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[AHW24-P10] Evaluation of the spatial distribution of seagrass-seaweed beds including different species in shallow coastal waters using UAV
Keywords:coastal seaweed beds, UAV, NDVI
Seagrass-seaweed beds (hereafter SSB) play roles in providing habitats for aquatic organisms and purifying water quality in shallow coastal waters. In recent years, the number of SSB has been decreasing worldwide due to land reclamation and deterioration of water quality. Therefore, it is important to manage SSB through the monitoring and observations. UAV is a useful tool for monitoring SSB because they are not affected by clouds, they allow easy observation, and they can acquire high-resolution photographs.
In the previous studies, the habitat status of SSB using UAV has been evaluated mainly on the basis of the surface area. However, it can be said that just the surface area is insufficient for evaluating properly the habitat status and carbon stock of SSB.
The purpose of this study was to estimate the spatial distribution of SSB with classification of different species in shallow coastal waters using aerial images taken by UAVs.
Habitat characteristics were used to distinguish between types. Habitat characteristics include the fact that seagrasses are mainly found in sandy soil and seaweeds are on rocky substrates. In addition, there is a difference in the depth of water inhabited by coelenterate and large eelgrass. Since we were able to confirm these characteristics of the habitats of each species during the field survey, we classified the species that make up the seagrass beds by comparing them with the results of the field survey.
Regarding the spatial distribution of SSB, NDVI (Normalized Difference Vegetation Index) was applied as an indicator. However, it is difficult to understand NDVI in water because near-infrared radiation is greatly affected by water. In this study, we focused on the bottom index(BI), which is a method of estimating the sediment quality using two different wavelength bands and can eliminate the effect of water. By creating a regression equation between this BI and NDVI, we estimated NDVI in water.
The survey and analysis of the seaweed bed was conducted on Ikuchijima Island in the Seto Inland Sea. Visible images were acquired using a camera installed on the UAV at an altitude of 100m with a resolution of approximately 2.8cm/pixel. For near-infrared images, we used a camera called yubaflex. The resolution of this camera was 3.7 cm. At the same time, we also conducted a foot survey on the tidal flat to collect samples of the seaweed bed and to understand its distribution.
As a result of NDVI estimation, the values were higher for seaweed, large eelgrass, and small eelgrass, in that order. We would like to use the results to understand quantitatively the amount of SSB biomass in the future.
This research was supported by JSPS Grant-in-Aid for Scientific Research (B) (No. 21H03650, PI: Mitsuyo Saito).
In the previous studies, the habitat status of SSB using UAV has been evaluated mainly on the basis of the surface area. However, it can be said that just the surface area is insufficient for evaluating properly the habitat status and carbon stock of SSB.
The purpose of this study was to estimate the spatial distribution of SSB with classification of different species in shallow coastal waters using aerial images taken by UAVs.
Habitat characteristics were used to distinguish between types. Habitat characteristics include the fact that seagrasses are mainly found in sandy soil and seaweeds are on rocky substrates. In addition, there is a difference in the depth of water inhabited by coelenterate and large eelgrass. Since we were able to confirm these characteristics of the habitats of each species during the field survey, we classified the species that make up the seagrass beds by comparing them with the results of the field survey.
Regarding the spatial distribution of SSB, NDVI (Normalized Difference Vegetation Index) was applied as an indicator. However, it is difficult to understand NDVI in water because near-infrared radiation is greatly affected by water. In this study, we focused on the bottom index(BI), which is a method of estimating the sediment quality using two different wavelength bands and can eliminate the effect of water. By creating a regression equation between this BI and NDVI, we estimated NDVI in water.
The survey and analysis of the seaweed bed was conducted on Ikuchijima Island in the Seto Inland Sea. Visible images were acquired using a camera installed on the UAV at an altitude of 100m with a resolution of approximately 2.8cm/pixel. For near-infrared images, we used a camera called yubaflex. The resolution of this camera was 3.7 cm. At the same time, we also conducted a foot survey on the tidal flat to collect samples of the seaweed bed and to understand its distribution.
As a result of NDVI estimation, the values were higher for seaweed, large eelgrass, and small eelgrass, in that order. We would like to use the results to understand quantitatively the amount of SSB biomass in the future.
This research was supported by JSPS Grant-in-Aid for Scientific Research (B) (No. 21H03650, PI: Mitsuyo Saito).