Distribution Of Chlorophyll-a Research Articles

Long‐Term Trends in the Distribution of Ocean Chlorophyll

AbstractThe concentration of chlorophyll‐a (CHL) is an important proxy for autotrophic biomass and primary production in the ocean. Quantifying trends and variability in CHL are essential to understanding how marine ecosystems are affected by climate change. Previous analyses have focused on assessing trends in CHL mean, but little is known about observed changes in CHL extremes and variance. Here we apply a quantile regression model to detect trends in CHL distribution over the period of 1997–2022 for several quantiles. We find that the magnitude of trends in upper quantiles of global CHL (>90th) are larger than those in lower quantiles (≤50th) and in the mean, suggesting a growing asymmetry in CHL distribution. On a regional scale, trends in different quantiles are statistically significant at high latitude, equatorial, and oligotrophic regions. Assessing changes in CHL distribution has potential to yield a more comprehensive understanding of climate change impacts on CHL.

Analysis of Chlorophyll-a Variability in the Eastern Indonesian Waters Using Sentinel-3 OLCI from 2020-2021

The Eastern Indonesian waters are significant in influencing the global climate system and oceanic connectivity. However, the Indonesian Through Flow (ITF) facilitates the movement of waters from the Pacific Ocean to the Indian Ocean. This flow vertically mixes water masses in the Eastern regions, leading to the concentration of phytoplankton. In addition, the distribution of phytoplankton, indicative of chlorophyll-a concentration, is influenced by upwelling and downwelling phenomena. Chlorophyll-a, responsible for capturing carbon and producing oxygen in marine ecosystems, is important in regulating climate change. Moreover, oceanographic conditions play a significant role in the dispersion of chlorophyll-a concentration. Therefore, this study adopted ocean colour remote sensing technology to assess chlorophyll-a distribution. Monthly ocean colour data was collected by the multi-temporal Sentinel-3 Ocean and Land Colour Instrument (OLCI). The analysis output included chlorophyll-a concentration associated with currents and the El Nino Southern Oscillation (ENSO). Data processing using the Case-2 Regional Coast Colour (C2RCC) processor resulted in an average chlorophyll-a concentration in the Eastern Indonesian waters ranging from 0.16 to 0.52. The results showed higher chlorophyll-a levels during the southeast monsoon (July to September) and lower levels during the northwest monsoon (January to March).

Vertical and Horizontal Variability of Chlorophyll-a and Its Relationship with Environmental Parameters in the Waters of Sangihe and Talaud Islands, North Sulawesi, Indonesia

The chlorophyll-a is an important biological parameter that could act as a proxy to indicate the abundance of phytoplankton and the primary productivity of an aquatic ecosystem. This paper investigates the vertical and horizontal variability of chlorophyll-a in the waters of Sangihe and Talaud Islands, Indonesia, and its correlation with water environmental parameters. In this study, the distribution of chlorophyll-a, temperature, salinity, and nutrients (NO3 and PO4) from the surface to a depth of 200 m (photic zone) was measured at 29 research stations. The results showed that the distribution of chlorophyll-a in the waters of the Sangihe-Talaud Islands was varied vertically and horizontally. The waters around the Sangihe Islands generally exhibited a higher chlorophyll-a distribution and shallower Deep Chlorophyll Maxima compared to the water around the Talaud Islands. The concentration of chlorophyll-a varied between 0.0017 and 1.2155 mg.m-3, with most of the water column in Sangihe-Talaud considered oligotrophic, although some stations or depths were mesotrophic or slightly eutrophic. The maximum chlorophyll-a concentration was found in the sub-surface layer at depths between 46 and 101 m. The low N:P ratio (<16) and N:Si ratio (<1) indicate that the water columns of Sangihe-Talaud, up to a depth of 200m, were N-limited. Based on the GAM analysis, chlorophyll-a concentration in Sangihe-Talaud waters was primarily regulated by temperature, salinity, and the N:P ratio, with weak influence from phosphate and the N:Si ratio. The analysis also suggests that primary productivity in Sangihe-Talaud is sensitive to temperature changes, indicating its vulnerability to future warming events.

Vertical distribution patterns of chlorophyll-a during autumn in a coastal environment inside the Gulf of California

An evaluation of the vertical distribution of chlorophyll-a (Chl-a) levels, as an indicator of phytoplankton biomass, has strong repercussions in any marine ecosystem since it allows evaluating its productive potential and the amount of matter that is available for the higher trophic levels of the pelagic food web. This short communication aims to report the vertical distribution patterns of Chl-a levels and some selected hydrographic parameters during autumn in the Bay of La Paz, the biggest and deepest coastal environment inside the Gulf of California, a site also recognized for its high biodiversity. Two oceanographic research cruises were carried out during November 2014 and 2016. A CTD probe configured with dissolved oxygen and active fluorescence sensors, all pre-calibrated by the manufacturer, was used to acquire high-resolution data along the water column. The results showed two distribution patterns of Chl-a: 1) deep peaks (>60 m depth, with concentrations >6 mg m-3) associated with the bottom at nearshore stations, and 2) maximum concentration peaks associated with the thermocline/pycnocline with concentrations up to 7.40 mg m-3 observed at deep stations. A multivariate statistical analysis confirmed the role played by some physical variables in the distribution patterns described. The results shown in this study complement the previous research and fill in the existing gaps for the transition periods between the warm and the cold.

Using a Combination of High-Frequency Coastal Radar Dataset and Satellite Imagery to Study the Patterns Involved in the Coastal Countercurrent Events in the Gulf of Cadiz

This study exploits the combination of High-Frequency Coastal Radar (HFR) information with satellite-derived observations to characterize the patterns involved in the coastal countercurrents (CCCs) events in the Gulf of Cadiz (GoC), which is situated in the SW of the Iberian Peninsula. The westward alongshore currents are observed throughout the year, but the main drivers necessary to develop this flow and its extension in both parts of the basin are not fully clear. In order to identify the main physical processes (both local and remote) that induce the development of these countercurrents and to evaluate the connection of the circulation patterns between the eastern and the western part of the GoC, we make use of several data sources available for the region. First of all, a land-based system of HFR antennas located at four different sites of the GoC provides the velocity field of the surface circulation of the basin. To achieve a significant characterization of the CCCs in the Gulf, the dataset analyzed is processed by means of a series of operations, including the Empirical Orthogonal Functions (EOFs) analysis used to identify spatial and temporal variability of the flow, a low-pass filter used to isolate the sub-inertial signal of the current and temporal interpolation to fill in the missing values. Secondly, given the known importance of the zonal component of the local winds combined with the variations in the mean pressure at sea level over the Western Mediterranean during these events, time series of meteorological data are processed and correlated with the current velocity series via a statistical analysis. Finally, sea surface temperature fields and chlorophyll-a distribution patterns are used as tracers to obtain information on the extension of the countercurrents where HFR data are missing in four cases studied during the year 2017. The conducted analysis revealed the consistent occurrence of westward coastal currents throughout the year, driven in the most intense cases by a combination of the zonal component of the local wind and atmospheric pressure fluctuations over the Western Mediterranean Sea. During those events, CCCs reached the Portuguese side of the Gulf and facilitated the advection of biological material and warmer waters.

Geospatial analysis for prediction of potential fishing zones in the Banggai Waters using hydroacoustic data and satellite remote sensing

Banggai waters are a strategic area rich in fish resources and a source of livelihood for most of the local population. Mapping fish stocks is important to ensure the sustainability of fishing in the area. One of them uses hydroacoustic data and satellite imagery to provide more comprehensive results on fish stocks and more accurate predictions of potential fishing zones. This study will spatially estimate fish density in Banggai waters using hydroacoustic data and investigate the distribution of chlorophyll-a and sea surface temperature using Himawari-8 imagery. Hydroacoustic data processing will produce target strength (TS) and volume backscatter strength (Sv) values to determine the value and distribution of fish density. Meanwhile, ZPPI results are obtained from the overlay of chlorophyll-a and sea surface temperature. From the results of mapping the potential fishing zone, the high category has the highest percentage of 43%, low 28% and medium 29%. The ZPPI point distribution map then shows the concentration of ZPPI points in the southern Banggai waters. The fish density distribution map shows that the spatial distribution tends to be around the coast of Banggai Peninsula, dominated by small and very small fish categories. Correlation of fish density point distribution with ZPPI results and oceanographic parameters has a positive influence on fish density from GAM modelling.

Analysis of Chlorophyll-A Distribution And Sea Surface Temperature for Estimation of Skipjack Fishing Grounds (Katsuwonus Pelamis) Based on Different Seasons in South Buton Waters

The success of fishing activities is certainly greatly influenced by the conditions of the Fishing Ground (FG). Therefore, information about potential fishing areas is needed by fishermen in fishing activities. Chlorophyll-a is the primary productivity in water. The development of aquatic chlorophyll is influenced by Sea Surface Temperature (SST). The purpose of this study was to analyze the distribution of chlorophyll-a and sea surface temperature (SST) for estimating fish fishing areas based on different seasons in South Buton Waters. This type of research is exploratory research. This study used remote sensing method. The direct interview method with respondents aims to validate data (fishermen) from remote sensing processing. The study used Aqua Modis satellite imagery data to derive parameter values of chlorophyll-a and sea surface temperature. The results of the analysis of the distribution of chlorophyll-a in South Buton waters are the highest with chlorophyll-a concentrations ranging from 0.3-3.0 mg / m³ spread in the waters of Sampolawa and Batauga Districts with surface temperatures ranging from 26 °C and 33 °C. Eastern and Transitional Season II have high levels of chlorophyll-a concentration. This season will be a potential skipjack fishing area in South Buton Waters.

Decision tree ensemble with Bayesian optimization to predict the spatial dynamics of chlorophyll-a concentration: A case study in Bay of Bengal

An accurate prediction of the spatial distribution of phytoplankton biomass, as represented by Chlorophyll-a (CHL-a) concentrations, is important for assessing ecological conditions in the marine environment. This study developed a hyperparameter-optimized decision tree-based machine learning (ML) models to predict the geographical distribution of marine phytoplankton CHL-a in the Bay of Bengal. To predict CHL-a over a large spatial extent, satellite-derived remotely sensed data of ocean color features (CHL-a, colored dissolved organic matter, photosynthetically active radiation, particulate organic carbon) and climatic factors (nighttime sea surface temperature, surface absorbed longwave radiation, sea level pressure) from 2003 to 2022 are used to train and test the models. Results obtained from this study have shown the highest concentrations of CHL-a occurred near the Bay's coastal belts and river estuaries. Analysis revealed that aside from photosynthetically active radiation, organic components exhibited a stronger positive relationship with CHL-a than climatic features, which are correlated negatively. Results showed the chosen decision tree methods to all possess higher R2 and lower root mean square error (RMSE) errors. Furthermore, XGBoost outperforms all other models in predicting the geographic distribution of CHL-a. To assess the model efficacy on seasonal basis, a best performing XGBoost model was validated in the Bay of Bengal region which has shown a good performance in predicting the spatial distribution of Chl-a as well as the pixel values during the summer, winter and monsoon seasons. This study provides the best ML model to researchers for predicting CHL-a in the Bay of Bengal. Further it helps to improve our knowledge of CHL-a spatial dynamics and assist in monitoring marine resources in the Bay of Bengal. It worth noting that the water quality in the Indian Ocean is very dynamic in nature, therefore, additional efforts are needed to test the efficacy of this study model over different seasons and spatial gradients.

Improvement in Spatiotemporal Chl-a Data in the South China Sea Using the Random-Forest-Based Geo-Imputation Method and Ocean Dynamics Data

The accurate estimation of the spatial and temporal distribution of chlorophyll-a (Chl-a) concentrations in the South China Sea (SCS) is crucial for understanding marine ecosystem dynamics and water quality assessment. However, the challenge of missing values in satellite-derived Chl-a data has hindered obtaining complete spatiotemporal information. Traditional methods for deriving Chl-a are based on the modeling of measured sensor data and in situ measurements. Spatiotemporal imputation of Chl-a is difficult due to the inaccessibility of the measured Chl-a. In this study, we introduce an innovative approach that incorporates an ocean dynamics dataset and utilizes the random forest algorithm for predicting the Chl-a concentration in the SCS. The method combines the spatiotemporal feature pattern of Chl-a and the main influencing factors, and it introduces ocean dynamics data, which has a high correlation with the spatiotemporal distribution of Chl-a, as the input data through feature engineering. Also, we compared Random Forest (RF) with other Machine Learning (ML) methods. The results show that (1) ocean dynamics datasets can provide important data support for Chl-a imputation by capturing the impact of dynamical processes on ecological roles in the South China Sea. (2) The RF method is the superior imputation method for the reconstruction of Chl-a in the South China Sea, with better model performance and smaller errors. This study provides valuable insight for researchers and practitioners in choosing suitable machine learning methods for the imputation of the Chl-a concentration in the SCS, facilitating a better understanding of the region’s marine ecosystems and supporting effective environmental management.

Prediction on daily spatial distribution of chlorophyll-a in coastal seas using a synthetic method of remote sensing, machine learning and numerical modeling

Harmful algal blooms (HABs) pose a severe environmental issue and have significant economic and ecological consequences on coastal oceans. Predicting the occurrence of these blooms has become increasingly vital for coastal communities. To facilitate this, chlorophyll-a (Chl-a) levels have been widely used to forecast algal blooms. Although Hydro-biogeochemical (HBGC) process-based models display reasonable accuracy in predicting hydrodynamic variables and nutrients, they are not as effective in predicting Chl-a. Purely data-driven machine learning techniques also have limitations in accurately predicting Chl-a of high spatio-temporal resolutions. In this study, a coupled HBGC-Convolutional Neural Network (CNN) model was developed to predict the daily surface Chl-a distribution. The HBGC-CNN model integrates the information gathered by the HBGC model on temperature, salinity, dissolved inorganic nitrogen, dissolved organic phosphorus, and zooplankton with the remote sensing Chl-a products for the CNN model training. The results revealed that the HBGC-CNN model can effectively reproduce both daily and seasonal Chl-a variations, and interpret spatiotemporal information related to an HAB event triggered by the heavy rainfall during typhoon Lekima in 2019. Furthermore, this method can be used for data reconstruction, producing gap-free Chl-a products for historical reanalysis, especially in nearshore regions. The successful implementation of the HBGC-CNN model in predicting Chl-a highlights its potential in being incorporated into an operational forecasting system from a regional scale to a global scale, reducing the adverse impact of HAB disasters and facilitating emergency treatment.

Analysis of Fringescale Sardine Fishing Grounds (Sardinella fimbriata) Based on Sea Surface Temperature and Chlorophyll-A in Tegal Waters, Indonesia

An alternative that can be done in determining the fishing area is to use remote sensing technology through the approach of sea surface temperature distribution and chlorophyll-a. The purpose of this study was to determine the value of the distribution of sea surface temperature and chlorophyll-a, as well as analyze the relationship between sea surface temperature and chlorophyll-a on Fringescale sardine (Sardinella fimbriata). The research method used is the descriptive method. The research data taken are in-situ data and SNPP Viirs satellite imagery data, as well as capture data from 2018 – 2022. Data analysis used multiple linear regression methods and Pearson correlation analysis. The results showed that the sea surface temperature distribution value ranged from 29.5°C – 29.9°C and chlorophyll-a ranged from 0.0545 mg m-3 – 0.4022 mg m-3. The value of sea surface temperature and chlorophyll-a in Tegal waters on land tends to have a higher value compared to high seas waters. Analysis conducted between sea surface temperature and chlorophyll-a on catches found a coefficient of determination of 0.74 or 74% affecting the total catch. The partial correlation between sea surface temperature and catch is 0.81 or 81%. The correlation between chlorophyll-a and catch is 0.86 or 86% where this value is included in the very strong category in influencing the catch of Sardinella fimbriata. Fishermen no longer rely on instinct to predict potential fishing zone each season, but it has taken advantage of variables that affect the presence of S. fimbriata using potential fishing zone map.

Phytoplankton Analysis and Mapping in Tuban Coastal Based on Chlorophyll-A Concentration Value Using Terra Modis Satellite Imagery

The coast of Tuban is a fairly dynamic area where the lives of fishermen depend on fishing. Ecosystem changes that occur in the coastal area of Tuban greatly affect the fishing effort. The presence of the phytoplankton themselves cannot be sensed directly. However, the green leaf substance present in the bodies of the phytoplankton can be sensed by sensors in the form of the chlorophyll-a content of the water bodies on the coast. In this study, the distribution of chlorophyll-a on the coast will be mapped using Terra Modis satellite imagery and then analyzed by modeling the distribution so that future predictions can be made on handling water quality on the coast of Tuban so that the economy of fishing communities on the coast of Tuban can be improved. The method used for the mapping is Remote Sensing. The result is a liner mathematical model y = 28.012 x, y is Chlorophyll and x reflectance Rrs_488 which is blue with a degree of determination R2 = 0.9535, and the availability of thematic maps of chlorophyll-a content on the coast of Tuban.

CALENDAR FOR PLANTING SEAWEED EUCHEUMA SP. IN MALLASORO BAY, JENEPONTO DISTRICT, BASED ON LANDSAT-8 IMAGES

Seaweed cultivation activities in Jeneponto Regency have been practised for a long time and have become the main livelihood for most of the Mallasoro Bay community. In cultivating seaweed, obstacles often arise in the form of failures experienced by seaweed farmers or poor-quality yields. This study was aimed to develop alternative planting calendars for Eucheuma sp. in Mallasoro Bay, Jeneponto Regency based on sea surface temperature and distribution of chlorophyll-a obtained from Landsat-8 imagery. Image Processing Sea Surface Temperature and Chlorophyll-a were processed using ENVI 4.8 AND 5.3 software, the satellite images used were clean and without cloud disturbance. In this study, data analysis was carried out descriptively. The water temperature that is good for seaweed growth is 27-30˚C, for Mallasoro Bay Sea Surface Temperature which is suitable for seaweed cultivation, namely April, May, June, July, August, September, October and November. While the classification is based on the criteria for chlorophyll-a trophic status in marine waters, namely the range < 1 mg/L is classified as Oligotrophic, ≥ 1–3 mg/L is classified as Mesotrophic, ≥ 3–5 mg/L is classified as Eutrophic, and > 5 mg/L is classified as Hypertrophic. , from the results of image analysis for the distribution of chlorophyll-a in Mallasoro Bay, it shows that Mallasoro Bay is at the Mesotrophic level throughout the year or the fertility level of the waters is quite fertile because it is in the range of ≥ 1–3 mg/L. so that a seaweed planting calendar can be obtained in Mallasoro Bay, namely in January, February and December, preparation of tools such as cleaning and repair of seaweed planting tools can be carried out, then at the end of March, the end of May, the end of July and the end of September, the procurement of seaweed seeds is carried out. , in early April, early June, early August, and early October, seaweed seeds can be spread, then in mid-May, mid-July, mid-September, and mid-November, harvesting can be carried out, so that seaweed cultivation can be carried out 4 times in one year. cycle.

The Utilization of Seasonal Variation of Ocean Wave Characteristics Identification in Monitoring the Chlorophyll-a Distribution in South Sulawesi

South Sulawesi is one of Indonesia’s provinces with various forms of waters around its territory, such as the sea, strait, the Spermonde archipelago, and bay. This geographical condition makes it interesting to examine what phenomena can affect the characteristics and variability of ocean waves in each type to increase efficiency and effectiveness in carrying out activities in the waters, especially fishing. By utilizing the BMKG Ina-Waves wave model and NASA’s Aqua MODIS satellite image, the characteristics and variations of ocean waves and the level of fertility of the waters around South Sulawesi Province can be seen. Based on the climatological analysis of wind, ocean waves, and the distribution of chlorophyll-a in this study, the results showed that the Asian and Australian monsoon phenomena greatly influenced these three parameters. The monsoon winds increase the average wave height and affect the distribution of chlorophyll-a concentrations in the waters of South Sulawesi and its surroundings. Efficiency and effectiveness of fishing at sea are carried out during the transition season in the coastal waters of South Sulawesi so that the risk of sea accidents can be minimized.

Pemetaan Zona Potensial Penangkapan Ikan (ZPPI) Di Perairan Selat Bali Menggunakan Citra Satelit Aqua MODIS


 
 
 
 The abundance and distribution of fish are influenced by environmental productivity, such as chlorophyll-a distribution and variations in sea surface temperature (SST). The lack of technology fishermen use in the waters of the Bali Strait has resulted in the high potential of fish resources not being optimally utilized. This study aimed to determine potential fishing zones by utilizing Aqua MODIS satellite data which can detect SST and chlorophyll-a. The image data observed was from October to December 2018 to 2020. The method used is the Single Image Edge Detection Algorithm, where this algorithm is used to determine potential fishing areas designed to detect fronts in SST images. The results showed that in December 2018, 7 potential fishing points were produced, marked with a chlorophyll-a value of 0.25 mg/m3 and an SST of 29.25°C. In December 2020, 82 potential fishing points were produced, with a chlorophyll-a value of 0.31 mg/m3 and an SPL of 29.79°C. Validation was carried out by analyzing the result of image processing with fish catch data obtained from Pengambengan PPN, Muncar PPP, and Kedonganan TPI. SST, with a value of 27°C, is the ideal temperature for fish where when the temperature increases, the number of potential fish catch points decreases and vice versa. The distribution of chlorophyll-a content with a value of > 0.2 mg/m3 affects the number of potential fishing points, where the higher the chlorophyll-a content, the more fishing ground points and vice versa.
 
 
 

The analysis of chlorophyll-a distribution in fishing areas of Aceh Waters

The distribution and abundance of fish are strongly influenced by several oceanographic parameters, one of which is chlorophyll-a. Chlorophyll-a is one of the oceanographic parameters that play an important role in primary productivity in the sea. The distribution of chlorophyll-a contained in phytoplankton can be used as a measure of fertility in water. With advances in geographic information systems or remote sensing, the distribution of chlorophyll-a can be used to estimate fish catchment areas. This study aims to look at the distribution of chlorophyll-a in Aceh waters both spatially and temporally to predict fishing locations. This study used satellite imagery combined with a Geographic Information System (GIS) to detect chlorophyll-a’s distribution. The results of satellite image processing show that the average monthly value of chlorophyll-a in Aceh waters ranges from 2.71 – 23.54 mg/m3, areas with high chlorophyll-a are generally located in the western waters of Aceh to the southwestern waters of Aceh, while areas with The lowest chlorophyll-a is generally in areas far from the coast (towards the open sea) with a range of 0.04 – 0.5 mg/m3 where the lowest average occurs in the east monsoon. However, in general, the potential locations for fishing grounds are more in the waters west of Aceh than in the waters north of Aceh, Aceh waters.Keywords:Chlorophyll-aSatellite imageFishing areaGeographic information SystemAceh Waters

Analysis of the effect of ENSO and IOD on the productivity of yellowfin tuna (Thunnus albacares) in the South Indian Ocean, East Java, Indonesia

Abstract. Sambah AB, Noor’izzah A, Intyas CA, Widhiyanuriyawan D, Affandy DP, Wijaya A. 2023. Analysis of the effect of ENSO and IOD on the productivity of yellowfin tuna (Thunnus albacares) in the South Indian Ocean, East Java, Indonesia. Biodiversitas 24: 2689-2700. Yellowfin tuna (Thunnus albacares) is one of the fish that migrates through the Indian Ocean and is primarily caught in the south Java waters which are directly adjacent to the Indian Ocean. Fish abundance and migration are influenced by oceanographic factors, including climatic factors which affect annual and interannual variations, such as the Indian Ocean Dipole (IOD) and the El Nino Southern Oscillation (ENSO). This study aimed to determine the effect of climate anomalies on the productivity of yellowfin tuna in the Indian Ocean south of East Java, Indonesia. Data collection was carried out at Coastal Fishing Port of Pondokdadap East Java, involving 58 respondents consisting of fishermen with yellowfin tuna catches. The boundaries of the research area are at coordinates 110.9°-114.5° East Longitude and 8°-11° South Latitude. The data used in the analysis consisted of Sea Surface Temperature (SST) data, chlorophyll-a data, Nino 3.4 data, Dipole Mode Index (DMI) data, and yellowfin tuna catch data for the year 2017-2021. The results showed that of the fifteen GAM models, the combination of variables that most affected fish productivity was the distribution of chlorophyll-a and the ENSO phenomenon with values ??of AIC (1503.33) and DE (64.30%). Pearson correlation analysis showed that the IOD phenomenon was influenced by SST and chlorophyll-a, while SST and chlorophyll-a did not significantly influence the ENSO phenomenon. These results indicated that the phenomenon of climate anomalies and the oceanographic conditions in the waters indirectly affect fish productivity through the food chain process.

Three-dimensional spatial interpolation for chlorophyll-a and its application in the Bohai Sea

Assessing the health of coastal ecosystems is crucial for maintaining ecological balance. One significant indicator of water eutrophication is the distribution of chlorophyll-a (Chl-a), which makes obtaining a complete three-dimensional spatial distribution of Chl-a essential for assessment. This study utilized the linear radial basis function (RBF-Linear) method to obtain a comprehensive and reasonable spatial distribution of Chl-a. The method was applied to obtain the three-dimensional spatial field of Chl-a concentration in the Bohai Sea in March, May, August, and October from 2016 to 2018. The distribution pattern of Chl-a concentration in the Bohai Sea displayed characteristic spatial and temporal variations. Spatially, high Chl-a concentration was most concentrated in coastal waters, particularly in estuaries and mariculture areas. Temporally, there were two peaks in March and August. The total Chl-a and areas with high Chl-a concentration in four sub-regions of the Bohai Sea were also calculated to enable a comprehensive assessment of the marine ecological environment. By analyzing the temporal and spatial variation of Chl-a in the Bohai Sea and evaluating the marine ecological environment, we confirmed the feasibility and rationality of RBF-Linear. Our findings have the potential to contribute to improve the accuracy of ecological models and assessment of the satellite products.

Mapping Area Of Potential Arresting Rajungan (Portunus pelagicus) In Medan Belawan Waters, North Sumatera Province

Crab (Portunus pelagicus) is one of the fishery commodities that has high economic value, but fishermen in the waters of Medan Belawan still have difficulty in determining the location of the fishing area. Therefore, information is needed regarding the condition of potential arrest areas in an effort to speed up the trip of arrest operations and reduce fuel consumption. The purpose of this study is to map the distribution of sea surface temperature and chlorophyll-a as well as crab catches and also to analyze potential crab fishing areas. The method used is a survey method by recording the coordinates of the fishing area and the number of catches. Sea surface temperature and chlorophyll-a are obtained by downloading MODIS satellite imagery. The analysis used in this study is a linear regression analysis that serves to see the influence of sea surface temperature and chlorophyll-a on crab catches. The results obtained were the distribution of SPL and chlorophyll-a in Medan Belawan waters during February varied greatly, SPL ranged from 24.13ºC to 29.67ºC with an average value of 27.73ºC. While chlorophyll-a ranged from 0.11 mg/m3 to 3.52 mg/m3 with an average value of 1.613 mg/m3. The catch of crabs during February reached 6,253 kg, with the most catches of 2,380 kg, namely on February 18-25 and at least 264 kg on January 25 to February 1. The results of the analysis of the indicators of estimating the potential area of crabs show that those that are included in the potential category are KM. Khadijah, KM. Sifolin-folin 012, KM. Lugage, KM. Maduma 05, KM. SS XIII, KM. Aura, and KM. Malika Pala 1, while others fall into the potential medium category.

Effects of typhoon and upwelling on Chlorophyll-a distribution in the northeastern coast of Hainan during Summer.

Typhoons or upwelling are thought to promote higher phytoplankton chlorophyll-a (Chl-a) concentration in many previous studies. However, the combined effects of typhoons and upwelling have been less studied in the South China Sea. Based on satellite remote sensing data, we investigated potential contributions of temperature-characterizing upwelling and typhoon events to Chl-a changes in the Hainan northeast area. Results showed that the Chl-a concentration was 0.80 mg m-3 at the coastal upwelling index (CUI) of 1.7°C in the summer of 2020 when there were no typhoons crossing the area. The CUI (1.01°C) of typhoon-influenced period in 2019 was 0.21°C higher than that of typhoon-free period in 2019. And the Chl-a also increased from 0.70 mg m-3 to 0.99 mg m-3. In comparison, during the typhoon-free period, with the higher CUI, there was the higher concentration of Chl-a. In addition, the typhoon affected Chl-a concentration is significantly higher than that in the other two typhoon-free periods of 2019 and 2020. Though the typhoon has a limited effect on the upwelling intensity, the Chl-a concentration is much higher than when the upwelling acts alone. This is due to the combined effect of typhoon (vertical mixing and runoff) and upwelling. The above results indicate that upwelling dominated the changes in Chl-a concentration in the Hainan northeast upwelling area during the typhoon-free period. In contrast, strong vertical mixing and runoff dominated the changes of Chl-a concentration during the typhoon-influenced period in the above area.