Monitoring spatial and temporal distribution of Sea Surface Temperature with TIR sensor data

2012 ◽  
pp. 97-107 ◽  
Author(s):  
Eufemia Tarantino
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Temporal Distribution ◽  
Sea Surface ◽  
Sensor Data ◽  
Spatial And Temporal Distribution

scholarly journals KETERKAITAN KONDISI OSEANOGRAFI DENGAN PERIKANAN PELAGIS DI PERAIRAN SELAT BALI

2018 ◽  
Vol 23 (4) ◽  
pp. 275 ◽  
Author(s):  
Komang Iwan Suniada ◽  
Eko Susilo
Keyword(s):  
Spatial Distribution ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Coastal Waters ◽  
Temporal Distribution ◽  
Pelagic Fish ◽  
Sea Surface ◽  
Catch Per Unit Effort ◽  
Chl A ◽  
Terra Modis

Perikanan pelagis di perairan Selat Bali telah diusahakan sejak lama. Data runtut tahun hasil tangkapan yang didaratkan cenderung berfluktuasi. Kajian ini bertujuan untuk mengetahui kondisi spasial dan temporal karakteristik oseanografi, terkait dengan dengan dinamika perikanan pelagis di Selat Bali. Data parameter oseanografi meliputi suhu permukaan laut (SPL) dan klorofill-a (chl-a) yang diperoleh dari citra satelit penginderaan jauh Aqua/Terra MODIS, sedangkan data sumberdaya perikanan pelagis berupa hasil tangkapan/satuan upaya (Catch per Unit Effort, CPUE) ikan pelagis diperoleh dari Tempat Pendaratan Ikan (TPI) Pengambengan, Jembrana - Bali pada periode Januari 2007 hingga Desember 2015. Uji statistik regresi linear berganda digunakan untuk mengetahui pengaruh parameter oseanografi terhadap kondisi perubahan sumberdaya perikanan pelagis. Sebaran spasial SPL menunjukkan bahwa pada Mei hingga November suhu permukaan laut cenderung rendah serta tidak ada perbedaan yang signifikan antara suhu di perairan pantai dan di laut lepas. Secara temporal terlihat bahwa suhu terendah terjadi pada musim timur yaitu pada Agustus 2007. Sebaran spasial chl-a menunjukkan bahwa peningkatan konsentrasi chl-a dimulai dari Mei hingga Oktober serta terdapat perbedaan yang signifikan antara chl-a perairan pantai dan laut lepas. Secara temporal terlihat bahwa konsentrasi chl-a tertinggi terjadi pada Oktober 2015. Faktor iklim yang merupakan faktor eksternal memberikan pengaruh terhadap perubahan konsentrasi chl-a pada lokasi penelitian. Faktor iklim tersebut adalah kecepatan angin dan kejadian El-Nino. Hasil analisis statistik menunjukkan bahwa perubahan parameter SPL dan chl-a secara bersama-sama berpengaruh signifikan terhadap perubahan CPUE ikan pelagis, namun secara parsial parameter chl-a memberikan pengaruh yang lebih signifikan dibandingkan dengan parameter SPL.Pelagic fisheries around Bali Strait have been exploited since decades. Based on monthly and annual landing data, fish production around Bali strait are very fluctuated. This study aims to determine the spatial and temporal conditions of oceanographic characteristics and how they relate to the dynamics of pelagic fisheries in the Bali Strait. The oceanographic parameter data consist of sea surface temperature (SST) and chlorophyll-a (chl-a) that obtained from Aqua / Terra MODIS remote sensing satellite imagery, while the dynamics of pelagic fish resource data indicated by Catch per Unit Effort (CPUE) derrived from landing place (TPI) Pengambengan, Jembrana - Bali in the period January 2007 to December 2015. Multiple linear regression analysis were applied to determine the effect of oceanographic parameters on the changing conditions of pelagic fishery resources. Spatial distribution of SPL indicates that in May to November sea surface temperature tends to be low and there is no significant difference between the temperature in coastal waters and on high seas. The temporal distribution shows that the lowest temperature occurred during the southeast monsoon in August 2007. The spatial distribution of chl-a showed that the chl-a concentration starts to increase from May to October and there were significant differences between chl-a coastal waters and high seas. The temporal distribution shows that the highest chl-a concentration occurred in October 2015. Climate factor which is an external factor has an effect on the change of chl-a concentration at the research location. Climatic factors are wind speed and El-Nino events. The result of the statistical analysis shows that the change of SPL and chl-a parameters together significantly influence on the CPUE of pelagic fish, but partially chl-a parameter gives highly significant effect than SST parameter.

scholarly journals Relationship of Temperature and Chlorophyll-a with Distribution of Yellowstripe Scad (Selaroides leptolepis) in East Coastal Waters of North Sumatra

2021 ◽  
pp. 11-23
Author(s):  
Hasan Sitorus ◽  
Zulham Apandy Harahap ◽  
Tifani Zianida
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Chlorophyll A ◽  
Coastal Waters ◽  
Sea Surface ◽  
Sensor Data ◽  
The West ◽  
A Value ◽  
North Sumatra ◽  
West Monsoon

The east coast of North Sumatra is part of the Malacca Strait, a highly utilized fishing area. One of the commodities that can be harvested is the Yellowstripe Scad (Selaroides leptolepis). Through oceanography parameters, specifically temperature and chlorophyll-aconcentrations, it is possible to determine optimal fishing grounds which can be used as guidelines. One of the satellites that can detect sea surface temperature (SST) and chlorophyll-a concentrations is Aqua (EOS PM),which is equipped with a Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. Data on the Yellowstripe Scad (Selaroide sleptolepis) were obtained from the Belawan Ocean Fishing Port (OFP). Based on the analysis of MODIS images from the Aqua satellite, the sea surface temperature of theeast coastal waters of North Sumatra in 2012 - 2016 ranged from 29oC – 32oC with chlorophyll-a concentrations ranging from 0.19 - 5.26 mg/m³. The largest harvest occurred during the west monsoon with a value of 143.46 tons and the lowest was during the east monsoon with a value of 139.87 tons. Yellowstripe Scad harvest has a negative correlation with the sea surface temperature with a correlation value of -0.365. Chlorophyll-a concentrations and harvest amount have a positive correlation value of 0.660. Yellowstripe Scad is predicted to yield the largest harvest during the west monsoon (December-February) between the Asahan and Labuhanbatu Regencies at coordinates 2.68oN - 2.74oN and 100.37oE - 100.44oE.

scholarly journals Chlorophyll-a variability during positive IOD - the east season period in 2019 in Padang Sea, Indonesia

2020 ◽  
Vol 200 ◽  
pp. 06002
Author(s):  
Dandi Arianto Pelly ◽  
Muh Aris Marfai ◽  
Evita Hanie Pangaribowo ◽  
Akhmad Fadholi
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Chlorophyll A ◽  
Satellite Image ◽  
Temporal Distribution ◽  
Image Data ◽  
Sea Surface ◽  
Chlorophyll A Concentration ◽  
Positive Indian Ocean Dipole ◽  
Point Data

This study aimed to identify the effect of the positive Indian Ocean Dipole (IOD) phenomenon on the spatial, temporal distribution of chlorophyll-a concentrations in the East Season in Padang Sea in 2019. The method used in this research was the Kriging analysis method applied in oceanographic parameter satellite imagery extraction point data. By applying the method, we produced the maps of the spatial distribution variation of chlorophyll-a content and Sea Surface Temperature (SST). The data of IOD events in 2019 showed the occurrence of a strong positive IOD phenomenon that caused anomaly in the Sea Surface Temperature (SST) in Padang Sea. The interpretation of Aqua-Modis level 2 satellite image data showed that the sea surface temperature during the East Season was relatively cold, which was in the minimum temperature ranging from 18.5-22°C with a normal temperature condition of 28-29°C. The minimum chlorophyll-a concentration in the East Season was 0.252 mg/m3; while the maximum value reached 18.5 mg/m3. The distribution value of chlorophyll-a concentration was 1.028 mg/m3.The RMSe Cross Validation value obtained was 0.504 for SST and 0.363 for chlorophyll-a with a mean SST of -0.0005 and mean chlorophyll-a of -0.0039.

scholarly journals SEBARAN SUHU PERMUKAAN LAUT (SPL) SECARA SPASIAL DAN TEMPORAL DI PERAIRAN SELAT ALAS PROVINSI NUSA TENGGARA BARAT

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Didik Santoso
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Satellite Image ◽  
Temporal Distribution ◽  
Secondary Data ◽  
Sea Surface ◽  
Primary Data ◽  
Average Temperature ◽  
Level 3 ◽  
Research Period

Abstrak: Tujuan dari penelitian ini adalah unutk Menentukan sebaran suhu permukaan laut (SPL) secara spasial dan temporal  di Selat Alas Provinsi NTB. Lokasi penelitian di wilayah perairan Selat Alas. Waktu penelitian selama 5 bulan yaitu dari bulan Agustus-Desember 2015. Data penelitian berupa data primer SPL Selat Alas dan data sekunder berupa data citra satelit Aqua MODIS Level-3 dengan resolusi spasial 0,05o x 0,05o dan resolusi temporal 8 harian sebagai data bulanan yang cakupan waktunya dari Agustus 2008 sampai dengan Desember 2012. Data dianalisis dengan menggunakan algoritma Miami Pathfinder SST (MPFSST). Hasil penelitian menunjukkan bahwa secara spasial sebaran SPL Selat Alas didominasi oleh suhu rendah terutama yang berlokasi di bagian selatan, dan bagian tengah selat dengan suhu rata-rata sebesar 26.50C. Sedangkan secara temporal sebaran SPL wilayah perairan Selat Alas menunjukkan bahwa penurunan SPL terjadi disetiap bulan Agustus dan mulai meningkat pada bulan berikutnya, hingga mencapai suhu tertinggi pada bulan Desember, dan hal ini berulang setiap tahunnya. Suhu tinggi mendominasi seluruh bagian selat dengan suhu rata-rata 29.50C pada bulan November dan 29.00C pada bulan Desember. Akan tetapi pada bulan Desember, pada bagian utara selat suhu rata-rata nya  lebih tinggi dari daerah lainnya yaitu sebesar 29.70C.  Kata kunci: Citra Satelit , Selat Alas, Suhu Permukaan Laut, Spasial, Temporal Abstract: The purpose of this study is to determine the spatial and temporal distribution of sea surface temperature (SST) in the Alas Strait of NTB Province. Research location in the Alas Strait waters. The research period  is 5 months, from August to December 2015. The research data are in the form of primary data on the Alas Strait Sea Surface Temperature and secondary data in the form of Aqua satellite image MODIS Level-3, with spatial resolution of 0.05o x 0.05o and 8 daily temporal resolutions, as monthly data from August 2008 to December 2012. Data were analyzed using the Miami Pathfinder SST algorithm (MPFSST). The results of the study indicate that spatially the distribution of Alas Strait Surface Temperature is dominated by low temperatures, especially those located in the south, and the central part of the strait with an average temperature of 26.50C. While temporally the distribution of Sea Surface Temperature in the Alas Strait waters region shows that the decline occurred every August and began to increase the following month, until it reached the highest temperature in December, and this recurs every year. High temperatures dominate the entire strait with an average temperature of 29.50C in November and 29.00C in December. However, in December, in the northern part of the strait the average temperature was higher than other regions, which amounted to 29.70C. Keywords: Satellite Imagery, Alas Strait, Sea Surface Temperature, Spatial, Temporal 

scholarly journals The long-term spatio-temporal variability of sea surface temperature in the Northwest Pacific and the Near China Sea

2019 ◽  
Author(s):  
Zhiyuan Wu ◽  
Changbo Jiang ◽  
Mack Conde ◽  
Jie Chen ◽  
Bin Deng
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Temporal Distribution ◽  
Sea Surface ◽  
Northwest Pacific ◽  
Atmospheric Parameters ◽  
China Sea ◽  
The Past ◽  
Increasing Trend ◽  
Spatio Temporal

Abstract. The variability of the sea surface temperature (SST) in the Northwest Pacific has been studied on seasonal, annual and interannual scales based on the monthly datasets of ERSST 3b (1854–2017, 164 years) and OISST V2 (1988–2017, 30 years). The overall trends, spatial-temporal distribution characteristics, regional differences in seasonal trends, and seasonal differences of SST in the Northwest Pacific have been calculated over the past 164 years based on these datasets. In the past 164 years, the SST in the Northwest Pacific has been increasing linearly year by year with a trend of 0.033 °C/10 yr. The period from 1880 to 1910 is a slow decreasing trend period in the past 164 years and the SST during the 1910–1930 period was a trough of the past 164 years. After 1930, SST has continued to increase until now. The increasing trend in the past 30 years has reached 0.132 °C/10 yr and the increasing trend in the past 10 years is 0.306 °C/10 yr, which is around ten times in the past 164 years. The SST in most regions of the Northwest Pacific showed a linear increasing trend year by year, and the increasing trend in the offshore region was stronger than that in the ocean and deep-sea region. The change trend of the SST in the Northwest Pacific shows a large seasonal difference, and the increasing trend in autumn and winter is larger than that in spring and summer. There are some correlations between the SST and some climate indexes and atmospheric parameters, the correlation between the SST and some atmospheric parameters have been discussed, such as NAO, PDO, SOI anomaly, TCW, Nino 3.4, SLP, Precipitation, T2 and wind speed. The lowest SST in the Near China Sea basically occurred in February and the highest in August. The SST fluctuation in the Bohai Sea and Yellow Sea (BYS) is the largest with a range from 5 °C to 22 °C, the SST in the East China Sea (ECS) is from 18 °C to 27 °C, the smallest fluctuations occurs in the South China Sea (SCS) maintained at range of 26 °C to 29 °C. There are large differences between the mean and standard deviation in different sea regions.

Is summer sea surface temperature over the Arctic Ocean connected to winter air temperature over North America?

2016 ◽  
Vol 70 (1) ◽  
pp. 19-27
Author(s):  
M Ogi ◽  
S Rysgaard ◽  
DG Barber ◽  
T Nakamura ◽  
B Taguchi
Keyword(s):  
North America ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Arctic Ocean ◽  
Air Temperature ◽  
Sea Surface ◽  
The Arctic ◽  
The Arctic Ocean ◽  
Winter Air Temperature
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