Mapping of Sargassum meadow for estimation biomass by side scan sonar

doi:10.21203/rs.3.rs-2758304/v1

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Mapping of Sargassum meadow for estimation biomass by side scan sonar

https://doi.org/10.21203/rs.3.rs-2758304/v1

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Sargassum meadows play important roles in coastal ecosystems, CO2 fixing, and others. Estimation of their biomass helps conservation of meadows and estimation of the amount of fixed CO2 for blue carbon projects. Most sonars are inefficient in shallow waters because their swaths are limited by water depth. Side scan sonar (SSS) creates a detailed image of the bottom efficiently, even in shallow waters transmitting wide beams.

We towed SSS from the ship’s side and scanned the meadow of Sargassum horneri at a small beach in Otsuchi Bay on the Sanriku Coast of Japan during the growing season of 2022. For the truthing, we surveyed with the underwater camera and Single beam echo sounder (SBES) to compare with SSS data.

SSS scanned the sea floor of the beach to detect seaweed echoes. The beach consists of a sandy area and a rocky shore. The seaweeds were discriminated from the sand and rocks by their acoustic shadows, and we pinpointed their locations, although the background reflectance strength and pattern were different between the two areas. To estimate the canopy heights, we measured the acoustic shadow lengths of the seaweeds. However, the shadows of the seaweeds, which are located very near to the sonar, are too short to measure. Therefore, we developed a new method to measure their heights from acoustic reflections instead.

Our study demonstrated the effectiveness of side scan sonar for mapping Sargassum meadows in shallow waters and estimating their canopy heights with a novel method, which leads to precise biomass estimation.

Side scan sonar

blue carbon

Sargassum

Habitat mapping

Biomass

Sargassum species are brown macroalgae, and they stand up on the sea bottom, reaching several m and creating complex forests (Komatsu et al. 1982; Maegawa 2000). Their meadow plays important roles in coastal ecosystems. For example, experiments by Levin and Hay (1996) suggested that the Sargassum meadow has a fish aggregation effect, and it was reported that the number of migrating fish decreased with a decrease the Sargassum meadow in Hokkaido (Sakurai et al. 2009). Sargassum meadow also serves as food for abalone and other useful fishery species (Uki et al. 1986).

Sargassum is also expected to serve as blue carbon. Interest in blue carbon has increased in recent years from the perspective of global warming mitigation. Seagrass beds, salt marshes, and mangroves are called blue carbon ecosystems because the carbon they absorb is buried in the bottom sediment and soil and is stored and sequestered without returning to the atmosphere (Nelleman et al. 2009). On the other hand, Krause-Jensen & Duarte (2016) suggested that dead seaweed is transported to the deep sea and sequestered. In addition, a blue carbon offset system is emerging in Japan, in which companies and others purchase blue carbon credits to compensate for their inability to reduce CO2 emissions by using seagrass beds as blue carbon ecosystems. Although seaweed meadows, including Sargassum meadows, are not recognized as blue carbon ecosystems by UNEP (United Nations Environment Programme) because it's not clear how much of the carbon derived from the seaweed meadow is sequestered in storage. In order to clarify the contribution of seaweed beds as blue carbon ecosystems, it is necessary to accurately estimate the amount of carbon in seaweed meadows.

Survey methods of seaweed meadows are divided into direct methods and indirect methods. Quadrat, underwater cameras, and diving are mentioned as examples of the direct method. When doing these methods, the operator has to approach near the meadow, and it is difficult to survey in an area whose water depth is too shallow or the current is too fast to approach by ship. In addition, the methods are easily affected by water turbidity, and it is difficult to survey large areas efficiently.

The optic method and acoustic method enable to survey without approaching near meadow. Satellite remote sensing and drone survey are mentioned as an example of optic methods. These methods enable to survey of a large area, but it is difficult to measure the target height. Additionally, the method is easily affected by water turbidity and water depth. Controversially, the Acoustic method hardly to be affected by water turbidity, and it is possible to detect the target from a faraway area. There are previous studies that suggest a method to survey Sargassum distribution by Quantitative Echo Sounder (QES) (Minami et al. 2012) and Multi Beam Echo Sounder (MBES) (Hamana and Komatsu, 2021). QES transmits the pulse downward and detects the target just below the sonar and cannot survey areas outside of the ship track. Minami et al. use Kriging for interpolation. However, this method is not suitable for surveying the meadow in which Sargassum stands irregularly on the bottom. On the other hand, there is no necessity for interpolation at the MBES survey because the swath width is wider than QES. However, the swath width of MBES is changed by the water depth and is narrow in a shallow area. It needs much time to survey the meadow, which is in a shallow area. Additionally, it is difficult to survey near the shoreline because it is dangerous for the ship to run aground to land. In contrast, Side Scan Sonar (SSS) is appropriate for surveys in a shallow area. SSS transmit beam to side direction, and their swath width is very wide. It is possible to survey shallow areas effectively without getting closer to the shoreline. In addition, SSS produces a high-resolution 2D image of the seafloor from the intensity of beam reflectance. The beam width for track direction is very thin, and the sonar image of SSS is high resolution so that the operator can find a small target.

This study aimed to develop a method to locate the Sargassum body distributed irregularly in shallow waters and estimate its carbon content by SSS, and compare the method with MBES to evaluate our method. Thereby contributing to the meadow’s management and blue carbon study of the seaweed meadow.

Survey area

The survey area was Akahama Beach in Otsuchi Bay in Iwate, Japan(Fig. 1). Otsuchi Bay is a closed bay with a north-south length of 3.5 km, an east-west length of about 8 km, and a bay mouth width of about 4 km. There are Sargassum meadows which mainly consist of Sargassum muticum and Sargassum horneri. Akahama Beach consists of a sandy and rocky beach, and the Sargassum meadow is concentrated in a shallow area near the shoreline where ships cannot enter. Therefore, only the edges of the meadow could be approached by the ship.

Sonar survey

A Sonar survey was conducted in May 2022, when the meadow was thriving. A preliminary survey was conducted on the 9. Multiple survey lines were planned from offshore to shore. The reason for setting the survey line direction is that a homogeneous acoustic image can be obtained when there is no depth variation in the cross-track direction. However, due to the shallow water depth, it was not possible to get close to the meadow by ship, and few plants were observed in the deeper areas that could be surveyed. Therefore, a survey line was planned parallel to the shoreline, and a survey was conducted on the 10. For the analysis, data from only one survey line acquired on the 10, 2022, was used. We towed side scan sonar, MA-X View 600 (Klein a mind technology business Inc., USA) from the ship side. The sonar frequency is 600 kHz, and the beam angle is 0.23 degrees. Because the water depth was shallow, we decided to cable out a length of about 1 m, and the sonar was at 0.5m depth. We set the sonar range to 25 m. The sonar system was operated with GNSS antennae, Crescent A100 (Hemisphere GNSS Inc., USA), and navigation data was recorded into SDF files by SonarPro software v14.2 (Klein a mind technology business Inc., USA).

After the field survey, the recorded data was imported into mosaic software, CleanSweep3 (Oceanic Imaging Consultants Inc., USA) for detailed analysis.

Pinpointing the locations and heights of plant

SSS image is produced by the reflection intensity of the beam. Objects, which reflect the beam strongly, are drawn brighter, and acoustic shadows are created behind standing structures like a shadow that was drawn in photography because structures block the beam. Sargassum body has many gas-filled vesicles (Huruka 1971) and stands on the bottom of the water. The Sargassum body was expected to reflect the SSS beam strongly and was drawn brightly. Additionally, it was thought that acoustic shadows could form behind the plant. To validate this hypothesis, we surveyed the meadow with an uplifted camera (Hayashizaki submitted 2023) on 30 May 2022 and compared with the SSS image. In addition, we did SSS survey on 2 September 2022, when the meadow was declined. After validation, we tried to pinpoint the plants. SSS beam approach in order of 1) top of the plant, 2) bottom of the plant, and 3) ground behind the plant. The horizontal axis of the sonar image reveals a slant distance from the sonar to the target. We decided that the sonar-side end of the plant reflection is the top of the plant, and the boundary between the plant reflection and acoustic shadow is the plant bottom of the plant. The boundary between plant reflection and acoustic shadow was recorded as the location of the plant. Then, the locations are plotted on a map by QGIS software v3.31 (QGIS Development Team). Additionally, the bottom types at the plant’s location are classified by the SSS image.

In sonar surveys, there is a dead zone that cannot be surveyed due to the instrument's characteristics. For example, Hamana and Komatsu (2021) pointed out the existence of dead zones of MBES near the sea surface. MBES detects the depth measurement point from within a fan-shaped area extended downward. Therefore, an area outside the fan becomes a dead zone. On the other hand, SSS produces acoustic images from reflections arriving from all directions. Therefore, there are no dead zones for cross-track direction. On the other hand, a dead zone exists for track direction. In the case of most SSS, it is not possible to transmit and receive the beam at the same time, and a time gap exists between pings(Yet-Chung et al. 2010). In addition, since the survey is conducted while the ship is running, unmeasured areas are created when the ship moves forward while the sonar does not transmit its beam. Because the beam is diffusing during propagation, the closer the range, the smaller the footprint. Thus, the closer to the sonar, the larger the unmeasured area. The narrower the transmit interval or the slower the ship speed, the smaller the unmeasured area, and the transmit interval depends on the sonar range setting. MA-X View 600 transmits 23.8 pings per second when the range is set to 25m range. The distance traveled by the vessel from one ping to the next ping (d, m) is determined by the following equation.

$$\begin{array}{c}d=\frac{v}{f}\end{array}\left(1\right)$$

v is ship speed (m s^− 1), and f is ping rate (ping s^− 1). If the distance was sufficiently small relative to the plant width, the survey was considered to have been conducted without missing plants. After the survey, we compared the distance referring to ship speed which was recorded by GNSS, with the width of the Plant to evaluate the possibility of overlooking the Plants.

Next, we tried to measure canopy height. It is general to measure the length of acoustic shadow to estimate target height. However, in the case of surveying Sargassum meadow, there are problems with the method. On the other hand, the closer the distance from the sonar to the target, the longer the acoustic shadow and the longer the reflection length (Fig. 2a). The greater the distance from the sonar to the target, the longer the acoustic shadow and the shorter the reflection length (Fig. 2b). The acoustic shadow of the plants, which is located near the sonar, is too short to measure. In addition, some shadows overlap rocks or other plants, and it is difficult to measure accurately. In contrast, the plant reflection, which is located near the sonar, is long, and it is easy to measure the length. For a plant located less than 5 m from the sonar, we estimated the height from the reflection length, and for a plant located far away, over 5 m from the sonar, we estimated from acoustic shadow length.

To estimate canopy height by reflection length, we measured the distance from the sonar to shore, the water depth at the location of the sonar, and the distance from the sonar to the top and bottom of the plant. At first, we estimated the tilt of the bottom from the water depth at the ship and the horizontal distance from the ship to the shoreline. Next, we decided the location of the plant bottom from slant distance from sonar to plant bottom, water depth at the location of sonar, and tilt of the bottom. Finally, we estimated the canopy height from the horizontal distance from the sonar to the plant bottom and the slant distance from the sonar to the plant top.

To validate the height measurement result, a Single Beam Echo Sounder (SBES) survey was conducted (Hayashizaki & Okawa unpublished data) after the SSS survey. The survey was conducted by MX Acoustic Habitat Echosounder (BioSonics Inc., USA), and the data was collected by Visual Acquisition software v.6.4 (BioSonics Inc., USA). An underwater camera was also mounted next to the SBES to capture images of the seafloor. After the survey, measuring canopy height was performed by Visual Aquatic software v1.0.0.13146 (BioSonics Inc., USA).

Estimation of carbon weight of Sargassum meadow

We investigated the relationship between thallus length, width, and dry weight. We collected Sargassum muticum at Namiita Beach in Okirai Bay, which is located near Otsuchi Bay, on 5 May 2022. The lengths and widths were measured for every plant in the field. After being transported to our laboratory, which is located near Namiita Beach, the plants were dried at room temperature over 24 hours and weighed their dry weight. We obtained a formula to convert height to dry weight from the result.

Observations on the vessel and camera surveys confirmed that multiple plants were attached to one substrate. SSS resolution for track direction (R_t, m) is calculated by the following equation (Burghard 1976).

$$\begin{array}{c}{R}_{t}={R }\text{sin}\theta \#\left(2\right)\end{array}$$

R is the range from sonar to target (m), and θ is the beam angle for track direction. The beam angle of MA-X View600 for track direction is 0.23 degrees, and the resolution at 25m away is 0.1m. It was difficult to detect every individual plant, and it is considered that clustered plants were detected as one block by SSS. To simplify the theory, we assume all plants in a cluster were the same height. We estimated dry weight per one clustered plant (DW_c, kg) by following the equation.

$$\begin{array}{c}DWc=DWs {\left(\frac{{W}_{Ref}}{{W}_{s}}\right)}^{2}\#\left(3\right)\end{array}$$

DWs is the dry weight per single plant (kg). W_Ref is the width of plant reflectance for track direction (m), and W_s is the width of a single plant (m). The average width of the plant collected at Namiita Beach was applied to W_s. Mengqiu et al. (2018) reported carbon ratio per dry weight of combined Sargassum fluitans and Sargassum natans is 27.16%. We estimated the carbon weight of clustered plants for every cluster of plants by the ratio.

Pinpointing the location of the clustered plants

SSS survey area covered an area of 10916.1 square meters in May. By classification of SSS, the sandy beach was 4467.88 square m, and the rocky beach was 6448.22 square m. We pinpointed 151 clustered plants in a sandy area and 168 clustered plants at a rocky beach (Fig. 3). plant reflections could be distinguished from rocks by 1) unclear reflection contours, 2) elongated echoes of reflections near the sonar, and 3) long acoustic shadows in areas away from the sonar. The location of the plants pinpointed by SSS is similar to the result of the uplifted camera, and the reflection, which was considered a plant, was not confirmed in the SSS image, which was recorded in September.

In this survey, the maximum ship speed was 2.2knots (1.13m/s). The distance between pings was 0.047 m when sailing at the maximum ship speed. Since the average width of Sargassum muticum, which was collected in Okirai Bay, is 0.18 m, it is considered that the plant was not missed. In other words, SSS can be used to pinpoint all plant locations, even in the discretely distributed meadow.

Measuring canopy height

The canopy height was estimated for 63 well-conditioned points. Of these, 19 points were estimated from reflection length, and the remaining 44 points were estimated from acoustic shadow length. The canopy height measured by SBES at 4 to 5 m depth ranged from 0.408 m to 1.751 m. The canopy height measured by SSS ranged from 0.49 m to 2.33 m within the same depth range. There was a correlation between canopy height, estimated by SSS, and water depth (Fig. 4). We obtained a regression curve between water depth and canopy height. We measured depth at the bottom of the plant and estimated canopy height for all clustered plants by the regression. The average estimated canopy height was 3.431 ± 0.271 m.

Estimation carbon weight

We collect 15 plants whose thallus lengths are 0.90 to 2.76 m and the width is 0.18 ± 0.05m in Okirai Bay. Average of the dry weight is 0.075 ± 0.042 kg. There is a positive correlation between Thallus length (TL, m) and dry weight per single plant (r = 0.7670), and the relationship is following.

$$\begin{array}{c}DWs=0.200 {{T}{L}}^{2.0123}\left(4\right)\end{array}$$

We used the average width (0.18) of plants, which was collected in Okirai Bay for W_s of formula, and estimated the carbon weight of the meadow at Akahama Beach (Table 1)

The number of plants pinpointed in this study was more significant on the rocky beach than in the sandy beach area. This is thought to be due to the larger area of substrate attached to the rocky beach. On the other hand, the amount of carbon weight was more significant in the sandy area than in the rocky beach. When comparing sandy beaches and boulders at every depth, there was no significant difference in carbon weight between the two areas at depths less than 3 m. On the other hand, in the 3–4 m depth zone, the carbon weight of the sandy area was more than 200 kg, while the carbon weight of the rocky beach was less than 50 kg.

Sea urchins are known as herbivorous vermin in Otsuchi Bay. Underwater camera results showed that many sea urchins were observed on the rocky beach and few in the sandy area. Kawamata et al.(1994) reported that sea urchin grazing is suppressed by waves, suggesting that seaweed communities develop in shallow areas favorable for photosynthetic production but are affected by sea urchin grazing from deeper areas where wave effects are less severe. These suggest that the reason why the present amount of rocky beach is significantly smaller in this study area than in the sandy area is due to the influence of sea urchin grazing in deep are of rocky beach.

Sargassum meadow in Akahama was mainly distributed in an area less than 5m deep. The swath width of MBES, which is said to be able to survey a large area at a time, is generally three times the depth of the water (Verner et al 2006), and in the case of this area, the maximum swath width would be 15m. The SSS produced a clear acoustic image of the seafloor at 50 m from sonar, the upper limit of the set range, and could detect the plant’s reflection. The swath width of SSS was 100 m, which indicates that the survey efficiency of the SSS was over six times greater than that of MBES. We demonstrated a survey to estimate the total amount of carbon in the Sargassum meadow.

The Pinpointing process was very time-consuming and also required skilled techniques. Depending on the skill of the operator, the plants may be misjudged. Therefore, it is necessary to develop technology for automatic detection using machine learning or other methods. It is also necessary to evaluate the accuracy of the height estimation method for SSS.

To the best of our knowledge, this is the first study to estimate the total carbon amount of the Sargassum meadow. Our method is an efficient way to survey discretely distributed meadows without overlooking them. In the near future, side-scan sonar surveys of the Sargassum meadow will help manage the meadow and promote blue carbon studies.

Funding – Japan Society for the Promotion of Science JSPS KAKENHI Grant Number JP22K05808, Japan Society for the Promotion of Science Core-to-Core Program JSPS CREPSUM JPJSCCB20200009, the Cooperative Program (No. 119, 2022) of Atmosphere and Ocean Research Institute, The University of Tokyo.

Competing interests – We declare no competing interests

Availability of data and material – see more details here.

Code availability - Not applicable

Contribution

Conceptualization, HO. and KH; Data curation, HO. and KE.; Formal analysis, OH and KH; Funding acquisition, KH.; Methodology, HO and KH.; Supervision, KH.; Visualization, HO and KH; Writing—original draft, HO.; Writing—review & editing, HO. and KH.

Acknowledgment

We are grateful to Otsuchi Coastal Research Center for supporting our field survey. The University of Tokyo, and Hydro Systems Development Inc. for lending Klein’s sonar system. We also wish to thank Taichi Mase, an undergraduate student at Kitasato University, for his support of data analysis. This research was supported by the Cooperative Program (No. 119, 2022) of Atmosphere and Ocean Research Institute, Japan Society for the Promotion of Science JSPS KAKENHI Grant Number JP22K05808, and Japan Society for the Promotion of Science Core-to-Core Program JSPS CREPSUM JPJSCCB20200009.

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Table 1 Results of estimated carbon amount. Bottom type classification and measuring area were conducted by SSS mosaic.

Water Depth(m)	Sandy Area			Rocky beach
Water Depth(m)	n	Carbon Amount (kg)	Area (m²)	n	Carbon Amount (kg)	Area (m²)
1-2	28	18.11		38	24.41
2-3	67	112.54		82	110.67
3-4	56	195.04		31	51.02
4-5	0	0.00		17	24.56
Combined	151	325.69	4467.9	168	210.66	6448.2

No competing interests reported.

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You are reading this latest preprint version

Mapping of Sargassum meadow for estimation biomass by side scan sonar

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Version 1

Abstract

Figures

Introduction

Method

Survey area

Sonar survey

Pinpointing the locations and heights of plant

Result

Pinpointing the location of the clustered plants

Measuring canopy height

Estimation carbon weight

Discussion

Declarations

References

Tables

Additional Declarations

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