# Quantifying intertidal macroalgae stocks in the NW Iberian Peninsula using unmanned aerial vehicle (UAV) multispectral imagery

Miguel Peidro-Devesa, Andrea Martínez-Movilla, Juan Luis Rodríguez-Somoza, Joaquín Martínez Sánchez, Marta Román

Rodríguez-Somoza, Joaquín Martínez Sánchez, Marta Román

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Miguel Peidro-Devesa, Andrea Martínez-Movilla, Juan Luis Rodríguez-Somoza, Joaquín Martínez Sánchez, Marta Román. Quantifying intertidal macroalgae stocks in the NW Iberian Peninsula using unmanned aerial vehicle (UAV) multispectral imagery. Regional Studies in Marine Science, 2024, 77, pp.103621. 10.1016/j.rsma.2024.103621. hal-04765062

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# Quantifying intertidal macroalgae stocks in the NW Iberian Peninsula using unmanned aerial vehicle (UAV) multispectral imagery

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Miguel J. Peidro-Devesa<sup>a, b, 1</sup>, Andrea Martínez-Movilla<sup>c, 2</sup>, Juan Luis Rodríguez-Somoza<sup>c, 3</sup>, Joaquín Martínez Sánchez<sup>c, 4</sup>, Marta Román<sup>a, d, *, 5</sup>

## ARTICLE INFO

### Keywords:

- Remote sensing

- Intertidal habitats

- SCP

- QGIS

- Drone mapping

## ABSTRACT

Intertidal macroalgae play a fundamental role in providing various ecosystem services, and they must be managed effectively for conservation purposes. Using remote sensing techniques to monitor macroalgae is a promising way of optimizing efforts in relation to logistics and personnel. This study aimed to quantify the cover and biomass of the predominant macroalgae in a rocky intertidal zone (of area 5.9 ha) in NW Spain. Drone monitoring with multispectral sensors, *in situ* photography and biomass sampling were conducted in June and July, 2021 (i.e. in summer, when macroalgae are most abundant in the study area). The macroalgae were classified using Minimum Distance, Spectral Angle Mapping and Maximum Likelihood algorithms, the latter of which proved the most accurate. The fresh weight of macroalgae determined in the quadrat sampling was regressed on the cover of July to enable subsequent estimation of biomass from the classification maps. The area occupied by brown algae (phylum Ochrophyta) in July 2021 was estimated to be 5574 m<fnref n="2" /> with a biomass of 28.5 t. The remote sensing method, which did not require in-depth knowledge of programming, proved to be a rapid, effective means of estimating macroalgal cover. The method may be useful for managing commercial macroalgal stocks and monitoring protected marine areas.

## 1. Introduction

Intertidal macroalgal communities are important ecosystem engineers and form the basis of marine trophic food webs, guaranteeing the stability of coastal and marine ecosystems. Macroalgae contribute significantly to oxygen production, carbon sequestering and nutrient dynamics (Pfister et al., 2019). They dissipate wave energy (Denny, 2021) and retain large amounts of sediment, enabling the coexistence of diverse epifaunal communities (Anandavelu et al., 2013). During low tide, the vegetation canopy that they form shelters vertebrate and

invertebrate organisms from solar radiation and minimizes water loss (Umanzor et al., 2019; Coombes et al., 2013). Macroalgal communities protect small herbivores against larger predators and also protect the rocky surface from erosion (Lasley-Rasher et al., 2011; Gowell et al., 2015). They play an essential role within trophic webs, supporting a large number of herbivorous communities (Pereira, 2021).

From an anthropocentric perspective, intertidal macroalgae provide a wide variety of ecosystem services. For instance, macroalgae farms can buffer the harmful effects of global warming, including acidification, deoxygenation of the marine environment and increased CO₂ levels at

<footnote><sup>a</sup> Centro de Investigación Mariña, Universidade de Vigo, EcoCost, Facultade de Ciencias do Mar, Edificio C.C. Experimentais, Campus de Vigo, As Lagoas-Marcosende, Vigo 36310, Spain</footnote>

<footnote><sup>b</sup> CIBIO - Research Centre in Biodiversity and Genetic Resources, InBIO Associate Laboratory, Pólo dos Açores - Departamento de Biologia da Universidade dos Açores, Ponta Delgada 9501-801, Portugal</footnote>

<footnote><sup>c</sup> GeoTECH Research Group, Universidade de Vigo, As Lagoas, Marcosende, Vigo 36310, Spain</footnote>

<footnote><sup>d</sup> Nantes Université, Institut des Substances et Organismes de la Mer, ISOMer, UR2160, Nantes F-44000, France</footnote>

<footnote>* Corresponding author at: Centro de Investigación Mariña, Universidade de Vigo, EcoCost, Facultade de Ciencias do Mar, Edificio C.C. Experimentais, Campus de Vigo, As Lagoas-Marcosende, Vigo 36310, Spain.</footnote>

<footnote>E-mail address: marroman@uvigo.es (M. Román).</footnote>

<footnote n="1">1 https://orcid.org/0009-0006-1646-8187</footnote>

<footnote n="2">2 https://orcid.org/0000-0002-6114-9074</footnote>

<footnote n="3">3 https://orcid.org/0000-0003-4031-762X</footnote>

<footnote n="4">4 https://orcid.org/0000-0003-0320-4191</footnote>

<footnote n="5">5 https://orcid.org/0000-0002-7082-0763</footnote>
local scales (Xiao et al., 2021). They also protect human health, as they are used in the phytoremediation of marine pollution and to bioabsorb heavy metals (Agarwal et al., 2022). Owing to their high nutritional value (rich in carbohydrates and low in lipids), they are used as an alternative to animal protein in human diets and in aquaculture (Fleurence, 2018). Within the health sector, endogenous compounds with antitumoral and antioxidant activity have been used to develop various therapies and medicines (Ye et al., 2008). Extraction of macro-algal products such as alginates, ulvans, carrageenates and agar, which are used in a wide range of applications, has led to the development of an important industry (McHugh, 2003). All of these features have led to an increase in the harvesting pressure on macroalgae in western countries.

Monitoring the distribution and abundance of macroalgal communities is essential for good management and protection of these assemblages and to guarantee their sustainable growth. Intertidal habitats have traditionally been studied by *in situ* monitoring techniques. However, the main drawbacks of *in situ* sampling is the inaccessibility of some macroalgae beds, the logistical requirements for the field sampling, the time-consuming sample collection/processing and the need for highly trained personnel. In addition, traditional methods cannot be used to assess heterogeneity patterns at large spatial scales, making long-term monitoring complicated (Godet et al., 2009). More recently developed remote sensing techniques, such as the acquisition and analyses of multi and/or hyperspectral data collected from satellites and/or unmanned aerial vehicles (UAVs) can be used for the large-scale assessment of intertidal resources, as they generate large amounts of data on the surveyed area without physical contact.

Remote sensing involves detection of the electromagnetic energy reflected from the earth's surface (Yang and Everitt, 2011). Although satellite imagery is available for marine environments, the low resolution of images prevents accurate taxonomic identification and reliable prediction of the area occupied by different taxa in heterogeneous intertidal habitats (Brodie et al., 2018). Other limitations of the use of satellite images are the difficulty in synchronizing the presence of satellites in the study area with low tide periods, as well as the appearance of obstacles such as light refraction, interference within the water column and cloud cover (Traganos and Reinartz., 2018). Satellite imaging studies have thus focused on detecting homogeneous cover, such as phytoplankton blooms and large monospecific brown macroalgae beds (Bresciani et al., 2018; Nijland et al., 2019).

The use of UAVs or drones has solved many of the problems related to satellite imagery. Synchronization of flights with low tide periods and selection of flight times to avoid adverse weather conditions enable high quality images to be acquired. UAVs can fly close to the earth's surface, and they carry sensors that can capture the high-resolution images that are essential for accurate taxonomic identification (Anderson and Gaston, 2013). UAVs are generally equipped with RGB sensors, capable of detecting red, green and blue light within the visible spectrum. The use of multispectral cameras increases the detection of spectral bands by, for instance, adding near-infrared light and the transition zone between red light and infrared. The use of five bands improves the detection of marine vegetation in emerging or shallow waters, where the infrared light has not yet been absorbed by the water column (Ventura et al., 2018). The variety of photosynthetic pigments in different macroalgae phyla facilitates taxonomic differentiation based on differential reflection of the infrared band during active photosynthesis (Enríquez et al., 1994; Schmidt and Skidmore, 2003). In this sense, the photosynthetic organisms show a high reflectance in the near-infrared region of the spectrum, thus this band is employed for the estimation of the quantity and health of vegetation through indices such as the Normalised Difference Vegetation Index (NDVI). NDVI is based on the differential absorption of near-infrared light versus red light in the visible spectrum and has been used not only to quantify terrestrial vegetation, but also macroalgae and seagrass cover (Barillé et al., 2010; Taddia et al., 2020; Karki et al., 2021).

Nonetheless, there are some limitations related to the use of drones, including the vulnerability to adverse weather conditions, such as heavy rain and high-speed winds. Their autonomy is also limited and the short flight times prevent surveys of large areas being conducted with a single drone flight (Green et al., 2018). In spite of this, UAV flights can be used to address the gap in temporal and spatial scale information that cannot be recorded by traditional sampling and satellite imagery (Escobar-Sánchez et al., 2021). UAV flights have therefore recently been confirmed as effective tools for monitoring intertidal zones (Konar and Iken, 2018, Cavalli, 2024).

For all of the aforementioned reasons, remote sensing studies of marine environments have increased in the last few years. Studies involving marine fauna focus on the health and density of benthic invertebrates of commercial interest, such as oysters and mussels (Gomes et al., 2018; Ridge et al., 2020). Regarding intertidal flora, the success of the surveys depends on the sensors used. In some studies conducted with RGB and multispectral cameras, functional groups are considered instead of taxonomic groups, to increase the accuracy of results across heterogeneous intertidal zones (James et al., 2020). In other cases, composite images have been generated from two sensors to increase the number of bands, improving the accuracy of results and the taxonomic discrimination (Tait et al., 2021). Although drone-acquired images of the water column are of lower quality, areas of the benthic vegetation in the higher subtidal fringe can be successfully mapped and classified (Svane et al., 2021). Within the Iberian Peninsula, areas of marine macrophytes within the Gulf of Cadiz have been accurately mapped by drones equipped with cameras able to capture 10 spectral bands (Román et al., 2021), and macroalgal mapping has been carried out in northern Portugal by using multispectral and RGB cameras (Borges et al., 2021, Borges et al., 2023).

Drones have been used to study coastal dynamics for the purposes of oil spill clean-ups on the coasts of Galicia (NW Spain) (Bernabeu et al., 2021; Gómez-Pazo and Pérez-Alberti, 2021). However, advanced remote sensing techniques have rarely been used to monitor the cover and abundance of macroalgae in this region. Around 600 species of macroalgae have been recorded on this coast (Bárbara et al., 2005), and increased harvesting of intertidal macroalgae is an emerging pressure. Thus, there is a need to implement rapid diagnosis tools for the monitoring and mapping of these increasingly exploited marine resources. The analyses of multispectral images acquired by UAVs to estimate the cover (Taddia et al., 2020; Borges et al., 2021; Karki et al., 2021) and biomass (Svane et al., 2021; Borges et al., 2023) of intertidal macroalgae has been already demonstrated. However, the application of these methodologies to intertidal areas of NW Spain, that are characterized by a different distribution and diversity of species, has not been performed yet. The application of a semi-automated classification methodology to more case studies offers new outcomes that can test the viability of these new sampling methodologies. Moreover, they can amplify the knowledge about the distribution and cover of relevant intertidal macroalgae habitats at different regions. Therefore, the main objectives of the present study were to 1) test the accuracy of a semi-automatic classification methodology of intertidal macroalgae based on the UAV-based acquisition of multispectral images and *in situ* collection of RGB photographs, 2) quantify and map the cover of macroalgae of a rocky intertidal zone on the coast of NW Spain, and 3) estimate the biomass of macroalgae from the obtained maps by creating biomass-cover relationships with data and samples collected *in situ*. We expected the method to provide reliable estimates of macroalgal cover and biomass, which would confirm the use of drone flights as valuable tools for the study, protection, and management of intertidal habitats.

## 2. Materials and methods

### 2.1. Data and samples collection

The study was carried out on an intertidal rocky shore in Galicia, NW
Spain (42°00′ 11.7′′N, 8°52′40.7′′W) (Fig. 1). The tidal range in the area varies between 2 m and 4 m, with a semidiurnal cycle (Martínez, 1997). Macroalgae populations occur in the lower intertidal and subtidal fringe in the study site. The predominant species of brown algae (phylum Ochrophyta) were *Bifucaria bifurcata* R. Ross, *Saccorhiza polyschides* (Lightfoot) Batters and *Undaria pinnatifida* (Harvey) Suringar, and the predominant green algae (phylum Chlorophyta) were *Codium* spp. and *Ulva* spp. Field visits were carried out during low spring tides in June and July, 2021. The meteorological conditions during the visits in June and July were optimal for sampling, with no rainfall. In June, the water temperature ranged between 13 C° and 23 C° and there were light gusts wind, of speed no more than 15 km/h in a south-easterly direction. In July, the water temperature ranged between 14 and 20 C° and winds were light, in a predominantly north-westerly direction (https://www.meteogalicia.gal).

The sites were visited on two consecutive days in each month. Image collection (drone flights and in situ photographs) was done in June in July, whereas biomass sampling was done only in July, coinciding with the period of maximum biomass. On the first day, images of intertidal zones were captured using UAVs equipped with multispectral cameras (Table 1). Due to logistic constraints, two multispectral cameras, with different band ranges and widths, were used in June and July. The MICASENSE Altum camera, used in June, detected the following bands (bandwidths): blue 475 (32) nm, green 560 (27) nm, red 668 (14) nm, red edge 717 (12) nm, and NIR 842 (57) nm, whereas the P-4 multispectral sensor, used in July, detected the blue 450 (16) nm, green 560 (16) nm, red 650 (16) nm, red edge 730 (16) nm and NIR 840 (26) nm. Therefore, as we expected different reflectances between the target classes captured in June and July caused by the different sensors used, the data for each month was analysed separately. On the following day,

in situ sampling was carried out, to determine the “ground truth” for classifier training and cover quantification of June and July and for macroalgae biomass estimation for July. Two 100-m transects were delimited on each side of the inlet to cover the maximum spatial representativeness of the macroalgae populations (Fig. 1). In each transect, 20 quadrats of 0.25 m² were photographed with a Fujifilm JV-200 (14 Megapixels) digital camera, therefore a total of 40 quadrats were sampled each month. The position of the upper right and lower left corner of each quadrat was recorded with a topographic GPS with centimetric precision (Trimble, R8 antenna), then, quadrats were removed. Additionally, in July, coinciding with the period of maximum biomass, the macroalgae photographed in each quadrat were also extracted. We collected both the upper macroalgae canopy that was visible in the photographs (apparent macroalgae cover), and the algae that was below the upper canopy (secondary macroalgae cover). Samples were stored in plastic bags, taken to the laboratory and frozen until further processing.

## 2.2. Spatial data processing

The multispectral images captured by the UAV were processed using Agisoft Metashape photogrammetric software and the reflectance of bands was normalized to dimensionless units, in the 0–1 range (https://agisoft.freshdesk.com/support/solutions/articles/31000148381-micasense-altum-processing-workflow-including-reflectance-calibration-in-agisoft-metashape-professi). The photographs of the sampling quadrats taken in situ were georeferenced and positioned by assigning the GPS coordinates measured in the two opposite corners of the quadrats. The obtained products were, for each month, a georeferenced multispectral orthoimage and a set of 40 georeferenced in situ photographs.

<figure><img src="image_4.png" /><figcaption>**Fig. 1.** A) Location of the study site, B) detail of NW Spain and C) scheme of fieldwork and acquired multispectral orthoimages of the study site in June and July. The points indicate the location of the 40 sampling quadrats used to generate the ground truth each month (June and July) and to quantify the macroalgae biomass (July). Coordinate reference systems: WGS84 EPSG 4326 in A), ETRS89 EPSG 25829 in B) and C).</figcaption></figure>
<caption>**Table 1**</caption>

Description of flights and characteristics of the sensors. GSD: Ground sampling distance. Information obtained from manufacturer's websites (¹https://www.dji.com/es/p4-multispectral/specs; ²https://support.micasense.com/hc/en-us/articles/360010025413-Altum-Integration-Guide#h.5ow085yb2oll). Low tide hour and height with respect to the hydrographic zero (https://www.meteogalicia.gal/web/predicion/mareas-e-luas).

<table><thead><tr><th>Drone</th><th>Sensor</th><th>Date and hour</th><th>Low tide hour and height (m)</th><th>Duration (min)</th><th>Flight height (m)</th><th>GSD (cm)</th><th>Focal distance (mm)</th></tr></thead><tbody><tr><td>DJI Matrice 300</td><td>Altum micasense¹</td><td>24/06/2021 9:30</td><td>10:12<br>0.6</td><td>15</td><td>75</td><td>2.5</td><td>8</td></tr><tr><td>DJI P-4 multiespectral</td><td>P-4 multiespectral²</td><td>27/07/2021<br>12:30</td><td>13:00<br>0.8</td><td>20</td><td>50</td><td>2.5</td><td>5.74</td></tr></tbody></table>

Both sources of data were loaded into geographic information system (GIS) software QGIS 3.16.16 Hannover. The geo-referenced photographs taken of *in situ* quadrats in each sampling month were used as ground truth to create the training dataset. A vector *shapefile* layer was created, where polygons corresponding to the coverages of each macroalgae species contained in each *in situ* photograph were delimited. Other classes present in the area, such as water, sand and rock, were not delimited in the *in situ* photos, since they could be directly labelled in the orthoimages. The area (cm²) occupied by each species and phylum in the sampling quadrats was calculated with the *field calculator* function from QGIS (Fig. 2). A free available image classification program was used within QGIS: Semi-Automatic Classification Plug-in (SCP) (Congedo, 2021). The supervised classification performed by this plug-in is based on the differential spectral signatures obtained from the values of all the pixels from Regions of Interest (ROIs), which are used as training data for the classification algorithms. The ROIs were then generated by clipping the multispectral orthoimages with the contours of the training polygons created in the vector *shapefile*. As the ROIs of inert elements (rocks, concrete, sand) and of water were not included in the photographs taken *in situ*, they were manually delimited directly on the multispectral raster. Seventy percent of the ROIs were used to feed the classification algorithms, and the remaining 30% were retained as validation points to check whether the result matched the real conditions and to calculate accuracy evaluation statistics (Olofsson et al., 2014).

The spectral signatures of the classes were extracted from the ROIs by the SCP plug-in. Once the separability of spectral distances between the classes (Tables S1-S3) was verified by the Jeffries-Matusita distance (Carrasco-Escobar et al., 2019), the images from June and July were used to perform two separate classifications, one per month, according to the classes specified in the ROIs by using three algorithms.

The Minimum Distance (MD) algorithm (Richards and Jia, 2006) calculates the distance between the spectral signature of the training classes and of the image pixels ($d(x, y)$) and assigns the class of the closest training spectral signature to each pixel, following Eq. (1):

$$d(x, y) = \sqrt{\sum_{i=1}^{n} (x_i - y_i)^2} \quad (1)$$

Where $x$ is the spectral signature vector of an image pixel, and $y$ is the spectral signature vector of a training area (Congedo, 2021).

<figure><img src="image_5.png" /><figcaption>**Fig. 2.** Delimitation of training regions for the method being evaluated. A) Georeferenced RGB image of a 50 × 50 cm quadrat. B) Elaboration of the ROIs on the ground truth information. C) Labelled shapefile.</figcaption></figure>

The Maximum Likelihood (ML) algorithm (Richards and Jia, 2006) calculates the probability distribution of the spectral signature of the training classes and estimates whether an image pixel belongs to a particular cover class. The discriminant function $g_k(x)$ of the algorithm follows the Eq. (2):

$$g_k(x) = \ln p(C_k) + \frac{1}{2} |\Sigma_k| - \frac{1}{2} (x - y_k)^T \Sigma_k^{-1} (x - y_k) \quad (2)$$

Where $x$ is the spectral signature of the image pixel, $C_k$ is the class, $p(C_k)$ is the probability that the correct class is $C_k$, $|\Sigma_k|$ is the determinant of the covariance matrix of the data in class $C_k$, $y_k$ is the spectral signature vector of class $k$, and $\Sigma_k^{-1}$ is the inverse of the covariance matrix (Congedo, 2021).

The Spectral Angle Mapping (SAM) algorithm calculates the angle between the spectral signature of the training classes and of the image pixels ($\theta(x, y)$), assigning each pixel to the class against which it has the lowest angle (Kruse et al., 1993; Richards and Jia, 2006). It follows the Eq. (3):

$$\theta(x, y) = \cos^{-1}\left(\frac{\frac{\sum_{i=1}^{n} x_i y_i}{\left(\sum_{i=1}^{n} x_i^2\right)^{\frac{1}{2}} \cdot \left(\sum_{i=1}^{n} y_i^2\right)^{\frac{1}{2}}}}{\frac{1}{\left(\sum_{i=1}^{n} x_i^2\right)^{\frac{1}{2}}} \cdot \frac{1}{\left(\sum_{i=1}^{n} y_i^2\right)^{\frac{1}{2}}}}\right) \quad (3)$$

Where $x$ is the spectral signature vector of an image pixel, $y$ is the spectral signature vector of a training area and $n$ is the number of image bands (Congedo, 2021).

## 2.3. Biomass processing

The apparent (upper layer) and secondary (species not visible by the drone, placed below the upper layer) macroalgae canopies collected from each quadrat were pooled. The fresh weight of the target species in each quadrat collected in July was determined in the laboratory. For this purpose, the target macroalgae species present in each sampling quadrat were separated and cleaned to remove sand or other epiphytic algae. Excess moisture was removed by blotting with filter paper, and the fresh weight (g) of each species in each quadrat was determined by weighing the material on a precision balance (± 0.1 g).

## 2.4. Data analysis and statistics

### 2.4.1. Spatial data

The classification algorithms were applied to predict each class as a function of the reflectance of the 5 multispectral bands. The accuracy of the three classification algorithms was assessed by means of four statistical parameters. The overall accuracy (OA) indicates the total number of reference sites that were correctly mapped and is calculated by dividing the total number of correctly classified pixels by the total number of pixels from the training dataset. The Cohen's Kappa (κ) coefficient ranges between -1 and 1 and assesses the performance of the classification by comparing it with a random classification. Negative values indicate higher accuracy of random classification relative to the classification performed, while positive values indicate better results for
the classification performed than for a random one (Fielding and Bell, 1997). User accuracy (UA) quantifies the false positives for each class, indicating the area that is incorrectly classified as a known class when it should have been classified as another one. It is calculated by dividing the correctly classified pixels by the total number of pixels classified for each class. Producer accuracy (PA) quantifies the false negatives, indicating how accurately the classification results match with the training data. It is calculated by dividing the correctly classified pixels by the total training pixels used for each class (Congalton, 1991; Lillesand et al., 2015). OA, UA and PA ranged from 0 to 1, being 1 the best accuracy. Those classifications with an OA greater than 0.9 were used to calculate the macroalgae cover (Fig. 3).

### 2.4.2. Macroalgae biomass

Linear regression models ($y = a + b \times x$) between the fresh weight in grams of macroalgae biomass ($y$) and the cover in square centimetres ($x$), previously quantified in the *in situ* quadrats, were computed in order to estimate macroalgae biomass by species and phyla. They were calculated with data of July, when both images and biomass were collected. The residuals of the linear regressions were graphically checked for normality with Q-Q plots and by the Shapiro-Wilk test. When the residuals did not fit a normal distribution, the dependent variable was replaced with the square root transformation. The regression equations were then used to estimate the macroalgae biomass from the cover of macroalgae previously obtained with the most accurate classifications (those with OA > 0.90) performed with the data collected in July. All statistical analyses were performed with R software (R Core Team, 2022).

## 3. Results

### 3.1. Spectral properties of the classification groups

The mean and dispersion of the reflectance values of the spectral signatures of each category measured with the drone multispectral cameras were analysed for both study months (Fig. 4). The spectral signature of water was stable in all the classifications, and the reflectance values were low along the spectrum (Fig. 4). By contrast, spectral signatures of the inert class varied between the study months. In June, the reflectance remained constant over the entire spectrum, with values around 0.4. In July, lower reflectance values of around 0.1 were

recorded, and slightly higher values of around 0.2 recorded at wavelengths close to 700 nm (Fig. 4).

Similar trends were observed for all of the spectral signatures of the macroalgae, with low reflectance values at the lower wavelengths until reaching 650 nm, after which there was a sharp increase in reflectance values (Fig. 4). In the classification by phyla, there were no differences between the spectral signatures of Ochrophyta and Chlorophyta in June. However, in July, greater separation of the signatures was detected between 700 nm and 850 nm, with higher reflectance values for the phylum Ochrophyta. In the classification by species, there was little separation between all species in either month, with a slight separation of the spectral signature of *B. bifurcata* at longer wavelengths (Fig. 4).

The Jeffries Matusita distances indicated that the spectral signatures of the general classifications were sufficiently separated for correct classification, with a magnitude greater than 1.9 (Table S1). From the phylum classification, a low level of separability of spectral signatures between algae of the phylum Chlorophyta and Ochrophyta was observed in both months (Table S2). In the case of species classification, the statistic indicated a low level of separation between most of the species (red values in Table S3).

### 3.2. Classification

In both months, the values of the kappa coefficient and OA decreased as the complexity of the classification increased (Table 2), owing to a larger number of categories and the similarity of spectral signatures. The MD algorithm yielded the worst results, in both June and July for all classifications (general, phyla and species) (Table 2). The accuracy of the classifications performed with ML and SAM algorithms was comparatively higher and similar to each other (Table 2).

The general classification yielded the highest OA, exceeding 0.90 in June and July. The accuracy indexes of the classifications were always greater in July than in June. When looking at the most accurate algorithms for each month, the SAM produced the most accurate results in June, while ML produced the most accurate results in July (Table 2). In the general classification, the UA and PA values for the macroalgae class were high, both in June and in July (Table 3), indicating that the algorithm was able to correctly differentiate the macroalgae zones (Tables S4 and S5). The estimated emerged macroalgae cover in the study area was 23708 m² in June and 10960 m² in July (Fig. 5, Figs. S1-S2).

<figure><img src="image_6.png" /><figcaption>**Fig. 3.** Flow diagram of fieldwork and spatial data processing. Classification algorithms abbreviations: SAM= Spectral Angle Mapping, ML= Maximum likelihood, MD= Minimum distance. Accuracy of classifications abbreviations: OA= Overall accuracy, κ= Kappa index, UA= User accuracy, PA= Producer accuracy.</figcaption></figure>
<figure><img src="image_7.png" /><figcaption>Fig. 4. Spectral signatures for each classification category in both months of study.</figcaption></figure>

<caption>**Table 2**</caption>

Summary of the accuracy metrics of the different algorithms for each classification. MD: Minimum Distance. ML: Maximum Likelihood. SAM: Spectral Angle Map. OA: Overall accuracy. κ: kappa coefficient. The highest values for each month are highlighted in bold.

<table><thead><tr><th rowspan="3"></th><th colspan="4">General</th><th colspan="4">Phylum</th><th colspan="4">Species</th></tr><tr><th colspan="2">June</th><th colspan="2">July</th><th colspan="2">June</th><th colspan="2">July</th><th colspan="2">June</th><th colspan="2">July</th></tr><tr><th>OA</th><th>κ</th><th>OA</th><th>κ</th><th>OA</th><th>κ</th><th>OA</th><th>κ</th><th>OA</th><th>κ</th><th>OA</th><th>κ</th></tr></thead><tbody><tr><th>MD</th><td>0.70</td><td>0.56</td><td>0.90</td><td>0.85</td><td>0.52</td><td>0.34</td><td>0.74</td><td>0.62</td><td>0.40</td><td>0.27</td><td>0.63</td><td>0.51</td></tr><tr><th>ML</th><td>0.80</td><td>0.71</td><td><strong>0.98</strong></td><td><strong>0.96</strong></td><td><strong>0.82</strong></td><td><strong>0.74</strong></td><td><strong>0.95</strong></td><td><strong>0.91</strong></td><td>0.60</td><td>0.51</td><td><strong>0.86</strong></td><td>0.81</td></tr><tr><th>SAM</th><td><strong>0.96</strong></td><td><strong>0.94</strong></td><td>0.97</td><td>0.95</td><td>0.81</td><td>0.73</td><td>0.84</td><td>0.78</td><td><strong>0.67</strong></td><td>0.57</td><td>0.77</td><td>0.69</td></tr></tbody></table>

The best results for the phyla classification were achieved with the ML algorithm, both in June and, more remarkably, in July, exceeding 0.9 of OA (Fig. 6, Fig. S3, Table 2). Values of UA for Chlorophyta and Ochrophyta were lower in June than in July, whereas the PA of each phylum was more similar in both months. The PA for the phylum classification was higher for Ochrophyta than for Chlorophyta in both months (Table 3). The lower UA values can be attributed to misallocation of validation areas between Chlorophyta and Ochrophyta phyla (Tables S6 and S7). The macroalgae cover estimated with the phylum classification in July (OA>0.9) was 11080 m², consistent with the general classification. This classification for July calculated cover by Chlorophyta and Ochrophyta phyla of 5507 and 5573 m², respectively (Fig. 6).

The most accurate species classification was achieved with the SAM algorithm in June, and with the ML algorithm in July (Table 2). In June, the best UA for species classification was observed for *S. polyschides*.

Moreover, PA values below 0.70 indicated that the algorithms misidentified the macroalgae species (Table 3), as some areas of *Ulva* spp., *Codium* spp. and *U. pinnatifida* cover were classified as *S. polyschides*. More than half of the *B. bifurcata* cover was classified incorrectly as *S. polyschides* or *Ulva* spp. (Table S8, Fig. S4). In the case of *Ulva* spp., *Codium* spp., *U. pinnatifida*, and *S. polyschides*, the accuracy of classification was low, as much of the correctly classified validation area was mapped as *B. bifurcata*. Within *B. bifurcata*, more than half of the area was erroneously mapped as *Ulva* spp. (Table S8). In the classification by species for July, UA values were higher than in June, exceeding 0.65 for all

species except *Codium* spp. and *U. pinnatifida* (Table 3). Approximately half of the area of *Codium* spp. and *U. pinnatifida* cover was incorrectly classified as *Ulva* spp. The PA was highest for *S. polyschides*, *Ulva* spp. and *B. bifurcata* (Table S9, Fig. S5). The different classification performances found for each algorithm and classification type caused
<caption>**Table 3**</caption>

User accuracy (UA) and producer accuracy (PA) for each classification by the algorithm with the highest percentage of overall accuracy (OA) in both study months.

<table><thead><tr><th rowspan="2">Classification</th><th rowspan="2">Class</th><th colspan="2">June</th><th colspan="2">July</th></tr><tr><th>UA<br>SAM</th><th>PA</th><th>UA<br>ML</th><th>PA</th></tr></thead><tbody><tr><td rowspan="3">General</td><td>Macroalgae</td><td>0.94</td><td>0.98</td><td>0.99</td><td>0.93</td></tr><tr><td>Water</td><td>1</td><td>0.88</td><td>0.99</td><td>0.98</td></tr><tr><td>Inert</td><td>0.99</td><td>0.92</td><td>0.97</td><td>0.99</td></tr><tr><td rowspan="4">Phyla</td><td>Chlorophyta</td><td>0.62</td><td>0.70</td><td>0.80</td><td>0.74</td></tr><tr><td>Ochrophyta</td><td>0.66</td><td>0.84</td><td>0.73</td><td>0.78</td></tr><tr><td>Water</td><td>0.99</td><td>0.97</td><td>0.99</td><td>0.96</td></tr><tr><td>Inert</td><td>0.95</td><td>0.75</td><td>0.98</td><td>0.99</td></tr><tr><td rowspan="8">Species</td><td>Ulva spp.</td><td>0.30</td><td>0.67</td><td>0.89</td><td>0.55</td></tr><tr><td>Codium spp.</td><td>0.08</td><td>0.22</td><td>0.04</td><td>0.08</td></tr><tr><td>Bifurcaria bifurcata</td><td>0.01</td><td>0.58</td><td>0.67</td><td>0.50</td></tr><tr><td>Saccorhiza polyschides</td><td>0.87</td><td>0.16</td><td>0.76</td><td>0.87</td></tr><tr><td>Undaria pinnatifida</td><td>0.09</td><td>0.06</td><td>0.16</td><td>0.28</td></tr><tr><td>Water</td><td>0.95</td><td>0.95</td><td>0.99</td><td>0.97</td></tr><tr><td>Inert</td><td>0.89</td><td>1</td><td>0.99</td><td>0.98</td></tr></tbody></table>

differences in the estimated percentage of cover per class (Table S10).

### 3.3. Biomass estimation

Positive and significant correlations between biomass and cover were detected for both phyla with the biomass and cover data of July. The variability in the biomass of the phylum Chlorophyta explained by its cover was lower than 50 % (R²=0.44), whereas in the model for the biomass of the phylum Ochrophyta, the cover explained more than 90 % of the biomass (R²=0.92) (Fig. 7).

Positive, significant correlations were observed among the five taxa identified in the species classification. The percentage of variance explained by the model exceeded 50 % for all species, except *Ulva* spp. (R²=0.32). Within this classification, *B. bifurcata* was the species for which the highest amount of variance was explained by the model (R²=0.97) (Fig. 8). The higher precision of the classification of phyla than of species (Tables 2 and 3), together with the high correlation of the regressions for the phylum Ochrophyta, enabled us to estimate the biomass of macroalgae in July with a high degree of confidence. Based on a brown algal cover of 5574 m², we estimated the fresh weight of algae of the phylum to be 28494.25 kg in the study area

## 4. Discussion

### 4.1. Spectral properties of the macroalgae

The reflectance values of the spectral signatures and their dispersion for each category were generally higher in June than in July. These differences were related to the differences in the flight times in the two months, and therefore in the angle of incidence of solar radiation on the substrate, which ultimately affected the spectral signature of ROIs. The variable micro-topography, as crevices and/or overhangs, is also a source of noise for the reflectance spectra (Borges et al., 2023). As well, the different multispectral cameras used in each month detected different bandwidths, what could have increased the variability of spectral signatures between June and July of the target classes. Nonetheless, in both months the spectral signatures obtained from the multispectral flights were not sufficiently separated to enable very accurate classification of species. By contrast, for the general classification and classification of phyla, separation of spectral signatures enabled precise classification (Fig. 3). Other analyses of intertidal communities using multispectral or RGB sensors have experienced similar difficulties for

<figure><img src="image_8.png" /><figcaption>**Fig. 5.** Close up of the study area showing the results of the general classifications performed with MD (minimum distance), ML (maximum likelihood) and SAM (spectral angle mapping) algorithms for June and July.</figcaption></figure>
<figure><img src="image_9.png" /><figcaption>**Fig. 6.** Map of the study area showing the results of the phylum classification performed with ML (maximum likelihood) algorithm for July.</figcaption></figure>

species classification. The spectral response of mixtures of macroalgae has previously been analysed using methods that enable correct discrimination, provided that the macroalgae belong to different phyla (Uhl et al., 2013). However, multispectral sensors do not capture enough bands to correctly differentiate between macroalgae species belonging to the same phylum, especially within heterogeneous areas (Rossiter et al., 2020a; Konar and Iken, 2018). The classification capacity of remote sensing methodologies may increase when multispectral sensors are replaced by hyperspectral sensors, which detect a larger number of bands. The inclusion of more wavelengths increases the probability of finding regions of the spectra with greater distances between algal phyla, which are mainly related to the differential absorption of different types of chlorophyll (Diruit et al., 2022; Valle et al., 2015).

One possible way of optimizing the separation of spectral signatures between species and of improving the classification is to incorporate the spectral signatures captured *in situ* with hyperspectral radiometers, measured in the species of interest, which can be compiled in spectral libraries (Davies et al., 2023). The separation of signatures captured *in situ* by hyperspectral radiometers is much higher than that captured with the same sensor attached to UAVs (Olmedo-Masat et al., 2020; Douay et al., 2022). However, data should be collected on the same species under different conditions of humidity and during different life stages in order to improve classifications. The spectral characteristics of the macroalgae often undergo spatio-temporal variations, mainly linked to the variations in colour that occur throughout their life cycle. These variations prevent the use of libraries of data for all seasons and all study areas. Moreover, the variable water content of the algae during emersion also affects the spectral signatures. For example, in the upper part of the subtidal fringe, modification of the absorbed and reflected radiation by the water column affected calculation of the spectral signatures of *S. polyschides* and *U. pinnatifida* (Chao Rodriguez et al., 2017). Direct measurement of spectral signatures in the study sites under different environmental conditions would therefore be appropriate to refine classifications.

## 4.2. Complexity of macroalgae classification

The accuracy of the general classification was sufficiently high to determine the macroalgal cover in the rocky intertidal zone with a high degree of confidence, both in June (OA=0.96, κ=0.94) and in July (OA= 0.98, κ=0.96). The mean values of UA (0.70) and PA (0.77) for classification of phyla were lower than reported in other studies; for instance,

Tait et al. (2021) obtained an average user accuracy of 0.78 and a producer accuracy of 0.96. The OA and Kappa coefficient of our classifications both in June (OA=0.67, κ=0.57) and in July (OA=0.86, κ=0.81) were lower than reported in other studies, i.e. magnitudes higher than 0.9 (Rossiter et al., 2020b; Diruit et al., 2020). The higher precision is mainly explained by the use of hyperspectral sensors, which are able to detect more bands, and the greater separation between classes. Nonetheless, the methodology developed here performed satisfactorily, with more accessible multispectral sensors, and offers promising opportunities for the rapid screening of macroalgal stocks.

The presence of shadows in the multispectral images is another possible source of uncertainty in the classification. When the flights are performed in early morning or late afternoon, or in periods when the sun elevation is low such as late autumn or winter, the inclined position of the sun generates shadows in the irregular surfaces of the rock, such as in crevices and in areas below overhangs (Borges et al., 2023). Therefore, areas that should be classified as inert (rock, sand, concrete) yield a different spectral signature from that of inert ROIs and are therefore classified within other categories, thus decreasing the accuracy of the method. To reduce this source of error, flights should be performed always at noon. However, optimal weather conditions obviously do not always coincide with low tide and midday hours. Other studies have incorporated 3D models, terrain elevation models, and topographic features of the study area such as slope or the shaded area in order to improve the accuracy assessment (Chust et al., 2010; Yuan et al., 2023).

The ML algorithm performed best in most of the classifications, as also observed in other studies (Rossiter et al., 2020b; Román et al., 2021; Diruit et al., 2022). However, in the general and species classification for June, the SAM algorithm was the most accurate (Table 3). In general, the SAM algorithm is more useful in those hyperspectral images in which large numbers of spectral bands are recorded (Kruse et al., 1993), and therefore the small number of bands (5) in our images reduced the accuracy of most classifications. Other algorithms such as Random Forests and Naïve-Bayes (Riniatsih et al., 2021; Borges et al., 2021; Tait et al., 2021) or indexes as the NDVI (Borges et al., 2023) could be used in future studies to improve the accuracy of the classifications.

The use of different sensors that detected different bandwidths each month is another source of variability to the spectral data observed. The classifications were more accurate in July, suggesting a better performance of the DJI 4 sensor. Nonetheless, other factors could affect spectral signatures observed of the target classes and cause differences in the accuracy of classifications between June and July. The performance of both sensors could not be directly compared because they were used under different atmospheric conditions, at different times of the day and with macroalgae at different life stages. Further experiments with both drones flying at the same study site and during the same period could allow to correctly compare their performance.

## 4.3. Biomass estimation

Overall, the correlation coefficients of biomass (g)-cover (cm²) regressions for data of July were satisfactory (above 0.7). However, the corresponding values for the phylum Ochrophyta and *Ulva* spp. were below 0.5, implying some limitations for the accurate estimation of the macroalgae stocks from the cover in quadrats. The low correlation coefficients could be caused by the inability of the method to detect part of the species, as previously noted (Chen et al., 2022). The macroalgae communities in NW Iberia are diverse and heterogeneous, with overlapping of species, whereby the largest individuals form an upper canopy, below which the sub-canopy species occupy the substrate. This is particularly evident during low tide, when the larger macroalgae lie horizontally and cover other species. Given that the drone flights only register the apparent cover, i.e. the upper layer of the macroalgae beds, subcanopy species (secondary macroalgae cover) are not detected. Biomass estimation from multispectral images acquired with UAVs has been found to be more accurate in areas of macroalgal aquaculture,
<figure><img src="image_10.png" /><figcaption>**Fig. 7.** Linear regressions between total macroalgae biomass (g, fw) and spatial cover (cm²) of target species in the *in situ* sampling quadrats (50 ×50 cm) sampled in July.</figcaption></figure>

where there are fewer species and the macroalgae cover is more homogeneous (Chen et al., 2022; Xu et al., 2018). Another source of uncertainty in biomass estimation from two-dimensional UAV images is the impossibility of quantifying the different volumes occupied by each species. The use of biomass-volume regressions could allow us to obtain more accurate biomass estimations from geospatial data. Future studies combining digital surface and digital terrain models could help to resolve this issue.

Despite the observed limitations of the methodology, it enabled us to
carry out large-scale estimation of the biomass of macroalgae in the
month of July, coinciding with the period of maximum biomass, in the
phylum Ochrophyta with a high degree of confidence (R²=0.92). This
data is valuable for managing macroalgae, exploitation of which is
increasing in Galicia (García Tasende and Peteiro, 2015; Cremades et al.,
2004), and can also be extrapolable to other coastal regions in which
macroalgae is an emerging or already exploited resource. The data can
also be used to calculate the carbon capture capacity of the commu-
nities, by combining biomass data with productivity rates, obtained both
from direct measurements and available datasets (Pessarrodona et al.,
2022). In this respect, in addition to live macroalgae, we also detected

drifting macroalgae in the upper zones of the rocky intertidal zone. This
detritus can be commercially important and also plays a role in recycling
carbon and nitrogen and in providing refuges for small invertebrates
(Rodil et al., 2019). Indeed, some studies have attempted to calculate the
biomass and estimate the C stored by drift macroalgae by the combined
use of drone flights and carbon and nitrogen analyses (Li et al., 2023).
These methodologies may prove useful for analysing the importance of
macroalgae communities in global biogeochemical cycles.

**5. Conclusions**

Analysis of UAV multispectral images proved a satisfactory method
for estimating the total cover of the phyla Ochrophyta and Chlorophyta
and for quantifying cover and biomass of intertidal macroalgae from the
phylum Ochrophyta. However, the method did not perform well for
classifying macroalgae species. The poor classification was mainly due
to the spectral similarity between species, particularly of those within
the same phylum, and to the limited number of wavelengths recorded
with the multispectral sensors. These problems could be overcome in
future studies by using digital surface models, which can add
<figure><img src="image_11.png" /><figcaption>**Fig. 8.** Linear regressions between total macroalgae biomass (g, fw) and spatial cover (cm²) of target phyla in the *in situ* sampling quadrats (50 × 50 cm) sampled in July.</figcaption></figure>

information about topography and roughness and thus complement classification based solely on spectral signatures. Biomass estimation models performed well for the phylum Ochrophyta, but not for Chlorophyta. Specific models did not perform well due to subcanopy species being covered by larger ones. Nonetheless, the methodology enabled rapid, inexpensive evaluation of the macroalgae stocks in a rocky intertidal area, which may be useful for conservation and management purposes. Use of the method does not require knowledge of complex programming language, and it is therefore accessible to all final users. However, the use of remote sensing techniques in ecological studies must be accompanied by *in situ* field sampling to obtain a complete picture of the taxonomic features of the populations and of the study site morphology.

## Funding sources

This research was funded by the Consellería de Educación e Ordenación Universitaria of the Autonomous government Xunta de Galicia, specifically by the European Regional Development Funds (project ED431C 2021/42: "ERDF A way of making Europe"). It was also funded by the Consellería do Mar through a research contract (Article 83, L.O. 6/2001) "Análisis de las poblaciones de macroalgas de hábitats intermareales rocosos. Uso de técnicas geoespaciales de bajo coste", executed between june and october 2021. Marta Román received funding through a Xunta de Galicia Post-doc fellowship (ED481B-2023-006).

## CRediT authorship contribution statement

**Joaquín Martínez-Sánchez:** Project administration, Funding acquisition, Conceptualization. **Marta Román:** Writing – review & editing, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. **Miguel J. Peidro-Devesa:** Writing – review & editing, Writing – original draft, Methodology, Investigation, Formal analysis. **Juan Luis Rodríguez-Somoza:** Methodology, Investigation, Formal analysis, Data curation, Conceptualization. **Andrea Martínez-**

## Movilla: Methodology, Investigation.

## Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Data availability

Data will be made available on request.

## Acknowledgments

The authors thank Celia Olabarria for scientific and technical support and Esther Pérez and Antonio Fuentes for assistance in field and laboratory work. Funding for open access charge: Universidade de Vigo/ CRUE-CISUG.

## Appendix A. Supporting information

Supplementary data associated with this article can be found in the online version at doi:10.1016/j.rsma.2024.103621.

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