Monitoring Seabirds and Marine Mammals by Georeferenced Aerial Photography

Abstract

ABSTRACT:The assessment of anthropogenic impacts on the marine environment is challenged by the accessibility, accuracy and validity of biogeographical information. Offshore wind farm projects require large-scale ecological surveys before, during and after construction, in order to assess potential effects on the distribution and abundance of protected species. The robustness of site-specific population estimates depends largely on the extent and design of spatial coverage and the accuracy of the applied census technique. Standard environmental assessment studies in Germany have so far included aerial visual surveys to evaluate potential impacts of offshore wind farms on seabirds and marine mammals. However, low flight altitudes, necessary for the visual classification of species, disturb sensitive bird species and also hold significant safety risks for the observers. Thus, aerial surveys based on high-resolution digital imagery, which can be carried out at higher (safer) flight altitudes (beyond the rotor-swept zone of the wind turbines) have become a mandatory requirement, technically solving the problem of distant-related observation bias. A purpose-assembled imagery system including medium-format cameras in conjunction with a dedicated geo-positioning platform delivers series of orthogonal digital images that meet the current technical requirements of authorities for surveying marine wildlife at a comparatively low cost. At a flight altitude of 425 m, a focal length of 110 mm, implemented forward motion compensation (FMC) and exposure times ranging between 1/1600 and 1/1000 s, the twin-camera system generates high quality 16 bit RGB images with a ground sampling distance (GSD) of 2 cm and an image footprint of 155 x 410 m. The image files are readily transferrable to a GIS environment for further editing, taking overlapping image areas and areas affected by glare into account. The imagery can be routinely screened by the human eye guided by purpose-programmed software to distinguish biological from non-biological signals. Each detected seabird or marine mammal signal is identified to species level or assigned to a species group and automatically saved into a geo-database for subsequent quality assurance, geo-statistical analyses and data export to third-party users. The relative size of a detected object can be accurately measured which provides key information for species-identification. During the development and testing of this system until 2015, more than 40 surveys have produced around 500.000 digital aerial images, of which some were taken in specially protected areas (SPA) of the Baltic Sea and thus include a wide range of relevant species. Here, we present the technical principles of this comparatively new survey approach and discuss the key methodological challenges related to optimizing survey design and workflow in view of the pending regulatory requirements for effective environmental impact assessments.

Monitoring marine mammals using unmanned aerial vehicles: quantifying detection certainty

“…, Kemper et al. ). Though glare was not included in Kaldfjord, image brightness (luminance) was found to have a positive effect on certainty of sightings in Kaldfjord (Table ).…”

“…, Kemper et al. ). Particularly during the polar night, light is a limiting factor that can significantly affect animal detection and certainty of sightings.…”

Applications of digital imaging and analysis in seabird monitoring and research

“…This is because aerial surveyors must be close enough to the birds to allow accurate identification and counts, whereas images can be magnified during analysis (Thaxter & Burton 2009, Kemper et al . 2016).…”

“…Taking digital photographs and videos from manned aircraft can further lower behavioural stress responses, as this allows the vehicle to be flown at higher altitude. This is because aerial surveyors must be close enough to the birds to allow accurate identification and counts, whereas images can be magnified during analysis (Thaxter & Burton 2009, Kemper et al 2016.…”