Mapping with confidence; delineating seagrass habitats using Unoccupied Aerial Systems (UAS)

Abstract

There is growing interest in the use of Unoccupied Aerial Systems (UAS) for mapping and monitoring of seagrass habitats. UAS provide flexibility with timing of imagery capture, are relatively inexpensive, and obtain very high spatial resolution imagery compared to imagery acquired from sensors mounted on satellite or piloted aircraft. However, research to date has focused on UAS applications for exposed intertidal areas or clear tropical waters. In contrast, submerged seagrass meadows in temperate regions are subject to high cloud cover and water column turbidity, which may limit the application of UAS imagery for coastal habitat mapping. To test the constraints on UAS seagrass mapping, we examined the effects of five environmental conditions at the time of UAS image acquisition (sun angle, tidal height, cloud cover, Secchi depth and wind speed) and five site characteristics (eelgrass patchiness and density, presence and density of non‐eelgrass submerged aquatic vegetation, sediment tone, eelgrass deep edge and site exposure) at 26 eelgrass (Zostera marina) monitoring sites in British Columbia, Canada. Eelgrass was delineated in UAS orthomosaics using object‐based image analysis, combining image segmentation with manual classification. Each site was ranked according to the analysts’ confidence in the delineated eelgrass. Robust Linear Regression revealed sun angle and ‘theoretical visibility’ (an aggregate of tidal height, Secchi depth, and eelgrass deep edge conditions) to be the most important variables affecting mapping confidence. In general, ideal environmental conditions to obtain high confidence eelgrass mapping included: (1) sun angles below 40°; (2) positive theoretical visibility with Secchi depths >5 m; (3) cloud cover conditions of <10% or >90%; and (4) wind speeds less than 5 km h−1. Additionally, high mapping confidence was achieved for sites with dense, continuous, and homogeneous eelgrass meadows. The results of this analysis will guide implementation of UAS mapping technologies in coastal temperate regions.

Unmanned Aerial Vehicles (UAVs) for Monitoring Macroalgal Biodiversity: Comparison of RGB and Multispectral Imaging Sensors for Biodiversity Assessments

“…Targeting optimal meteorological and oceanographic conditions for high-resolution remote sensing with UAVs ideally fills a gap between high spatial cover, low-resolution satellites and low spatial cover, high-resolution in situ sampling [25,27]. High-resolution remote sensing using UAVs has great potential to detect changes in rocky reef ecosystems, but importantly may have the spatial and taxonomic resolution to detect subtle changes preceding ecological collapse (i.e., tipping points).…”

“…UAVs are increasingly being used for a range of environmental and ecological monitoring campaigns [20][21][22][23][24][25][26][27] and, although the use of aerial platforms for ecological monitoring is hardly new, the deployment of imaging sensors on unmanned aerial drones has some advantages (as well as disadvantages) over other manned and unmanned platforms. The key advantages of copter drones are high pixel resolution, highly flexible deployment, and relatively low cost per unit of time [25,28].…”

Deep learning for coastal resource conservation: automating detection of shellfish reefs

“…With increased resolution, differences between similar environments become more pronounced and challenging classes become more easily separable. This added resolution has proven beneficial for seagrass mapping (Nahirnick et al 2019) and effective as a source for refining habitat classification training data in lower resolution satellite data (Gray et al 2018). The cryptic nature of shellfish reef habitat in remotely sensed imagery makes it an ideal candidate to test the limits of combining high-resolution imagery with deep learningbased habitat classification systems in conservation science.…”

Spatial and temporal persistence of nearshore kelp beds on the west coast of British Columbia, Canada using satellite remote sensing

“…From these images, further criteria were developed to determine the reliability of the images for accurately mapping kelp. Similar to the methods by Nahirnick et al (2019), each image was scored from 1 to 3 with one being the poorest condition, according to the following criteria: (1) time of collection within the growing season; (2) tide height, where lower tides allow for better ability to detect kelp floating on the water surface; (3) intensity of glint on the water, where high glint obstructs the ability to detect kelp; (4) water surface roughness (WSR), which describes the texture of the water surface, where calm flat water is best for detecting kelp and breaking waves or white caps present difficult conditions (Table 2). The total score (maximum of 12) for each image was calculated and images with scores lower than seven were deemed too unreliable for kelp detection.…”