[A-HW22] Material transportation and cycling at the land-sea interface: from headwaters to the ocean

[AHW22-16] An evaluation of seagrass area before and after heavy rain around Ikuchijima Island using GCOM-C SGLI data

★Invited Papers

*Yuji Sakuno¹ (1.Graduate School of Advanced Science and Engineering)

Keywords:seagrass, heavy rain, remote sensing, bottom index

During the 2018 heavy rain disaster in western Japan, which killed more than 200 people, heavy rains caused a large amount of sediment to flow into the Seto Inland Sea from large and small rivers. Due to such frequent heavy rains in recent years, there are concerns that the distribution of seagrass beds in the sea will decrease. Conventionally, high spatial resolution satellite data such as WorldView-3, Sentinel-2, and Landsat-8 have been used to evaluate seagrass bed area. However, the use of high-spatial resolution data has drawbacks in terms of temporal spatial resolution and cost. Therefore, in this study, we investigated changes in the distribution of seaweed beds before and after heavy rain using GCOM-C SGLI data with a medium spatial resolution of 250 m and approximately 2-day repeat cycle. In this study, we used a seaweed bed extraction algorithm called Bottom Index (BI) that is not affected by water depth around Ikuchijima Island. The map obtained by applying BI to SGLI was qualitatively similar to the actual survey results. Additionally, the seagrass area (%) determined by SGLI showed a pattern of decreasing before heavy rains, then increasing and decreasing thereafter. On the other hand, according to the actual measurement results of the Ministry of the Environment, the increasing trend continues until August 2020. However, actual measurements are limited to a portion around the island. The above results show that by using SGLI data, it is possible to understand long-term trends in seagrass bed maps to some extent.