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Using Unoccupied Aerial Vehicles (UAVs) to map and monitor changes in emergent kelp canopy after an ecological regime shift

Cite this dataset

Saccomanno, Vienna et al. (2022). Using Unoccupied Aerial Vehicles (UAVs) to map and monitor changes in emergent kelp canopy after an ecological regime shift [Dataset]. Dryad.


Kelp forests are complex underwater habitats that form the foundation of many nearshore marine environments and provide valuable services for coastal communities. Despite their ecological and economic importance, increasingly severe stressors have resulted in declines in kelp abundance in many regions over the past few decades, including the North Coast of California, USA. Given the significant and sustained loss of kelp in this region, management intervention is likely a necessary tool to reset the ecosystem and geospatial data on kelp dynamics are needed to strategically implement restoration projects. Because canopy-forming kelp forests are distinguishable in aerial imagery, remote sensing is an important tool for documenting changes in canopy area and abundance to meet these data needs. We used small unoccupied aerial vehicles (UAVs) to survey emergent kelp canopy in priority sites along the North Coast in 2019 and 2020 to fill a key data gap for kelp restoration practitioners working at local scales. With over 4,300 hectares surveyed between 2019 and 2020, these surveys represent the two largest marine resource-focused UAV surveys conducted in California to our knowledge. We present remote sensing methods using UAVs and a repeatable workflow for conducting consistent surveys, creating orthomosaics, georeferencing data, classifying emergent kelp, and creating kelp canopy maps that can be used to assess trends in kelp canopy dynamics over space and time. We illustrate the impacts of spatial resolution on emergent kelp canopy classification between different sensors to help practitioners decide which data stream to select when asking restoration and management questions at varying spatial scales. Our results suggest that high spatial resolution data of emergent kelp canopy from UAVs have the potential to advance strategic kelp restoration and adaptive management.


Priority survey site selection:

We selected sites for UAV emergent kelp canopy surveys using a prioritization framework for kelp recovery efforts based on data from OAV surveys, subtidal surveys, areas of cultural significance, areas of economic significance, accessibility, and proximity to marine protected areas (MPAs) (1). A total of 37 sites were identified in Mendocino and Sonoma Counties (i.e., the “North Coast”), hereafter referred to as ‘priority sites’. Ten of the sites are in actively managed state MPAs and 27 are in the Greater Farallones National Marine Sanctuary (NMS). Thirty-six of the 37 sites were surveyed with UAVs between 2019 and 2020, with 21 sites surveyed in both 2019 and 2020. The average priority site area was 1 km2 (range 0.2-1.7 km2).       

UAV flights, timing and environmental sources of variation and error

Due to the 90 km stretch of coastline within which the noncontiguous priority sites are located, numerous pilots participated in data collection and we developed a repeatable workflow building upon the efforts of Katherine C. Cavanaugh et al. 2021 to ensure data consistency. We obtained state and federal permits to allow UAV use in restricted areas and we established criteria for UAV launch sites (i.e., public coastal access, no large obstacles, flat area with minimal ecological impact potential, and located mid-way in the survey area to maintain telemetry link between the UAV and controller). We used small UAV platforms from the same manufacturer and each pilot selected their own flight software. Pilots flew at an altitude of 120 m above mean sea level with a minimum front and side overlap of 75%, nadir angle of the sensor, auto white balance, and UAV speeds between 10 to 12 m/s. The image processing softwares used included Agisoft Metashape, DroneDeploy, and Pix4D; all orthomosaics were reviewed by expert annotators and when output orthomosaics were incomplete or contained significant defects, the imagery was reprocessed using at least one of the two other software options.


All UAV pilots acquired imagery using the built-in Red-Green-Blue (RGB) sensor. We coordinated flights to coincide with the annual peak biomass of bull kelp, which typically occurs in late summer/early fall on the North Coast. Our team surveyed during the lowest tide series of the month and aimed to survey at the lowest tide of the day, as tidal height and surface currents have been shown to impact the amount of kelp canopy exposed on the water surface (2) and these impacts can vary regionally (3). Because sun angle, wind, and weather conditions varied significantly throughout the data collection process, surveys were not restricted to a specific daily tidal height or current speed; data were collected when field conditions allowed for stable UAV launch and landing and this structure resulted in random sampling throughout the tidal range within and between years, addressing sampling bias in our data. 

Kelp Detection, Classification, and Quantification 

We identified kelp pixels in each UAV image using a band combination between the red and blue bands (Red - Blue), which has been shown to best distinguish kelp from water in RGB-UAV imagery relative to other RGB vegetation indices (3). Before applying a threshold to our image, we manually masked all terrestrial objects (e.g., land and intertidal rocks). Due to radiometric and spectral variability present in the imagery, we manually selected thresholds to distinguish kelp from seawater. For individual sites with high levels of spectral variability due to turbidity, sun glint, or other artifacts, a single threshold could not be used for kelp identification because the threshold varied throughout the image within a site (3). For these sites, we gridded images into subsets (ranging from 1000 x 1000 m areas to 5000 x 5000 m areas, depending on the level of variability), and each grid was assigned a unique threshold. As a result, multiple thresholds were used for classification for these sites. We mosaicked the classified grids back to their original extent and manually reviewed all classified mosaics for quality assurance. We used binary classification values (i.e., “Kelp” or “Not Kelp”) except for mixed-species marine algal beds and the occasionally blurred image, which were assigned “No Data” values. We worked in a GIS environment to determine the area of kelp at a given site by multiplying the number of kelp pixels by the area of the pixels (ArcGIS Pro 2.7).


Comparison to multi-decadal Landsat data:


To give multi-decadal temporal context to the UAV surveys, we examined long-term trends in kelp canopy dynamics along the North Coast using Landsat satellite imagery. (n=36). To control for differences in available reef habitat between priority sites, we selected the maximum area of kelp canopy (m2) that occurred within a site in each year and normalized that amount by the historical maximum extent of emergent kelp canopy (i.e., the cumulative area within a site where kelp was ever observed between 1984-2020) to produce a time series of annual, proportional coverage values. We also used Landsat emergent kelp canopy data to produce maps of canopy persistence at our case-study sites, where relative persistence was defined as the number of years from 1984-2020 in which a pixel contained kelp canopy (4). Maps of emergent kelp canopy for case-study sites during a given year used the maximum canopy area observed.

(1) Hohman, R., Hutto, S., Catton, C. and F. Koe. 2019. Sonoma-Mendocino Bull Kelp Recovery Plan. Plan for the Greater Farallones National Marine Sanctuary and the California Department of Fish and Wildlife. Greater Farallones Association. San Francisco, CA. 166 pp.

(2) Britton-Simmons, K., Eckman, J. E. & Duggins, D. O. Effect of tidal currents and tidal stage on estimates of bed size in the kelp Nereocystis luetkeana. Marine Ecology Progress Series vol. 355 95–105 (2008).

(3) Cavanaugh, K. C., Cavanaugh, K. C., Bell, T. W. & Hockridge, E. G. An Automated Method for Mapping Giant Kelp Canopy Dynamics from UAV. Front. Environ. Sci. Eng. China 0, (2021).

(4) Bell, T. W., Allen, J. G., Cavanaugh, K. C., & Siegel, D. A. (2020). Three decades of variability in California’s giant kelp forests from the Landsat satellites. In Remote Sensing of Environment (Vol. 238, p. 110811).


Usage notes


Data repository metadata for Using Unoccupied Aerial Vehicles (UAVs) to map and monitor changes in emergent kelp canopy after an ecological regime shift 

This repository contains the following data and files: 

  1. The 2019 and 2020 orthomosaics for all surveyed priority sites. These files have been georeferenced and masked to remove the coast with individuals’ homes for privacy purposes. The relevant nomenclature for these files is as follows: SITENAME_YEAR.tif (n = 57) 

  1. The 2019 and 2020 classification layers built off of the orthomosaics for all surveyed priority sites. These files are available as shapefiles that can be opened in QGIS. The relevant nomenclature for these sites is as follows: SITENAME_finalYEAR.shp/.shp.xml/shx/prj/dbf/sbx/sbn/cpg 

  1. The R code and relevant CSV files for the spatial statistics and figures. The relevant nomenclature for these files is as follows: 

    1. Saccomanno_etal_UAVkelp_stats.Rmd (this script is well-commented) 
    2. kelp_landsat_area_FINAL.csv 

  1. kelp_drone_area_FINAL.csv (Years and sites with no data are marked NaN)

  1. kelp_drone_case_study_FINAL.csv 

  2. kelp_drone_latitude030722.csv
  3. kelp_drone_tides_FINAL.csv
  1. The Landsat kelp coverage data for all of the priority sites from 1984-2020 ( Landsat_TimeSeries_Charts_36sites_2021_data.xlsx) 

  1. KMZ of the priority sites extent


National Philanthropic Trust, Award: NPTTNCKelp

NOAA Center for Coastal & Marine Ecosystems, Award: NA16SEC4810009

National Marine Sanctuary Foundation, Award: 19-12-B-245

National Aeronautics and Space Administration, Award: 80NSSC21K1429

National Science Foundation, Award: NSF OCE 1831937