반사도를 활용한 비 적조 해역으로부터의 유해 적조 탐지에 관한 연구

Abstract

We investigated the possibility of optically discriminating harmful algal blooms (HABs) from non-HABs based on the simulated remote sensing reflectance (Rrs) spectra under a wide range of bio-optical conditions. We focused on C. polykrikoides, the major HAB causative dinoflagellate species in Korean waters. We produced a large dataset of Rrs spectra simulated using radiative transfer model (Hydrolight software) with bio-optical data provided by International Ocean-Color Coordinating Group (IOCCG). This enabled us to generate Rrs under a wide range of bio-optical conditions in natural waters. We determined critical differences between the Rrs spectra of high-density C. polykrikoides blooms and those of unspecified phytoplankton assemblages. Based on these, two Rrs band ratios (Rrs(555)/Rrs(531) and Rrs(488)/Rrs(443)) were identified as effectively discriminating high-density C. polykrikoides blooms. The results were consistent with in situ observations and seem applicable to diverse coastal environments. The results provide theoretical and quantitative criteria upon which in-water HAB detecting algorithms can be developed. Such algorithms can be extended to satellite remote sensing providing synoptic monitoring tools for detecting HABs in a large expanse of water.es, the major HAB causative dinoflagellate species in Korean waters. We produced a large dataset of Rrs spectra simulated using radiative transfer model (Hydrolight software) with bio-optical data provided by International Ocean-Color Coordinating Group (IOCCG). This enabled us to generate Rrs under a wide range of bio-optical conditions in natural waters. We determined critical differences between the Rrs spectra of high-density C. polykrikoides blooms and those of unspecified phytoplankton assemblages. Based on these, two Rrs band ratios (Rrs(555)/Rrs(531) and Rrs(488)/Rrs(443)) were identified as effectively discriminating high-density C. polykrikoides blooms. The results were consistent with in situ observations and seem applicable to diverse coastal environments. The results provide theoretical and quantitative criteria upon which in-water HAB detecting algorithms can be developed. Such algorithms can be extended to satellite remote sensing providing synoptic monitoring tools for detecting HABs in a large expanse of water.