Tree flowers are important for flower–insect relationships, seeds, fruits, and honey production. Flowers are difficult to analyze, particularly in complex ecosystems such as forests. However, unmanned aerial vehicles (UAVs) enable detailed analyses with high spatial resolution, and avoid destruction of sensitive ecosystems. In this study, we hypothesize that UAVs can be used to estimate the number of existing flowers, the quantity of nectar, and habitat potential for honeybees (Apis mellifera). To test this idea, in 2017 we combined UAV image analysis with manual counting and weighing of the flowers of eight-year-old black locust (Robinia pseudoacacia L.) trees to calculate the number of flowers, their surface area, and their volume. Estimates of flower surface area ranged from 2.97 to 0.03% as the flying altitude above the crowns increased from 2.6 m to 92.6 m. Second, for the horizontal analysis, a 133 m² flower area at a one-hectare black locust plantation was monitored in 2017 by a UAV. Flower numbers ranged from 1913 to 15,559 per tree with an average surface area of 1.92 cm² and average volume of 5.96 cm³. The UAV monitored 11% of the total surface and 3% of the total volume. Consequently, at the one-hectare black locust study area we estimate 5.3 million flowers (69 kg honey), which is sufficient for one bee hive to survive for one year.

Black locust is a drought-resistant tree species with high biomass productivity during juvenility; it is able to thrive on wastelands, such as former brown coal fields and dry agricultural areas. However, research conducted on this species in such areas is limited. This paper aims to provide a basis for predicting tree woody biomass for black locust based on tree, competition, and site variables at 14 sites in northeast Germany that were previously utilized for mining or agriculture. The study areas, which are located in an area covering 320 km × 280 km, are characterized by a variety of climatic and soil conditions. Influential variables, including tree parameters, competition, and climatic parameters were considered. Allometric biomass models were employed. The findings show that the most important parameters are tree and competition variables. Different former land utilizations, such as mining or agriculture, as well as growth by cores or stumps, significantly influenced aboveground woody biomass production. The new biomass models developed as part of this study can be applied to calculate woody biomass production and carbon sequestration of Robinia pseudoacacia L. in short rotation coppices in previous mining and agricultural areas.

Varying reproduction strategies are an important trait that tree species need in order both to survive and to spread. Black locust is able to reproduce via seeds, stump shoots, and root suckers. However, little research has been conducted on the reproduction and spreading of black locust in short rotation coppices. This research study focused on seed germination, stump shoot resprout, and spreading by root suckering of black locust in ten short rotation coppices in Germany. Seed experiments and sample plots were analyzed for the study. Spreading was detected and measured with unmanned aerial system (UAS)-based images and classification technology—object-based image analysis (OBIA). Additionally, the classification of single UAS images was tested by applying a convolutional neural network (CNN), a deep learning model. The analyses showed that seed germination increases with increasing warm-cold variety and scarification. Moreover, it was found that the number of shoots per stump decreases as shoot age increases. Furthermore, spreading increases with greater light availability and decreasing tillage. The OBIA and CNN image analysis technologies achieved 97% and 99.5% accuracy for black locust classification in UAS images. All in all, the three reproduction strategies of black locust in short rotation coppices differ with regards to initialization, intensity, and growth performance, but all play a role in the survival and spreading of black locust.