scholarly journals Model of Habitat Characteristics of Sardinella lemuru in the Bali Strait, Indonesia

2021 ◽  
Vol 56 (3) ◽  
pp. 229-240
Author(s):  
Adi Wijaya ◽  
Abu Bakar Sambah ◽  
Daduk Setyohadi ◽  
Umi Zakiyah
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Chlorophyll A ◽  
Environmental Conditions ◽  
Generalized Additive Models ◽  
Additive Models ◽  
Sea Surface ◽  
Model Parameters ◽  
Photosynthetically Available Radiation ◽  
Chlorophyll A Concentration

This article describes a new approach to the study of the environmental conditions that relate to the Sardinella lemuru habitat in the Bali Strait, through remote sensing data and fish catch data using the generalized additive model. Data that are acquired daily and then compiled into monthly data for sea surface temperature, sea surface chlorophyll-a concentration, photosynthetically available radiation, and sea surface depth (SSD) were used for the years 2008–2010. The objectives of the study are to describe the variability of the environmental conditions in the Bali Strait, to analyze a combination model of environmental factors in estimating the Sardinella lemuru habitat, and to map potential Sardinella lemuru fishing areas. We illustrate the proposed method by constructing seven generalized additive models with catches of Sardinella lemuru as a variable response and use sea surface temperature, sea surface chlorophyll-a concentration, photosynthetically available radiation, and SSD as covariant models for assessing the environmental characteristics of the abundance of Sardinella lemuru. Predicted values were validated using a linear model. Based on the three model parameters, habitat selection for Sardinella lemuru was significantly (P < 0.0001) influenced by photosynthetically available radiation (50–55 Einstein m-2 d-1), sea surface chlorophyll-a concentration (0.2–2.0 mgm-3), sea surface temperature (28.95–29.64 °C), and SSD (60–150 m). Catch predictions show a consistent trend toward environmental conditions and water depth. Our method allows for improvement with the validation of catch predictions that were observed and collected monthly, and the result was significant (P < 0.001, r2 = 0.816). Photosynthetically available radiation explains the highest deviation in continued generalized additive models; therefore, it was considered to be the best predictor of habitat, followed by sea surface chlorophyll-a concentration, sea surface temperature, and then SSD. New research results supplement several previous studies that relate to the analysis of environmental parameters in estimating the fish habitat and can be used in mapping the distribution of potential Sardinella lemuru fishing areas in spatial and temporal scales.

scholarly journals Perbandingan Model GAM dan Gamboost dalam Fitting Dataset Sea Surface Temperature

2018 ◽  
Vol 18 (1) ◽  
pp. 39-50
Author(s):  
Miftahuddin Miftahuddin
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Risk Model ◽  
Generalized Additive Models ◽  
Information Criteria ◽  
Additive Models ◽  
Sea Surface ◽  
Akaike Information Criteria ◽  
Fitting Model

Fitting model GAM (generalized additive models) dan Gamboost (generalized additive models by boosting) untuk dataset SST (sea surface temperature) dimaksudkan sebagai upaya mencapai perbaikan fitting model terhadap data SST. Secara umum, model GAM dapat memvisualisasikan masing-masing kovariat, sedangkan model gamboost dapat memvisualisasikan lebih detail kovariatnya dalam beberapa bentuk, baik secara linier dan nonlinier. Pengukuran performance yang digunakan terhadap model adalah nilai AIC (Akaike Information Criteria) dan CV-risk. Model GAM dengan boosting menunjukkan lebih sesuai dalam struktur model, pemilihan model terbaik dan seleksi variabel pada dataset SST. Fitting model GAM dapat menghasilkan pola dan trend masing-masing kovariat meskipun memiliki beberapa gap, sedangkan pada model gamboost memiliki lebih banyak pilihan simultan dalam bentuk linier, nonlinier dan smooth untuk masing-masing kovariat. Kedua pendekatan fitting memiliki kelebihan yang dapat saling melengkapi dalam memodelkan dataset SST.

scholarly journals VARIABILITY OF CHLOROPHYLL-A CONCENTRATION AND SEA SURFACE TEMPERATURE OF NATUNA WATERS

2012 ◽  
Vol 4 (1) ◽  
Author(s):  
Bisman Nababan ◽  
Kristina Simamora
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Chlorophyll A ◽  
Southern Oscillation ◽  
Sea Surface ◽  
Wide Field ◽  
The West ◽  
Chlorophyll A Concentration ◽  
Sst Variability ◽  
Noaa Avhrr

Variability of chlorophyll-a concentration and sea surface temperature (SST) in Natuna waters were analyzed using satellite data Sea-viewing Wide Field-of-view Sensor (SeaWiFS) and the National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR). SeaWiFS data with a resolution of 9×9 km2 and AVHRR with a resolution of 4×4 km2 were the monthly average data downloaded from NASA website. Chlorophyll-a concentrations and SST were estimated using OC4v4 and MCSST algorithms. In general, the concentration of chlorophyll-a in Natuna waters ranged between 0.11-4.92 mg/m3 with an average of 0.56 mg/m3 during the west season and 0.09-2.93 mg/m3 with an average of 0.66 mg/m3 during the east season. Chlorophyll-a concentrations were relatively high seen in coastal areas, especially around the mouth of the Kapuas, Musi, and Batang Hari rivers allegedly caused by the high nutrient intake from the mainland. SST variability in Natuna waters ranged from 23.46-30.88 °C during the west season and tended to be lower than that the east season (27.91-31.95 °C). In addition, the SST values tended to be lower in the offshore than that inshore. During the west season (Nov-Feb) and the transitional season (Apr) in the years of Elnino Southern Oscillation (ENSO), the concentration of chlorophyll-a and the SST in Natuna waters was generally higher than that in non-ENSO years. The results of wind analyses showed that ENSO caused the change of direction and speed of wind from its normal conditions.Keywords: Sea surface temperature, chlorophyll-a, Natuna waters, ENSO, SeaWiFS, AVHRR

scholarly journals RESPONSE OF SEA SURFACE TEMPERATURE (SST) AND CHLOROPHYLL-A ON MADDEN JULIAN OSCILLATION (MJO) IN INDONESIAN SEAS

2016 ◽  
Vol 7 (2) ◽  
Author(s):  
Nabil Balbeid ◽  
Agus Saleh Atmadipoera ◽  
Alan Frendy Koropitan
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Chlorophyll A ◽  
Mixed Layer Depth ◽  
Phase Fluctuation ◽  
Active Phase ◽  
Sea Surface ◽  
Phytoplankton Production ◽  
Madden Julian Oscillation ◽  
Chlorophyll A Concentration

<p class="Paragraf"><em>Madden-Julian Oscillation (MJO) is a large-scale phenomenon that occurs in equatorial area, parti-cularly Indonesia. This research aimed to investigate the MJO propagation process and studied the correlation between MJO and sea surface temperature (SST) and chlorophyll-a. Sea variables (SST and chlorophyll-a) and atmosphere variables (</em><em>outgoing longwave radiation</em><em>/OLR, 1,5 km wind,</em><em> and</em><em> surface wind) were band-pass filtered for 20-100 days period. Spectral density from OLR and 1,5 km wind (2003-2012) shows that the MJO period was dominantly occurred for </em><em>40–50</em><em> days. </em><em>Average </em><em>pro-pagation</em><em> of</em><em> </em><em> MJO</em><em> </em><em>velocity </em><em>resulted from the atmospheric variable analysis by </em><em>Hovmöller</em><em> diagram was 4,7 m/s. Cross correlation between SST and OLR in South Java and Banda Sea result</em><em>s</em><em> a strong corre-lation during MJO active phase, where </em><em>MJO too</em><em>k </em><em> place first and was then followed by</em><em> the </em><em>decreasing </em><em>SST </em><em>along the equatorial region</em><em>.</em><em> Increasing chlorophyll-a concentration occured at some areas du</em><em>-</em><em>ring MJO active phase with relatively short phase delay. </em><em>During the MJO active phase, fluctuation of wind velocity generates variation over mixed layer depth and triggers upwelling /entrainment. Nutri-ent was upwelled to the water surface and hence increase phytoplankton production and chlorophyll-a concentration.</em></p><p><em> </em><strong><em>Keywords</em></strong><em>:</em><em> Madden Julian Oscillation, OLR, </em><em>sea surface temperature, surface chlorophyll-a</em></p>

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.

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