UASea: A Data Acquisition Toolbox for Improving Marine Habitat Mapping

Abstract

Unmanned aerial systems (UAS) are widely used in the acquisition of high-resolution information in the marine environment. Although the potential applications of UAS in marine habitat mapping are constantly increasing, many limitations need to be overcome—most of which are related to the prevalent environmental conditions—to reach efficient UAS surveys. The knowledge of the UAS limitations in marine data acquisition and the examination of the optimal flight conditions led to the development of the UASea toolbox. This study presents the UASea, a data acquisition toolbox that is developed for efficient UAS surveys in the marine environment. The UASea uses weather forecast data (i.e., wind speed, cloud cover, precipitation probability, etc.) and adaptive thresholds in a ruleset that calculates the optimal flight times in a day for the acquisition of reliable marine imagery using UAS in a given day. The toolbox provides hourly positive and negative suggestions, based on optimal or non-optimal survey conditions in a day, calculated according to the ruleset calculations. We acquired UAS images in optimal and non-optimal conditions and estimated their quality using an image quality equation. The image quality estimates are based on the criteria of sunglint presence, sea surface texture, water turbidity, and image naturalness. The overall image quality estimates were highly correlated with the suggestions of the toolbox, with a correlation coefficient of −0.84. The validation showed that 40% of the toolbox suggestions were a positive match to the images with higher quality. Therefore, we propose the optimal flight times to acquire reliable and accurate UAS imagery in the coastal environment through the UASea. The UASea contributes to proper flight planning and efficient UAS surveys by providing valuable information for mapping, monitoring, and management of the marine environment, which can be used globally in research and marine applications.

Experimentally Determining Optimal Conditions for Mapping Forage Fish with RPAS

“…Environmental conditions for detecting forage fish were evaluated through infield experiments using forage fish-like fishing lures and considering the influence of sun altitude, turbidity (secchi depth as a proxy), cloud cover, wind speed, and wave height. Generally, our findings showed that calm conditions with clear skies, moderate sun angles, and low turbidity to be optimal, similar to others working with different taxa, including eelgrass, sharks, and marine mammals [4,5,[21][22][23][28][29][30][31][32][33]. Defining these optimal conditions will help make RPAS surveys the basis of a practical, efficient, non-destructive, fishery independent technique for quantifying forage fish distribution and abundance [5].…”

“…The optimal range for sun altitudes between 20 • and 40 • balances the need for adequate solar irradiance to illuminate forage fish targets and avoid sun glint on the surface. Solar altitude is important, as a minimum amount of solar irradiance is required to adequately illuminate targets for visual identification [22,31,43] associated with the light attenuation properties of the water constituents, the characteristics of the target (e.g., color, size, depth in the water column), and the contrast between the target and the background [33]. Higher solar altitudes will deliver more solar irradiance.…”

“…Lastly, minimum solar altitudes seen in the literature are generally in a range of 25-30 • . However, some research suggested that lower altitudes may provide adequate illumination [22,28,31,32]. Authors from [28] found that the minimum 30 • angle recommended for piloted aerial benthic habitat surveys from [32] offered more than sufficient light to illuminate benthic eelgrass vegetation.…”

“…While there was no sun glint directly blocked visualization of the model fish school in any video sample (Figure 5E), glint was observed at the edges of the imagery with glassy conditions at angles above 43 • . Upper thresholds found in the literature were either 40 • or 45 • for the same rationale of avoiding sun glint [4,22,28,[30][31][32].…”

Designing Unmanned Aerial Survey Monitoring Program to Assess Floating Litter Contamination

“…Dark Set-was to understand how differences in exposure and contrast affect the reliability of the automated pixel selection and object detection models. Indeed, one of the biggest problems with nadir images collected over the ocean is the glare from sunlight backscatter, resulting in "specs" of high reflectance that can be misidentified as white floating objects [95]. The use of these contrasting exposure settings were expected to have a major influence on colorand contrast-based analysis and identification of floating items due to the homogenization of the background (i.e., seawater) in underexposed images [36,78].…”