Using drones for mapping seagrass and coastal carbon inventories

SeaBee is being proven in the field through different applications and subprojects. ZosMap is a SeaBee subproject with focus on developing applications for seagrass mapping and monitoring using drones (Unoccupied Aerial Vehicles, UAVs), high-resolution aerial imaging, and Machine Learning (ML) technology for image analysis and thematic mapping.  

The aim is to explore the possibilities of measuring seagrass distribution, biomass, carbon content, and health status using flying drones. Not only are seagrass meadows essential to sustain marine biodiversity and provide nursery grounds for fish, but they also host large amount of organic carbon bound in living biomass and in the sediments below the seagrass meadows.   

Mapping seagrass mission

In mid-June, a team of NIVA researchers and drone pilots once again went to map seagrass meadows and explore the amount of organic carbon that is stored in these vital coastal habitats.

The fieldwork is part of an annual monitoring campaign with drone missions flown every month. Five researchers from NIVA participated in this fieldwork. In addition, engineering researchers and two Master students from NTNU are involved in the project work.

A bit outside Larvik, around Ølbergholmen in the Oslofjord, ZosMap has a test ground for developing methods and testing out new hypotheses. Two small bays define the test area that hosts both dense and sparse seagrass meadows, rockweed beds, sandy sediments with microalgae, and a few scattered kelp occurrences. 

The two adjacent field sites close to Larvik. Named "Ølberg S" the southern bay and "Ølberg N" the northern bay (named after the peninsula on the outside of the two bays). N59.007, E10.132.
NIVA partners working in SeaBee, in the field and ready to to map seagrass meadows and explore the amount of organic carbon that is stored in these vital coastal habitats. Photo Kasper Hancke.

A 360 degree panorama of the field area in Larvik, taken using a Mavic mini Pro3. Panorama by Kasper Hancke.

Flying drones

Over four days in June the team flew multiple drone missions for collecting imaging data covering both a larger overview of the region (at high altitude) and high-resolution images for detailed studies (at low altitude). To assess the meaning of pixel resolution for seagrass and organic carbon mapping drone mission were flown at altitudes from 20 to 100 m.  

The drones used in the field mission (top and lower right), and the seaweed species that were mapped (lower left). Photo by Kasper Hancke.

A DJI Matrice 300 drone was deployed equipped with an RGB camera (SeaBee Tech) and a multispectral spectral (MSI) camera (Micasense Altum). In addition a DJI M600 drone was deployed with a hyperspectral (HSI) sensor (SPECIM AFX-10). The RGB images will be used primarily for image annotation and provide good true color overview image, while MSI and HIS image data will be used for generating thematic vegetation maps and quantifying seagrass biomass, organic carbon content and ecosystem health status.  

In addition to drone data collection, the team was busy collecting ground truth data using traditional techniques in order to annotate (categorise) image data and to build a database for training of machine learning algorithms. 

Measuring of seagrass using ground-based methods. Photo by Kasper Hancke.

The algorithms will in turn be able to identify seagrass and other coastal vegetated habitats automatically from drone data, in the same way as a smart phone identifies the face of its owner.

In June we found the seagrass meadows of the Larvik region to be both well developed, in good ecological condition and rich in organic carbon hosted in its dense seagrass canopies – Kasper Hancke, NIVA


Importance for seagrass meadows 

Seagrass meadows in the Oslofjord are often under strong pressure from anthropogenic stressors, so it was great to experience healthy and live seagrass meadows in these areas. These areas are not only a SeaBee/ZosMap test ground, but are also a well-visited recreational area for beach drifters, picnic groups, kayakers and many others.

Next Steps

Next steps are to analyse the images further in the SeaBee data pipeline. The results will be published when the data and laboratory samples have been processed.

For more information on how SeaBee works with coastal habitat mapping, visit ‘What We Do’

Two weeks, 440 drone missions, 100 thousand images – The SeaBee Research Infrastructure is taking off!

SeaBee is creating a comprehensive national research infrastructure, enabling sharing and use of data, collected with drones, to better understand Norway’s natural environment. SeaBee trains machine learning algorithms to analyse drone data and facilitate automated data analysis. This allows researchers to do their work faster, better, and more efficiently.  

SeaBee and SeaBirds – collecting drone images in the field 

Two field teams from NINA were out in May 2023. Led by Sindre Molværsmyr (NINA), the teams mapped sea gull and cormorant breeding colonies along the southern Norwegian coast from the Swedish border to Karmøy (more than ~500 km of coastline). The goal was to count seabirds through collecting drone images of seabird populations. The data collected from the drone images can then be compared with historical datasets, and data collected using traditional methods.

Traditional methods of data collection are people going to the seabird colony and counting the seabirds, or taking thousands of photos with a handheld camera from the window of a small airplane. The seabird population counting work also contributes to SEAPOP (Om SEAPOP – SEAPOP). 

Map of missions flown to map breeding seabird colonies, during field work by NINA. Photo Sindre Molværsmyr

In total, 440 drone missions were flown by two teams during two weeks from 15 – 29th May, 2023, each using a DJI MAVIC 3 equipped with a RGB camera (SeaBee tech). The surveys mostly covered open-nesting species, like gulls and cormorants.

With very nice weather for most of the field mission, the teams collected around 100 thousand drone images of seabird populations during the 440 missions. The largest missions collected around 2000 images to cover larger islands along the coast. Both teams covered about 150 km of coastline each day, one team with their own boat and the other with good help from Skjærgårdstjenesten or other local boat drivers.

The teams covered most known bird populations in the mission area. All missions were logged and added to the map and data visualisations. This will help for next year’s field mission planning, as colonies that were missed this year are easily seen and located in the map visualisations.


Our field work and data collection could not have been done at this scale without SeaBee – sending data for processing by pressing enter on my computer in the evenings was very efficient. The amount of data collected would be impossible to analyse manually, it would take many months to analyse drone images from 400 colonies by hand – Sindre Molværsmyr, NINA

Preparing for drone flying mission from the boat. Photo by Sine Dagsdatter Hagestad
Pilot flying drone out from the boat to capture images for counting seabird populations during NINA's field mission in May 2023. Photo by Sine Dagsdatter Hagestad.
Large scale data upload

NINA’s field campaign is one of the first examples of the SeaBee infrastructure operating on large-scale data handling, with drone images uploading directly to the SeaBee data server directly from the field at the end of each day. This is a great achievement for SeaBee, which not only saves time in the field, but allows more efficient mapping and monitoring of large geographic areas with better data quality.

James Sample (NIVA) and the SeaBee Data Platform team have worked hard to get to this point (SeaBee Data Platform components and how it works). Using code deployed on Sigma2, an automated script runs every hour to identify, process and publish new datasets uploaded by scientists in the field.

It was an exciting first test of SeaBee as “data infrastructure”. I really enjoyed being able to explore the new mission data published each morning to see what I could find – James Sample, NIVA

Ready for reviewing drone data collected during the day, before uploading to the SeaBee Data Pipeline
SeaBee Data Pipeline

The SeaBee data infrastructure was valuable during this mission, as having data processed in near real-time allowed the field teams to review data and adjust settings and drone flying strategies during the mission, to ensure the best quality drone images were collected.

In total, around 1 TB of raw images from drone flights were collected and uploaded to the SeaBee data repository, which led to around 15 TB of data once it had been processed (Digital Terrain models (DTMs), orthophotos, point clouds, etc).

The SeaBee data infrastructure ran well for processing such a large amount of data. On average, missions took around 90 minutes to process. Missions with approximately 2000 images took around 1 day to process. After processing, the final products were automatically published to SeaBees GeoNode server. Currently, there are more than 500 data resources published on the server.

This project has been an important achievement for SeaBee as it demonstrates how useful a drone-based infrastructure is for seabird research and mapping, and it serves as a good example of how efficient SeaBee can be for large-scale mapping projects and handling of big data. – Kasper Hancke, NIVA


Drone image of seabird population, collected during NINA's field work in May 2023. Photo by Sine Dagsdatter Hagestad
Next steps

The next steps for this seabird mapping project include analysing the drone images that have been collected to estimate seabird population numbers. This will involve training a new AI model, and annotating drone images work as bird species were encountered that are not in the current AI models (for more information, check using Artifical Intelligence and drone data). Also, some comparisons will be made between the data collected using drones and that collected using traditional methods.

In terms of what will happen next for the SeaBee data infrastructure, this field mission provided some very useful lessons for processing large scale datasets. Better error handling systems will be developed, and the machine learning from NR will be integrated to further automate the infrastructure.