GEO DATA

Original Papers

Improvement of Algal Bloom Identification Using Satellite Images by the Algal Spatial Monitoring and Machine Learning Analysis in a New Dam Reservoir: Hye-Suk Yi, Sunghwa Choi, Dong-Kyun Kim, Hojoon Kim; GEO DATA. 2023;5(3):126-136. Published online September 25, 2023; DOI: https://doi.org/10.22761/GD.2023.0021

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Abstract PDF: Algal blooms are major issues and an ongoing cause of water quality problems in inland waters globally. In the case of harmful algal blooms, the water temperature rises after nitrogen and phosphorus inflow, which occurs in the summer, is the main cause of the algae bloom. In South Korea, algae monitoring methods have been performed by collecting water in point monitoring stations. Recently, in order to overcome the limitations of these existing monitoring methods, spatial monitoring methods using hyperspectral images and satellite images has been researched. We used satellite images for analysis of the spatial algal variation. The accuracy of algal identification is imperative for effective spatial monitoring of algal blooms in the context of ecological health and assessment. In this study, we generated algal big-data with simultaneously observed chlorophyll-a concentrations based on fluorescence measurement and predicted chlorophyll-a concentrations using 13- band satellite images derived from Sentinel-2. In order to validate the values from the satellite images, we compared them with simultaneously observed chlorophyll-a concentrations based on fluorescence measurement. The goal of this study is to improve the accuracy of predictions induced from satellite images. The analytical techniques were comparatively evaluated. The results showed that Artificial Neural Networks exhibited the best performance among them, improving more than 30% accuracy compared to that of multiple linear regression. Furthermore, the accuracy of identifying algal blooms has been shown to increase at high algal concentrations. In the end, it was successful to create algal bloom maps using a new algorithm to analyze algal bloom management.

Distribution Characteristics of the Clithon retropictus in the Estuarine Wetland: Yeounsu Chu, Pyoungbeom Kim; GEO DATA. 2023;5(2):60-65. Published online June 12, 2023; DOI: https://doi.org/10.22761/GD.2023.0011

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Abstract PDF Supplementary Material: This study analyzed the distribution characteristics of Clithon retropictus (C. retropictus), an endangered species, using data from the benthic macroinvertebrate survey on estuarine ecosystems conducted in 2021-2022. A total of 5,906 individuals of C. retropictus were identified in 60 estuarine wetlands located along the eastern coast, southern coast, and Jeju area. It was confirmed to be a dominant species in certain estuarine wetlands such as Obangcheon, Gohyeoncheon, and Osucheon. The southern coast of Gyeongsangnam-do was identified as a major distribution area, indicating the need for systematic conservation and management of C. retropictus in this region. Furthermore, as a basic survey of benthic macroinvertebrates is currently being conducted in Jeolla-do, it is expected that nationwide distribution data for C. retropictus will be obtained.

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