Journal articles: 'Sea monitoring'

1

Gornitz, Vivien. "Monitoring sea level changes." Climatic Change 31, no. 2-4 (1995): 515–44. http://dx.doi.org/10.1007/bf01095160.

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Tajima, Keisuke. "Monitoring Ship Performance at Sea." Marine Engineering 57, no. 2 (2022): 145–49. http://dx.doi.org/10.5988/jime.57.145.

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Korotaev, G. K., V. V. Malinovsky, V. V. Pustovoitenko, L. N. Radaikina, and S. V. Stanichny. "Sea area monitoring space experiment." Kosmìčna nauka ì tehnologìâ 8, no. 2-3 (2002): 227–30. http://dx.doi.org/10.15407/knit2002.02.227.

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4

Ben-Michael, Chai, and Gilad Even-Tzur. "GNSS-Based Sea Level Monitoring." Marine Geodesy 30, no. 4 (2007): 333–44. http://dx.doi.org/10.1080/01490410701568426.

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Woodworth, PL, A. Aman, and T. Aarup. "Sea level monitoring in Africa." African Journal of Marine Science 29, no. 3 (2007): 321–30. http://dx.doi.org/10.2989/ajms.2007.29.3.2.332.

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GRONVALL, H. "Towards optimal sea-ice monitoring in the Baltic Sea." ICES Journal of Marine Science 56 (December 1999): 165–71. http://dx.doi.org/10.1006/jmsc.1999.0628.

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7

黄, 业霆. "Design of Sericulture Information Monitoring and Management System." Software Engineering and Applications 10, no. 03 (2021): 372–81. http://dx.doi.org/10.12677/sea.2021.103042.

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肖, 永红. "Design and Implementation of Graphical Network Monitoring System." Software Engineering and Applications 07, no. 02 (2018): 84–90. http://dx.doi.org/10.12677/sea.2018.72010.

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9

周, 俊贤. "Radio Environment Monitoring Software Based on LabVIEW Platform." Software Engineering and Applications 12, no. 06 (2023): 774–84. http://dx.doi.org/10.12677/sea.2023.126075.

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10

Zeng, Tao, Lijian Shi, Yingni Shi, Dunwang Lu, and Qimao Wang. "Polar Sea Ice Monitoring Using HY-2B Satellite Scatterometer and Scanning Microwave Radiometer Measurements." Remote Sensing 16, no. 13 (2024): 2486. http://dx.doi.org/10.3390/rs16132486.

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The Ku band microwave scatterometer (SCA) and scanning microwave radiometer (SMR) onboard HaiYang-2B (HY-2B) can simultaneously supply active and passive microwave observations over the polar region. In this paper, a polar ice water discrimination model and Arctic sea-ice-type classification model based on the support vector machine (SVM) method were established and used to produce a daily sea ice extent dataset from 2019 to 2021 with data from SCA and SMR. First, suitable scattering and radiation parameters are chosen as input data for the discriminant model. Then, the sea ice extent was obtained based on the monthly ice water discrimination model, and finally, the ice over the Arctic was classified into multiyear ice (MYI) and first-year ice (FYI). The 3-year ice extent and MYI extent products were consistent with the similar results of the National Snow and Ice Data Center (NSIDC) and Ocean and Sea Ice Satellite Application Facility (OSISAF). Using the OSISAF similar product as validation data, the overall accuracies (OAs) of ice/water discrimination and FYI/MYI discrimination are 99% and 97%, respectively. Compared with the high spatial resolution classification results of the Moderate Resolution Imaging Spectroradiometer (MODIS) and SAR, the OAs of ice/water discrimination and FYI/MYI discrimination are 96% and 86%, respectively. In conclusion, the SAC and SMR of HY-2B have been verified for monitoring polar sea ice, and the sea ice extent and sea-ice-type products are promising for integration into long-term sea ice records.

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Tuen, K. L. "Monitoring of sea surface temperature in the South China Sea." Hydrobiologia 285, no. 1-3 (1994): 1–5. http://dx.doi.org/10.1007/bf00005648.

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12

Weaver, Ronald L. S., Konrad Steffen, John Heinrichs, James A. Maslanik, and Gregory M. Flato. "Data assimilation in sea-ice monitoring." Annals of Glaciology 31 (2000): 327–32. http://dx.doi.org/10.3189/172756400781820039.

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AbstractThe detection of small changes in concentration or thickness in the Arctic or Antarctic ice cover is an important topic in the current global-climate-change debate. Change detection using satellite data alone requires rigorous error analysis for their derived ice products, including inter-satellite validation for long time series. All models of physical processes are only approximations, and the best models of complicated physical processes have errors and uncertainties. A promising approach is data assimilation, combining model, in situ data and satellite remote-sensing data. Sea-ice monitoring from satellite, ice-model estimates, and the potential benefit of combining the two are discussed in some detail. In a case-study we demonstrate how the sea-ice backscatter for the Beaufort Sea region was derived using a backscattering model in combination with an ice model. We conclude that, for data assimilation, the first steps include the use of simple models, moving, with success at this level, to progressively more complex models. We also recommend reconfiguring the current remote-sensing data to include precise time tags with each pixel. For example, the current Special Sensor Microwave Imager data might be reissued in a time-tagged orbital (or gridded) format as opposed to the currently available daily averaged gridded data. Finally, error statistics and quality-control information also need to be readily available in a form useful for assimilation. The effectiveness of data-assimilation techniques is directly linked to the availability of data error statistics.

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Kang, Hye‐Jin, In‐Ky Cho, Jung‐Ho Kim, Hwan‐Ho Yong, Sung‐Ho Song, and Young‐Gyu Park. "SP Monitoring at a Sea Dike." Near Surface Geophysics 12, no. 1 (2013): 83–92. http://dx.doi.org/10.3997/1873-0604.2013063.

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14

Homes, Gordon M. "Changing technology—Monitoring sea level change." Australian Surveyor 37, no. 1 (1992): 13–22. http://dx.doi.org/10.1080/00050326.1992.10438770.

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15

Kravtsova, V. I., and T. V. Tarasenko. "Space monitoring of Aral Sea degradation." Water Resources 37, no. 3 (2010): 285–96. http://dx.doi.org/10.1134/s0097807810030036.

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16

Wilbur, Grant, Bryce MacMillan, Kyle M. Bade, and Igor Mastikhin. "MRI monitoring of sea spray freezing." Journal of Magnetic Resonance 310 (January 2020): 106647. http://dx.doi.org/10.1016/j.jmr.2019.106647.

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17

Wedekind, Ch, G. Schilling, M. Grüttmüller, and K. Becker. "Gamma-radiation monitoring network at sea." Applied Radiation and Isotopes 50, no. 4 (1999): 733–41. http://dx.doi.org/10.1016/s0969-8043(98)00062-1.

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18

Bett, Brian. "Environmental monitoring in the deep sea." Marine Pollution Bulletin 21, no. 1 (1990): 3–4. http://dx.doi.org/10.1016/0025-326x(90)90129-v.

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19

Reise, Karsten. "Monitoring the Wadden Sea — an introduction." Helgoländer Meeresuntersuchungen 43, no. 3-4 (1989): 259–62. http://dx.doi.org/10.1007/bf02365887.

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20

吴, 章浩. "Design of Water Quality Monitoring System Based on LoRa." Software Engineering and Applications 11, no. 01 (2022): 109–18. http://dx.doi.org/10.12677/sea.2022.111013.

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21

赵, 鹏泽. "Design and Implementation of Intelligent Water Environment Monitoring Platform." Software Engineering and Applications 09, no. 01 (2020): 72–81. http://dx.doi.org/10.12677/sea.2020.91009.

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22

汪, 晗. "Exploration on Short-Wave Data Monitoring and Analysis Method." Software Engineering and Applications 11, no. 06 (2022): 1439–45. http://dx.doi.org/10.12677/sea.2022.116148.

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23

付, 雨欣. "Portable Cardiovascular Health Monitoring System Based on Android System." Software Engineering and Applications 12, no. 05 (2023): 722–36. http://dx.doi.org/10.12677/sea.2023.125070.

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24

Sun, Hequan, Chunhua Li, and Yifeng Cheng. "Monitoring Polar Sea Ice Using Optical and SAR Data." Marine Technology Society Journal 53, no. 6 (2019): 35–41. http://dx.doi.org/10.4031/mtsj.53.6.4.

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AbstractWith the movement and destructiveness of sea ice, conventional in situ instruments are restricted or unavailable for polar sea ice observation. Satellite remote sensing is a feasible way to monitor sea ice in the polar area. Multispectral imaging spectrometer and synthetic aperture radar are applied to monitoring the sea ice in this article with high spatial resolution, wide swath, and continuous imaging. The sea ice distribution and motion can be measured by analyzing satellite remote sensing images. The image segmentation method is presented in the article to obtain the sea ice distribution. Meanwhile, the cross-correlation algorithm to extract sea ice motion is proposed as well as the processed vector results.

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刘, 亚国. "Real-Time Health Monitoring System Based on Smart Wearable Devices." Software Engineering and Applications 11, no. 05 (2022): 959–67. http://dx.doi.org/10.12677/sea.2022.115098.

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26

冯, 丹. "Design and Realization of Intelligent Monitoring System Based on TMS320DM8168." Software Engineering and Applications 05, no. 02 (2016): 164–70. http://dx.doi.org/10.12677/sea.2016.52018.

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27

王, 志昌. "Research on Meteorological Data Partitioning Strategy for Forest Fire Monitoring." Software Engineering and Applications 07, no. 01 (2018): 46–52. http://dx.doi.org/10.12677/sea.2018.71005.

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28

毛, 鑫玉. "Intelligent Monitoring System for Organic Vegetable Pests with Full Vision." Software Engineering and Applications 14, no. 02 (2025): 432–40. https://doi.org/10.12677/sea.2025.142039.

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29

Frigione, Barbara Maria, and Michele Pezzagno. "The Strategic Environmental Assessment as a “Front-Line” Tool to Mediate Regional Sustainable Development Strategies into Spatial Planning: A Practice-Based Analysis." Sustainability 15, no. 3 (2023): 2378. http://dx.doi.org/10.3390/su15032378.

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The 2030 Agenda for Sustainable Development of the United Nations calls upon all signatory countries to localize its goals through National and Regional Sustainable Development Strategies (SDS). As in Italy the SDS constitute the framework of the Strategic Environmental Assessment (SEA) of Plans and Programmes (P/P), the question arises as to whether the SEA can represent a fundamental tool for SDS. Although the mutual relationship between 2030 Agenda goals and SEA is recognized in the literature, there is a lack of focus on SDS and SEA. The SEA monitoring system is an essential instrument to redirect P/P trajectories, although it represents a constant weakness of the SEA process. Opening a discussion about the relationship between SDS and SEA, the present contribution aims at assessing SEA monitoring potential in mediating the 2030 Agenda SDS’s objectives into P/P. To this end, the study delves into the SEA monitoring structure through a qualitative and comparative approach, the feasibility of which is illustrated by an application to a set of spatial plans. Results show both good potential and the criticalities of the SEA monitoring system, which allow us to outline practical inputs to update SEA monitoring guidelines and new paths to foster the mutual relationship between the SDS and SEA.

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30

Kostianoy, Andrey G., and Vladimir Pešić. "Advances in Environmental Monitoring of the Caspian Sea." Ecologica Montenegrina 76 (December 26, 2024): 201–10. https://doi.org/10.37828/em.2024.76.12.

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The paper provides a brief overview of 11 papers published in the Special Issue of Ecologica Montenegrina entitled "Advances in Environmental Monitoring of the Caspian Sea". These papers are devoted to different aspects of environmental issues of the Caspian Sea from regional climate change and dramatic sea level decline to oil pollution and algal bloom among the others. Special attention is paid to the ecologically and biologically significant marine protected areas in the Caspian Sea and the need to organize Marine Protected Areas in the region. The contributions in this special issue not only shed light on the environmental problems facing the Caspian Sea, but also emphasize the importance of interdisciplinary approaches and constant improvement of methods aimed at obtaining reliable and timely environmental data of this unique ecosystem. A combination of satellite remote sensing methods with in-situ measurements allows to improve a quality of remote sensing data via calibration and elaboration of regional algorithms. From the other side, this will improve a quality of satellite monitoring of the ecological state of whole area of the Caspian Sea which is of vital importance during a continuous decline of the sea level. The presented results further evidence the need for continuous satellite monitoring of areas subjected to ecological risks in the Caspian Sea.

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31

Karvonen, J. "Operational SAR-based sea ice drift monitoring over the Baltic Sea." Ocean Science 8, no. 4 (2012): 473–83. http://dx.doi.org/10.5194/os-8-473-2012.

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Abstract. An algorithm for computing ice drift from pairs of synthetic aperture radar (SAR) images covering a common area has been developed at FMI. The algorithm has been developed based on the C-band SAR data over the Baltic Sea. It is based on phase correlation in two scales (coarse and fine) with some additional constraints. The algorithm has been running operationally in the Baltic Sea from the beginning of 2011, using Radarsat-1 ScanSAR wide mode and Envisat ASAR wide swath mode data. The resulting ice drift fields are publicly available as part of the MyOcean EC project. The SAR-based ice drift vectors have been compared to the drift vectors from drifter buoys in the Baltic Sea during the first operational season, and also these validation results are shown in this paper. Also some navigationally useful sea ice quantities, which can be derived from ice drift vector fields, are presented.

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32

Karvonen, J. "Operational SAR-based sea ice drift monitoring over the Baltic Sea." Ocean Science Discussions 9, no. 1 (2012): 359–84. http://dx.doi.org/10.5194/osd-9-359-2012.

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Abstract. An algorithm for computing ice drift from pairs of synthetic aperture radar (SAR) images covering a common area has been developed at FMI. The algorithm has been developed based on the C-band SAR data over the Baltic Sea. It is based on phase correlation in two scales (coarse and fine) with some additional constraints. The algorithm has been running operationally in the Baltic Sea from the beginning of 2011, using Radarsat-1 ScanSAR wide mode and Envisat ASAR wide swath mode data. The resulting ice drift fields are publicly available as part of the MyOcean EC project. The SAR based ice drift vectors have been compared to the drift vectors from drifter buoys in the Baltic Sea during the first operational season and also these validation results are shown in this paper. Also some navigationally useful sea ice quantities, which can be derived from ice drift vector fields, are presented.

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33

El-Sikaily, Amany, Azza Khaled, and Ahmed El Nemr. "Heavy Metals Monitoring using Bivalves from Mediterranean Sea and Red Sea." Environmental Monitoring and Assessment 98, no. 1-3 (2004): 41–58. http://dx.doi.org/10.1023/b:emas.0000038178.98985.5d.

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34

Andersen, Ole B., Per Knudsen, and Brian Beckley. "Monitoring sea level and sea surface temperature trends from ERS satellites." Physics and Chemistry of the Earth, Parts A/B/C 27, no. 32-34 (2002): 1413–17. http://dx.doi.org/10.1016/s1474-7065(02)00081-5.

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35

Temimi, Marouane, Peter Romanov, Hosni Ghedira, Reza Khanbilvardi, and Kim Smith. "Sea-ice monitoring over the Caspian Sea using geostationary satellite data." International Journal of Remote Sensing 32, no. 6 (2011): 1575–93. http://dx.doi.org/10.1080/01431160903578820.

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36

吕, 艳芳. "Low Power Intelligent Underground Well Monitoring System Based on NB-IoT." Software Engineering and Applications 11, no. 04 (2022): 701–11. http://dx.doi.org/10.12677/sea.2022.114073.

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37

杨, 成慧. "Target Tracking and Alarm Monitoring Research Based on Wireless Sensor Network." Software Engineering and Applications 02, no. 01 (2013): 35–41. http://dx.doi.org/10.12677/sea.2013.21007.

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38

瞿, 杏元. "Design and Implementation of a Non Intrusive Household Appliance Monitoring System." Software Engineering and Applications 11, no. 06 (2022): 1473–78. http://dx.doi.org/10.12677/sea.2022.116152.

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39

胡, 焱兵. "An Early Warning Method for Resource Monitoring of Edge Service System." Software Engineering and Applications 12, no. 02 (2023): 318–29. http://dx.doi.org/10.12677/sea.2023.122032.

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40

李, 志翔. "Design of Intelligent Monitoring System for the Elderly Based on RK3588." Software Engineering and Applications 13, no. 02 (2024): 234–43. http://dx.doi.org/10.12677/sea.2024.132024.

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41

Zhao, Y., P. Wei, H. Zhu, and B. Xing. "SEA ICE DRIFT MONITORING IN THE BOHAI SEA BASED ON GF4 SATELLITE." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3 (April 30, 2018): 2419–25. http://dx.doi.org/10.5194/isprs-archives-xlii-3-2419-2018.

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The Bohai Sea is the inland sea with the highest latitude in China. In winter, the phenomenon of freezing occurs in the Bohai Sea due to frequent cold wave influx. According to historical records, there have been three serious ice packs in the Bohai Sea in the past 50 years which caused heavy losses to our economy. Therefore, it is of great significance to monitor the drift of sea ice and sea ice in the Bohai Sea. The GF4 image has the advantages of short imaging time and high spatial resolution. Based on the GF4 satellite images, the three methods of SIFT (Scale invariant feature &amp;ndash; the transform and Scale invariant feature transform), MCC (maximum cross-correlation method) and sift combined with MCC are used to monitor sea ice drift and calculate the speed and direction of sea ice drift, the three calculation results are compared and analyzed by using expert interpretation and historical statistical data to carry out remote sensing monitoring of sea ice drift results. The experimental results show that the experimental results of the three methods are in accordance with expert interpretation and historical statistics. Therefore, the GF4 remote sensing satellite images have the ability to monitor sea ice drift and can be used for drift monitoring of sea ice in the Bohai Sea.

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42

Stepanova, O. "Results of Black Sea algal viruses monitoring." Limnology and Freshwater Biology, no. 4 (2020): 1030–31. http://dx.doi.org/10.31951/2658-3518-2020-a-4-1030.

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43

Matthijs, E., and M. Stalmans. "Monitoring of LAS in the North Sea." Tenside Surfactants Detergents 30, no. 1 (1993): 29–33. http://dx.doi.org/10.1515/tsd-1993-300109.

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44

Nystuen, Jeffrey A. "Monitoring air–sea exchange using ambient sound." Journal of the Acoustical Society of America 103, no. 5 (1998): 2910. http://dx.doi.org/10.1121/1.422071.

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45

Liu, Jianqiang, Runheng Huang, Zhengang Jin, Kuiqiao Wu, and Congrong Sun. "Bohai Sea Ice Monitoring Using Satellite Images." Journal of Cold Regions Engineering 14, no. 2 (2000): 93–100. http://dx.doi.org/10.1061/(asce)0887-381x(2000)14:2(93).

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46

Abdallah, Renee I., Nagla M. Khalil, and Mohamid I. Roushdie. "Monitoring of pollution in Egyptian Red Sea." Egyptian Journal of Petroleum 24, no. 1 (2015): 59–70. http://dx.doi.org/10.1016/j.ejpe.2015.02.006.

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47

Konings, H. "Oil pollution monitoring on the North Sea." Spill Science & Technology Bulletin 3, no. 1-2 (1996): 47–52. http://dx.doi.org/10.1016/s1353-2561(96)00028-x.

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48

Ruhl, Henry A., and Imants G. Priede. "Open up monitoring of deep-sea drilling." Nature 473, no. 7346 (2011): 154. http://dx.doi.org/10.1038/473154b.

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49

Soulat, F., M. Caparrini, O. Germain, P. Lopez-Dekker, M. Taani, and G. Ruffini. "Sea state monitoring using coastal GNSS-R." Geophysical Research Letters 31, no. 21 (2004): n/a. http://dx.doi.org/10.1029/2004gl020680.

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50

Snow, Mary M., and Richard K. Snow. "Modeling, monitoring, and mitigating sea level rise." Management of Environmental Quality: An International Journal 20, no. 4 (2009): 422–33. http://dx.doi.org/10.1108/14777830910963753.

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Journal articles on the topic 'Sea monitoring'

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