Ryu, J.-H.; Jung, H.-S.; Lee, S., and Cui, T., 2019. Special Issue on “Advances in Remote Sensing and Geoscience Information Systems of the Coastal Environments”. In: Jung, H.S.; Lee, S.; Ryu, J.H., and Cui, T. (eds.), Advances in Remote Sensing and Geoscience Information Systems of Coastal Environments. Journal of Coastal Research, Special Issue No. 90, pp. v–xi. Coconut Creek (Florida), ISSN 0749-0208.

Advanced remote sensing (RS) and geoscience information system (GIS) have become more essential to understanding the coastal environmental characteristics of Earth surfaces. In this special issue, a total of 52 papers have been published. These papers studied on a variety seas including the Yellow Sea (YS), East China Sea (ECS), South China Sea, Arctic Ocean, North West Pacific, and the Greenland Sea. Forty of these papers studied on the YS and the ECS. Remotely sensed data from various platforms, including satellite, airborne, unmanned aircraft, Helikite and Unmanned Surface Vehicle (USV) images, were used for analysis, and GIS spatial data, reanalysis data and models were also utilized. Ocean colour images were mainly applied to detect marine environment changes (SST, chlorophyll-a and suspended particle matter) and benthic and floating vegetation. High-resolution images were mainly used in the analysis of topographic changes, sedimentary phases and habitat changes in small study areas. SAR images were mainly used for detection of oil spill and sea ice, and could also be used in studies to estimate the moving speed of the target using dual receive antenna mode of the SAR sensor. Unmanned aerial vehicles were mainly used to analyze geographical features and topographic deformation along the coast. Furthermore, hyperspectral images were used for precise detection of vegetation and oil spill studies.

Hong, S.H.; Kim, J.H.; Park, J.W., and Won, J.S., 2019. Detection and velocity measurement of brash ice in the Arctic Ocean by TerraSAR-X quad-pol SAR. In: Jung, H.S.; Lee, S.; Ryu, J.H., and Cui, T. (eds.), Advances in Remote Sensing and Geoscience Information Systems of Coastal Environments. Journal of Coastal Research, Special Issue No. 90, pp. 1–10. Coconut Creek (Florida), ISSN 0749-0208.

A new method is presented using a TerraSAR-X quad-pol synthetic aperture radar (SAR) observation for detection and velocity measurement of sea ice drift, which can be useful information to improve sea ice models in the Arctic and Antarctic Oceans. It is very difficult to detect and measure slow moving natural objects using only a space-borne SAR observation without any terrestrial measurement. The core idea is to exploit a slight time difference between different polarizations (i.e., H- and V-pol transmitted signals). The ground motion can be estimated by measuring the slope of residual Doppler frequency versus azimuth time difference without the knowledge of different scattering centers. The results demonstrate effective detection of the flow of brash ice. The SAR-measured velocities were approximately 0.2-0.3 m/s for ice floes and 1.4-3.5 m/s for brash ice flowing through ice fractures. The method was validated by using a pursuit monostatic TanDEM-X mode observation, by which two satellites observed the same sea ice with about a ten-second time interval. The sea ice velocities measured by the proposed approach using only one dataset well correlated with the results from offset tracking method applied to the two datasets with a correlation determination R² of 0.93. The cross-pol (HV- and VH-pol) pair is more effective to measure the velocity because the scattering center distance decreases significantly in the cross-pol pair, and data with high coherence are required for velocity estimation.

Park, J.; Kwon, Y.S.; Baek, S.H.; Lim, W.A.; Park, J.; Jang, J., and Park, Y., 2019. Identifying environmental effects on an annual variation in Margalefidinium polykrikoides in the South Korean sea using statistical analysis. In: Jung, H.-S.; Lee, S.; Ryu, J.-H., and Cui, T. (eds.), Advances in Remote Sensing and Geoscience Information Systems of Coastal Environments. Journal of Coastal Research, Special Issue No. 90, pp. 11-18. Coconut Creek (Florida), ISSN 0749-0208.

Understanding harmful algal blooms is imperative for protecting aquatic ecosystems and human health. This study describes spatial and temporal distributions of dinoflagellate Margalefidinium polykrikoides blooms to understand the relationship between blooms and environmental factors in the South Korean Sea. A regression tree model, which is a binary recursive partitioning method, analyzed 20 years of long-term monitoring data such as hydrodynamic, water quality, and bloom density data to investigate the relationship. The results showed that all independent (i.e., hydrodynamic and water quality data) and dependent (i.e., bloom density) variables were not significantly different between the Goheung-Yeosu and Yeosu-Namhae areas (p-values > 0.05). Variations in the M. polykrikoides blooms predominantly depended on variations in the amount of Yangtze River discharge of china (first split variable in Yeosu-Namhae area) and current velocity (second and fourth split variables in the Goheung-Yeosu and Yeosu-Namhae areas, respectively) from the mouth of the Yangtze River to the South Korean Sea. The increase in Yangtze River discharge, which led to changes in ratio of nitrogen to phosphorus, had a negative effect on the increase in M. polykrikoides cell. Interpretation of relationship using a regression tree can be a useful approach to evaluate the causality between environmental factors and M. polykrikoides blooms.

Qing, S.; Hao, Y.L., and Bao, Y.H., 2019. Retrieval of inorganic suspended particle size with Landsat-8 OLI data in the Yellow River estuary. In: Jung, H.-S.; Lee, S.; Ryu, J.-H., and Cui, T. (eds.), Advances in Remote Sensing and Geoscience Information Systems of Coastal Environments. Journal of Coastal Research, Special Issue No. 90, pp. 19-26. Coconut Creek (Florida), ISSN 0749-0208.

Particle size distribution (PSD) is relevant to many physical and biogeochemical processes in marine ecosystems. Field investigation or satellite data with coarse resolution have limitations in description of detailed PSD in a small scale region, e.g. river estuary. In this study, Landsat-8 Operational Land Imager (OLI) data was used for estimating inorganic suspended particle size in the Yellow River estuary. A simple empirical model using ratio of green to red bands was developed based on in situ measured remote sensing reflectance (R_rs) and median particle size (Dv50) for inorganic materials. Validation against in situ measurements showed that retrieved Dv50 was in good accordance with in situ measurements with the mean absolute percentage error (MAPE) of 24.0 % and root mean square error (RMSE) of 4.310µm. Application of the model to OLI data retrieved a clear and reasonable spatial distribution of Dv50 in the Yellow River estuary. OLI retrieved Dv50 agreed well with that from MODIS data (R²=0.89, MAPE=6.4 %, RMSE=0.6 µm). Dv50 maps characterized a river mouth to offshore changing trend, demonstrating the model potential for understanding of the detailed information of a local scale region. The empirical algorithm developed here can be easily applied to other satellite data with similar bands of Landsat-8 OLI data and fine spatial resolution.

Sun, W.; Zhang, J.; Meng, J., and Liu, Y., 2019. Sea surface temperature characteristics and trends in China offshore seas from 1982 to 2017. In: Jung, H.-S.; Lee, S.; Ryu, J.-H., and Cui, T. (eds.), Advances in Remote Sensing and Geoscience Information Systems of Coastal Environments. Journal of Coastal Research, Special Issue No. 90, pp. 27-34. Coconut Creek (Florida), ISSN 0749-0208.

This paper examines the spatial distribution and temporal variability of sea surface temperature (SST) in marginal seas off China at seasonal and inter-annual scales based on long-term high spatial-resolution optimum interpolation sea surface temperature (OISST) data from 1982–2017. Based on these data, annual mean SST ranges from 12 °C to 30 °C. Annual mean SST for the entire China offshore region is 25.03 °C; 12.75 °C for the Bohai Sea (BHS), 15.31 °C for the Yellow Sea (YS), 23.70 °C for the East China Sea (ECS), and 27.62 °C for the South China Sea (SCS). From south to north, SST exhibits a slow decrease with increasing latitude followed by a rapid decrease. Latitudinal SST gradients increase from south to north and are smallest in the SCS, followed by the ECS, and the YS and BHS. Over the past 36 years, annual mean SST has increased at a rate of approximately 0.0181 °C/yr and different marginal seas exhibit different features and inter-annual changes. Overall, the China offshore region exhibited long-term warming over the past 36 years. The strongest center of warming, which exceeds 0.04 °C/yr, is located in the ECS near the Yangtze River estuary and the Taiwan Strait. Variations in SST in the China offshore region are related to global natural climate variability and changes in the East Asian Monsoon.

Jin, J.-C; Zhang, J.; Liu, D.-Q; Shao, F.; Wang, D.; Shi, J.-N., and Li, F.-X., 2019. Design and experiment for an offshore nuclear radiation emergent observation system based on an unmanned surface vehicle. In: Jung, H.-S.; Lee, S.; Ryu, J.-H., and Cui, T. (eds.), Advances in Remote Sensing and Geoscience Information Systems of Coastal Environments. Journal of Coastal Research, Special Issue No. 90, pp. 35-40. Coconut Creek (Florida), ISSN 0749-0208.

The nuclear leakage is a super disaster, and the offshore approaching observation is seriously restricted when nuclear power station leaks. An observation system based on an Unmanned Surface Vehicle ‘Jiu Hang 490’ is designed and developed for nuclear radiation emergent observation. First, the design and system integration of the USV are introduced in detail. Second, sea trials are performed near the nuclear power station at Shi-Island bay in Rongcheng City, Shandong Province, China at September 2017 and at Nanjiang port in Qingdao, Shandong Province, China at July 2018 respectively. Last, the ability for data acquirement for nuclear radiation and autonomous control of the USV are tested and performed. The practicability and reliability of the system are validated for nuclear radiation emergent observation through the sea trials.

Oh, H.-J.; Syifa, M.; Lee, C.-W., and Lee, S., 2019. Ruditapes philippinarum habitat mapping potential using SVM and Naïve Bayes. In: Jung, H.-S.; Lee, S.; Ryu, J.-H., and Cui, T. (eds.), Advances in Remote Sensing and Geoscience Information Systems of Coastal Environments. Journal of Coastal Research, Special Issue No. 90, pp. 41-48. Coconut Creek (Florida), ISSN 0749-0208.

The aim of this study was to compare the performance of Support Vector Machine (SVM) and Naïve Bayes (NB) models for potential R. philippinarum habitat mapping in the Geunso Bay, Korea. R. philippinarum samples were collected during field observation. Remote sensing data were used to identify the factors controlling the distribution of R. philippinarum. Habitat potential maps were constructed and eight controlling factors were generated from satellite imagery, namely elevation of intertidal zone, aspect, exposure time, slope, density of tidal channel, distance from tidal channel, surface-sediment distribution, and near-infrared reflectance (NIR). Validation of the maps was conducted by comparison with surveyed habitat locations. Performance of the SVM model (AUC=0.777) is better compared with NB model (0.733). The GIS-based SVM and NB models combined with remote sensing techniques are efficient tools for mapping potential R. philippinarum habitat in tidal flats.

Zhu, H.; Li, K.; Wang, L.; Chu, J.; Gao, N., and Chen, Y., 2019. Spectral characteristic analysis and remote sensing classification of coastal aquaculture areas based on GF-1 data. In: Jung, H.-S.; Lee, S.; Ryu, J.-H., and Cui, T. (eds.), Advances in Remote Sensing and Geoscience Information Systems of Coastal Environments. Journal of Coastal Research, Special Issue No. 90, pp. 49-57. Coconut Creek (Florida), ISSN 0749-0208.

In this research, the offshore area of Bohai Sea, which is located at Yantai City in Shandong Province, was selected as the experimental region and the GF-1 data was used as experimental data. First, the spectral characteristics of different target objects in the study area were investigated using the sample point analysis method. The corresponding spectral discriminant function was also constructed. Second, the object-oriented multi-scale segmentation method was employed to perform the object segmentation of GF-1 image. Finally, the image segmentation results were classified through the constructed discriminant function of the spectral characteristics of target objects. The remote sensing classification results of coastal aquaculture areas were also obtained. The overall accuracy of such classification results was 91.6 %. Compared with traditional classification methods, classification accuracy improved greatly and the classified aquaculture types increased.

Zhang, K.; Jiang, T., and Huang, J., 2019. Spatial–temporal variation in sea surface temperature from Landsat time series data using annual temperature cycle. In: Jung, H.-S.; Lee, S.; Ryu, J.-H., and Cui, T. (eds.), Advances in Remote Sensing and Geoscience Information Systems of Coastal Environments. Journal of Coastal Research, Special Issue No. 90, pp. 58-65. Coconut Creek (Florida), ISSN 0749-0208.

Sea surface temperature (SST) plays an important role in aquatic ecosystems and the biogeochemical cycle. Multi-temporal remote-sensing observations may be desirable alternatives to traditional in situ sensors for SST measurement. However, the frequently used low-to-moderate-resolution remote sensors usually cannot identify subtle SST variations in coastal areas due to the pixel radiance contamination caused by shoreline influences. For alleviating this problem, the SST of Jiaozhou Bay (JZB) between 1986 and 2017 was estimated by means of Landsat thermal infrared data and the single-channel retrieval algorithm. The retrieved results were validated by field-measured water temperature and bootstrap method. Then, the estimated SST was divided into five-year intervals and calculated the SST climatology for each period. With use of the annual temperature cycle fitting model, the time series data not only demonstrated the spatial–temporal variation of the water temperature of JZB but also effectively compensated for the lack of remote sensing data due to adverse weather in summer. The estimation results showed that the SST of JZB increased gently during the past 30 years, especially in coastal areas. The increase of SST near artificial facilities was evident, indicating the influence of urbanization and industrialization in coastal area. A correlation analysis of SST and meteorological factors revealed that air temperature influenced SST variation, especially in the central bay, whereas wind speed and precipitation had nearly no influence.

Li, W.K.; Tian, L.Q.; Li, J.; Zhou, Q.; Li, Y., and Li, S., 2019. Impact of natural and anthropogenic changes on the spatial–temporal variations of total suspended matter in the Pearl River Estuary, China. In: Jung, H.-S.; Lee, S.; Ryu, J.-H., and Cui, T. (eds.), Advances in Remote Sensing and Geoscience Information Systems of Coastal Environments. Journal of Coastal Research, Special Issue No. 90, pp. 66-76. Coconut Creek (Florida), ISSN 0749-0208.

The Pearl River Delta and its adjacent coastal water is one of most developed regions of China, and high variability in its environment has been observed due to its subtropical monsoon climate as well as high-intensity human activity. This study aims to interpret the spatial–temporal variations of total suspended matter (TSM) in the Pearl River Estuary (PRE) and its driving factors. An empirical band ratio TSM retrieval algorithm was developed using Moderate Resolution Imaging Spectroradiometer (MODIS)/Aqua images and in situ data. The coefficient of determination (R²) and mean relative error (MRE) of the algorithm are 0.925 and 15.67 %, respectively. Significant spatial and temporal (seasonal and inter-annual) TSM variations were revealed from 2003 to 2015, which were found to gradually decrease from the western mouths of the river to its central. The monthly mean TSM was higher in winter than in summer, with a peak concentration of 33.2 mg/L in December and a low of 16.4 mg/L in August. Wind is a main driving force of TSM spatial–temporal variation, and sediment yield of the river and tide also play positive roles. Meanwhile, dam construction in the Pearl River Basin (PRB) was found to be crucial for decreasing sediment yield, and sediment yield become the main factor when the average wind speed was steadily decreased. Moreover, the uncertainty from limited Landsat observations was assessed using MODIS data, which demonstrated that the MRE was about –50 % and 50 % during most of the year, and thus higher frequency observations are required. These results gave an environmental basis for the optimized development and management of rivers and estuary in the PRB and the PRE.

Chu, J.L.; Suo, A.N.; Liu, B.Q.; Zhao J.H., and Wang, C.Y., 2019. Remote sensing-based life cycle analysis of land reclamation processes: Case study on Tianjin Binhai new area. In: Jung, H.-S.; Lee, S.; Ryu, J.-H., and Cui, T. (eds.), Advances in Remote Sensing and Geoscience Information Systems of Coastal Environments. Journal of Coastal Research, Special Issue No. 90, pp. 77-85. Coconut Creek (Florida), ISSN 0749-0208.

Land reclamation plays an important role in the development of coastal regions as it creates additional space that can be utilized for production and living spaces expansion and the construction of projects like ports, seafront industries, and coastal towns. In land reclamation, marine areas are enclosed and filled with stones to construct functional areas like industrial zones, townships, ports, and recreation areas. To address the need for further refinement in the monitoring of land reclamation developments in China, time series remote sensing images were used to continuously monitor land reclamation. Based on continuous monitoring of land reclamation processes using remote sensing images, the development of reclaimed land is divided into four stages according to life cycle theory, namely, growth, subsidence, stocking and consumption. Taking the Tianjin Binhai New Area as a case study, a method for the classification of land surface states according to their life cycle stages was constructed, and it was applied to analyze the duration of each life cycle stage in land reclamation development. It was found that time-series remote sensing images can be used in conjunction with the life cycle method to elucidate the development and land surface states of reclaimed land; this approach is therefore suitable for the monitoring and assessment of land reclamation developments. In 2015, the total reclaimed area in the Tianjin Binhai New Area was 22689.21 hm², with 44.99% and 34.44% of these lands being in the stocking and consumption stages, respectively. “Stock” lands are dominated by lands that have been idle for eight years or five years (3953.42 hm² and 3005.90 hm², respectively). The lands that first transitioned into the consumption stage in 2010, 2006 and 2008 account for the largest proportion of all “consumption” lands, at 19.67%, 19.36% and 19.11%, respectively.

Cui, B.-G.; Zhong, Y.; Fei, D.; Zhang, Y.-H.; Liu, R.-J.; Chu, J.-L., and Zhao, J.H., 2019. Floating raft aquaculture area automatic extraction based on fully convolutional network. In: Jung, H.-S.; Lee, S.; Ryu, J.-H., and Cui, T. (eds.), Advances in Remote Sensing and Geoscience Information Systems of Coastal Environments. Journal of Coastal Research, Special Issue No. 90, pp. 86-94. Coconut Creek (Florida), ISSN 0749-0208.

In the extraction of floating raft aquaculture areas from remote sensing images, the method of visual interpretation is time-consuming and laborious, and the traditional machine learning method has poor generalization ability and fitting ability for remote sensing data. To overcome these two problems, this paper proposes a method based on using a fully convolutional neural network to automatically extract floating raft aquaculture areas. The proposed method uses multiple convolution layers, pooling layers and nonlinear ReLU functions to extract nonlinear and invariant deep features of floating raft aquaculture areas, which effectively improves the accuracy of recognizing floating raft aquaculture areas. At the same time, L2 regularization and dropout strategies are added to the neural network model to avoid overfitting. The offshore area of Lianyungang in China was selected as the research region of the floating raft aquaculture. The experimental results show that the proposed method area in this paper effectively identifies and extracts the aquaculture areas.

Xiao, Y.F.; Zhang, J., and Qin, P., 2019. An algorithm for daytime sea fog detection over the Greenland Sea based on MODIS and CALIOP data. In: Jung, H.-S.; Lee, S.; Ryu, J.-H., and Cui, T. (eds.), Advances in Remote Sensing and Geoscience Information Systems of Coastal Environments. Journal of Coastal Research, Special Issue No. 90, pp. 95-103. Coconut Creek (Florida), ISSN 0749-0208.

Sea fog is one of the dangerous weather disasters affecting scientific investigation and maritime transportation on the Arctic Ocean. In this paper, a detection algorithm for daytime sea fog over the Arctic Ocean is proposed taking the Greenland Sea as an example. With the assistance of satellite lidar data from CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations)/CALIOP (Cloud Aerosol Lidar with Orthogonal Polarization), a large number of sample points of sea fog, low level clouds, mid-high level clouds, the sea surface, ice, and snow were extracted from MODIS (Moderate Resolution Imaging Spectroradiometer) images. The radiance (reflectance and emissivity) and texture (mean, variance, homogeneity, contrast, dissimilarity, entropy, second moment, and correlation) characteristics of each MODIS channel for sea fog and other features were analyzed. Thereafter, a new algorithm for sea fog detection is proposed. The step-by-step algorithm first masks the sea surface, sea ice, and snow using the reflectance of MODIS channels 2 and 7. Then, the sea fog is separated from low and medium-high level clouds using the homogeneity of channel 18. The validation compared with CALIOP data shows encouraging accuracy for sea fog detection with probabilities of detection (POD) ∼80 %, and probabilities of falsity (POF) ∼8.5 %.

Wang, R.F.; Zhang, Y.; Li, J.G.; Zhao, W.; Wang, F.Z.; Cao, H.J., and Duan, Y.P., 2019. Development of green tide monitoring with satellite images. In: Jung, H.-S.; Lee, S.; Ryu, J.-H., and Cui, T. (eds.), Advances in Remote Sensing and Geoscience Information Systems of Coastal Environments. Journal of Coastal Research, Special Issue No. 90, pp. 104-111. Coconut Creek (Florida), ISSN 0749-0208.

Since the large-scale bloom in 2008, green tide, as a marine natural disaster, happens every year along the coast of Qingdao. It brings huge economic losses to society every year. Therefore, it is urgent to monitor the green tide in real time to obtain its dynamic information. Generally, researches on green tide are mainly focused on the coverage area. For Operational Application of Disaster Emergency Response, the influence range of the green tide is what people is concerned about. The influence range of the green tide can not only give information about the gaps between small green tide patches but also show the trend of development of greed tide. The research is mainly about the influence range of the green tide. An algorithm is designed for extracting the green tide distribution boundaries automatically. Principle of the algorithm is based on mathematical morphology dilation/erosion operation. This paper mainly improves in the following aspects: the partition of the green tide blocks, the accurate and efficient extraction of the distribution range and distribution contour of the green tide, and the filtering of the island. Since green tide mainly bursts along the Qingdao Coast and there is no established system so far, a system for monitoring green tides is established. On the basis of IDL/GIS secondary development technology, the system integrated environment of RS and GIS. It can be used for remote sensing monitoring and information extraction. Optical sensor data and microwave sensor data are used in this system. Special processing flow and algorithms for extracting information are designed based on the different characteristics of these data. Without using this system, a complete data process from beginning to ending needs 2 hours, but it can be finished in 10-15 minutes now in this system. The system runs smoothly and successfully in the State Oceanic Administration for three years till now.

Lee, S.M.; Oh, H.J.; Lee, S., and Lee, M.J., 2019. Spatial distribution analysis of Ruditapes philippinarum habitat using data mining. In: Jung, H.-S.; Lee, S.; Ryu, J.-H., and Cui, T. (eds.), Advances in Remote Sensing and Geoscience Information Systems of Coastal Environments. Journal of Coastal Research, Special Issue No. 90, pp. 112-119. Coconut Creek (Florida), ISSN 0749-0208.

The purpose of this study is to analyze the spatial distribution of Ruditapes philippinarum habitat in Geunso Bay, South Korea. R. philippinarum samples were acquired through in-situ observation. Remotely sensed data were used to derive factors related to R. philippinarum habitat. A spatial distribution map was generated using a data-mining model focused on the Chi-squared Automatic Interaction Detection (CHAID) model with a spatial dataset of eight R. philippinarum habitat-related factors: training data of R. philippinarum observations, spectral reflectance factor, sediment type factor, tidal channel factors (density of and distance from the tidal channel), and morphological factors (elevation, slope gradient, slope aspect and exposure time). Validation analysis was performed through comparison with observed habitat locations. The accuracy rate of the CHAID model was 84.7 % (area under the curve [AUC] = 0.847) using success rate analysis and 74.5 % (AUC = 0.745) using prediction rate analysis. The data-mining model based on remote sensing data and geographic information system (GIS) tools is an efficient method for mapping the spatial distribution of R. philippinarum habitat in tidal flats.

Liu, R.-J.; Zhang, J.; Cui, B.-G.; Ma, Y.; Song, P.J., and An, J.-B., 2019. Red tide detection based on high spatial resolution broad band satellite data: A case study of GF-1. In: Jung, H.-S.; Lee, S.; Ryu, J.-H., and Cui, T. (eds.), Advances in Remote Sensing and Geoscience Information Systems of Coastal Environments. Journal of Coastal Research, Special Issue No. 90, pp. 120-128. Coconut Creek (Florida), ISSN 0749-0208.

Traditional red tide remote sensing detection methods are based on ocean color satellite data with high spectral resolution (spectral band width < 20 nm), but low spatial resolution. Clearly, fine-scale remote-sensing monitoring of red tide requires high spatial resolution satellite data. Yet, satellite data with high spatial resolution often lack high spectral resolution, with spectral band width > 50 nm. Moreover, systematic research has yet to demonstrate effective detection of red tide using high spatial resolution, but low spectral resolution, satellite data. In this paper, high spatial resolution (16 m), but low spectral resolution, data from the satellite, GF-1, are analyzed to determine if effective fine-scale detection of red tide is possible. The spectral response characteristics of red tide in GF-1 data are analyzed using a 2014 red tide event that occurred in Guangdong Province, China. It was found that, despite broad spectral band widths, GF-1 WFV imaging spectrometer data still exhibits a clear tide detection algorithm for GF-1 WFV data based on this red tide spectral signal. The detection accuracy of red tide for this algorithm is better than 92 %, and the F1-Score value is better than 87 %. Spatial analysis of the Guangdong 2014 red-tide event based on this algorithm showed that the maximum red tide area extracted from GF-1 satellite data is 10 km², and that the red tide area decreases greatly during the extinction phase. A comparative analysis of MODIS satellite data indicates that, despite the availability of multiple ocean color bands, no ocean color anomaly is detected because of coarse spatial resolution. The developed method was also successfully used to detect red tide occurred in Rizhao, Shandong Province.

Ren, P.; Yu, Z.-Q.; Dong, G.S.; Wang, G.-X., and, Wei, K., 2019. Sea ice classification with first-order logic refined sliding bagging. In: Jung, H.-S.; Lee, S.; Ryu, J.-H., and Cui, T. (eds.), Advances in Remote Sensing and Geoscience Information Systems of Coastal Environments. Journal of Coastal Research, Special Issue No. 90, pp. 129-134. Coconut Creek (Florida), ISSN 0749-0208.

This paper proposes an automatic framework for classifying sea ice types using remote sensing imageries. Firstly, polarization features are extracted to form polarization characteristic vectors for representing individual pixels in a remote sensing image. Secondly, a multiple classifier ensemble strategy, i.e., sliding bagging, is exploited for classifying each polarization characteristic vector into a sea ice type. The sliding bagging strategy not only avoids the limitation of one single classifier for characterizing the sea ice variability over a large-scale region, but also alleviates the data imbalance over different sea ice types. Finally, the spatial structural relationships of sea ice types are encoded with first-order logic (FOL) to refine the sea ice classification resulted from the sliding bagging scheme. Experimental evaluations on RADARSAT-2 data validate the effectiveness of the proposed framework.

Ren, G.-B.; Wang, J.-J.; Wang, A.-D.; Wang, J.-B.; Zhu, Y.-L.; Wu, P.-Q.; Ma, Y., and Zhang, J.B., 2019. Monitoring the invasion of smooth cordgrass Spartina alterniflora within the modern Yellow River Delta using remote sensing. In: Jung, H.-S.; Lee, S., and Ryu, J.-H. (eds.), Advances in Remote Sensing and Geoscience Information Systems of the Coastal Environments. Journal of Coastal Research, Special Issue No. 90, pp. 135-145. Coconut Creek (Florida), ISSN 0749-0208.

The Smooth Cordgrass, Spartina alterniflora, was introduced into the modern Yellow River Delta in 1989 to protect banks and beaches within the intertidal zone. By 2018, its distribution area had expanded to 4005.89 ha, which was 2557 times its initial colonization area. The distribution of S. alterniflora in the modern Yellow River Delta at given times and its ecological and biological effects have been studied previously; however, its invasion rate and characteristics, which affect its comprehensive management, are not known. Using multi-resolution and multi-platform remote sensing time series data from 1989 to 2018, combined with years of field data, the invasion of S. alterniflora within the modern Yellow River Delta was studied. These data show that S. alterniflora began to expand rapidly in 2011, after a 22-y incubation period within the modern Yellow River Delta. Its expansion mainly occurred via seed reproduction. In addition, S. alterniflora preferentially invaded intertidal deltaic areas, using local seagrass beds as colonization sites. There was a significant correlation between the increase in area of S. alterniflora and the decrease in pollutant concentration of the Yellow River. Clearly, the improvement in water quality of the Yellow River accelerated the invasion of S. alterniflora within the delta.

Chen, X.-Y.; Zhang, J.; Tong, C.; Liu, R.-J.; Mu, B., and Ding, J., 2019. Retrieval algorithm of chlorophyll-a concentration in turbid waters from satellite HY-1C coastal zone imager data. In: Jung, H.-S.; Lee, S.; Ryu, J.-H., and Cui, T. (eds.), Advances in Remote Sensing and Geoscience Information Systems of Coastal Environments. Journal of Coastal Research, Special Issue No. 90, pp. 146-155. Coconut Creek (Florida), ISSN 0749-0208.

The satellite HY-1C was successfully launched in September 2018 with the Coastal Zone Imager (CZI) onboard, which can provide optical images of 50-m resolution at four broad bands (blue, green, red and near-infrared). In this study, 305 sets of in-situ measurements collected between 2005 and 2015 in the Bohai, Yellow and East China seas were used to assess the performance of HY-1C in retrieving chlorophyll-a concentration in the coastal waters. The results showed that two algorithms based on blue-green band ratios (OC3 and OC3L) performed well. The median of the ratio of retrieved to in-situ values was about 1.1, and the median absolute percentage difference was about 40 %. After applying the two algorithms to HY-1C CZI data of Jiaozhou Bay (Qingdao, China), the spatial distribution of retrieved Chl-a data was found to be in good agreement with results based on data from another satellite (Landsat-8 OLI) with a median absolute percentage difference less than 15 %. The results showed that the broadband channels of the HY-1C CZI can retrieve Chl-a concentration in turbid waters, which indicates potential for its application in coastal water quality and eutrophication monitoring.

Tong, C.; Mu, B.; Liu, R.-J.; Ding, J.; Zhang, M.W.; Xiao, Y-.F.; Liang, X.-J., and Chen, X.-Y., 2019. Atmospheric correction algorithm for HY-1C CZI over turbid waters. In: Jung, H.-S.; Lee, S.; Ryu, J.-H., and Cui, T. (eds.), Advances in Remote Sensing and Geoscience Information Systems of Coastal Environments. Journal of Coastal Research, Special Issue No. 90, pp. 156-163. Coconut Creek (Florida), ISSN 0749-0208.

China's HY-1C ocean-observing satellite was launched successfully on September 7, 2018. It carries a four-channel wide-band coastal zone imager (CZI) that has 50 m spatial resolution and 950 km swath width. To exploit the potential of quantitative ocean color inversion, accurate atmospheric correction is needed. However, because of the CZI band settings, the realization of this goal is a challenge, especially for turbid water with complex optical properties. This study investigated the atmospheric correction algorithm for the CZI over turbid water. First, using the 6SV radiative transfer model, CZI Rayleigh lookup tables (LUTs) were built to correct for atmospheric molecular Rayleigh scattering, greatly shortening the time required for operational data processing. Second, CZI aerosol scattering was removed using the Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol LUTs and quasi-synchronous MODIS aerosol products. The accuracy of the CZI atmospheric correction was validated using data from the highly turbid Bohai Sea. In comparison with synchronous in situ data, the results showed the average relative error of CZI remote sensing reflectance (Rrs) in the blue, green and red bands was 32.51 %, 25.38 % and 42.10 %, respectively. Comparison of CZI Rrs with quasi-synchronous MODIS data revealed similar spatial distributions, although the spatial information from the CZI was more detailed. The results proved the validity and accuracy of the CZI atmospheric correction algorithm over turbid water, which lays a foundation for quantitative ocean color inversion with high spatial resolution in the coastal zone.

Bing, L.; Xing, Q.-G.; Liu, X., and Zou, N.-N., 2019. Spatial distribution characteristics of oil spills in the Bohai Sea based on satellite remote sensing and GIS. In: Jung, H.-S.; Lee, S.; Ryu, J.-H., and Cui, T. (eds.), Advances in Remote Sensing and Geoscience Information Systems of Coastal Environments. Journal of Coastal Research, Special Issue No. 90, pp. 164-170. Coconut Creek (Florida), ISSN 0749-0208.

In recent years, on account of the vast and persistent damage of oil spill accidents, it becomes essential to carry out a further study on oil spill distribution characteristics on sea. Routine satellite remote sensing surveillance on oil spills with Synthetic Aperture Radar (SAR) proved to be ideal for analyzing distribution of oil pollution in macro scale. In this research work, considering the presence of “look-alikes” phenomena on SAR images as well as current operational application of “confidence level”, a confidence-oriented oil spill geodatabase is initially designed and built for quantification analysis. Then, in view of the requirement of marine grid management, a common framework based on remote sensing and Geographic Information System (GIS) is proposed to map and reveal the spatial distribution of oil pollution, in the process of which, oil pollution index (OPI) is put forward to evaluate oil pollution levels, then oil pollution distribution map can be compiled in terms of OPI, and the relationship between oil pollution and oil spill risk source can be further analyzed. Finally, a case study of the Bohai Sea and the north of the Yellow Sea with five-year's inter-annual satellite monitoring data was studied. The results showed that, during the period between 2009 and 2013, the high frequency grids of potential oil spills mainly distributed in the Bohai Bay and the south of the Liaodong Bay. 77.03 % of detected oil spills were within a certain distance along the sea lanes or near the offshore platforms, indicating a high risk of oil pollution of these areas. Generally, this research work also benefits for yield for the first time a rather comprehensive distribution characteristics of potential oil spills in macro scale in the Bohai Sea combined with remote sensing and GIS technique, which will be beneficial for oil spill response, preparedness and risk management.

Mu, B.; Qin, P.; Liu, C.; Liang, X.-J., and Huang, T.-X., 2019. An assessment of atmospheric correction methods for GOCI images in the Yellow River Estuary. In: Jung, H.-S.; Lee, S.; Ryu, J.-H., and Cui, T. (eds.), Advances in Remote Sensing and Geoscience Information Systems of Coastal Environments. Journal of Coastal Research, Special Issue No. 90, pp. 171-182. Coconut Creek (Florida), ISSN 0749-0208.

The Yellow River Estuary is a typical case II water body with high turbidity. Atmospheric correction of Geostationary Ocean Color Imager (GOCI) data is difficult in such areas. The applicability of existing atmospheric correction methods suffers from a lack of systematic assessment. In this study, the nearest-neighbor, near-infrared (NIR)-ratio, and ultraviolet atmospheric correction (UV-AC) techniques are applied to GOCI images of the Yellow River Estuary, and the quality of the water-leaving reflectance ρw(λ) obtained by these methods is evaluated. The results show that the performance of the NIR-ratio and UV-AC methods is almost the same compared with in situ synchronous data, with the absolute percentage difference (APD) of each band ranging from 6–48 % and 9–47 %, respectively. The accuracy of the 660 nm and 680 nm bands is the highest (APD less than 10 %). The values of ρw(λ) retrieved by the nearest-neighbor method are obviously underestimated, with an APD ranging from 30–196 %. Moreover, negative values appear in the NIR and blue bands. A rationality evaluation of the spectral shape of ρw(λ) extracted from the GOCI images further confirms the consistency of the results obtained by the UV-AC and NIR-ratio methods. Approximately 86 % of pixels were scored the same by the two methods. Based on the above evaluation results, the NIR-ratio and UV-AC methods are concluded to have the same accuracy, and both perform better than the nearest-neighbor method. The input parameters of the NIR-ratio method are determined in advance using in situ measured data. On account of the limited amount of measured data, the representativeness and applicability of the input parameters are yet to be confirmed.

Eom, J.; Park, W.; Syifa, M.; Lee, C., and Yoon, S., 2019. Monitoring variation in sea surface temperature in the Nakdong River estuary, Korea, using multiple satellite images. In: Jung, H.-S.; Lee, S.; Ryu, J.-H., and Cui, T. (eds.), Advances in Remote Sensing and Geoscience Information Systems of Coastal Environments. Journal of Coastal Research, Special Issue No. 90, pp. 183-189. Coconut Creek (Florida), ISSN 0749-0208.

Annual and monthly sea surface temperatures (SSTs) of the coastal waters around Nakdong River, Korea, were analyzed using data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat. SST values derived from MODIS data were consistent with the in-situ data (R² = 0.63), whereas Landsat-derived SST values were moderately correlated with in-situ data (R² = 0.44). The mean annual SST over 18 years was 21.7 °C. The coldest temperatures were recorded in 2001, and the warmest were recorded in 2016. SSTs were higher during the summer than in the winter, mainly because of higher air temperatures in summer. Across all years, March was the coldest month, with a mean SST of 14.4 °C, whereas August was the warmest month at 28 °C. As MODIS data have a spatial resolution of 4 km, it cannot be used to detect SST variation at fine spatial scales (least 100 m or less). However, it can be monitored temporal trends in SST. Spatially detailed SST variation was monitored using Landsat data. According to the Landsat data, warm SSTs were measured in the area extending from the southwest to northeast of the Nakdong Estuary, which is an area affected by the Kuroshio Current. Climate data are needed in addition to image data to accurately monitor SST variation in the future.

Park, N.-W., 2019. Geostatistical integration of field measurements and multi-sensor remote sensing images for spatial prediction of grain size of intertidal surface sediments. In: Jung, H.-S.; Lee, S.; Ryu, J.-H., and Cui, T. (eds.), Advances in Remote Sensing and Geoscience Information Systems of Coastal Environments. Journal of Coastal Research, Special Issue No. 90, pp. 190-196. Coconut Creek (Florida), ISSN 0749-0208.

The objective of this paper is to demonstrate the potential benefit of using high-resolution optical and SAR images for the geostatistical mapping of grain size of intertidal surface sediments. The grain size values from field measurements are integrated with reflectance and backscattering coefficients from multi-sensor images via regression kriging. The trends of grain size variations are estimated using support vector regression (SVR) modeling to account for a nonlinear relationship, and rank transformation is applied to original input variables to highlight the relative differences in input values from multi-sensor images. Unlike the conventional regression-based mapping approach, the residual component that cannot be explained by the multi-sensor remote sensing images is considered and predicted via kriging. The final grain size values are then obtained by adding these two components. From a case study on the Baramarae tidal flats in Korea with KOMPSAT-2 and COSMO-SkyMed images, the integration of multi-sensor images with field measurements via SVR and rank transformation could explain 58 % of grain size variance, leading to a significant improvement in predictive performance (approximately 29 %) over ordinary kriging based on field measurements only. Furthermore, using reflectance and scattering information from multi-sensor images generated the grain size distribution with more detailed variations in the study area. Therefore, the synergistic use of multi-sensor images within an advanced geostatistical integration framework is expected to be very effective for the reliable mapping of the grain size of intertidal surface sediments when only a limited number of field measurements are available.

Syifa, M.; Park, S.-J.; Achmad, A.-R.; Lee, C.-W., and Eom, J., 2019. Flood mapping using remote sensing imagery and artificial intelligence techniques: A case study in Brumadinho, Brazil. In: Jung, H.-S.; Lee, S.; Ryu, J.H., and Cui, T. (eds.), Advances in Remote Sensing and Geoscience Information Systems of Coastal Environments. Journal of Coastal Research, Special Issue No. 90, pp. 197-204. Coconut Creek (Florida), ISSN 0749-0208.

Floods are considered to be among the most devastating disasters and can threaten human life and environmental ecosystems. On January 25, 2019, the Brumadinho dam wall collapsed and waste material from the Córrego do Feijão mine flooded the area beneath the dam. At least hundreds of people were killed, animal habitats were swamped, and the flood invaded the river and agricultural fields. Brazilian authorities are examining how this destructive flood might threaten the water quality of the contaminated river. It is important to determine the flood distribution to prevent additional contamination by dangerous material from the flood. In this study, remote sensing was effectively used to map and calculate the dimensions of the flood. Pre- and post-flood images from Landsat-8 and Sentinel-2 were employed to devise a pixel-based classification using two artificial intelligence techniques: artificial neural network (ANN) and support vector machine (SVM). The flood area was successfully determined using the two classifiers. The resulting post-flood damage map should be beneficial for mitigating damage from a future flood event.

Koo, S.; Song, Y.J.; Lim, S.H.; Oh, M.H.; Seo, S.N., and Baek, S.J., 2019. Development of a remote supervisory control and data acquisition system for offshore waste final disposal facility. In: Jung, H.-S.; Lee, S.; Ryu, J.-H., and Cui, T. (eds.), Advances in Remote Sensing and Geoscience Information Systems of Coastal Environments. Journal of Coastal Research, Special Issue No. 90, pp. 205-213. Coconut Creek (Florida), ISSN 0749-0208.

In this paper, a case study implementation of a remote supervisory control and data acquisition system (RSCDA) for offshore waste final disposal facilities is presented. The RSCDA system is composed of three main parts—a fixed sensor-communication-integrated interface that controls multiple sensors and conducts wireless transmission of the data at fixed points, a mobile sensing platform that measures bathymetry and landfill status at irregular points, and a central control center for data collection, storage, analysis, and display. Field tests on water tanks and open water sites are conducted to verify that the proposed RSCDA can effectively collect and maintain the status of offshore waste disposal facilities.

Achmad, A.R.; Syifa, M.; Park, S.J.; and Lee, C.W., 2019. Geomorphological transition research for affecting the coastal environment due to the volcanic eruption of Anak Krakatau by satellite imagery. In: Jung, H.-S.; Lee, S.; Ryu, J.-H., and Cui, T. (eds.), Advances in Remote Sensing and Geoscience Information Systems of Coastal Environments. Journal of Coastal Research, Special Issue No. 90, pp. 214-220. Coconut Creek (Florida), ISSN 0749-0208.

Volcano eruptions of Anak Krakatau had a great impact on the coastal area at the western part of Java island in Indonesia. In the middle of 2018, the volcanic activity at Anak Krakatau in Indonesia increased. Since June 2018, the eruption begun, accompanied by volcanic earthquakes, emission, and tremor vibration. However, on December 22, 2018, the eruption of Anak Krakatau caused a landslide on the southwest part of the volcano. The landslide also caused a tsunami in coastal area and caused many people to become victims in Banten province. There were significant geomorphological changes at Anak Krakatau Island where the volcano is situated. In this study, we monitored the geomorphological change of the Anak Krakatau Island and calculated the total area of the island for pre-eruption, post-eruption, and recent times. By using SNAP 6.0 program and ArcMap 10.4, Sentinel-1 data were processed to generate maps and calculate the extent of the island. The geomorphological changes were successfully detected and quantified. This kind study using all-weather satellite radar imagery can be used to detect and calculate the total area of any geomorphological change from future volcanic activity.

Chen, Y.-L.; Wan, J.-H.; Zhang, J.; Ma, Y.-J.; Wang, L.; Zhao, J.-H., and Wang, Z.-Z., 2019. Spatial-temporal distribution of golden tide based on high-resolution satellite remote sensing in the South Yellow Sea. In: Jung, H.-S.; Lee, S.; Ryu, J.-H., and Cui, T. (eds.), Advances in Remote Sensing and Geoscience Information Systems of Coastal Environments. Journal of Coastal Research, Special Issue No. 90, pp. 221-227. Coconut Creek (Florida), ISSN 0749-0208.

A new marine ecological disaster called the golden tide occurred in the Southern Yellow Sea at the end of 2016. This disaster damaged the Porphyra yezoensis aquaculture in the Jiangsu Shoal, causing a direct economic loss of nearly 500 million CNY. The floating brown macroalgae in the golden tide was identified as Sargassum horneri, which have been frequently growing in coastal waters in recent years. Effectively detecting this golden tide using traditional satellites is difficult because of the small patches or slicks of bloom. This study used multi-source and high-resolution satellite data to identify the floating Sargassum with a maximum multispectral resolution of 4 m (GF-2). Satellite and in-situ spectral data were used to analyze the spectral characteristics of the Sargassum and compare them with those of Ulva. Both sets of spectral characteristics exhibited the “red-edge” effect, and the difference was obvious between the green and red bands. Combined with the normalized difference vegetation index algorithm, monitoring and backtracking were performed for the golden tide disaster. Results show that the golden tide originated from the Rongcheng–Haiyang sea area of the Shandong peninsula and drifted to the south and westward. In early December 2016, Sargassum affected the sea area of Yancheng in Jiangsu Province. In late December 2016, it arrived at the Jiangsu Shoal and finally entered the Yangtze River estuary around mid-January 2017. The drifting path was mainly controlled by wind vector products. A comprehensive analysis of environmental factors showed that the sea surface temperature and chlorophyll-a in the South Yellow Sea were higher than normal during the golden tide disaster, which may be attributed to the rapid growth of the Sargassum biomass. Therefore, this study hypothesizes that an internal cause exists between the golden tide and the abovementioned factors

Park, S.-J.; Achmad, A.R.; Syifa, M., and Lee, C.-W., 2019. Machine learning application for coastal area change detection in Gangwon province, South Korea using high-resolution satellite imagery. In: Jung, H.-S.; Lee, S.; Ryu, J.-H., and Cui, T. (eds.), Advances in Remote Sensing and Geoscience Information Systems of Coastal Environments. Journal of Coastal Research, Special Issue No. 90, pp. 228-235. Coconut Creek (Florida), ISSN 0749-0208.

The coastal area is one of an essential region for the ecosystem. Various kinds of effects that occur due to natural phenomena and human intervention make an impact on the change in the coastal area. In recent years, coastal erosion caused severe problems along the Korea coastline with 143 sites are experienced erosion throughout 2013. In this study, six beaches from 3 cities in Gangwon province, Republic of Korea are chosen to monitor its coastal area in 2016 and 2018. Those areas were chosen due to the high erosion severity grades according to Coastal Maintenance Project of the Ministry of Land, Infrastructure, and Transport. By using a support vector machine (SVM) classifier, high-resolution imagery from PlanetScope satellite was processed to generate the map that separate coastal area with others. The coastal area is successfully detected and calculated using SVM classifier. The result of the coastal area map is beneficial for the government or the authorities as mitigation and prevention plan.

Shin, J.; Kim, S.M.; Son, Y.B.; Kim, K., Ryu, J.-H., 2019. Early prediction of Margalefidinium polykrikoides bloom using a LSTM neural network model in the South Sea of Korea. In: Jung, H.-S.; Lee, S.; Ryu, J.-H., and Cui, T. (eds.), Advances in Remote Sensing and Geoscience Information Systems of Coastal Environments. Journal of Coastal Research, Special Issue No. 90, pp. 236-242. Coconut Creek (Florida), ISSN 0749-0208.

Harmful algal blooms (HABs) have been occurring within the South Sea of Korea (SSK) for decades, causing significant ecological impacts and economic problems for many fish farms concentrated in coastal areas. The occurrence of HABs is related to various factors, including meteorological and physical factors, making it difficult to predict the timing of their occurrence. However, it is essential to make a preliminary forecast of HAB occurrence through analysis of such factors to minimize the damage. In this study, a deep neural network model of long short-term memory (LSTM) that can predict the occurrence time of Margalefidinium polykrikoides blooms is presented and evaluated. Satellite data were used to extract sea surface temperature (SST) and photosynthetically available radiation (PAR), which are environmental factors known to be related to HABs occurrence. M. polykrikoides blooms that have occurred in the past 21 years (1998–2018) have been shown to be initiated when SST reaches around 25 °C within the summer season. The prediction performance of LSTM-based neural network was evaluated using test data from 2017 and 2018, and the root-mean-squared-error (RMSE) and prediction accuracy were determined. Prediction for 2017 matched well with the test data; however, for 2018, the network predicted that a HAB would occur between July 19 and Sep. 03, with an RMSE of 0.233 and accuracy of 94.8 %, but the actual occurrence of HAB was between July 24 and Aug. 20. This study shows that the trained LSTM-based network would be useful for early prediction of the future red tide blooms in SSK.

Lee, Y.-S.; Park, S.-H.; Cho, Y.-H.; Lee, W.-J, Jung, H.-S.; Lee, M.-J., and Kim, S.-H., 2019. Classification of halophytes from airborne hyperspectral imagery in Ganghwa Island, Korea using multilayer perceptron artificial neural network. In: Jung, H.-S.; Lee, S.; Ryu, J.-H., and Cui, T. (eds.), Advances in Remote Sensing and Geoscience Information Systems of Coastal Environments. Journal of Coastal Research, Special Issue No. 90, pp. 243-250. Coconut Creek (Florida), ISSN 0749-0208.

Surveying methods have limitations when investigating the vegetation of mudflats, which are difficult to access due to tidal variations. The use of remote sensing data to explore inaccessible targets and regions is an effective approach to analyzing vegetation species. Recently, hyperspectral images, composed of several tens to several hundreds of spectral bands, have been effectively utilized to classify and analyze halophytes using spectral reflectance differences. In this study, halophytes were classified from an airborne hyperspectral image of the Dongmak beach using an artificial neural network (ANN) approach. Dongmak beach is located on Ganghwa Island, Korea, where mudflats are well developed. Since the hyperspectral image has many spectral bands, it was tried to reduce the number of bands. To do this, the statistical values of halophytes and mudflats from all of the hyperspectral bands were first calculated by using several methods, including parallelepiped, Mahalanobis distance, minimum distance, spectral information divergence, and a spectral angle mapper. The statistical images were then used to classify halophytes and mudflats by applying a multilayer perceptron artificial neural network (MLP-ANN). The achieved accuracy of the MLP-ANN classification was about 95.02 %. Some areas composed of halophytes were misclassified as mudflats. These results will reduce the time and cost of field investigations in large areas.

Park, S.-H.; Jung, H.-S.; Lee, M.-J.; Lee, W.-J., and Choi, M.-J., 2019. Oil spill detection from PlanetScope satellite image: Application to oil spill accident near Ras Al Zour area, Kuwait in August 2017. In: Jung, H.-S.; Lee, S.; Ryu, J.-H., and Cui, T. (eds.), Advances in Remote Sensing and Geoscience Information Systems of Coastal Environments. Journal of Coastal Research, Special Issue No. 90, pp. 251-260. Coconut Creek (Florida), ISSN 0749-0208.

Oil spill accidents are major marine disasters that can destroy the ocean ecosystem. Satellite images are widely used to respond immediately to oil spill accidents. A miniaturized satellite equipped with an optical sensor has an advantage that the imaging period is very short because a large number of satellites can be operated. In this study, oil slick areas were detected by applying the artificial neural network (ANN) technique to the PlanetScope satellite optical image captured near Ras Al Zour, Kuwait on August 10, 2017. However, the image included sunglint effects owing to ocean waves and yellow dust areas, making it difficult to classify the oil slick area. In addition, the spilled oil had three different spectral information characteristics through interaction with the sea surface of the Persian Gulf. The image processing was divided into the pre-processing step and the oil slick classification step to detect the oil slick area and to mitigate these limitations. In the pre-processing step, a directional median filter was applied to reduce the sunglint effects caused by ocean waves, and the negative effects from the sea surface and dust were mitigated using the differences with the low-pass filtered image. In the oil slick detection step, three types of oil probability maps and one sea surface probability map were produced using the ANN technique. Subsequently, an oil slick classification map was produced by applying the maximum probability choosing method and dust area rejection. Through validation, the overall accuracy of the oil classification map was obtained to be 82.01 % and the kappa coefficient was 72.42 %. The proposed oil detection method can effectively detect different types of oil spill areas in optical images with sunglint and dust handicaps. Moreover, it has high potential to be used in the future because the revisit time of the satellite image used is shorter than that of other optical images.

Baek, Y.-H. and Moon, I.-J., 2019. Estimation of satellite-based upper-ocean temperature profile in the western North Pacific and its application to tropical cyclone intensity predictions. In: Jung, H.-S.; Lee, S.; Ryu, J.-H., and Cui, T. (eds.), Advances in Remote Sensing and Geoscience Information Systems of Coastal Environments. Journal of Coastal Research, Special Issue, No. 90, pp. 261-266. Coconut Creek (Florida), ISSN 0749-0208.

Satellite measurements have limitations in obtaining information below sea surface, because they assess only the ocean surface. However, combining the satellite-measured sea surface temperature and heights with a large number of ARGO and reanalysis profiles allows estimating upper-ocean temperature profiles (UTPs) below the surface. In this study, a satellite-based UTP estimation algorithm was developed using a massive data set of 128,136 ARGO profiles and ocean reanalysis data collected over 17 years (2000–2016). The algorithm has the advantage of producing UPTs in all tropical cyclone (TC)-passing areas in the western North Pacific (WNP) without missed points. The verification results revealed that the estimated UTPs and TC-intensity-related predictors, such as depth-averaged temperatures up to 80 m and 100 m, isothermal depths at 20 °C and 26 °C, ocean heat content, and maximum potential intensity, overall are in good agreement with the observations, although there still exists a relatively large error in the higher latitudes (north of 40°N) and the Kuroshio extension area where spatial and temporal variations are large. Based on the relationships between the TC intensity change and the predictors obtained for WNP TCs during 2004–2014, this study finally provides a guideline for predicting satellite-based TC intensity using estimated predictors.

Lee, D.; Jeong, J.-Y.; Jang, H.K.; Min, J.-O.; Kim, M.J.; Youn, S.H.; Lee, T., and Lee, S.H., 2019. Comparison of particulate organic carbon to chlorophyll-a ratio based on the ocean color satellite data at the Ieodo and Socheongcho ocean research stations. In: Jung, H.-S.; Lee, S.; Ryu, J.-H., and Cui, T. (eds.), Advances in Remote Sensing and Geoscience Information Systems of Coastal Environments. Journal of Coastal Research, Special Issue No. 90, pp. 267-271. Coconut Creek (Florida), ISSN 0749-0208.

The Ieodo ocean research station (IORS) and the Socheongcho ocean research station (SORS) have been operated in South Korea to carry out a research across multiple disciplines. The IORS and the SORS are affected by various water masses such as Tsushima Warm Current (TWC), Yellow Sea Warm Current (YSWC) and Changjiang Diluted Water (CDW). Therefore, the IORS and the SORS are suitable for the research on the physio-ecological response of phytoplankton to environmental changes. The particulate organic carbon to chlorophyll-a ratio (POC:Chl-a) has been widely used for an indicator of the ecological and physiological conditions of the phytoplankton. The purpose of this study is to investigate the POC:Chl-a ratio and its controlling factors at the IORS and the SORS by using satellite dataset. At the IORS, POC:Chl-a ratio ranged from 166.16 to 431.20, and the average was 322.14 ± 46.35. On the other hand, at the SORS, POC:Chl-a ratio ranged from 131.92 to 703.98, and the average was 385.05 ± 123.95. The POC:Chl-a ratio was higher at the SORS from autumn to winter. However, POC:Chl-a ratio in the IORS showed similar ranges in every month. The difference in POC:Chl-a ratio between the IORS and the SORS appear to be caused by differences of environmental conditions such as light intensity, temperature, and nutrients. However, to reveal the detailed relationship between environmental conditions and POC:Chl-a ratio, a further study on seasonal variations of the phytoplankton community structure and several environmental parameters would be warrant.

Liang, X.-J.; Qin, P.; Xiao, Y.-F.; Kim, K.-Y.; Liu, R.-J.; Chen, X.-Y., and Wang, Q,-B., 2019. Automatic remote sensing detection of floating macroalgae in the yellow and east china seas using extreme learning machine. In: Jung, H.-S.; Lee, S.; Ryu, J.-H., and Cui, T. (eds.), Advances in Remote Sensing and Geoscience Information Systems of Coastal Environments. Journal of Coastal Research, Special Issue No. 90, pp.272-281. Coconut Creek (Florida), ISSN 0749-0208.

In the past 10 years, floating macroalgae blooms have occurred repeatedly in the Yellow Sea. For the purpose of disaster prevention and mitigation, it is very important to monitor floating macroalgae blooms using satellite imagery. The traditional macroalgae remote sensing detection methods based on the vegetation indices are very sensitive to the threshold value which is affected by many factors in the complex atmospheric–oceanic environment. The threshold has obvious temporal and spatial variations, and is difficult to determine accurately. The expert experience is required to assist the value of threshold which leads to the low automation of detection. Aiming at this problem, this study introduces an Extreme Learning Machine (ELM) into the field of macroalgae remote sensing detection. Taking the four-bands GF-1 WFV optical images with 16-m resolution as an example, an automatic remote sensing detection model of macroalgae is constructed. The evaluation based on independent data shows that the accuarcy of this method is up to 86 %. The method is not disturbed by thin clouds, sun glint, high-turbidity water, and other factors. In addition, no manual intervention is required which suggests that the proposed method has strong potential of automated detection for the floating macroalgae blooms.

Jung, D.; Lee, J.S.; Baek, J.Y.; Nam, J.; Jo, Y.H.; Song, K.M., and Cheong Y.I., 2019. High temporal and spatial resolutions of sea surface current from low-altitude remote sensing. Jung, H.-S.; Lee, S.; Ryu, J.-H., and Cui, T. (eds.), Advances in Remote Sensing and Geoscience Information Systems of Coastal Environments. Journal of Coastal Research, Special Issue No. 90, pp. 282-288. Coconut Creek (Florida), ISSN 0749-0208.

The Saemangeum coast in Korea, which is the study area, is divided from the outer side and the inner side of the dyke. Because exchanging seawater are occurred by only two gates (Sinsi and Garyeok), coastal erosion and sedimentation, migration of suspended solids, and chemical component exchange occur extensively along the gates. Therefore, it is critical to observe the movement of seawater flowing through the gates. In this study, an algorithm to extract high-resolution sea surface current information using a Helikite (the compound word for Helium and Kite) and a drone was examined. First, direct georeferencing was performed using Global Positioning System/Inertial Measurement Unit (GPS/IMU) data from the aircraft to assign ground coordinates into the images without a Ground Control Point (GCP). Subsequently, the sea surface current was estimated using a Robust Optical Flow (ROF) algorithm. ROF computes motion by analyzing changes in the brightness values of successive images. The accuracy of the estimated sea surface current was verified by comparing with observations from a surface drifter, and the results showed that the correlation coefficient was 0.57 and the Root Mean Square Error (RMSE) was 8.85 cm/s. Generally, the ROF-based flow rates were underestimated compared to the field measurements. Furthermore, image aliasing occurred due to a 5-s time interval and the currents that had a strong wave frequency could not be completely captured. To fully restore the sea surface current that appeared on the images, it is necessary to obtain the images at various sampling frequencies using video recordings.

Jeong, Y.; Kim, D.; Jo, Y.-H.; Kim, D.-W.; Jo, Y.-H., 2019. Interactions of eddies with the Kuroshio Current based on satellite altimeter measurements In: Jung, H.-S.; Lee, S.; Ryu, J.-H., and Cui, T. (eds.), Advances in Remote Sensing and Geoscience Information Systems of Coastal Environments. Journal of Coastal Research, Special Issue No. 90, pp. 289-293. Coconut Creek (Florida), ISSN 0749-0208.

Ocean eddy is a closed circulation system having a vortex that creates a closed flow. These eddies vary in size from a few centimeters to a few hundreds of kilometers and can lead to changing in sea surface level, sea surface temperature, and nutrient concentration, which can be used to detect eddies using various satellite sensors. Thus, such a vortex plays an important role in the ocean flow in transferring the heat, energy, and the substance inside it. In order to detect eddies objectively in this study, the particle tracking method was used to estimate spatial eddy boundaries using satellite altimeter measurements. This method has an advantage over conventional methods, allowing to detect the eddy's physical boundary changes over the time. Following results were obtained from the understanding the relationship between eddies' evolution and the Kuroshio Current. The cold and warm eddies in two different locations were analyzed to examine the influence of the Western Boundary Current (WBC): Kuroshio Current. Eddies located off the Kuroshio Current revealed that the developmental stages were divided into two stages, and there is a time delay between circular motions and amplitude of eddies. However, eddies located in the Kuroshio Current were divided into three stages of development, and there was a little time delay between circular motions and amplitudes of the eddies. Results of this study suggest that the eddies in the Kuroshio Current were interacted each other through the exchanging the kinetic energy. Although there are some previous researches for analyzing how the ocean current influences to the eddy kinetic energy based on numerical models, there are no investigations based on the actual eddy measurements like this study.

Zhang, J.-Y.; Zhang, J.; Ma, Y.; Chen, A.-N.; Cheng, J., and Wan, J.-X., 2019. Satellite-derived bathymetry model in the Arctic waters based on support vector regression. In: Jung, H.-S.; Lee, S.; Ryu, J.-H., and Cui, T. (eds.), Advances in Remote Sensing and Geoscience Information Systems of Coastal Environments. Journal of Coastal Research, Special Issue No. 90, pp. 294-301. Coconut Creek (Florida), ISSN 0749-0208.

Bathymetric data are essential for navigation and marine engineering. Satellite-derived bathymetry (SDB) is an effective supplement to the ship-based echo sounder, especially for bathymetric measurement in the area that is difficult to reach. Accurate SDB in the Arctic waters is more complex by weak light, cloud, mist, floating ice and so on than in middle or lower latitude clean waters. This paper performs SDB based on Support vector regression (SVR) from multi-spectral imagery of Sentinel-2 in shallow water (0-20 m) of Pomorskiy Proliv of Northern Russia, and corrects the estimated water depth with the interpolation of residuals. The results indicate that the highest accuracy of SDB based on SVR is achieved by employing radial basis function on the full feature inputs, with the mean absolute error (MAE) of 2.5 m and the mean relative error of 44.9 %. After the residual interpolation correction, the overall MAE reduces to 2.0 m, and the root mean square errors decrease by at least 0.3 m among different depth ranges. The approach provides technical support for obtaining a wide range and relatively high spatial resolution of bathymetric data in the Arctic region.

Kim, S.M.; Shin, J.; Baek, S., and Ryu, J.-H., 2019. U-Net convolutional neural network model for deep red tide learning using GOCI. In: Jung, H.-S.; Lee, S.; Ryu, J.-H., and Cui, T. (eds.), Advances in Remote Sensing and Geoscience Information Systems of Coastal Environments. Journal of Coastal Research, Special Issue No. 90, pp. 302-309. Coconut Creek (Florida), ISSN 0749-0208.

GOCI launched in 2010 is a geostationary satellite image sensor that monitors ocean color. It captures 8-band spectral satellite images of northeast Asian regions hourly, eight times a day. The spatial resolution of GOCI is about 500 m. GOCI is capable of monitoring a large ocean area for sensing various events such as red tide occurrences, tidal movement changes and ocean disasters. In this study, we propose a deep convolutional neural network model, U-Net, for automatic pixel-based detection of red tide occurrence from the spectral images captured by GOCI. We construct two training datasets with GOCI images and the corresponding red-tide index maps (RI maps) accumulated through 2011 to 2018. The RI maps indicate where red tides occurred and what kind of red tide species were there. U-Net consists of five U-shaped encoder and decoder layers to extract spectral features relating to red-tide species from GOCI images. We compared the performances of U-Nets trained from two datasets (i) consisting of only four spectral bands and (ii) consisting of all six spectral bands. The RI maps predicted by the trained U-Nets showed considerably matching spatial occurrence tendencies of three red tide species to the ground truths for validation images. The mean target accuracy with the four-band dataset was 13 % lower than that with the six-band dataset. The trained U-Net for pixel-wise red tide detection would be able to effectively inspect red tide occurrences in the huge area of water surrounding the Korean peninsula.

Min, S.H.; Hwang, J.D.; Oh, H., and Son, Y.B., 2019. Reflectivity characteristics of the green and golden tides from the Yellow Sea and East China Sea. In: Jung, H.-S.; Lee, S.; Ryu, J.-H., and Cui, T. (eds.), Advances in Remote Sensing and Geoscience Information Systems of Coastal Environments. Journal of Coastal Research, Special Issue No. 90, pp. 310-316. Coconut Creek (Florida), ISSN 0749-0208.

The Yellow Sea (YS) and East China Sea (ECS) have the world's largest supply of floating algae. The green and golden tides appear mainly in the YS and ECS, respectively, but become entangled as they drift. The floating algae obstructs navigation and is a huge socioeconomic problem in the vicinity of coastal areas. To manage these floating algae more systematically, a method of distinguishing between the green and golden tides is required. In this study, the reflectivity characteristics of the green and golden tides appearing in the YS and ECS were investigated based on which a classification method was proposed and applied to satellite sensors. First, the reflectance of Ulva prolifera and Sargassum horneri was measured, which are the major causative species of the green and golden tides in the YS and ECS, respectively. Under visible light, the reflectance of U. prolifera increased at 555 nm, and that of S. horneri increased at 602 and 646 nm and decreased at 632 nm. To distinguish the two algae, slope of red-green (SRG) method was applied with the red and green reflectance slope. U. prolifera exhibited SRG that was always negative, whereas that of S. horneri was always positive. When SRG was applied to the difference in reflectance values between floating algae and nearby water detected in satellite imagery, the green tide was always negative, and the golden tide was always positive. Classification and detection of floating algae using multi-satellite sensors and SRG system can reduce the associated management cost and time.

Kim, K.; Shin, J.; Kim, K.Y., and Ryu, J.H., 2019. Long-term trend of green and golden tide in the eastern Yellow Sea. In: Jung, H.-S.; Lee, S.; Ryu, J.-H., and Cui, T. (eds.), Advances in Remote Sensing and Geoscience Information Systems of Coastal Environments. Journal of Coastal Research, Special Issue No. 90, pp. 317-323. Coconut Creek (Florida), ISSN 0749-0208.

Since 2008, floating green tides (Ulva sp.) have been occurring continuously in the Yellow Sea (YS), and after 2013 floating golden tides (Sargassum sp.) have also occurred. The distribution, areal coverage, and migration of floating green tides have been actively studied, but most research has focused only on the western YS. Little is known about the floating golden tides in the eastern YS. The purpose of this study was to determine the long-term distribution of floating green and golden tides in the eastern YS using Geostationary Ocean Color Imager (GOCI), Moderate Resolution Imaging Spectroradiometer (MODIS), and Landsat satellite images from 2008 to 2017. In addition, the migration of floating macroalgae with Global Hybrid Coordinate Ocean Model (HYCOM) surface current data were compared. Green tides were observed in 2008, 2009, 2011, 2015, and 2016 in the eastern YS. From a satellite image backtracking analysis, it was confirmed that the green tides observed in the eastern YS were supplied from the western YS. When the maximum areal coverage of green tide was compared between the eastern and western YS, the coverage in the eastern YS was found to be about 4 % of that of the western YS. However, in 2011, the largest amount of floating macroalgae was found in the eastern YS and it accounted for about 45 % of the amount in the western part of the YS. In the eastern YS, floating golden tides were found in 2013, 2015, 2016, and 2017, with the largest amount of floating macroalgae occurring in 2017. Although there were no long-term golden tide data for the western YS, such that it was not compared to the areal coverage of the eastern YS, it was confirmed that the amount of golden tide supplied to the eastern YS gradually increased. A comparison between the migration of floating macroalgae and HYCOM surface current data suggested that the migration and flow directions were not identical, and were considerably affected by surface ocean currents during their passage into the eastern YS. From this study, the long-term distribution and changes in areal coverage of green and golden tides in the eastern YS were obtained for the first time. This information will be useful for understanding the long-term patterns of green and golden tides, and provides basic data for predicting the occurrence and migration of floating macroalgae.

Ma, Y.; Zhang, J.; Zhang, Z., and Zhang, J.Y., 2019. Bathymetry retrieval method of LiDAR waveform based on multi-Gaussian functions. In: Jung, H.-S.; Lee, S.; Ryu, J.-H., and Cui, T. (eds.), Advances in Remote Sensing and Geoscience Information Systems of Coastal Environments. Journal of Coastal Research, Special Issue No. 90, pp. 324-331. Coconut Creek (Florida), ISSN 0749-0208.

This paper provides a water depth inversion approach to laser radar waveform data based on multi-Gauss function in order to address the following two problems: the impact of noise on the traditional bathymetry and the poor accuracy of the water depth inversion in deep water. This approach employs L-M optimization algorithm to make multiple Gauss functions iterate and fit the LiDAR waveform data, and then uses peak detection method to extract the echo signal from the surface and the bottom of the water. In this paper, the water depth inversion is carried out using the simulation data of LiDAR water echo and the Aquarius LiDAR waveform data around the water area of the Ganquan Island respectively. Taking the mean relative error (MRE) and the mean absolute error (MAE) as the evaluation index of accuracy, the result shows that the detection model of LiDAR bathymetry has a higher ability of water depth inversion in the range of detectable water depth. For simulation data, the MAE is 20 cm and the MRE is below 7 % in the water depth range of 2 m to 10 m. While for real LiDAR waveform data, the MAE of water depth inversion is between 30 cm and 75 cm, and the MRE is below 12 %.

Yang, J.-F.; Wan, J.-H.; Ma, Y.; Zhang, J.; Hu, Y.-B., and Jiang, Z.-C., 2019. Oil spill hyperspectral remote sensing detection based on DCNN with multi-scale features. In: Jung, H.-S.; Lee, S.; Ryu, J.-H., and Cui, T. (eds.), Advances in Remote Sensing and Geoscience Information Systems of Coastal Environments. Journal of Coastal Research, Special Issue No. 90, pp. 332-339. Coconut Creek (Florida), ISSN 0749-0208.

In this paper, a deep convolutional neural network (DCNN) model is developed for sea surface oil spill accurate detection using multi-scale features with AISA+ airborne hyperspectral remote sensing image. Based on multi-scale features after wavelet transform (WT), a deep convolution neural network classification algorithm with seven-layer network structure is proposed to detect oil spill of the Penglai 19-3C platform in 2011, and the accuracy evaluation is conducted on the overall situation. The detection results of proposed method are compared with those of the classical SVM, RF and DBN method. The results show that the accuracies of DCNN for oil spill detection based on different-scale features are all more than 85 %, which are much better than those of SVM, RF and DBN method, and the detection results can maintain the continuity of oil film at sea. Among them, the detection result of DCNN model based on spectral feature information combined with low-frequency component of 1-level wavelet transform has the best effect and highest detection accuracy, reaching 87.51 %.

Kim, H.-J. and Moon, I.-J., 2019. Determination of rain-/wind-dominant type for typhoons approaching South Korea based on satellite-estimated rainfall and best-track data. In: Jung, H.-S.; Lee, S.; Ryu, J.-H., and Cui, T. (eds.), Advances in Remote Sensing and Geoscience Information Systems of Coastal Environments. Journal of Coastal Research, Special Issue, No. 90, pp. 340-345. Coconut Creek (Florida), ISSN 0749-0208

Typhoons are one of the most influential natural hazards in South Korea. When a typhoon approaches the Korean Peninsula (KP), detailed disaster plans are needed to prevent typhoon-induced damage. In general, the official forecast in South Korea provides basic information on the track and the intensity of the typhoon, but the forecast does not provide detailed information on the impact of the typhoon on the KP based on the type of damage (i.e., rain-, wind-, or rain-wind-dominant). Impact-based information is crucial to prevent disasters, because preparations for an approaching typhoon should be planned differently depending on the type. Based on satellite-estimated rainfall and best-track wind data, this study developed an algorithm for estimating the rainfall and maximum wind speed (MWS) percentiles induced by a typhoon, compared to those induced by previous tropical cyclones (TCs) that have moved through the same area, at intervals of 1° latitude. This information is used to determine the dominant types of typhoons along their tracks. From the estimated rainfall and MWS percentiles, all TCs that approached the KP during 2001–2016 were classified as wind-, rain-, and wind-rain-dominant types. As a result, Maemi in 2003 was the most wind-dominant type, while Nabi in 2005 was the most rain-dominant type. This result has important implications for providing a guidance tool based on impact-based TC prediction for decision makers preparing disaster prevention plans based on real-time satellite-estimated rainfall data.

Jang, J.Y.; Yoon, J.H.; Cho, N.W., and Lee, M.J., 2019. Expanding the use of environmental conservation value assessment maps to marine environments: A case study in South Korea In: Jung, H.-S.; Lee, S.; Ryu, J.-H., and Cui, T. (eds.), Advances in Remote Sensing and Geoscience Information Systems of Coastal Environments. Journal of Coastal Research, Special Issue No. 90, pp. 346-353. Coconut Creek (Florida), ISSN 0749-0208.

Environmental issues affect not only inland areas (land), but also marine locations. South Korea is surrounded by water on three sides and is therefore significantly affected by marine environments. Conventional marine environmental conservation value assessments have tended to focus on specific incidents, and environmental conservation values that link inland and marine environments have not been well researched. South Korea's environmental conservation value assessment maps have been widely used to assess the environmental value of the entire territory comprehensively, to grade environmental value relatively, and to analyze areas that can be developed. Against this backdrop, this study analyzed conventional environmental conservation value assessment maps and examined the applicability of these maps to marine environments. The status of marine spatial data available in South Korea was analyzed. Because these spatial data are produced, managed, and distributed by government agencies, they are reliable and accurate. In addition, five assessment items related to marine environments (four legal items and one environmental–ecological item) and assessment criteria (divided into every 500m) were proposed to incorporate marine spatial data into environmental conservation value assessment maps. It is necessary to conduct additional studies of the value of marine environments to determine assessment grades based on the suggested assessment items and criteria. Environmental conservation value assessment maps incorporating marine spatial data have potential value for comprehensive assessment of the environmental values of both inland and marine areas across the country, and for improving the efficiency of territorial management based on objective data.

Bae, S.; Yu, J.; Lei, W.; Kim, J., and Park, C., 2019. Experiments on unmanned aerial vehicle survey for detection of micro beach features. In: Jung, H.-S.; Lee, S.; Ryu, J.-H., and Cui, T. (eds.), Advances in Remote Sensing and Geoscience Information Systems of Coastal Environments. Journal of Coastal Research, Special Issue No. 90, pp. 354-361. Coconut Creek (Florida), ISSN 0749-0208.

This study tested the effectiveness of using UAV point clouds at different densities to delineate micro beach features. A profile-based method was applied to three different types of geometric patterns of ripples: featureless, regular pattern, and irregular pattern. A terrestrial laser scanner (TLS) based profile was used as the baseline. In general, the overall RMSE between TLS-based profile and UAV-based profiles increased with UAV flight altitude. For the featureless test zone, the UAV beach profile matched the TLS profile with acceptable errors for all three test flight altitudes. In the irregular ripple area, UAV profiles showed much lower RMSE than the regular ripple area. Furthermore, in the irregular ripple area, the UAV profiles captured more extreme elevation points than in the regular ripple area. The results suggest that in beach areas with an irregular spatial pattern of features, UAV surveys could take higher flight altitudes to achieve the same level of accuracy as in an area with a regular pattern of features. This study proved that UAV-based elevation surveys could achieve high-level accuracy with controlled flight heights and could replace the costly TLS-based surveys.

Ko, K. and Lee, H.-J., 2019. Detecting geological structures in coastal areas with unmanned aerial vehicle photogrammetric surveys. In: Jung, H.-S.; Lee, S.; Ryu, J.-H., and Cui, T. (eds.), Advances in Remote Sensing and Geoscience Information Systems of Coastal Environments. Journal of Coastal Research, Special Issue No. 90, pp. 362-368. Coconut Creek (Florida), ISSN 0749-0208.

This study attempted to use unmanned aerial vehicle (UAV) photogrammetry for structural mapping at limited exposure outcrops in the west coast area of southwestern Korea. The west coast area of the Korean Peninsula has a large tidal range, and there are restrictions for traditional structure mapping. A study site was selected, and high spatial resolution images (< 5 cm per pixel) were obtained at low tide. The UAV survey identified 50 brittle structures (fractures and faults that were divided into three groups) and changes in the bedding trace. The bedding trace demonstrates various directional verging of the fold geometry that indicates slump-fault structures. While more research is still necessary, this study demonstrated that UAV mapping techniques are very useful for geological structural analysis in coastal areas.

Chun, J.-H. and Lee, H.-J., 2019. Subaerial and subaqueous investigations of volcanic debris avalanche and lahar deposits on the northern coast of Ulleung Island, Korea. In: Jung, H.-S.; Lee, S.; Ryu, J.-H., and Cui, T. (eds.), Advances in Remote Sensing and Geoscience Information Systems of Coastal Environments. Journal of Coastal Research, Special Issue No. 90, pp. 369-376. Coconut Creek (Florida), ISSN 0749-0208.

Volcanic debris avalanche and lahar deposits associated with small-scale lava dome collapse have been poorly documented in volcanic islands. The Chusan Formation was emplaced on the collapsed northern flank of Ulleung Island, Korea. Subaerial and subaqueous investigations of the collapsed northern part of Ulleung Island have been performed based on shaded relief images generated from a digital elevation model (DEM), a GeoEye satellite image, outcrop observations, and multi-beam echosounder and high-resolution Chirp sub-bottom profiling systems. The Chusan Formation is an elongated (length, 800 m; width, 250 m) system of valley-confined deposits connected to the Albong lava dome within the Nari caldera depression. A 40-m-thick outcrop of the Chusan Formation consists of three aggradational units that were emplaced by lahars, volcanic debris avalanches, and mixed deposition due to these processes. The peat layer between the overlying Chusan Formation and underlying reworked sediments was dated at 3,070–3,275 cal B.P., matching the emplacement of the Chusan Formation. Equivalent subaqueous deposits of the Chusan Formation were not detected in the northern shelf; thus, large-scale caldera collapse deposits covered the marine terrace before emplacement of the Chusan Formation. The valley-confined Chusan Formation is the result of an aggradational succession of lahar and volcanic debris avalanche deposits associated with the Albong lava dome collapse, corresponding to the most recent volcanic activity of Ulleung Island.

Kim, H.C.; Baek, S.K., and Hwang, J.H., 2019. GIS analysis and evaluation on the geothermal reserves of the costal area of Jeju island and Ulleung island, volcanic islands in South Korea. In: Jung, H.-S.; Lee, S.; Ryu, J.-H., and Cui, T. (eds.), Advances in Remote Sensing and Geoscience Information Systems of Coastal Environments. Journal of Coastal Research, Special Issue No. 90, pp. 377-385. Coconut Creek (Florida), ISSN 0749-0208.

South Korea has the greatest growth in greenhouse gas (GHG) emissions among Organization for Economic Cooperation and Development countries. To reduce emissions through voluntary GHG reduction measures, at the 21st Summit of the Conference on Climate Change in France, geothermal energy was selected as a key technology for the achievement of 50 % reduction in national GHG emissions by 2020. To meet this challenge, selection of the most suitable (reasonable and efficient) geothermal energy development site is necessary. The development of economical geothermal power through the efficient execution of the state budget for future geothermal energy development is also necessary. In other countries, geothermal power is being developed using the heat around active volcanoes. In Korea, geothermal abnormalities are related mostly to the geothermal heat produced by the radioactive decay of elements. The research area selected for this study comprised Korea's representative volcanic islands, Jeju Island and Ulleung Island. The geothermal investigation on Jeju Island was assessed through several boreholes, revealing generally lesser heat flow than that of inland of South Korea. On the other hand, a high temperature gradient was found through recent drilling on Ulleung Island. To determine the cause of this high gradient, rock thermal properties and borehole irradiation data from the coastal areas of Jeju and Ulleung islands were compared. It need to be calculated the geothermal reserve and constructed geothermal parameter maps of the volcanic Jeju and Ulleung islands in South Korea for regional analysis and evaluation. As a result, the average thermal conductivity of rocks is 1.69 W/mK (Ulleung) and 1.76 W/mK (Jeju), lower than inland. In the case of geothermal gradient, heat flow, and heat production rate, Ulleung island is average 86.55 °C/km, 127.06 mW/m², 3.75 µW/m³ and Jeju island is average 24.65 °C/km, 40.13 mW/m², 0.76 µWm³. It is the value significantly higher Ulleung island than inland, but Jeju island is lower than inland. It seems to be the reason related to the occurrence origin of the volcanic island.

Kim, S.; Park, S.; Han, J.; Son, S.; Lee, S.; Han, K.; Kim, J., and Kim, J., 2019. Feasibility of UAV photogrammetry for coastal monitoring: A case study in Imlang Beach, South Korea. In: Jung, H.-S.; Lee, S.; Ryu, J.-H., and Cui, T. (eds.), Advances in Remote Sensing and Geoscience Information Systems of Coastal Environments. Journal of Coastal Research, Special Issue No. 90, pp. 386-392. Coconut Creek (Florida), ISSN 0749-0208.

This study assessed the potential of unmanned aerial vehicle (UAV) photogrammetry to accurately monitor coastal zone features, such as vertical profiles, and to detect shoreline. In total, 245 images with a ground spatial distance (GSD) of 1.59 cm were captured using a Zenmuse X7 camera mounted on an Inspire 2 UAV at Imlang Beach, Busan, Korea; 40 ground control points (GCPs) for UAV photogrammetry and 21 stations for terrestrial laser scanning (TLS) were surveyed using a network real-time kinematic (RTK) approach. The root mean square error (RMSE) values in the X, Y, and Z directions were 0.015, 0.017, and 0.040 m, respectively, based on bundle adjustment of 24 GCPs and 16 checkpoints. The root sum of squares error (RSSE) was 0.046 m. To assess the accuracy of the vertical profiles obtained for Imlang Beach, digital elevation models (DEMs) of six cross-shore profiles were constructed and compared based on UAV photogrammetry and TLS surveying. Vertical accuracy assessment showed an average difference in height between the models of 0.02 m and an RMSE of 0.04 m. A well-established object-based image segmentation approach was applied with standard parameters (size 100, shape 0.5, and compactness 0.5) to extract the shoreline from orthomosaic images of Imlang Beach. The results suggest that UAV photogrammetry has the capacity to achieve accurate and continuous coastal monitoring.

Oh, H.-J.; Koo, B.J.; Ryu, J.-H., and Lee, S., 2019. Spatial macrobenthos habitat on Ganghwa tidal flat, Korea: Part I – spatial relationship between Potamocorbula laevis and spatial variables. In: Jung, H.-S.; Lee, S.; Ryu, J.-H., and Cui, T. (eds.), Advances in Remote Sensing and Geoscience Information Systems of Coastal Environments. Journal of Coastal Research, Special Issue No. 90, pp. 393-400. Coconut Creek (Florida), ISSN 0749-0208.

This paper describes the spatial relationship between Potamocorbula laevis and spatial variables on an intertidal flat at Ganghwa, Korea, which is the largest tidal flat on the west coast of South Korea. In total, 5,700 Potamocorbula laevis were counted at 30 sampling locations, making it the dominant species in the study area. Eight spatial variables in the habitat were examined: distance from a channel network; density of channel network; elevation; slope; aspect; and the amounts of silt, clay, and sand on the tidal flat. The spatial distribution of Potamocorbula laevis in relation to each spatial variable was measured using a frequency ratio (FR) based on a geographic information system (GIS). The results indicate that Potamocorbula laevis prefer areas with an elevation of 407–498 m, less than 14 degrees of slope, a north aspect, within 30 m of a channel, near a high density of channels, and in areas with more silt and clay and less sand. The FR is a probability ratio that quantitatively describes specific relationships. The data provide important information for understanding intertidal estuarine ecosystems, and can be used as a baseline for comparing spatiotemporal macrobenthos distributions in the future.

Lee, S.; Syifa, M.; Koo, B.J.; Lee, C.-W., and Oh, H.-J., 2019. Spatial macrobenthos habitat on Ganghwa tidal flat, Korea: Part II - Habitat potential mapping of Potamocorbula laevis using probability models. In: Jung, H.-S.; Lee, S.; Ryu, J.-H., and Cui, T. (eds.), Advances in Remote Sensing and Geoscience Information Systems of Coastal Environments. Journal of Coastal Research, Special Issue No. 90, pp. 401-408. Coconut Creek (Florida), ISSN 0749-0208.

Macrobenthos is a well-known biological indicator of environmental pollution, and its habitat plays important roles in the food chain in intertidal estuary ecosystems. Therefore, determination of the macrobenthos distribution is essential to understand such ecosystems. Potamocorbula laevis is a macrobenthos species that is found on Ganghwa tidal flat, South Korea. To model the potential distribution of this species, a dataset consisting of 5700 Potamocorbula laevis was divided into training and validation datasets at a 1:1 ratio. Eight controlling factors were also included for evaluation using three models—frequency ratio (FR), weight of evidence (WoE), and evidential belief function (EBF)—to determine the correlations with Potamocorbula laevis habitat potential. From these models, a Potamocorbula laevis habitat potential map was generated and validated using the areas under the curves (AUCs); WoE had the highest accuracy (94.02 %), followed by EBF (81.72 %), and then FR (79.47 %). This Potamocorbula laevis habitat potential map can be used as basic information to understand the intertidal estuarine ecosystem and for future planning regarding the tidal flat area by the government, developers, or public policy makers.

Lee, Y.K.; Eom, J.; Do, J.D.; Kim, B.J., and Ryu, J.H., 2019. Shoreline movement monitoring and geomorphologic changes of beaches using lidar UAVs images on the coast of the East Sea, Korea. In: Jung, H.S.; Lee, S.; Ryu, J.H., and Cui, T. (eds.), Advances in Remote Sensing and Geoscience Information Systems of Coastal Environments. Journal of Coastal Research, Special Issue No. 90, pp. 409-414. Coconut Creek (Florida), ISSN 0749-0208.

Sandy beaches are important habitats for coastal organisms and act as buffer zones during coastal disasters. Most of the coastal zone along the eastern coast of Korea consists of sandy beaches. However, beach erosion has been accelerating in recent years. In this study, shoreline movement and topographical changes were analyzed in Uljin-gun on the East Sea coast of Korea using remotely sensed data from airborne Lidar and unmanned aerial vehicles (UAVs). The shoreline changes extracted were statistically quantified using net shoreline movement (NSM) and linear regression rate (LRR) in the digital shoreline analysis system (DSAS). Morphological changes were quantified based on a comparison of the digital surface maps (DSM) generated by Lidar and UAVs. Shoreline movement and morphological changes were analyzed over the short-term (February 2016 to February 2019) and long-term (June 2008 to June 2018). Seaward migration was dominant for 7.02 m, 1.64 m, and 9.22 m along three defined subareas over the long-term. Over the short-term, LRR results showed landward migration as 0.7 m and 1.10 m in two of the subareas. Morphological change showed erosion and accretion occurring at approximately 0.08 m³ and 0.42 m³, respectively. This reverse trend indicates that a detailed DSM can detect volumetric change due to relocation of the sediment around artificial construction.