DOI: 10.5061/dryad.n02v6wwzs
title: Using Unoccupied Aerial Vehicles (UAVs) to map and monitor changes in emergent kelp canopy after an ecological regime shift

description: 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.
description: <strong>Priority survey site
 selection:</strong> 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
 km<sup>2</sup> (range 0.2-1.7 km<sup>2</sup>).   
     <br><strong>UAV flights,
 timing and environmental sources of variation and
 error</strong> 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<em> </em>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. 
 <br><strong>Kelp Detection, Classification, and
 Quantification</strong>  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).   <strong>Comparison to multi-decadal Landsat
 data:</strong>   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 (m<sup>2</sup>) 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.
 https://farallones.org/wp-content/uploads/2019/06/Bull-Kelp-Recovery-Plan-2019.pdf. (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). <br>(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).<br>  (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 <em>Remote Sensing of Environment</em> (Vol. 238, p. 110811). https://doi.org/10.1016/j.rse.2018.06.039  
description: README  Data repository metadata for Using
 Unoccupied Aerial Vehicles (UAVs) to map and monitor changes in emergent
 kelp canopy after an ecological regime shift 
 <br>This repository contains the following data and
 files:  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)  
 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 
 The
 R code and relevant CSV files for the spatial statistics and figures. The
 relevant nomenclature for these files is as follows:  
 Saccomanno_etal_UAVkelp_stats.Rmd (this script is
 well-commented)  
 kelp_landsat_area_FINAL.csv  
 kelp_drone_area_FINAL.csv (Years
 and sites with no data are marked NaN) 
 
 kelp_drone_case_study_FINAL.csv  kelp_drone_latitude030722.csv
 kelp_drone_tides_FINAL.csv
 The Landsat kelp coverage data for
 all of the priority sites from
 1984-2020 ( Landsat_TimeSeries_Charts_36sites_2021_data.xlsx) 
 KMZ of the priority sites extent