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Knowing before harvesting how many plants have emerged and how they are growing is key in optimizing labour and efficient use of resources. Unmanned aerial vehicles (UAV) are a useful tool for fast and cost efficient data acquisition. However, imagery need to be converted into operational spatial products that can be further used by crop producers to have insight in the spatial distribution of the number of plants in the field. In this research, an automated method for counting plants from very high-resolution UAV imagery is addressed. The proposed method uses machine vision—Excess Green Index and Otsu’s method—and transfer learning using convolutional neural networks to identify and count plants. The integrated methods have been implemented to count 10 weeks old spinach plants in an experimental field with a surface area of 3.2 ha. Validation data of plant counts were available for 1/8 of the surface area. The results showed that the proposed methodology can count plants with an accuracy of 95% for a spatial resolution of 8 mm/pixel in an area up to 172 m2. Moreover, when the spatial resolution decreases with 50%, the maximum additional counting error achieved is 0.7%. Finally, a total amount of 170 000 plants in an area of 3.5 ha with an error of 42.5% was computed. The study shows that it is feasible to count individual plants using UAV-based off-the-shelf products and that via machine vision/learning algorithms it is possible to translate image data in non-expert practical information.

49To quantify potential nitrogen (N) deposition impacts on peatland carbon (C) uptake, we 50 explored temporal and spatial trends in N deposition and climate impacts on the production of net C-uptake remained small relative to 1900 due to the low peatland cover in high-N areas. 58The predicted impacts of likely changes in N deposition on Sphagnum productivity appeared 59 to be less than those of climate. Nevertheless, current critical loads for peatlands are likely to 60 hold under a future climate.

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Unmanned Aerial Vehicles (UAV) have already become an affordable and cost-efficient tool to quickly map a targeted area for many emerging applications in the arena of Ecological Monitoring and Biodiversity Conservation. Managers, owners, companies and scientists are using professional drones equipped with high-resolution visible, multispectral or thermal cameras to assess the state of ecosystems, the effect of disturbances, or the dynamics and changes of biological communities inter alia. It is now a defining time to assess the use of drones for these types of applications over natural areas and protected areas. UAV missions are increasing but most of them are just testing its applicability. It is time now to move to frequent revisiting missions, aiding in the retrieval of important biophysical parameters in ecosystems or mapping species distributions. This Special Issue is aimed at collecting UAV applications contributing to a better understanding of biodiversity and ecosystem status, threats, changes and trends. Submissions were welcomed from purely scientific missions to operational management missions, evidencing the enhancement of knowledge in: Essential biodiversity variables and ecosystem services mapping; ecological integrity parameters mapping; long-term ecological monitoring based on UAVs; mapping of alien species spread and distribution; upscaling ecological variables from drone to satellite images: methods and approaches; rapid risk and disturbance assessment using drones, ecosystem structure and processes assessment by using UAVs, mapping threats, vulnerability and conservation issues of biological communities and species; mapping of phenological and temporal trends and habitat mapping; monitoring and reporting of conservation status.

In 2003 Wageningen Environmental Research implemented the ISO 9001 certified quality management system. Since 2006 Wageningen Environmental Research has been working with the ISO 14001 certified environmental care system. By implementing the ISO 26000 guideline, Wageningen Environmental Research can manage and deliver its social responsibility.