Remote Sensing of the Ocean, Sea Ice, Coastal Waters, and Large Water Regions 2018最新文献

{"title":"Front Matter: Volume 10784","authors":"","doi":"10.1117/12.2517149","DOIUrl":"https://doi.org/10.1117/12.2517149","url":null,"abstract":"","PeriodicalId":104340,"journal":{"name":"Remote Sensing of the Ocean, Sea Ice, Coastal Waters, and Large Water Regions 2018","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132166601","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Coastal chlorophyll-a concentrations monitoring in complex coastal region using machine learning techniques (Conference Presentation)","authors":"Y. Kwon, S. Baek, Y. Lim, J. Pyo, Yongeun Park, K. Cho","doi":"10.1117/12.2325519","DOIUrl":"https://doi.org/10.1117/12.2325519","url":null,"abstract":"Frequency and intensity of the harmful algal blooms (HABs) increased globally since 1970s. The increase in HABs have negatively affected aquatic ecosystem and aquaculture industry. The economic losses were about $ 1 billion in Europe, $ 100 million in USA and $ 121 billion in Korea per year. There were various field monitoring campaigns for ecological and biological researches. However, traditional HABs monitoring has limitations on both spatial and temporal coverage. In these days, multispectral remote sensing methods using satellite sensors have been widely used to monitor HABs in ocean and coastal areas. However, the satellite systems used in ocean and coastal research, such as MODIS, SeaWiFS and etc. have limitations in study on complex coastline, because of their coarse spatial resolution (~ few km). In this research, we conducted two-year intensive monitoring on the South Sea of Korea from 2016 to 2017 at 62 sampling station and used landsat-8 operational land imager (OLI) satellite that has 30m spatial resolution. We used 4 band (band 1 to 4), 4-band ratio (band 1 over band 3 and 4, and band 2 over band 3 and 4) and mixed dataset of 4 band and 4-band ratio. The empirical OC algorithms showed poor performances, under 0.25 of r-squared. The machine learning techniques, i.e., artificial neural network (ANN) and support vector machine (SVM) were applied to enhance performance of estimating chl-a on landsat-8 application. Parameters for developing ANN and SVM model were optimized using a pattern search algorithm in MATLAB toolbox. All dataset were divided into 80 % of training and 20 % of validation data. In the training step, mixed dataset showed the best performance in both ANN and SVM models, whereas 4-band ratio and 4 band dataset in the validation step showed the best performance in ANN and SVM, respectively. The ANN model showed poor performance in low chl-a concentrations but SVM had more accurate performance in low and mid concentrations. Both models under-estimated chl-a in mid to high concentration range. For the mapping results, the ANN model using 4 band dataset showed very low concentration of chl-a in most of research area, whereas SVM showed high concentration of chl-a in coastal area and bay. The result using 4-band ratio dataset showed similar chl-a distribution in ANN and SVM. For mixed dataset results the ANN model estimated over 8 mg m-3 of chl-a at some of coastal, almost zero in near coastal area and over 2 mg m-3 chl-a concentration for off-shore area. In case of SVM, all region showed approximately 2 mg m-3 of chl-a concentration. Landsat-8 OLI was not proper system for OC algorithms. Machine learning techniques were effective tools for enhancing ocean chl-a estimation performance using landsat-8 OLI. Thus, this study showed potential of landsat-8 OLI application to coastal HAB monitoring.","PeriodicalId":104340,"journal":{"name":"Remote Sensing of the Ocean, Sea Ice, Coastal Waters, and Large Water Regions 2018","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127322174","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Measurement of ocean optical properties profiles using airborne lidar (Conference Presentation)","authors":"Liu Hang, Peng Chen, Mao Zhihua, Yan He, Difeng Wang, Zhu Qiankun, Gong Fang","doi":"10.1117/12.2325481","DOIUrl":"https://doi.org/10.1117/12.2325481","url":null,"abstract":"Abstract: The measurement of ocean optical parameters is an important part of ocean research. According to the transmission of the blue and green laser pulse, the lidar return signals were analyzed, and the vertical profiles of the lidar attenuation coefficient were studied with airborne polarization lidar. Simultaneously, the absorption coefficient and extinction coefficient of South China Sea were measured using AC-S. Comparing the lidar and in situ measurements, we found that the lidar attenuation coefficient is between the absorption and extinction coefficient. The correlation analyses of lidar attenuation coefficient with absorption and extinction coefficient were carried out respectively, and it was shown that they have a good correlation. Overall, the results indicated that the airborne polarization lidar is an efficient way to detect the profiles of ocean and the combination of airborne lidar and in situ measurements provide comparable and complementary information about ocean optical parameters.","PeriodicalId":104340,"journal":{"name":"Remote Sensing of the Ocean, Sea Ice, Coastal Waters, and Large Water Regions 2018","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117320068","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}