The Sea Route Planning for Survey Vessel Intelligently Navigating to the Survey Lines

Marine surveying is an important part of marine environment monitoring systems. In order to improve the accuracy of marine surveying and reduce investment in artificial stations, it is necessary to use high-precision GNSS for shipborne navigation measurements. The basic measurement is based on the survey lines that are already planned by surveyors. In response to the needs of survey vessels sailing to the survey line, a method framework for the shortest route planning is proposed. Then an intelligent navigation system for survey vessels is established, which can be applied to online navigation of survey vessels. The essence of the framework is that the vessel can travel along the shortest route to the designated survey line under the limitation of its own minimum turning radius. Comparison and analysis of experiments show that the framework achieves better optimization. The experimental results show that our proposed method can enable the vessel to sail along a shorter path and reach the starting point of the survey line at the specified angle.

Analysis and Mitigation of Crosstalk Effect on Coastal GNSS-R Code-Level Altimetry Using L5 Signals from QZSS GEO

Coastal Global Navigation Satellite System Reflectometry (GNSS-R) can be used as a valuable supplement for conventional tide gauges, which can be applied for marine environment monitoring and disaster warning. Incidentally, an important problem in dual-antenna GNSS-R altimetry is the crosstalk effect, which means that the direct signal leaks into the down-looking antenna dedicated to the reflected signals. When the path delay between the direct and reflected signals is less than one chip length, the delay waveform of the reflected signal is distorted, and the code-level altimetry precision decreases consequently. To solve this problem, the author deduced the influence of signal crosstalk on the reflected signal structure as the same as the multipath effect. Then, a simulation and a coastal experiment are performed to analyze the crosstalk effect on code delay measurements. The L5 signal transmitted by the Quasi-Zenith Satellite System (QZSS) from a geosynchronous equatorial orbit (GEO) satellite is used to avoid the signal power variations with the elevation, so that high-precision GNSS-R code altimetry measurements are achieved in the experiment. Theoretically and experimentally, we found there exists a bias in proportion to the power of the crosstalk signals and a high-frequency term related to the phase delay between the direct and reflected signals. After weakening the crosstalk by correcting the delay waveform, the results show that the RMSE between 23-h sea level height (SSH) measurements and the in-situ observations is about 9.5 cm.

A Completeness and Complementarity Analysis of the Data Sources in the NOAA In Situ Sea Surface Temperature Quality Monitor (iQuam) System

In situ sea surface temperatures (SST) are the key component of the calibration and validation (Cal/Val) of satellite SST retrievals and data assimilation (DA). The NOAA in situ SST Quality Monitor (iQuam) aims to collect, from various sources, all available in situ SST data, and integrate them into a maximally complete, uniform, and accurate dataset to support these applications. For each in situ data type, iQuam strives to ingest data from several independent sources, to ensure most complete coverage, at the cost of some redundancy in data feeds. The relative completeness of various inputs and their consistency and mutual complementarity are often unknown and are the focus of this study. For four platform types customarily employed in satellite Cal/Val and DA (drifting buoys, tropical moorings, ships, and Argo floats), five widely known data sets are analyzed: (1) International Comprehensive Ocean-Atmosphere Data Set (ICOADS), (2) Fleet Numerical Meteorology and Oceanography Center (FNMOC), (3) Atlantic Oceanographic and Meteorological Laboratory (AOML), (4) Copernicus Marine Environment Monitoring Service (CMEMS), and (5) Argo Global Data Assembly Centers (GDACs). Each data set reports SSTs from one or more platform types. It is found that drifting buoys are more fully represented in FNMOC and CMEMS. Ships are reported in FNMOC and ICOADS, which are best used in conjunction with each other, but not in CMEMS. Tropical moorings are well represented in ICOADS, FNMOC, and CMEMS. Some CMEMS mooring reports are sampled every 10 min (compared to the standard 1 h sampling in all other datasets). The CMEMS Argo profiling data set is, as expected, nearly identical with those from the two Argo GDACs.

Marine Environment Monitoring Based on Virtual Reality and Fuzzy C-Means Clustering Algorithm

In modern society, with the rapid increase of population and the serious shortage of resources, the marine environment has been destroyed; there are also many people who go out to sea without permission, regardless of the legal constraints, fishing. This kind of behavior leads the marine environment to get worse and worse, so the real-time monitoring of the marine environment is very necessary. The main article marine environment monitoring, virtual reality technology, and fuzzy C-means clustering algorithm combine to improve the efficiency of monitoring and processing power of the data information. Through the application of virtual reality technology in the marine environment monitoring base and real-time simulation of the dynamics of the ocean, the monitoring personnel can understand the emergencies on the sea in time; the fuzzy C-means clustering algorithm is applied to the server receiving the data to classify the data. It is found in the experiment that when virtual reality technology and fuzzy C-means clustering algorithm are not used, the data of marine environment monitoring takes more than 1.3 s to return to the server, but, after applying two advanced technologies, the return efficiency is greatly improved, and the time consumed is less than 0.82 s. The results show that virtual reality technology and fuzzy C-means clustering algorithm can improve the efficiency of environmental monitoring, and through virtual reality technology, real-time monitoring of the marine environment can be achieved; in the absence of people out to sea, the actual situation on the sea can be clearly understood; and fuzzy C-means clustering algorithm can improve the speed of data processing, so that the monitoring personnel can solve the problem faster.

Perubahan Garis Pantai pada Musim Timur dan Barat kaitannya dengan Karakteristik Gelombang di Pesisir Kabupaten Takalar, Sulawesi Selatan

Detection of shoreline changes needs to be done to determine changes so that supervision and management planning in a coastal area can be carried out, one of which is on the coast of Takalar Regency, South Sulawesi. This study aims to map changes in the coastline in Takalar Regency in different seasons and to see the effect of the waves on these changes. This study uses Landsat satellite imagery data from 1998-2018, and wave data obtained from Copernicus Marine Environment Monitoring Services (CMEMS). The shoreline data extraction was using the combination of single band and band ratio approach while the shoreline change rate calculation was using the Digital Shoreline Analysis System (DSAS) application. In addition, GeoDa application was used to obtain the regression analysis of the effect of waves on shoreline changes. The results showed that there were similar patterns of shoreline changes between monsoon and west monsoon, although the value were different. Coastal erosion occurs in almost all Takalar coastal area. Some areas that have a high coastal erosion value were the sub-district of South Galesong and Mappakasunggu while the areas that have a high accretion value were the sub-districts of Sanrobone, Mappakasunggu, and Mangarabombang. The waves had a significant influence on changes in shoreline in both the monsoon and west monsoon (P <0.05) with a percentage of 17,2% for the monsoon and 7.3% for the west monsoon which indicated there were other factors that influence shoreline change besides the wave factor on the Takalar Coast. Deteksi perubahan garis pantai perlu dilakukan dalam rangka pengawasan dan perencanaan pengelolaan di suatu kawasan, salah satunya di Pesisir Kabupaten Takalar, Sulawesi Selatan. Penelitian ini bertujuan untuk memetakan perubahan garis pantai di Kabupaten Takalar berdasarkan musim dan melihat pengaruh gelombang terhadap perubahan tersebut. Penelitian ini menggunakan data Citra Satelit Landsat tahun 1998-2018, dan data gelombang yang diperoleh dari Copernicus Marine Environment Monitoring Services (CMEMS). Ekstraksi data garis pantai menggunakan pendekatan perkalian single band dan ratio band sedangkan perhitungan laju perubahan garis pantai menggunakan aplikasi Digital Shoreline Analysis System (DSAS). Analisis regresi untuk melihat pengaruh gelombang terhadap perubahan garis pantai menggunakan aplikasi GeoDa. Hasil penelitian menunjukkan terdapat pola yang hampir sama antara perubahan garis pantai musim timur dan musim barat meskipun dengan besaran yang berbeda. Abrasi terjadi hampir di seluruh Pesisir Takalar. Daerah yang memiliki nilai abrasi yang tinggi yaitu Kecamatan Galesong Selatan dan Mappakasunggu sedangkan daerah yang memiliki nilai akresi tinggi yaitu Kecamatan Sanrobone, Mappakasunggu, dan Mangarabombang. Gelombang memberikan pengaruh yang signifikan terhadap perubahan garis pantai baik pada musim timur maupun barat (P<0,05) dengan persentase 17,2% untuk musim timur dan 7,3% untuk musim barat yang mengindikasikan terdapat faktor lain yang ikut mempengaruhi perubahan garis pantai selain faktor gelombang di Pesisir Takalar.

Spatiotemporal Variability and Trends of Marine Heat Waves in the Red Sea over 38 Years

Marine heat waves (MHWs) can have catastrophic consequences for the socio-environmental system. Especially in the Red Sea, which has the world’s second longest coral reef system. Here, we investigate the sea surface temperature (SST) variability and trends, as well as the spatiotemporal characteristics of marine heat waves (MHWs) in the Red Sea, using high resolution daily gridded (1/20°) SST data obtained from the Copernicus Marine Environment Monitoring Service (CMEMS) for the period 1982–2019. Results show that the average warming rate was about 0.342 ± 0.047 °C/decade over the entire Red Sea over the whole study period. The Empirical Orthogonal Function (EOF) analysis reveals that the maximum variability is over the central part of the Red Sea, while the minimum variability is in the southernmost part of the Red Sea. Over the last two decades (2000–2019), we have discovered that the average MHW frequency and duration increased by 35% and 67%, respectively. The results illustrate that the MHW frequency and duration trends have increased by 1.17 counts/decade and 1.79 days/decade, respectively, over the study period. The highest annual MHW frequencies were detected in the years 2018, 2019, 2010, and 2017. A strong correlation (R = 0.89) was found between the annual MHW frequency and the annual mean SST.

Sea Surface Temperature Of Manado Bay and Its Surroundings

Indonesia is a maritime country where as much as two-thirds of its territory is the ocean. The waters of Manado Bay and its surroundings which are located in North Sulawesi Province are waters with great potential. Bunaken National Park, which is an important tourist destination in Indonesia, is situated in Manado Bay. Sea surface temperature (SST) is an important factor that influences climate dynamics and also the life of marine organisms. The information regarding SST is needed not only on a local scale but also globally. SST is a key variable that underpins weather predictions, ocean forecasts, and ocean-atmospheric variability leading to the understanding and forecasting of short- and long-term climate variability. This study was conducted with the aim of describing and analyzing daily and seasonal SST in several places in the waters of Manado Bay and its surroundings. The study was conducted by using data from the Copernicus Marine Environment Monitoring Service (CMEMS), which is a global marine data provider institution. This digital data have scientific qualifications and is updated regularly. CMEMS provides maps and data for forecasting oceanographic conditions. Based on the study conducted, it was found that in 2020 the SST at the research location was varied according to the time and station determined. The highest SST in Manado Bay and the surrounding waters generally occurs around May, although the values are not exactly the same. The lowest SST value varies by station, but the value can be at a temperature slightly below 28oC and occurs around February to April.Keywords : SST; Manado Bay; Bunaken Island; CMEMSAbstrak Indonesia merupakan Negara maritim di mana sebanyak dua per tiga wilayah Indonesia adalah laut. Perairan Teluk Manado dan sekitarnya yang terletak di Provinsi Sulawesi Utara merupakan salah satu perairan yang sangat potensial. Taman Nasional Bunaken yang menjadi tempat tujuan wisata yang penting di Indonesia. Suhu permukaan laut (SPL) merupakan faktor penting yang mempengaruhi dinamika iklim dan juga kehidupan organisme laut. Kepentingan terhadap informasi menyangkut SPL dibutuhkan bukan hanya pada skala lokal, tetapi juga global. SPL adalah variabel kunci yang mendukung prediksi cuaca, prakiraan laut, dan variabilitas atmosfer laut yang mengarah pada pemahaman dan prakiraan variabilitas iklim jangka pendek dan jangka panjang. Penelitian ini dilakukan dengan tujuan mendeskripsikan dan menganalisis SPL harian dan musiman di beberapa tempat pada kawasan perairan Teluk Manado dan sekitarnya. Kajian dilakukan menggunakan data Copernicus Marine Environment Monitoring Service (CMEMS) yang adalah suatu lembaga penyedia data kelautan global. Data digital ini memiliki kualifikasi ilmiah dan diperbarui secara berkala. CMEMS menyediakan peta dan data untuk prakiraan kondisi oseanografi. Berdasarkan kajian yang dilakukan, selama tahun 2020 SPL pada lokasi penelitian berada pada kisaran yang berbeda menurut waktu maupun stasiun yang ditetapkan. SPL tertinggi Teluk Manado dan perairan sekitarnya umumnya terjadi pada sekitar bulan Mei, walaupun dengan nilai yang tidak persis sama. Nilai SPL terendah berbeda menurut stasiun, tetapi nilainya bisa berada pada suhu sedikit di bawah 28oC dan terjadi pada sekitar bulan Februari sampai April.