New tools for old problems - comparing drone- and field-based assessments of a problematic plant species.

Plant species that negatively affect their environment by encroachment require constant management and monitoring through field surveys. Drones have been suggested to support field surveyors allowing more accurate mapping with just-in-time aerial imagery. Furthermore, object-based image analysis tools could increase the accuracy of species maps. However, only few studies compare species distribution maps resulting from traditional field surveys and object-based image analysis using drone imagery. We acquired drone imagery for a saltmarsh area (18 ha) on the Hallig Nordstrandischmoor (Germany) with patches of Elymus athericus, a tall grass which encroaches higher parts of saltmarshes. A field survey was conducted afterwards using the drone orthoimagery as a baseline. We used object-based image analysis (OBIA) to segment CIR imagery into polygons which were classified into eight land cover classes. Finally, we compared polygons of the field-based and OBIA-based maps visually and for location, area, and overlap before and after post-processing. OBIA-based classification yielded good results (kappa = 0.937) and agreed in general with the field-based maps (field = 6.29 ha, drone = 6.22 ha with E. athericus dominance). Post-processing revealed 0.31 ha of misclassified polygons, which were often related to water runnels or shadows, leaving 5.91 ha of E. athericus cover. Overlap of both polygon maps was only 70% resulting from many small patches identified where E. athericus was absent. In sum, drones can greatly support field surveys in monitoring of plant species by allowing for accurate species maps and just-in-time captured very-high-resolution imagery.

Individual recognition of Eurasian beavers (Castor fiber) by their tail patterns using a computer-assisted pattern-identification algorithm.

Individual recognition of animals is an important aspect of ecological sciences. Photograph-based individual recognition options are of particular importance since these represent a non-invasive method to distinguish and identify individual animals. Recent developments and improvements in computer-based approaches make possible a faster semi-automated evaluation of large image databases than was previously possible. We tested the Scale Invariant Feature Transform (SIFT) algorithm, which extracts distinctive invariant features of images robust to illumination, rotation or scaling of images. We applied this algorithm to a dataset of 800 tail pattern images from 100 individual Eurasian beavers (Castor fiber) collected as part of the Norwegian Beaver Project (NBP). Images were taken using a single-lens reflex camera and the pattern of scales on the tail, similar to a human fingerprint, was extracted using freely accessible image processing programs. The focus for individual recognition was not on the shape or the scarring of the tail, but purely on the individual scale pattern on the upper (dorsal) surface of the tail. The images were taken from two different heights above ground, and the largest possible area of the tail was extracted. The available data set was split in a ratio of 80% for training and 20% for testing. Overall, our study achieved an accuracy of 95.7%. We show that it is possible to distinguish individual beavers from their tail scale pattern images using the SIFT algorithm.

Invasive alien acacias rapidly stock carbon, but threaten biodiversity recovery in young second-growth forests.

Under the UN-Decade of Ecosystem Restoration and Bonn Challenge, second-growth forest is promoted as a global solution to climate change, degradation and associated losses of biodiversity and ecosystem services. Second growth is often invaded by alien tree species and understanding how this impacts carbon stock and biodiversity recovery is key for restoration planning. We assessed carbon stock and tree diversity recovery in second growth invaded by two Acacia species and non-invaded second growth, with associated edge effects, in the Brazilian Atlantic Forest. Carbon stock recovery in non-invaded forests was threefold lower than in invaded forests. Increasingly isolated, fragmented and deforested areas had low carbon stocks when non-invaded, whereas the opposite was true when invaded. Non-invaded forests recovered threefold to sixfold higher taxonomic, phylogenetic and functional diversity than invaded forest. Higher species turnover and lower nestedness in non-invaded than invaded forests underpinned higher abundance of threatened and endemic species in non-invaded forest. Non-invaded forests presented positive relationships between carbon and biodiversity, whereas in the invaded forests we did not detect any relationship, indicating that more carbon does not equal more biodiversity in landscapes with high vulnerability to invasive acacias. To deliver on combined climate change and biodiversity goals, restoration planning and management must consider biological invasion risk. This article is part of the theme issue 'Understanding forest landscape restoration: reinforcing scientific foundations for the UN Decade on Ecosystem Restoration'.