Unoccupied Aerial Vehicles (UAVs), or drone technologies, with their high spatial resolution, temporal flexibility, and ability to repeat photogrammetry, afford a significant advancement in other remote sensing approaches for coastal mapping, habitat monitoring, and environmental management. However, geographical drone mapping and in situ fieldwork often come with a steep learning curve requiring a background in drone operations, Geographic Information Systems (GIS), remote sensing and related analytical techniques. Such a learning curve can be an obstacle for field implementation for researchers, community organizations and citizen scientists wishing to include introductory drone operations into their work. In this study, we develop a comprehensive drone training program for research partners and community members to use cost-effective, consumer-quality drones to engage in introductory drone mapping of coastal seagrass monitoring sites along the west coast of North America. As a first step toward a longer-term Public Participation GIS process in the study area, the training program includes lessons for beginner drone users related to flying drones, autonomous route planning and mapping, field safety, GIS analysis, image correction and processing, and Federal Aviation Administration (FAA) certification and regulations. Training our research partners and students, who are in most cases novice users, is the first step in a larger process to increase participation in a broader project for seagrass monitoring in our case study. While our training program originated in the United States, we discuss our experiences for research partners and communities around the globe to become more confident in introductory drone operations for basic science. In particular, our work targets novice users without a strong background in geographic research or remote sensing. Such training provides technical guidance on the implementation of a drone mapping program for coastal research, and synthesizes our approaches to provide broad guidance for using drones in support of a developing Public Participation GIS process.

Kelp forests are commonly classified within remote sensing imagery by contrasting the high reflectance in the near-infrared spectral region of kelp canopy floating at the surface with the low reflectance in the same spectral region of water. However, kelp canopy is often submerged below the surface of the water, making it important to understand the effects of kelp submersion on the above-water reflectance of kelp, and the depth to which kelp can be detected, in order to reduce uncertainties around the kelp canopy area when mapping kelp. Here, we characterized changes to the above-water spectra of Nereocystis luetkeana (Bull kelp) as different canopy structures (bulb and blades) were submerged in water from the surface to 100 cm in 10 cm increments, while collecting above-water hyperspectral measurements with a spectroradiometer (325–1075 nm). The hyperspectral data were simulated into the multispectral bandwidths of the WorldView-3 satellite and the Micasense RedEdge-MX unoccupied aerial vehicle sensors and vegetation indices were calculated to compare detection limits of kelp with a focus on differences between red edge and near infrared indices. For kelp on the surface, near-infrared reflectance was higher than red-edge reflectance. Once submerged, the kelp spectra showed two narrow reflectance peaks in the red-edge and near-infrared wavelength ranges, and the red-edge peak was consistently higher than the near-infrared peak. As a result, kelp was detected deeper with vegetation indices calculated with a red-edge band versus those calculated with a near infrared band. Our results show that using red-edge bands increased detection of submerged kelp canopy, which may be beneficial for estimating kelp surface-canopy area and biomass.

Kelp forests are vital marine habitats with significant ecological, cultural, and economic importance. These ecosystems, found along coastlines, are susceptible to regional and global stressors including coastal development and climate change. This paper presents Satellite-based Kelp Mapping (SKeMa), a novel framework for automatically mapping canopy-forming kelp forests along the coast of British Columbia using Sentinel-2 satellite scenes. SKeMa employs a deep learning semantic segmentation model, offering an efficient alternative to traditional, labor-intensive, and time-consuming kelp-mapping methods. Here, we initially evaluated SKeMa against two conventional, non-deep-learning baselines, NDVI thresholding and Random Forest, and several alternative deep learning architectures. For the best-performing model, an ablation study on input channels identifies a suitable input configuration, and a cross-validation study with independent test sets yields a mean Intersection over Union (IoU) of 0.6024 and F1-score of 0.7491. Average precision and recall are 0.7261 and 0.7951, respectively. Overall, the highest validation metrics were for images with medium to large kelp beds and the lowest for images with small kelp beds, likely because Sentinel-2’s spatial resolution limits mapping of small kelp beds. These results are achieved under a highly imbalanced class distribution, with labeled kelp pixels constituting, on average, less than 0.1% of all pixels in a Sentinel-2 scene. The results demonstrate the model’s ability to detect kelp canopies across diverse coastal regions, particularly for medium to large kelp beds.