VARIABILITY OF CHLOROPHYLL-A DISTRIBUTION AROUND BELITUNG ISLAND WATERS OBSERVED BY AQUA-MODIS SATELLITE DATA

2018 ◽  
Vol 20 (2) ◽  
pp. 78
Author(s):  
Ulung Jantama Wisha ◽  
Hanah Khoirunnisa
Keyword(s):  
Chlorophyll A ◽  
Southern Oscillation ◽  
Geographical Location ◽  
Southwest Monsoon ◽  
Fishing Ground ◽  
Northeast Monsoon ◽  
Surface Distribution ◽  
Nutrient Runoff ◽  
Chl A ◽  
Sst Variability

<p>Belitung Island has a strategic geographical location, which is directly bordered with Sumatera and Kalimantan also Karimata and Malacca Straits. Those conditions make the waters productivity being high due to the support from the biogeochemical cycle, nutrient runoff, and upwelling. This study aims to determine the seasonal variability of chlorophyll-a (Chl-a) around Belitung waters. The method used in this study was spatial analysis with IDW (inverse distance weighted) to interpolate the Chl-a surface distribution. Sea Surface Temperature (SST) acquired from Aqua-MODIS were retrieved from NASA (National Aeronautics and Space Administration) and wind data were obtained from ECMWF (European Centre for Medium-Range Weather Forecasts), data were analyzed statistically and spatially. The Chl-a concentration in the northeast monsoon ranged 0.38-3.5 mg.m-3, in the southwest monsoon ranged 0.15-18.7 mg.m-3, and in the transitional season ranged 0.29-9.04 mg.m-3. The Chl-a concentration during southwest and 1st transitional monsoons were higher due to the maximum sunlight intensity stimulating photosynthesis of autotroph biota. The condition of SST is indicating the upwelling event that involves wind-driven motion of dense, cooler, and usually nutrient-rich water towards the ocean surface. Seasonal SST variability ranged 22.6-28.3oC, 27.3-32.1oC, 30.7-32.3oC, and  29.1-32.8oC during northeast, 1st transitional, southwest, and 2nd transitional monsoons respectively. The existence of ENSO (El-Nino Southern Oscillation) contributes to enhance the Chl-a concentration. During ENSO years, the Chl-a concentration was higher than non-ENSO years due to the great impact of ENSO inducing upwelling and higher nutrient availability. Chl-a and SST conditions can be used to predict fishing ground and upwelling area. </p>

scholarly journals SPATIAL-TEMPORAL DISTRIBUTION OF CHLOROPHYLL-A IN SOUTHERN PART OF THE MAKASSAR STRAIT

2018 ◽  
Vol 4 (1) ◽  
Author(s):  
Wasir Samad Daming ◽  
Muhammad Anshar Amran ◽  
Amir Hamzah Muhiddin ◽  
Rahmadi Tambaru
Keyword(s):  
Chlorophyll A ◽  
Satellite Data ◽  
Chlorophyll Concentration ◽  
Temporal Distribution ◽  
Fishing Ground ◽  
Landsat 8 ◽  
Trade Winds ◽  
Chl A ◽  
June July ◽  
Potential Area

Surface chlorophyll-a (Chl-a) distribution have been analyzed with seasonal variation during southeast monsoon in southern part of Makassar Strait and Flores Sea. Satellite data of Landsat-8 is applied to this study to formulate the distribution of chlorophyll concentration during monsoonal wind period. The distribution of chlorophyll concentration was normally peaked condition in August during southeast monsoon. Satellite data showed that a slowdown in the rise of the distribution of chlorophyll in September with a lower concentration than normal is likely due to a weakening the strength of southeast trade winds during June – July – August 2016. Further analysis shows that the southern part of the Makassar strait is likely occurrence of upwelling characterized by increase in surface chlorophyll concentrations were identified as the potential area of fishing ground.

scholarly journals RELATIONSHIP BETWEEN CHLOROPHYLL-A AND SEA SURFACE TEMPERATUR WITH Sardinella lemuru CATCHING IN BALI STRAIT

2021 ◽  
Vol 15 (1) ◽  
pp. 12
Author(s):  
Niken Gustantia ◽  
Takahiro Osawa ◽  
I Gusti Bagus Sila Dharma ◽  
Wayan Sandi Adnyana
Keyword(s):  
Chlorophyll A ◽  
Sea Surface ◽  
Fishing Ground ◽  
Fish Production ◽  
Resource Potential ◽  
Chl A ◽  
Remote Sensing Technique ◽  
Fish Resources ◽  
Fish Catch ◽  
Total Fish

The Bali Strait is one of Indonesia's territorial waters that have high natural resource potential. The area is only about 2,500 km2 but has a high potential fishing ground. The Bali Strait has unique and dynamic waters that can cause fluctuations in fish production amount each year. The largest type of fish caught in the Bali Strait is lemuru (Sardinella lemuru), a fish found only in the Bali Strait. This fish plays a significant role in the economy of fishers in the Bali Strait. Each year the catch of lemuru has fluctuated, making fishing locations challenging to predict. Sea Surface Temperature (SST) and Chlorophyll-a (Chl-a) are oceanographic parameters that can affect the resources of the ocean. Oceanographic phenomena, such as upwelling, can also influence the condition of fish resources. Therefore, understanding the relationships between these factors is essential in practical fisheries management. Observation of oceanographic factors is very hard with the field observation due to time and cost limitations. The remote sensing technique is an efficient method to determine SST distributions and Chl-a concentrations using satellite imagery. This study analyzes SST and Chl-a concentration in the Bali Strait using the Global Change Observation Mission(GCOM-C) satellite and determines the correlation between Chl-a and SST with a total fish catch(lemuru) during 2019. The results showed the maximum average Chl-a concentration observed on August 1.62 mg/m3 and the lowest concentration observed on January 0.45 mg/m3, the maximum SST on March was 28.12° C, and on August (Dry season) with 22.40° C. The SST variable's influence provides a negative correlation (R = -0.209) with changes in lemuru catch, while the Chl-a parameter has a positive correlation (R = 0.375) with changes in the catch. The pattern of relationship between Chl-a with fish catching had a good relationship than the SST in 2019.Keywords: Bali Strait; Sardinella lemuru; SST; Chl-a.

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 Spatial and temporal variability of surface chlorophyll-a in the Gulf of Tomini, Sulawesi, Indonesia

2018 ◽  
Vol 19 (3) ◽  
pp. 793-801 ◽  
Author(s):  
QURNIA WULAN SARI ◽  
EKO SISWANTO ◽  
DEDI SETIABUDIDAYA ◽  
INDRA YUSTIAN ◽  
ISKHAQ ISKANDAR
Keyword(s):  
Chlorophyll A ◽  
El Niño ◽  
Temporal Variability ◽  
El Nino ◽  
Southern Oscillation ◽  
Surface Wind ◽  
Boreal Summer ◽  
Boreal Winter ◽  
Spatial And Temporal Variability ◽  
Chl A

Sari QW, Siswanto E, Setiabudidaya D, Yustian I, Iskandar I. 2018. Spatial and temporal variability of surface chlorophyll-a in the Gulf of Tomini, Sulawesi, Indonesia. Biodiversitas 19: 793-801. The Gulf of Tomini (GoT) is mostly influenced by seasonal and interannual events. So, the immensive aim of this study is to explore spatial and temporal variations of chlorophyll-a (chl-a) and oceanographic parameters in the GoT under the influences of monsoonal winds, El Niño Southern Oscillation (ENSO), and Indian Ocean Dipole (IOD). The data were collected from the satellite imaging of chl-a and sea and surface temperature (SST) as well as surface wind from the reanalysis data for a period of January 2003 to December 2015. Monthly variations of the chl-a and SST in the GoT reveal chl-a bloom in the center part to the mouth of the GoT during the southeast monsoon season (boreal summer). The chl-a concentrations were relatively higher (>0.1 mg m-3) and distributed throughout most of the areas near the Maluku Sea. The SST in the middle of the GoT was relatively lower than that near the Maluku Sea (the eastern part of the GoT). On the other hand, during the northwest monsoon (boreal winter), the chl-a concentration decreased (<0.1 mg m-3). During this season, the SST was relatively higher (28-29 °C) than that during the boreal summer (27-26 °C) and distributed uniformly. Meanwhile, on interannual timescale, the ENSO and IOD play important role in regulating chl-a distribution in the GoT. High surface chl-a concentration was observed during El Niño and/or positive IOD events. Enhanced surface chl-a concentration during El Niño and/or positive IOD events was associated with the upward Ekman pumping induced by the southeasterly wind anomalies. The situation was reversed during the Niña and/or negative IOD events.

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

<p>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.</p><p>Keywords: Sea surface temperature, chlorophyll-a, Natuna waters, ENSO, SeaWiFS, AVHRR</p>

scholarly journals Fishing Ground Mapping Model in The Semi-Enclosed Saleh Bay, West Nusa Tenggara

2021 ◽  
Vol 35 (1) ◽  
Author(s):  
Anang Dwi Purwanto ◽  
Ulung Jantama Wisha ◽  
Erick Karno Hutomo
Keyword(s):  
Southwest Monsoon ◽  
Fishing Ground ◽  
Northeast Monsoon ◽  
Statistical Regression ◽  
Thermal Front ◽  
Mapping Model ◽  
Fisheries Resources ◽  
Level 3 ◽  
Continued Use ◽  
Strategic Position

Saleh Bay is a semi-enclosed area of water in Nusa Tenggara Barat Province that is enriched by fisheries resources. The bay’s strategic position, surrounded by several small islands, makes it an area of fertile water. An area of water is considered a potentially ideal fishing ground if it contains several oceanographic phenomena, including thermal fronts and upwelling. Fishing activities in Saleh Bay have been found to be ineffective and inefficient due to local people’s continued use of traditional methods such as fishing by signs of nature (instincts), wind direction, astrological signs and previous experience. This study aimed to create a mapping model of the fishing grounds in Saleh Bay based on remote sensing satellite data. Spatial analysis of daily level 3 images from the Suomi-National Polar-Orbiting Partnership (SNPP) was conducted throughout January and August 2019. The image acquisition period was adapted based on the seasonal system of Indonesia. The study area was determined based on thermal front events as identified by sea surface temperature (SST) data analysed using statistical regression with a Non-Linear Multi-Channel SST (NLSST) approach. An ideal fishing ground is characterised by several oceanographic settings such as upwelling and thermal front occurrence. The average SST distribution in January 2019 was relatively high, ranging from 30.39 to 33.70 oC, while in August 2019, the temperature declined significantly, ranging from 25.09 to 29.30 oC. Concerning the fishing ground model, a plethora of potential fishing ground areas were identified in August compared to January 2019, at 144 and 42 points respectively. This reflected the density of the fishing grounds observed. The fishing grounds were most likely to be concentrated in the bay mouth during the southwest monsoon and within the bay near the plateau during the northeast monsoon. The seasonal variability of Saleh Bay played a significant role in the spatial extraction of the fishing ground data.

scholarly journals Seasonal variability of vertical patterns in chlorophyll-a fluorescence in the coastal waters off Kimbiji, Tanzania

2021 ◽  
Vol 20 (1) ◽  
pp. 21-33
Author(s):  
Nyamisi Peter ◽  
Masumbuko Semba ◽  
Charles Lugomela ◽  
Margareth Kyewalyanga
Keyword(s):  
Chlorophyll A ◽  
Coastal Waters ◽  
Mixed Layer Depth ◽  
Northeast Monsoon ◽  
Nutrient Input ◽  
Layer Depth ◽  
Chl A ◽  
The North ◽  
A Value ◽  
Chl A Fluorescence

A study on the vertical pattern of chlorophyll-a (Chl-a) fluorescence was undertaken in the Mafia Channel offKimbiji, Tanzania. Data was collected during the Southeast Monsoon (SEM) and Northeast Monsoon (NEM) seasons. There was higher Chl-a concentration of 0.1 to 1.1 mgm-3 in the surface layer off Kimbiji to about 50 m depth due to the presence of mixed layer depth (MLD) which allowed water mixing in the layer. A deep Chl-a maximum was recorded at around 40 m depth during the NEM and between 40 and 70 m in the SEM. Surface water between longitude 39.9°E and 40.2°E had low Chl-a from the surface to about 50 m depth due to poor nutrient input. The NEM had an insignificantly higher Chl-a value than the SEM (p > 0.05) which differed from other studies in which Chl-a was higher during the SEM than the NEM, than, the Chl-a concentration was higher at the surface during the SEM than during the NEM. Satellite data showed higher Chl-a in the SEM than NEM, localized along the Mafia Channel. During the SEM season the wind pushes higher Chl-a water from the Mafia Channel towards the north and leads to a higher concentration at Kimbiji.

scholarly journals Streamflow Variability in Mahaweli River Basin of Sri Lanka during 1990–2014 and Its Possible Mechanisms

Water ◽  
2019 ◽  
Vol 11 (12) ◽  
pp. 2485 ◽  
Author(s):  
Sherly Shelton ◽  
Zhaohui Lin
Keyword(s):  
Sri Lanka ◽  
Indian Ocean ◽  
River Basin ◽  
Southern Oscillation ◽  
Southwest Monsoon ◽  
Seasonal Rainfall ◽  
Northeast Monsoon ◽  
Total Rainfall ◽  
Factors Affecting ◽  
Streamflow Variability

This study investigates the variation of seasonal streamflow and streamflow extremes in five catchments of the Mahaweli River Basin (MRB) Sri Lanka from 1990 to 2014, and the relationship between streamflow and seasonal rainfall in each catchment is then examined. Furthermore, the influence of Indian Ocean Dipole (IOD) and El Nino and Southern Oscillation (ENSO) on the seasonal rainfall and streamflow in the upper (UMRB) and lower reaches (LMRB) of MRB are explored. It’s found that the rainfall amount in southwest monsoon (SWM) season contributes 29.7% out of annual total rainfall in the UMRB, while the LMRB records 41% of the total rainfall during the northeast monsoon (NEM) season. The maximum streamflow of upper (lower) Mahaweli catchments is observed in the SWM (NEM) season. Catchments in the UMRB (LMRB) recorded strong interannual variability of seasonal overall flow (Q50), Maximum 10-day, and 30-day flows during the SWM (NEM) season. It’s further revealed that the catchment streamflow in the UMRB is closely correlated with the SWM rainfall in the interannual time scale, while streamflow of catchments in the LMRB is closely associated with the NEM rainfall. The effects of ENSO and IOD on streamflow are consistent with their impacts on rainfall for all catchments in MRB, with strong seasonal dependent. These suggested that the sea surface temperature anomalies in the both Indian Ocean and tropical Pacific Ocean are important factors affecting the streamflow variability in the MRB, especially during the SWM season.

scholarly journals Spatiotemporal Variability of Surface Phytoplankton Carbon and Carbon-to-Chlorophyll a Ratio in the South China Sea Based on Satellite Data

2020 ◽  
Vol 13 (1) ◽  
pp. 30
Author(s):  
Wenlong Xu ◽  
Guifen Wang ◽  
Long Jiang ◽  
Xuhua Cheng ◽  
Wen Zhou ◽  
...  
Keyword(s):  
South China Sea ◽  
Chlorophyll A ◽  
South China ◽  
Satellite Data ◽  
Phytoplankton Biomass ◽  
The South China Sea ◽  
Spatiotemporal Variability ◽  
The South ◽  
Chl A ◽  
China Sea

The spatiotemporal variability of phytoplankton biomass has been widely studied because of its importance in biogeochemical cycles. Chlorophyll a (Chl-a)—an essential pigment present in photoautotrophic organisms—is widely used as an indicator for oceanic phytoplankton biomass because it could be easily measured with calibrated optical sensors. However, the intracellular Chl-a content varies with light, nutrient levels, and temperature and could misrepresent phytoplankton biomass. In this study, we estimated the concentration of phytoplankton carbon—a more suitable indicator for phytoplankton biomass—using a regionally adjusted bio-optical algorithm with satellite data in the South China Sea (SCS). Phytoplankton carbon and the carbon-to-Chl-a ratio (θ) exhibited considerable variability spatially and seasonally. Generally, phytoplankton carbon in the northern SCS was higher than that in the western and central parts. The regional monthly mean phytoplankton carbon in the northern SCS showed a prominent peak during December and January. A similar pattern was shown in the central part of SCS, but its peak was weaker. Besides the winter peak, the western part of SCS had a secondary maximum of phytoplankton carbon during summer. θ exhibited significant seasonal variability in the northern SCS, but a relatively weak seasonal change in the western and central parts. θ had a peak in September and a trough in January in the northern and central parts of SCS, whereas in the western SCS the minimum and maximum θ was found in August and during October–April of the following year, respectively. Overall, θ ranged from 26.06 to 123.99 in the SCS, which implies that the carbon content could vary up to four times given a specific Chl-a value. The variations in θ were found to be related to changing phytoplankton community composition, as well as dynamic phytoplankton physiological activities in response to environmental influences; which also exhibit much spatial differences in the SCS. Our results imply that the spatiotemporal variability of θ should be considered, rather than simply used a single value when converting Chl-a to phytoplankton carbon biomass in the SCS, especially, when verifying the simulation results of biogeochemical models.

scholarly journals Research on Inversion Mechanism of Chlorophyll—A Concentration in Water Bodies Using a Convolutional Neural Network Model

Water ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 664
Author(s):  
Yun Xue ◽  
Lei Zhu ◽  
Bin Zou ◽  
Yi-min Wen ◽  
Yue-hong Long ◽  
...  
Keyword(s):  
Neural Network ◽  
Regression Model ◽  
Convolutional Neural Network ◽  
Chlorophyll A ◽  
Language Processing ◽  
Water Bodies ◽  
Inversion Effect ◽  
Least Squares Regression ◽  
Chlorophyll A Concentration ◽  
Chl A

For Case-II water bodies with relatively complex water qualities, it is challenging to establish a chlorophyll-a concentration (Chl-a concentration) inversion model with strong applicability and high accuracy. Convolutional Neural Network (CNN) shows excellent performance in image target recognition and natural language processing. However, there little research exists on the inversion of Chl-a concentration in water using convolutional neural networks. Taking China’s Dongting Lake as an example, 90 water samples and their spectra were collected in this study. Using eight combinations as independent variables and Chl-a concentration as the dependent variable, a CNN model was constructed to invert Chl-a concentration. The results showed that: (1) The CNN model of the original spectrum has a worse inversion effect than the CNN model of the preprocessed spectrum. The determination coefficient (RP2) of the predicted sample is increased from 0.79 to 0.88, and the root mean square error (RMSEP) of the predicted sample is reduced from 0.61 to 0.49, indicating that preprocessing can significantly improve the inversion effect of the model.; (2) among the combined models, the CNN model with Baseline1_SC (strong correlation factor of 500–750 nm baseline) has the best effect, with RP2 reaching 0.90 and RMSEP only 0.45. The average inversion effect of the eight CNN models is better. The average RP2 reaches 0.86 and the RMSEP is only 0.52, indicating the feasibility of applying CNN to Chl-a concentration inversion modeling; (3) the performance of the CNN model (Baseline1_SC (RP2 = 0.90, RMSEP = 0.45)) was far better than the traditional model of the same combination, i.e., the linear regression model (RP2 = 0.61, RMSEP = 0.72) and partial least squares regression model (Baseline1_SC (RP2 = 0.58. RMSEP = 0.95)), indicating the superiority of the convolutional neural network inversion modeling of water body Chl-a concentration.

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