Testing the Use of Unmanned 

Aircraft Systems for Intertidal Surveys 

PRINCIPAL INVESTIGATORS

Dr. Brenda Konar

Dr. Katrin Iken

FINAL REPORT 

July 2016

OCS Study BOEM 2016-051
Contact Information: 

Coastal Marine Institute 

University of Alaska Fairbanks 

P. O. Box 757220 

Fairbanks, AK 99775-7220 

email: CMI@alaska.edu 

phone: 907.474.6782 

fax: 907.474.7204 

This study was funded in part by the U.S. Department of the Interior, Bureau of Ocean Energy 

Management (BOEM) through Cooperative Agreement M13AC00017 between BOEM, Alaska Outer 

Continental Shelf Region, and the University of Alaska Fairbanks. This report, OCS Study BOEM 2016-

051, is available through the Coastal Marine Institute. The views and conclusions contained in this 

document are those of the authors and should not be interpreted as representing the opinions or 

policies of the U.S. Government. Mention of trade names or commercial products does not constitute 

their endorsement by the U.S. Government. 
iii 

Table of Contents

Page 

List of Figures ................................................................................................................................ iii 

List of Tables ................................................................................................................................. iii 

Abstract .......................................................................................................................................... iv 

Introduction ......................................................................................................................................1 

Methods............................................................................................................................................2 

Results ..............................................................................................................................................6 

Discussion ......................................................................................................................................12 

Conclusion .....................................................................................................................................14 

Acknowledgements ........................................................................................................................15 

References ......................................................................................................................................16 

List of Figures 

Page 

Figure 1. Map showing the seagrass and rocky sites within Kachemak Bay ..............................3 

Figure 2. Transect layout and example images taken from the sUAS and ground observer 

cameras from the rocky intertidal MTL. ......................................................................4 

Figure 3. Transect layout and example images taken from the sUAS and ground observer 

cameras from the seagrass system. ...............................................................................5 

Figure 4. Comparison of percent cover (observer surveys versus sUAS images) of coarse 

taxonomic categories from the rocky intertidal site. ....................................................8 

Figure 5. nMDS ordinations of images taken by the sUAS and observer visual surveys for the 

MHHW, MTL, and MLLW tide levels. .......................................................................9 

Figure 6. nMDS ordinations of community structure from images taken by the sUAS versus 

images from the ground camera for MHHW, MTL, and MLLW tide levels .............10 

Figure 7. Percent cover of coarse categories from the seagrass bed as assessed by observer 

surveys and images taken by the sUAS ......................................................................10 

Figure 8. nMDS ordinations of fine and coarse taxonomic resolution community composition 

from images taken by the sUAS versus observer visual surveys. ..............................11 

Figure 9. Percent cover of seagrass derived from sUAS images. ..............................................11 

List of Tables

Table 1. ANOSIM results of comparisons between observer and sUAS data and between 

sUAS and camera data .................................................................................................7 
iv 

ABSTRACT 

Intertidal monitoring is critical for supporting sustained ecosystem health and functioning as 

coastal systems are particularly vulnerable to environmental stressors and human impacts. 

Intertidal monitoring projects are often limited in their practicality because the spatial extent for 

which data can be collected by traditional methods, such as visual surveys or removal of biota, is 

constrained to sampling at low tide exposure periods. Here, we used imagery from a small 

unmanned aerial system (sUAS) to test their potential use in rocky intertidal and intertidal 

seagrass surveys in the northern Gulf of Alaska. Images captured by the sUAS in the high, mid, 

and low intertidal strata on a rocky beach and within a seagrass bed were compared to data 

derived from concurrent observer visual surveys and images taken by observers on the ground. 

Observer visual data always resulted in the highest taxon richness; however, when observer data 

were aggregated to the same taxonomic resolution obtained by the sUAS images, overall 

community composition was mostly similar between the two methods. Ground camera images 

and sUAS images yielded mostly comparable community composition despite the typically 

higher taxonomic resolution obtained by the ground camera. We conclude that monitoring goals 

or research questions that can be answered on a relatively coarse taxonomic level can benefit 

from an sUAS-based approach because it allows much larger spatial coverage than is possible by 

observers on the ground. We demonstrated this large-scale applicability by using sUAS images 

to develop maps that show the distribution patterns and patchiness of seagrass. 
1 

INTRODUCTION 

Monitoring and assessing biological communities in intertidal habitats is a common practice and 

much needed in our changing climate because these communities will be among the first to 

exhibit impacts (Helmuth et al., 2006). Traditional intertidal monitoring is restricted to low tide 

events when researchers can manually quantify organisms using percent cover, abundance, 

biomass, or similar measurements (Konar et al., 2009; Leujak and Ormond, 2007; Menge et al., 

2015). In the field, this is typically done by a small team of surveyors who assess species 

composition from quadrats or transect lines along intertidal strata. Quadrat sampling is a tested, 

accurate, and efficient sampling method for quantifying benthic biological communities (Heltshe 

and Forrester, 1985; Obermeyer, 1998) and is a mainstay in monitoring programs (Delaney et al., 

2008; Konar et al., 2009) and impact studies (Cox and Foster, 2013; Minchinton and Fels, 2013). 

For example, in the Gulf of Alaska, quadrat sampling was broadly used in assessing damage and 

intertidal recovery after the Exxon Valdez oil spill (Boehm et al., 1996; Skalski et al., 2001; 

Stekoll and Deysher, 2000) and is still being used by the current Gulf Watch Alaska intertidal 

monitoring program (Konar et al., in press). The advantages of this type of traditional survey 

method are that experts can identify the species in the field at a high taxonomic resolution and 

can collect vouchers of unknown organisms for later identification. Despite its wide application, 

quadrat sampling in intertidal systems has the inherent problem of being spatially limited 

because only small stretches of beach (on the order of 10s to 100s of meters) can typically be 

surveyed per low tide interval. This spatial limitation can cause problems in representatively 

capturing species occurrence and distribution, intertidal community structure, and variability in 

community composition. 

In addition to traditional sampling, photographic surveys have proven to be an effective and cost 

efficient method for accessing benthic communities (Aronson et al., 1994; Bohnsack, 1979; 

Witman et al., 2004). Photographic methods provide advantages over traditional in situ surveys 

because photographs reduce the time required in the field to collect quadrat information, and 

images offer a permanent historical record (Reimers et al., 2014, although see Foster et al., 

1991). While use of imagery can reduce the time it takes to sample a single quadrat (Bohnsack, 

1979), the amount of beach that can feasibly be sampled remains limited to the short low tide 

period because surveyors need to walk along the beach stratum and photograph each sample. 

Recently, other means of acquiring photographs across broader areas have been tested. For 

example, kite aerial photography produced maps that covered 200 m of intertidal shoreline 

(Bryson et al., 2013). These photographs were used to construct high-resolution, threedimensional, multi-spectral terrain models of rocky intertidal shores. Similarly, low-altitude 

aerial photography from a balloon-mounted digital camera platform was used to acquire an 

intertidal landscape mosaic of eelgrass (Zostera marina) and blue mussels (Mytilus edulis) 

(Barrell and Grant, 2015). Also, multispectral LiDAR surveys have been used to evaluate the 

structural complexity of coastal habitats (kelp habitat, eelgrass meadow, beach, salt-marsh, farm, 

and urban coastal environments) and to classify the spatio-temporal distribution of these habitats 
2 

across a large spatial area (Collin et al., 2012). Photographic and video surveys from airplanes 

have become common for characterizing intertidal communities, as demonstrated by the National 

Oceanic and Atmospheric Administration (NOAA) ShoreZone mapping program along most of 

Alaska’s shoreline and much of the US west coast (www.shorezone.org). While the use of 

various types of aerial photography has allowed for the expansion of the spatial extent of 

intertidal surveys, the resulting imagery has not been used to identify intertidal organisms at the 

taxonomic resolution needed for biological monitoring. 

Ideally, an intertidal method should allow sampling to be completed quickly, cover much spatial 

area, and have high enough taxonomic resolution to inform accurate and detailed biological 

descriptions of large intertidal areas. We suggest that a small Unmanned Aerial System (sUAS) 

equipped with a high-resolution camera can be used to spatially expand biological intertidal 

sampling. We tested this methodology for an ongoing rocky intertidal and intertidal seagrass 

monitoring program in the Gulf of Alaska. In Alaska, sUASs have been successfully operated in 

marine mammal monitoring, resource mapping, and mapping sea ice and glaciers (Koski et al., 

2009; Walker, 2012). However, altitudes flown for those purposes are too high to produce 

images at the taxonomic resolution required for most biological intertidal monitoring programs. 

Here, we flew a small, camera-mounted sUAS at low altitude to test if resulting images would 

allow for the analysis of percent cover of individual intertidal taxa or taxon groups. We also 

tested the use of sUAS images of an intertidal seagrass bed to assess percent cover of seagrasses 

versus other taxa. In addition, we used imagery over the seagrass bed to produce large-scale (on 

the order of 100s of meters) coverage of the bed to assess variability in seagrass cover, which is 

difficult to achieve for a representative area doing ground surveys. Seagrass bed size and 

patchiness are important monitoring metrics because seagrass beds tend to shrink in size under 

high sedimentation conditions such as from coastal erosion (Cabaço et al., 2008). Therefore, our 

primary objective was to determine if sUASs can be used to spatially expand current traditional 

rocky intertidal and intertidal seagrass monitoring. We tested the hypotheses that observer data 

would yield higher taxonomic resolution than images taken by a camera mounted to an sUAS 

and that there would be no difference between methods when applied at a unified, coarse 

taxonomic level. We also hypothesized that community composition determined from images 

taken on the ground by observers would not be different to that determined from images taken by 

the sUAS. Lastly, we hypothesized that sUAS images could be used to create large-scale 

distribution maps of seagrass cover. 

METHODS 

This study took place in April to May 2015 in Kachemak Bay, Alaska, in the northern Gulf of 

Alaska at 59.7257 ̊ N and 151.1410 ̊ W, where ongoing intertidal monitoring efforts through the 

Gulf Watch Alaska program (www.gulfwatchalaska.org) include the biological assessment of 

rocky intertidal and intertidal seagrass systems. We paired the annual monitoring of two 

permanent intertidal sites with an sUAS team, which included an sUAS pilot, to conduct this 

study. For this, a camera-equipped sUAS was deployed at one rocky, macroalgal-dominated 
3 

beach and one soft-bottom seagrass intertidal site in Kachemak Bay, Alaska (Figure 1). The 

sUAS was a quadcopter Aeryon ScoutTM, which carried a GoPro Hero 3, 12-megapixel camera 

modified with a rectilinear lens to eliminate the fish-eye effect. At the rocky intertidal site, the 

sUAS was deployed at the mean higher high water (MHHW), the mean tidal level (MTL), and 

the mean lower low water (MLLW) level (see Konar et al., 2009). At the seagrass site, the sUAS 

was deployed from a single starting point on the beach and flown in various directions across the 

seagrass bed. In both intertidal systems, the sUAS was flown at an altitude of approximately 5 m 

at very low speeds of approximately 2 km/h to maximize the resolution of the imagery. Images 

were taken every 1-5 seconds, depending if images were taken solely or grabbed from video. 

Figure 1: Map showing the seagrass and rocky sites within Kachemak Bay. The red box on the inset map 

shows the location of the study area within Alaska. 

A field team conducted biological ground surveys at the same times and locations as the sUAS 

deployments. A 50-m transect tape was placed at each depth stratum (MHHW, MTL, and 

MLLW) at the rocky site and one 50-m transect was placed in the center of the seagrass site to 

orient the sUAS and ensure that the sUAS surveyed the same spatial area as the ground crew. A 

quadrat (1-m2

 at rocky site, 0.25-m2 at seagrass site) was placed along each transect to allow us 

to compare the size of an sUAS image to a ground image or survey plot. At the rocky intertidal 

site, the observer surveys included the visual estimation of percent cover of macroalgae, sessile 

invertebrates (such as mussels and barnacles), and bare substrate in ten randomly-placed 1-m2

quadrats along transects at each of the tidal strata (Figure 2). A photograph was taken of each 

quadrat and used for comparisons with the sUAS images. Each rocky intertidal stratum was 
4 

examined separately because strata differ significantly in community composition and are not 

comparable in this region (Konar et al., 2009). At the seagrass site, percent cover of seagrass and 

other groups such as mussels, macroalgae, diatom mats, and open substrate was calculated from 

five randomly-placed 0.25-m2 quadrats placed along a 50-m transect in the approximate center of 

the bed (Figure 3). A picture was taken of each quadrat to compare with five randomly selected 

sUAS image frames along this transect. To assess the distribution and patchiness in seagrass 

cover over a larger spatial extent, we flew the sUAS along extended haphazard flight paths over 

the seagrass bed. 

Figure 2: Transect layout and example images taken from the sUAS and ground observer cameras from 

the rocky intertidal MTL. 
5 

Figure 3: Transect layout and example images taken from the sUAS and ground observer cameras from 

the seagrass system. 

Percent cover was determined from images taken on the ground as well as ten randomly selected 

images taken by the sUAS along each 50-m transect in each system (Figure 2). Data were 

collected at the finest taxonomic resolution (fine) produced by each of the three methods (sUAS 

image, ground image, and observer visual). We then aggregated the observer ground data at the 

taxonomic resolution achieved from sUAS images to test our hypothesis that community 

composition at that coarse taxonomic level is comparable between the two methods. For the 

comparison of sUAS images and ground camera images, we only used the fine taxonomic 

resolution. 

For both intertidal systems, we compared the mean percent cover for the individual coarse taxon 

groups between observer data and sUAS images using t-tests at a significance level of α=0.05 

(SYSTAT v10). The overall community compositions for rocky intertidal and seagrass systems 

were compared at fine and coarse taxonomic resolutions for sUAS images versus observer data 

and at fine taxonomic resolution for sUAS images versus ground camera images. In the rocky 
6 

intertidal, this was done separately for each tidal stratum. We used nonmetric Multidimensional 

Scaling (nMDS) to visualize the data and Analysis of Similarity (ANOSIM) to determine 

differences between the sampling methods (Primer-e V7). An ANOSIM yields global R values 

that range from 0 to 1, with smaller values indicating little differences in community structure 

and R values above 0.45 considered to represent biologically relevant differences in community 

composition (Clarke et al., 2014). In cases where we detected differences between sUAS images 

and observer data at the coarse taxonomic level, we used a Similarity Percentage Analysis 

(SIMPER) to determine the taxon categories that contributed most to the differences in 

community composition. Similarly, we used SIMPER to determine the categories that 

contributed most to community differences detected in comparisons between the sUAS and 

ground images at the fine taxonomic resolution. 

For the large-scale analysis of patchiness in the seagrass bed, we selected non-overlapping 

images from a total of four flights with relatively even flight height (~ 5 m) as assessed from the 

altimeter information recorded by the sUAS camera. For each image (n=662 for all flights 

combined), we estimated the percent cover of seagrass in the image area. We then took the GPS 

coordinates of each image and converted them into eastings and northings to convert coordinates 

into actual distances (in meters) and to avoid spatial distortion. We spatially plotted the seagrass 

percent cover using a linear interpolation function to create a “heat map” to visualize regions of 

high and low seagrass cover or spatially plotted individual data points using Matlab. 

RESULTS 

In the rocky intertidal, observer percent cover surveys at the fine taxonomic resolution yielded 

10, 25, and 24 different taxa/categories at the MHHW, MTL, and MLLW, respectively (Table 1). 

This was much higher than obtained from the sUAS images at the fine resolution, with 4, 9, and 

10 taxa/categories at the same tidal strata. The observer camera images yielded similar or slightly 

higher taxon/category numbers as the sUAS images, with 5, 10, and 10 categories at the MHHW, 

MTL, and MLLW, respectively. The higher taxonomic resolution from the observer data 

primarily came from macroalgae that could be identified to species level by observers but 

required grouping into larger categories for camera and sUAS images (Table 1). Data from the 

ground observers and sUAS images were then unified at the coarser taxonomic level available 

for the sUAS images and grouped into 4 categories at the MHHW, 10 categories at the MTL, and 

10 at the MLLW (Figure 4). At the MHHW, significant differences between observer and sUAS 

data existed for the macroalgae and rock/shell categories (Table 1, Figure 4). At the MTL, the 

only significant differences in categories between the two methods were for kelp and other green 

algae; however, kelp occurred at a very low percentage in that tidal stratum. At the MLLW, 

significant differences between the two methods were observed for Palmaria spp., 

Polysiphonia/Pterosiphonia, and Cladophora/Acrosiphonia categories. Of these, the 

Cladophora/Acrosiphonia category was the only one of the significant groups that had higher 

percentages in the sUAS images than in the observer data (Figure 4). 
7 
8 

 

 

Figure 4: Comparison of percent cover (observer surveys versus sUAS images) of coarse taxonomic 

categories from the rocky intertidal site. A) MHHW, B) MTL, C) MLLW. Asterisks above bars indicate 

statistical differences for the respective category between sUAS images and observer surveys (at p≤0.05; 

results from t-test). 
9 

Similarities in the overall community composition between the observer and sUAS images 

depended mostly on the taxonomic resolution used but also varied slightly by the rocky intertidal 

stratum considered (Figure 5). As expected, observers always produced the highest taxon number 

in each tidal stratum, and community structure based on fine taxonomic resolution (i.e., the 

highest resolution yielded by either method) was always different between observer data and 

sUAS data (Figure 5 - fine, Table 1). When the taxonomic resolution of the observer data was 

brought to the same level as the sUAS images, community composition was no longer different 

between the two methods. However, at the MHHW, statistical results were near the threshold at 

which biologically meaningful differences could occur (ANOSIM R=0.436) (Figure 5 - coarse, 

Table 1). When combined, categories macroalgae and barnacles contributed the most (58%) to 

the differences in community structure at the MHHW (SIMPER analysis) and were both higher 

in the sUAS images than the observer data. When comparing the two image systems (sUAS and 

ground camera), we found a strong community difference at the MHHW but not at the other two 

strata (Figure 6, Table 1). As for the comparison between sUAS images and observer data, 

macroalgae and barnacles again contributed most to the community differences (56%, SIMPER), 

and both categories were higher in the sUAS images. Both image systems yielded very similar 

community compositions at fine resolution at both the MTL and MLLW (Figure 6, Table 1). 

Figure 5: nMDS ordinations of images taken by the sUAS and observer visual surveys for the MHHW, 

MTL, and MLLW tide levels. nMDS are shown for the fine (upper panel) and coarse (lower panel) 

taxonomic resolutions at each rocky intertidal stratum. All data were square-root transformed, and nMDS 

are based on Bray-Curtis similarity matrices. For ANOSIM results see Table 1. 
10 

Figure 6: nMDS ordinations of community structure from images taken by the sUAS versus images from 

the ground camera for MHHW, MTL, and MLLW tide levels. nMDS are shown for the fine taxonomic 

resolution at each rocky intertidal stratum. All data were square-root transformed, and nMDS are based 

on Bray-Curtis similarity matrices. For ANOSIM results see Table 1. 

Figure 7: Percent cover of coarse categories from the seagrass bed as assessed by observer surveys and 

images taken by the sUAS. Asterisks above bars indicate statistical differences for the respective category 

between drone images and observer surveys (at p≤0.05; results from t-test). 
11 

Figure 8: nMDS ordinations of A) fine, and B) coarse taxonomic resolution community composition from 

images taken by the sUAS versus observer visual surveys. All data were square-root transformed and 

nMDS are based on Bray Curtis similarity matrices. For ANOSIM results see text. 

Based on the result that seagrass percent cover taken by observers and from sUAS images were 

not significantly different (see Figure 7), we used the sUAS images from a larger spatial extent 

to assess variability in seagrass cover. The heat map based on percent cover of seagrass from 

sUAS images clearly identified the high-density center of the seagrass cover, with sharply 

decreasing concentrations towards the edges of the bed (Figure 9, left panel). Plotting the percent 

cover for each image, instead of using interpolation, can be a useful tool to document occasional 

but, strong small-scale variation in seagrass cover (Figure 9, right panel). Small-scale variability 

was especially obvious in the center of the bed where neighboring images showed seagrass cover 

from 10 to 80% (see circle in Figure 9, right panel). 

Figure 9: Percent cover of seagrass derived from sUAS images during flights across the seagrass bed. 

Left: Heat map of seagrass cover according to the scale on the right as % seagrass cover; black dots 

indicate pictures taken during the flight paths; dashed outline indicates the part of the plot displayed in 

detail in right panel. The “X” at the left side of the plot indicates the beach location where the sUAS 

started and returned. Right panel: Seagrass cover according to color scale given to the right for each 

individual image during flight paths, focusing on the center of the seagrass bed. The circle indicates a 

region of high patchiness in seagrass cover among neighboring images. 

X
12 

DISCUSSION 

The data support our hypothesis that observer percent cover surveys on the ground consistently 

achieve a higher resolution of taxonomic categories compared with sUAS images. Most of these 

differences resulted from the much higher taxonomic resolution of macroalgae obtained by the 

observers in both the rocky intertidal and seagrass systems (see Table 1). Naturally, observers on 

the ground have more means to determine taxonomic features of macroalgal species 

identification such as texture, branching patterns, holdfast structure, and natural color. Also, an 

observer can move organisms to get a better view of other partially hidden organisms, which is 

not possible with an image. These advantages may be especially important for taxa that occur at 

a very low percent cover. A study comparing various methods of estimating benthic cover in 

rocky intertidal systems found that photographic images can reliably identify taxa that occur at 

>30% in a plot (Meese and Tomich, 1992). The likelihood that taxonomic features will be 

discernable on an image should increase when a larger area is covered by a taxon. Many of the 

algal species in our study, in both rocky intertidal and seagrass systems, covered much less than 

30% of the area. In fact, many taxa covered just 1-5%, making it more likely that they were 

overlooked or not distinguishable on an image. Glare in many of the images and insufficient 

pixel resolution of the sUAS camera also made it difficult to distinguish small features. The 

number of macroalgal species in rocky intertidal systems has been proposed as a useful 

monitoring metric for water quality and system ecological health (Wells et al., 2007). That study, 

however, also recognized that reliability of species identification depends heavily on observer 

expertise and needs to be carefully evaluated. Thus, assessment of community structure on a 

coarser taxonomic level may be more reliable for monitoring purposes when taxonomic expertise 

cannot be guaranteed. 

In most cases, percent cover of individual categories and overall community composition were 

very similar once the observer data were aggregated at the taxonomic resolution level of the 

sUAS images in the rocky intertidal, supporting our hypothesis in most cases. We still detected 

notable differences in community structure at the coarse taxonomic resolution at the MHHW. On 

an individual species level at the MHHW, sUAS images yielded higher macroalgal cover while 

observer data yielded higher rock/shell cover (Figure 4A); however, on a community-wide level, 

macroalgae and barnacles contributed most to the community differences. It is possible that the 

white barnacles are not easily distinguished from light-colored rock on an image in the high 

intertidal and that the small size of many high intertidal macroalgal species (e.g., Gloiopeltis 

furcata and Endocladia muricata) renders them cryptic in an image. In addition, or alternatively, 

it is possible that spatial variability in the MHHW is so high that the randomly chosen observer 

plots and randomly chosen sUAS images happened to capture different aspects of this 

community. The fairly high scattering of replicate samples for both sUAS and observer data in 

the nMDS plots suggests that spatial variability is high in the MHHW in this system (Figure 5). 

While most of the spatial variability in rocky intertidal systems is associated with the vertical 
13 

extent of the system (i.e., the tidal strata), a significant amount can also be associated with the 

horizontal extent along a transect (Araújo et al., 2005; Konar et al., 2009). 

Similarly to the MHHW, we found significant differences in some individual species percent 

covers at the MTL and MLLW (Figures 4B, 4C), although the community-wide structures were 

highly similar (Figure 5, Table 1). Community analysis is a multi-species, permutational 

approach using rank-based data to calculate similarities of samples and, thus, the species that 

contribute to the multivariate differences in community structure are not always the same as 

those where abundances differ in single species comparisons (Clarke, 1993; Clarke et al., 2014). 

Therefore, the coarse taxonomic level achievable with an sUAS can be a useful tool if the goal of 

the monitoring is to assess changes in overall community structure or monitoring non-biological 

beach attributes. For example, sUASs were successfully used for monitoring disturbance in a 

rocky intertidal in northern Spain by comparing images of boulder position over time (PérezAlberti and Trenhaile, 2015). If the goal of the monitoring is to assess overall species richness or 

the abundance of specific species, the sUAS resolution may not be sufficient. It has been 

suggested that presence of opportunistic species is an important metric in rocky intertidal 

monitoring (Juanes et al., 2008; Wells et al., 2007), and it would be important to select such 

opportunistic species for a specific region in which to conduct monitoring. Should such species 

be recognizable on sUAS images, sUAS surveys could become a highly valuable tool. In 

previous work in the northern Gulf of Alaska, we determined that the genus or order levels of 

macroalgal identification, or groupings on a functional level, are sufficient to distinguish 

community patterns among various regions (Konar and Iken, 2009). While the sUAS did not 

consistently provide sufficient resolution on the genus or order level for all species, our results 

here are promising to test if the sUAS imaging approach could be useful to detect larger spatial 

scale (regional) differences in community structure on the coarse resolution. 

We hypothesized that ground camera and sUAS images would provide comparable communitylevel data. This was true for the rocky intertidal habitat at the MTL and MLLW but not at the 

MHHW. Observer surveys had an advantage over both imaging techniques in that observers 

could more reliably capture detailed taxonomic features and could move organisms aside to clear 

a partially hidden organisms underneath. However, the quality and resolution of ground images, 

typically taken from < 2 m height, was much higher than quality and resolution of sUAS images 

taken from a greater height. The better resolution of the camera images may have contributed to 

the greater detection of barnacles and small red algae, the two categories that contributed most to 

the differences in community structure. However, a slightly higher taxonomic resolution 

achieved with the ground camera versus the sUAS (see Table 1) did not affect results on the 

community level at the two lower tidal strata. Therefore, sUAS imagery could be an alternative 

to the more time-consuming method of walking along the tidal strata at low tide to capture 

ground images. Cost-benefit analyses would need to be done for specific projects to see whether 

sUAS imagery is also more cost effective. 
14 

In the seagrass system, the main difference between sUAS images and observer data was the 

higher resolution of macroalgae in the observer data. While overall macroalgal species richness 

in seagrass beds is much lower than in the rocky intertidal in Alaska, epiphytic macroalgae on 

seagrasses are often delicate and cryptic species that are difficult to distinguish on an image. For 

reasons similar to those discussed for rocky intertidal habitat, the limited resolution of sUAS 

imagery prohibited identification of these delicate species in seagrass, and the ground camera 

imagery proved more reliable for detecting these epiphytic macroalgal species. As seagrass 

system functioning depends heavily on seagrass-epiphyte interactions, the detection of epiphytes 

may be important to monitoring. For example, epiphytes are vital to nutrient recycling in the 

system when they take up a majority of nutrients released from seagrass leaves that would 

otherwise be diluted and lost from the benthic system (McRoy and Goering, 1974; Penhale and 

Thayer, 1980). Epiphytic algae also contribute significantly to the above-ground productivity of 

seagrass systems (Heijs, 1985), which then feeds into the associated food web (Kitting et al., 

1984). Additionally, epiphytic algae contribute significantly to the overall diversity in seagrass 

beds, a factor tied to resilience against perturbations (Blake and Duffy, 2010; Duffy, 2006; Duffy 

et al., 2015). Therefore, the lack of detection of epiphytic algae using sUAS images could be a 

significant hindrance in using the method when seagrass monitoring goals are associated with 

those functions. 

Seagrass cover itself was not significantly different between sUAS images and observer data 

(Figure 7). This is a very promising indication of sUASs usefulness for monitoring programs 

where the goal is to assess seagrass cover over large spatial scales. Seagrasses are foundation 

organisms that harbor a rich diversity of associated species, many of which are of commercial 

interest (Johnson et al., 2003). In Alaska, seagrasses also serve as spawning substrate for herring 

(Haegele and Schweigert, 1985) and provide an important nursery function for this important 

forage fish, which has experienced significant declines and little recovery after the 1989 Exxon 

Valdez oil spill in Prince William Sound and exposure to other stressors (Carls et al., 2002). 

Thus, reliably assessing seagrass cover itself is ecologically valuable. We successfully used 

sUAS images to create distribution maps of seagrass cover over a much larger scale than is 

feasible by observers on the ground. The maps demonstrate that the size of a seagrass bed can be 

reliably determined because the fairly sharp transition from seagrass cover to no cover was 

clearly recognizable. In addition, it was possible to gain an understanding of the small-scale 

variability and patchiness of seagrass cover, even in the densest region of the bed. 

CONCLUSION 

The use of sUASs in intertidal monitoring was assessed for a rocky intertidal system and an 

intertidal seagrass system in the northern Gulf of Alaska. The low resolution of the sUAS 

imagery created problems in reliably identifying many taxa to species. Some of these shortfalls 

could be remedied by using higher resolution cameras and an sUAS system that can carry the 

larger payload that a higher resolution camera may require, if necessary. Lower sUAS flight 

altitudes would also help improve image resolution; however, this can be challenging on beaches 
15 

with large boulders or other flight-path obstacles. Still, the value of using sUASs over the more 

traditional monitoring method of observer surveys remains dependent on the goals of a 

monitoring program. Programs with questions that can be addressed by a lower taxonomic 

resolution than observers produce could benefit greatly from the sUAS approach, mostly because 

of the much larger spatial extent that can be covered during a single low tide period. Our 

approach of assessing large-scale distribution patterns of seagrass provides an example where the 

coarse taxonomic resolution of sUAS images was sufficient to yield valuable results. Similar 

uses could be found for the rocky intertidal; for example, focusing on key taxa such as rockweed 

(Fucus sp.). 

Whether or not the sUAS approach is more cost effective and logistically easier must be 

determined for each application and depends on the specific logistical situation. For example, in 

more urban areas, sUAS use requires permits that can be difficult to obtain and that may require 

a licensed pilot. However, such limitations may become easier to overcome with the increased 

use and application of sUAS systems in monitoring science. Considerable time is needed for the 

sUAS image analysis when done individually by researchers. Time may be optimized by using 

automated imaging systems that can either 1) accurately calculate area of recognizable 

taxa/categories, 2) be trained to identify a library of images of common taxa, or 3) use spectral 

properties of the organisms in the image to automatically identify organism groups (Pech et al., 

2004). Therefore, while we obtained mixed results for the applicability of sUASs in the rocky 

intertidal and seagrass bed in our study, we have also shown that sUASs can be a valuable tool in 

monitoring and are likely to become an important tool in monitoring and ocean observing in the 

future (Lomax et al., 2005). More testing of sUAS use in various systems, and the optimization 

of camera resolution to flight height, will lay the groundwork for more routine applications. 

ACKNOWLEDGEMENTS 

This project was funded by the Coastal Marine Institute and the Bureau of Ocean Energy 

Management (Cooperative Agreement M14AC00017). We thank Richard Raymond (BOEM), 

Sam Vanderwaal (Northern Embedded Solutions), Kris Holderied (NOAA), the staff at the 

Alaska Center for Unmanned Aircraft Systems Integration, and the staff of the Kasitsna Bay Lab 

for excellent field support. We also thank Ken Arnoult for assistance with MATLAB for the 

spatial seagrass analysis. We acknowledge the Gulf Watch Alaska Long-term Monitoring 

Program (funded by the Exxon Valdez Oil Spill Trustee Council) for leveraging this project. 
16 

REFERENCES 

Araújo, R., Bárbara, I., Sousa-Pinto, I., Quintino, V., 2005. Spatial variability of intertidal rocky

 shore assemblages in the northwest coast of Portugal. Estuarine, Coastal, and Shelf

 Science, 64(4):658–670. 

Aronson, R.B., Edmunds, P.J., Precht, W.F., Swanson, D.W., Levitan, D.R., 1994. Large-scale,

 long-term monitoring of the Caribbean coral reefs: simple, quick, inexpensive techniques.

 Atoll Research Bulletin, 421:1–19. 

Barrell, J., Grant, J., 2015. High-resolution, low-altitude aerial photography in physical

 geography: a case study characterizing eelgrass (Zostera marina L.) and blue mussel

 (Mytilus edulis L.) landscape mosaic structure. Progress in Physical Geography DOI:

 10.1177/0309133315578943. 

Blake, R.E., Duffy, J.E., 2010. Grazer diversity affects resistance to multiple stressors in an

 experimental seagrass ecosystem. Oikos, 119(10):1625–1635. 

Boehm, P.D., Mankiewicz, P.J., Hartung, R., Neff, J.M., Page, D.S., Gilfillan, E.S., O’Reilly,

 J.E., Parker, K.R., 1996. Characterization of mussel beds with residual oil and the risk to

 foraging wildlife four years after the Exxon Valdez oil spill. Environmental Toxicology

 and Chemistry, 15:1289–1303. 

Bohnsack, J.A., 1979. Photographic quantitative sampling of hard-bottom benthic communities.

 Bulletin of Marine Science, 29:242–252. 

Bryson, M., Johnson-Roberson, M., Murphy, R.J., Bongiorno, D., 2013. Kite aerial photography

 for low-cost, ultra-high spatial resolution multi-spectral mapping of intertidal landscapes.

 PLOS One DOI: 10.1371/journal.pone.0073550.

Carls, M.G., Marty, G.D., Hose, J.E., 2002. Synthesis of the toxicological impacts of the Exxon

 Valdez oil spill on Pacific herring (Clupea pallasi) in Prince William Sound, Alaska,

 USA. Canadian Journal of Fisheries and Aquatic Sciences, 59(1):153–172. 

Clarke, K.R., 1993. Non-parametric multivariate analyses of changes in community structure.

 Australian Journal of Ecology, 18(1):117–143. 

Clarke, K.R., Gorley, R.N., Somerfield, P.J., Warwick, R.M., 2014. Change in marine

 communities: an approach to statistical analysis and interpretation, 3rd edition. PRIMER-

 E: Plymouth, UK. 

Cabaço, S., Santos, R., Duarte, C.M., 2008. The impact of sediment burial and erosion on

 seagrasses: a review. Estuarine, Coastal and Shelf Science, 79:354–366. 

Collin, A., Long, B., Archambault, P., 2012. Merging land-marine realms: Spatial patterns of

 seamless coastal habitats using a multispectral LiDAR. Remote Sensing of Environment,

 123:390–399. 

Cox, T.E., Foster, M.S., 2013. The effects of storm-drains with periodic flows on intertidal algal

 assemblages in ‘Ewa Beach (O‘ahu), Hawai‘i. Marine Pollution Bulletin, 70:162–170. 
17 

Delaney, D.G., Sperling, C.D., Adams, C.S., Leung, B., 2008. Marine invasive species:

 validation of citizen science and implications for national monitoring networks. 

 Biological Invasions, 10:117–128. 

Duffy, J., 2006. Biodiversity and the functioning of seagrass ecosystems. Marine Ecology

 Progress Series, 311:233–250. 

Duffy, J.E., Reynolds, P.L., Boström, C., Coyer, J.A., Cusson, M., Donadi, S., Douglass, G.J.,

 Eklöf, J.S., Engelen, A.H., Eriksson, B.K., Fredriksen, S., Gamfeldt, L., Gustafsson, C.,

 Hoarau, G., Hori, M., Hovel, K., Iken, K., Lefcheck, J.S., Moksnes. P.-O., Nakaoka, M., 

 O'Connor, M.I., Olsen, J.L., Richardson, J.P., Ruesink, J.L., Sotka, E.E., Thormar, J.,

 Whalen, M.A., Stachowicz, J.J., 2015. Biodiversity mediates top–down control in

 eelgrass ecosystems: a global comparative experimental approach. Ecology Letters,

 18:696–705. 

Foster, M.S., Harrold, C., Hardin, D.D., 1991. Point vs. photo quadrat estimates of the cover of

 sessile marine organisms. Journal of Experimental Marine Biology and Ecology,

 146:193–203. 

Haegele, C., Schweigert, J., 1985. Distribution and characteristics of herring spawning grounds

 and description of spawning behavior. Canadian Journal of Fisheries and Aquatic

 Sciences, 42:39–55 

Heijs, F.M., 1985. Some structural and functional aspects of the epiphytic component of four

 seagrass species (Cymodoceoideae) from Papua New Guinea. Aquatic Botany,

 23(3):225–247. 

Helmuth, B., Mieszkowska, N., Moore, P., Hawkins, S.J., 2006. Living at the edge of two

 changing worlds: forecasting the responses of rocky intertidal ecosystems to climate

 change. Annual Review of Ecology and Evolution, 37:373–404. 

Heltshe, J.F., Forrester, N.E., 1985. Statistical evaluation of the jackknife estimate of diversity

 when sampling quadrats. Ecology, 66:107–111. 

Johnson, S.W., Murphy, M.L., Csepp, D.J., Harris, P.M., Thedinga, J.F., 2003. A survey of fish

 assemblages in eelgrass and kelp habitats of southeastern Alaska. US Department of

 Commerce, National Oceanic and Atmospheric Administration, Technical Memo NMFS-

 AFSC-139. 39 pp. 

Juanes, J.A., Guinda, X., Puente, A., Revilla, J.A., 2008. Macroalgae, a suitable indicator of the

 ecological status of coastal rocky communities in the NE Atlantic. Ecological Indicators,

 8(4):351–359. 

Kitting, C.L., Fry, B., Morgan, M.D., 1984. Detection of inconspicuous epiphytic algae

 supporting food webs in seagrass meadows. Oecologia, 62(2):145–149. 

Konar, B., Iken, K., 2009. Influence of taxonomic resolution and morphological functional

 groups in multivariate analyses of macroalgal assemblages. Phycologia, 48(1):24–31. 
18 

Konar, B., Iken, K., Coletti, H., Monson, D., Weitzman, B., in press. Influence of static habitat

 attributes on local and regional rocky intertidal community structure. Estuaries and

 Coasts. 

Konar, B., Iken, K., Edwards, M., 2009. Depth-stratified community zonation patterns on Gulf

 of Alaska rocky shores. Marine Ecology, 30:63–73. 

Koski, W.R., Allen, T., Ireland, D., Buck, G., Smith, P.R., Macrander, M., Halick, M.A.,

 Rushing, C., Sliwa, D.J., McDonald, T.L., 2009. Evaluation of an unmanned airborne

 system for monitoring marine mammals. Aquatic Mammals, 35:347–357. 

Leujak, W., Ormond, R.F.G., 2007. Comparative accuracy and efficiency of six coral community

 survey methods. Journal of Experimental Marine Biology and Ecology, 351:168–187. 

Lomax, A.S., Corso, W., Etro, J.F., 2005. Employing unmanned aerial vehicles (UAVs) as an

 element of the Integrated Ocean Observing System. OCEANS, 2005. Proceedings of

 MTS/IEEE, pp. 184–190. 

McRoy, C.P., Goering, J.J., 1974. Nutrient transfer between the seagrass Zostera marina and its

 epiphytes. Nature, 248:173–174 

Meese, R.J., Tomich, P.A., 1992. Dots on the rocks: a comparison of percent cover estimation

 methods. Journal of Experimental Marine Biology and Ecology, 165:59–73. 

Menge, B.A., Gouhier, T.C., Hacker, S.D., Chan, F., Nielsen, K.J., 2015. Are meta-ecosystems

 organized hierarchically? A model and test in rocky intertidal habitats. Ecological

 Monographs, 85:213–233. 

Minchinton, T.E., Fels K.J., 2013. Sediment disturbance associated with trampling by humans

 alters species assemblages on a rocky intertidal seashore. Marine Ecology Progress

 Series, 472: 129–140. 

Obermeyer, B.K., 1998. A comparison of quadrats versus times snorkel searches for assessing

 freshwater mussels. The American Midland Naturalist, 139:331–339. 

Pech, D., Condal, A.R., Bourget, E., Ardisson, P.L., 2004. Abundance estimation of rocky shore

 invertebrates at small spatial scale by high-resolution digital photography and digital

 image analysis. Journal of Experimental Marine Biology and Ecology, 299(2):185–199. 

 Penhale, P.A., Thayer, G.W., 1980. Uptake and transfer of carbon and phosphorus by eelgrass

 (Zostera marina L.) and its epiphytes. Journal of Experimental Marine Biology and

 Ecology, 42(2):113–123. 

Pérez-Alberti, A., Trenhaile, A.S., 2015. An initial evaluation of drone-based monitoring of

 boulder beaches in Galicia, north-western Spain. Earth Surface Processes and Landforms,

 40: 105–111. 

Reimers, B., Griffiths, C.L., Hoffman, M.T., 2014. Repeat photography as a tool for detecting

 and monitoring historical changes in South African coastal habitats. African Journal of

 Marine Science, 36:387–398. 
19 

Skalski, J.R., Coats, D.A., Fukuyama, A.K., 2001. Criteria for oil spill recovery: a case study of

 the intertidal community of Prince William Sound, Alaska, following the Exxon Valdez

 oil spill. Environmental Management, 28:9–18. 

Stekoll, M.S., Deysher, L., 2000. Response of the dominant alga Fucus gardneri (Silva)

 (Phaeophyceae) to the Exxon Valdez oil spill and clean-up. Marine Pollution Bulletin,

 40:1028–2041. 

Walker, G., 2012. Augmenting Steller Sea Lion surveys in the western Aleutians with unmanned

 aircraft. Final Report to NOAA. 17 pp. 

Wells, E., Wilkinson, M., Wood, P., Scanlan, C., 2007. The use of macroalgal species richness

 and composition on intertidal rocky seashores in the assessment of ecological quality

 under the European Water Framework Directive. Marine Pollution Bulletin, 55(1):151–

 161. 

Witman, J.D., Etter, R.J., Smith, F., 2004. The relationship between regional and local species

 diversity in marine benthic communities: a global perspective. Proceedings of the

 National Academy of Sciences, 101(44):15664–15669. 
The Department of the Interior Mission

As the Nation’s principal conservation agency, the Department of the Interior has 

responsibility for most of our nationally owned public lands and natural resources. This 

includes fostering the sound use of our land and water resources, protecting our fish, 

wildlife and biological diversity; preserving the environmental and cultural values of our 

national parks and historical places; and providing for the enjoyment of life through 

outdoor recreation. The Department assesses our energy and mineral resources and 

works to ensure that their development is in the best interests of all our people by 

encouraging stewardship and citizen participation in their care. The Department also 

has a major responsibility for American Indian reservation communities and for people 

who live in island communities.

The Bureau of Ocean Energy Management 

The Bureau of Ocean Energy Management (BOEM) works to manage the exploration 

and development of the nation's offshore resources in a way that appropriately balances 

economic development, energy independence, and environmental protection through oil 

and gas leases, renewable energy development and environmental reviews and studies.