Drone Surveys Do Not Increase Colony-wide Flight Behaviour at Waterbird Nesting Sites, But Sensitivity Varies Among Species

Airborne imagery does not preclude detectability issues in estimating bird colony size

Aerial images obtained by drones are increasingly used for ecological research such as wildlife monitoring. Yet detectability issues resulting from animal activity or visibility are rarely considered, although these may lead to biased population size and trend estimates. In this study, we investigated detectability in a census of Malagasy pond heron Ardeola idae colonies on the island of Mayotte. We conducted repeated drone flights over breeding colonies in mangrove habitats during two breeding seasons. We then identified individuals and nests in the images and fitted closed capture-recapture models on nest-detection histories. We observed seasonal variation in the relative abundance of individuals, and intra-daily variation in the relative abundance of individuals-especially immature birds-affecting the availability of nests for detection. The detection probability of nests estimated by capture-recapture varied between 0.58 and 0.74 depending on flyover days and decreased 25% from early to late morning. A simulation showed that three flyovers are necessary to detect a 5-6% decline in colonies of 50 to 200 nests. These results indicate that the detectability of nests of forest-canopy breeding species from airborne imagery can vary over space and time; we recommend the use of capture-recapture methods to control for this bias.

Drones as a tool to study and monitor endangered Grey Crowned Cranes (Balearica regulorum): Behavioural responses and recommended guidelines

Crane populations are declining worldwide, with anthropogenically exacerbated habitat loss emerging as the primary causal threat. The endangered Grey Crowned Crane (Balearica regulorum) is the least studied of the three crane species that reside in southern Africa. This data paucity hinders essential conservation planning and is primarily due to ineffective monitoring methods and this species' use of inaccessible habitats. In this study, we compared the behavioural responses of different Grey Crowned Crane social groupings to traditional on-foot monitoring methods and the pioneering use of drones. Grey Crowned Cranes demonstrated a lower tolerance for on-foot monitoring approaches, allowing closer monitoring proximity with drones (22.72 (95% confidence intervals - 13.75, 37.52) m) than on-foot methods (97.59 (86.13, 110.59) m) before displaying evasive behaviours. The behavioural response of flocks was minimal at flight heights above 50 m, whilst larger flocks were more likely to display evasive behaviours in response to monitoring by either method. Families displayed the least evasive behaviours to lower flights, whereas nesting birds were sensitive to the angles of drone approaches. Altogether, our findings confirm the usefulness of drones for monitoring wetland-nesting species and provide valuable species-specific guidelines for monitoring Grey Crowned Cranes. However, we caution future studies on wetland breeding birds to develop species-specific protocols before implementing drone methodologies.

Monitoring Dropping Densities with Unmanned Aerial Vehicles (UAV): An Effective Tool to Assess Distribution Patterns in Field Utilization by Foraging Geese

Counting of droppings is often, with great effect, used as an indirect method to monitor the appearance and usage of an area by a population covering longer time spans. However, manual detecting and counting of droppings can be time-consuming and tedious, and with a risk of resulting in course estimations. In this context, we studied the use of imaging from unmanned aerial vehicles (UAVs) as a novel and enhanced tool to estimate the dropping densities and distributions of field foraging Arctic migratory geese, such as pink-footed goose Anser brachyrhynchus and barnacle goose Branta leucopsis. Aided by analysis in geographical information systems (GIS), we sought to detect and use fine-scale changes in the within-field dropping densities to evaluate avoidance distance to selected landscape elements. Data in the form of aerial photos from farmed grassland and pastures were collected in areas adjacent to Limfjorden, Northern Jutland, Denmark. The UAV proved usable for detecting droppings from field foraging geese, but with the applied UAV technology only at a low flying altitude (≤3 m), which rendered traditional methods for georeferencing inapplicable. A revised protocol for georeferencing of single aerial photos triggered from low altitudes was successfully developed, which was considered suitable for future use. Analyses based on the performed UAV data sampling allowed for an unprecedented fine-scale estimation of distribution patterns of the goose droppings and further for determination of optimal sampling frequencies (≤12 × 12 m spacing between photo samples) for calculation of density patterns, which reflected differences in foraging activity of geese across whole fields. Contagious dispersions in dropping densities were detected in the majority of fields indicating local, within-field displacements of the geese, which were illustrated by interpolated heatmaps. Additionally, avoidance distances were assessed for four landscape elements and detected with consistent results for windbreaks (100 m), roads (175 m) and wind turbines (1100 m) throughout the ten surveyed fields.

Networked Unmanned Aerial Vehicles for Surveillance and Monitoring: A Survey

As a typical cyber-physical system, networked unmanned aerial vehicles (UAVs) have received much attention in recent years. Emerging communication technologies and high-performance control methods enable networked UAVs to operate as aerial sensor networks to collect more complete and consistent information with significantly improved mobility and flexibility than traditional sensing platforms. One of the main applications of networked UAVs is surveillance and monitoring, which constitute essential components of a well-functioning public safety system and many industrial applications. Although the existing literature on surveillance and monitoring UAVs is extensive, a comprehensive survey on this topic is lacking. This article classifies publications on networked UAVs for surveillance and monitoring using the targets of interest and analyzes several typical problems on this topic, including the control, navigation, and deployment optimization of UAVs. The related research gaps and future directions are also presented.