series.

Technology advances can revolutionize Precision Forestry by providing accurate and fine forest information at tree level. This paper addresses the question of how and particularly when Unmanned Aerial System (UAS) should be used in order to efficiently discriminate deciduous tree species. The goal of this research is to determine when is the best time window to achieve an optimal species discrimination. A time series of high resolution UAS imagery was collected to cover the growing season from leaf flush to leaf fall. Full benefit was taken of the temporal resolution of UAS acquisition, one of the most promising features of small drones. The disparity in forest tree phenology is at the maximum during early spring and late autumn. But the phenology state that optimized the classification result is the one that minimizes the spectral variation within tree species groups and, at the same time, maximizes the phenologic differences between species. Sunlit tree crowns (5 deciduous species groups) were classified using a Random Forest approach for monotemporal, two-date and three-date combinations. The end of leaf flushing was the most efficient single-date time window. Multitemporal datasets definitely improve the overall classification accuracy. But single-date high resolution orthophotomosaics, acquired on optimal time-windows, result in a very good classification accuracy (overall out of bag error of 16%).

Conservation of natural ecosystems requires regular monitoring of biodiversity, including the estimation of wildlife density. Recently, unmanned aerial systems (UAS) have become more available for numerous civilian applications. The use of small drones for wildlife surveys as a surrogate for manned aerial surveys is becoming increasingly attractive and has already been implemented with some success. This raises the question of how to process UAS imagery in order to determine the surface area of sampling strips within an acceptable confidence level. For the purpose of wildlife surveys, the estimation of sampling strip surface area needs to be both accurate and quick, and easy to implement. As GPS and an inertial measurement units are commonly integrated within unmanned aircraft platforms, two methods of direct georeferencing were compared here. On the one hand, we used the image footprint projection (IFP) method, which utilizes collinearity equations on each image individually. On the other hand, the Structure from Motion (SfM) technique was used for block orientation and georeferencing. These two methods were compared on eight sampling strips. An absolute orientation of the strip was determined by indirect georeferencing using ground control points. This absolute orientation was considered as the reference and was used for validating the other two methods. The IFP method was demonstrated to be the most accurate and the easiest to implement. It was also found to be less demanding in terms of image quality and overlap. However, even though a flat landscape is the type most widely encountered in wildlife surveys in Africa, we recommend estimating IFP sensitivity at an accentuation of the relief.