• Korean Journal of Remote Sensing
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• v.39 no.5_1
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• pp.695-713
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• 2023

In coastal waters, phytoplankton,suspended particulate matter, and dissolved organic matter intricately and nonlinearly alter the reflectivity of seawater. Neural network technology, which has been rapidly advancing recently, offers the advantage of effectively representing complex nonlinear relationships. In previous studies, a three-stage neural network was constructed to extract the inherent optical properties of each component. However, this study proposes an algorithm that directly employs a deep neural network. The dataset used in this study consists of synthetic data provided by the International Ocean Color Coordination Group, with the input data comprising above-surface remote-sensing reflectance at nine different wavelengths. We derived inherent optical properties using this dataset based on a deep neural network. To evaluate performance, we compared it with a quasi-analytical algorithm and analyzed the impact of log transformation on the performance of the deep neural network algorithm in relation to data distribution. As a result, we found that the deep neural network algorithm accurately estimated the inherent optical properties except for the absorption coefficient of suspended particulate matter (R² greater than or equal to 0.9) and successfully separated the sum of the absorption coefficient of suspended particulate matter and dissolved organic matter into the absorption coefficient of suspended particulate matter and dissolved organic matter, respectively. We also observed that the algorithm, when directly applied without log transformation of the data, showed little difference in performance. To effectively apply the findings of this study to ocean color data processing, further research is needed to perform learning using field data and additional datasets from various marine regions, compare and analyze empirical and semi-analytical methods, and appropriately assess the strengths and weaknesses of each algorithm.

• Journal of Plant Biotechnology
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• v.50
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• pp.275-289
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• 2023

Since the implementation of the Living Modified Organisms (LMOs) Act in 2008, approximately 10 million tons of genetically modified corn, soybean, potato, canola, and other crops have been imported into South Korea. The import approval procedures have been completed for approximately 191 cases that include seven crops. Of these, approximately 90 cases, excluding crossbreeds of approved LMOs, were reviewed via consultation risk evaluation in four areas: human health, crop culture, natural ecology, and marine fishery environment. LMO developers in South Korea, who are major stakeholders in the import of LMO crops produced overseas, have raised concerns regarding procedural inefficiency in consultation reviews and the need of excessive reviews that are unsuitable for food-feed processing purposes. These procedures reflect the perspective of consultation agencies that deviate from the nature of risk assessment and demand specific supplementary data that do not reflect familiarity and substantial equilibrium. Based on frequent instances of unintentional environmental release of LMO crops imported into Korea, the ministries responsible for consultation insist on a review that considers the climate and natural environment of Korea. In addition, the ministries mandate that their reviews reflect the expertise of competent ministries and are based on risk assessment principles and methods in accordance with international guidelines. In this regard, considering that traits introduced into LMO crops involving familiar agricultural crops have been considered safe for more than two decades, we have suggested reasonable alternatives to several risk assessment items for agricultural LMOs. These alternatives can mitigate conflicts of interest among key stakeholders within the scope of the current LMO regulations.