Current Issue
2024 Vol. 42, No. 12
Display Method:
2024, 43(12): 1-1.
Abstract:
2024, 43(12): 1-2.
Abstract:
2024, 43(12): 1-12.
doi: 10.1007/s13131-024-2365-0
Abstract:
We introduce a new method, the piecewise Reynolds mean (PREM), for decomposing the flow velocity into the mean-flow and eddy-flow parts in the time domain for subsequent calculation of the mean flow kinetic energy (MKE) and eddy kinetic energy (EKE). Compared with conventional methods like the Reynolds mean and running mean (RUM), PREM has the advantage of exact balance between the MKE and EKE, without the additional residual kinetic energy (RKE), while retaining time-dependent mean-flow. It is mathematically simple and computationally lightweight, depending on a pre-defined separation scale for the mean-flow and eddies. Based on satellite observations and the separation scale of 1 year, we compare PREM with RUM, as well as another newly proposed method, the eddy detection and extraction (EDEX). The latter is based on objective identification of mesoscale eddies and eddy anomaly extraction algorithms, and is therefore only suitable for mesoscale eddy energetics, but independent of separation scales. It is shown that compared with RUM, PREM gives larger mean EKE and stronger interannual variability. In strong-current and eddy-rich regions, the two methods differ the most (max: Kuroshio Extension, root-mean-sqaure-difference = 60.3 J/m3); but in areas with weak current and eddy, the difference accounts for the largest fraction of total EKE (max: south of the Aleutian Islands, 208%). EKE estimated by the two methods is out of phase (min correlation coefficient = 0.38). The mean EKE and standard deviation from the EDEX method resemble the PREM with 1-year separation scale, but is generally smaller in magnitude.
We introduce a new method, the piecewise Reynolds mean (PREM), for decomposing the flow velocity into the mean-flow and eddy-flow parts in the time domain for subsequent calculation of the mean flow kinetic energy (MKE) and eddy kinetic energy (EKE). Compared with conventional methods like the Reynolds mean and running mean (RUM), PREM has the advantage of exact balance between the MKE and EKE, without the additional residual kinetic energy (RKE), while retaining time-dependent mean-flow. It is mathematically simple and computationally lightweight, depending on a pre-defined separation scale for the mean-flow and eddies. Based on satellite observations and the separation scale of 1 year, we compare PREM with RUM, as well as another newly proposed method, the eddy detection and extraction (EDEX). The latter is based on objective identification of mesoscale eddies and eddy anomaly extraction algorithms, and is therefore only suitable for mesoscale eddy energetics, but independent of separation scales. It is shown that compared with RUM, PREM gives larger mean EKE and stronger interannual variability. In strong-current and eddy-rich regions, the two methods differ the most (max: Kuroshio Extension, root-mean-sqaure-difference = 60.3 J/m3); but in areas with weak current and eddy, the difference accounts for the largest fraction of total EKE (max: south of the Aleutian Islands, 208%). EKE estimated by the two methods is out of phase (min correlation coefficient = 0.38). The mean EKE and standard deviation from the EDEX method resemble the PREM with 1-year separation scale, but is generally smaller in magnitude.
2024, 43(12): 13-33.
doi: 10.1007/s13131-024-2450-4
Abstract:
Accurate significant wave height (SWH) prediction is essential for the development and utilization of wave energy. Deep learning methods such as recurrent and convolutional neural networks have achieved good results in SWH forecasting. However, these methods do not adapt well to dynamic seasonal variations in wave data. In this study, we propose a novel method—the spatiotemporal dynamic graph (STDG) neural network. This method predicts the SWH of multiple nodes based on dynamic graph modeling and multi-characteristic fusion. First, considering the dynamic seasonal variations in the wave direction over time, the network models wave dynamic spatial dependencies from long- and short-term pattern perspectives. Second, to correlate multiple characteristics with SWH, the network introduces a cross-characteristic transformer to effectively fuse multiple characteristics. Finally, we conducted experiments on two datasets from the South China Sea and East China Sea to validate the proposed method and compared it with five prediction methods in the three categories. The experimental results show that the proposed method achieves the best performance at all predictive scales and has greater advantages for extreme value prediction. Furthermore, an analysis of the dynamic graph shows that the proposed method captures the seasonal variation mechanism of the waves.
Accurate significant wave height (SWH) prediction is essential for the development and utilization of wave energy. Deep learning methods such as recurrent and convolutional neural networks have achieved good results in SWH forecasting. However, these methods do not adapt well to dynamic seasonal variations in wave data. In this study, we propose a novel method—the spatiotemporal dynamic graph (STDG) neural network. This method predicts the SWH of multiple nodes based on dynamic graph modeling and multi-characteristic fusion. First, considering the dynamic seasonal variations in the wave direction over time, the network models wave dynamic spatial dependencies from long- and short-term pattern perspectives. Second, to correlate multiple characteristics with SWH, the network introduces a cross-characteristic transformer to effectively fuse multiple characteristics. Finally, we conducted experiments on two datasets from the South China Sea and East China Sea to validate the proposed method and compared it with five prediction methods in the three categories. The experimental results show that the proposed method achieves the best performance at all predictive scales and has greater advantages for extreme value prediction. Furthermore, an analysis of the dynamic graph shows that the proposed method captures the seasonal variation mechanism of the waves.
2024, 43(12): 34-46.
doi: 10.1007/s13131-024-2395-7
Abstract:
Over the past two decades, numerous countries have actively participated in the International Argo Program, working toward the global “OneArgo” goal. China’s Argo program has deployed over 500 autonomous profiling floats in the Indo-Pacific, with 55 Beidou (BD) floats, equipped with the Beidou satellite communication system, currently operational. During the operation of the BD float network, we found that in addition to the limitation of floats battery, the loss may also be caused by communication loss due to the floats escaping from the Beidou-2’s short message coverage. In this study, float trajectories are simulated using velocity fields from an eddy-resolved resolution Estimating the Circulation and Climate of the Ocean, Phase Ⅱ (ECCO2) model and a Lagrangian particle tracking model programmed to represent the vertical motions of profiling floats. The simulations can help to explore both the representativeness and the predictability of profiling float displacements. By deploying a large number of synthetic floats in the Lagrangian particle tracking system, we construct probability density functions (PDFs) of the simulated-float trajectory among key oceans, for example, a joint region of East Indian−South China Sea−Northwest Pacific Ocean (5°–40°N, 70°–140°E), which is generally similar to the location of the present BD float network. These statistics can help to estimate the chance of floats drifting into shallow seas (such as the East China Sea) and out of the coverage of the Beidou satellite communication. With this knowledge changes to the future China’s Argo observing system could be made.
Over the past two decades, numerous countries have actively participated in the International Argo Program, working toward the global “OneArgo” goal. China’s Argo program has deployed over 500 autonomous profiling floats in the Indo-Pacific, with 55 Beidou (BD) floats, equipped with the Beidou satellite communication system, currently operational. During the operation of the BD float network, we found that in addition to the limitation of floats battery, the loss may also be caused by communication loss due to the floats escaping from the Beidou-2’s short message coverage. In this study, float trajectories are simulated using velocity fields from an eddy-resolved resolution Estimating the Circulation and Climate of the Ocean, Phase Ⅱ (ECCO2) model and a Lagrangian particle tracking model programmed to represent the vertical motions of profiling floats. The simulations can help to explore both the representativeness and the predictability of profiling float displacements. By deploying a large number of synthetic floats in the Lagrangian particle tracking system, we construct probability density functions (PDFs) of the simulated-float trajectory among key oceans, for example, a joint region of East Indian−South China Sea−Northwest Pacific Ocean (5°–40°N, 70°–140°E), which is generally similar to the location of the present BD float network. These statistics can help to estimate the chance of floats drifting into shallow seas (such as the East China Sea) and out of the coverage of the Beidou satellite communication. With this knowledge changes to the future China’s Argo observing system could be made.
2024, 43(12): 58-65.
doi: 10.1007/s13131-024-2415-7
Abstract:
Aquaculture, as the fastest-growing food production sector in the world, is becoming an increasingly nonnegligible source of greenhouse gas emissions. Despite this, there has been limited research on nitrous oxide (N2O) emission from marine aquaculture in China, where more marine aquaculture occurs than anywhere else, globally. We estimated N2O emissions (E) from marine mariculture of 10 fish and 6 crustacean species in China from 2003 to 2022 using production data from the China Fishery Statistical Yearbook (2004–2023), and data for feed conversion rates and types from the literature. From 2003, marine aquaculture production, the annual N2O emissions (EA), and the annual N2O emissions per unit of aquaculture area (EIA) trend upward. The EA of fish culture was lower than that of crustaceans, but the EIA of fish culture was generally higher. Sea bass (0.308 Tg/a, in terms of N) and white shrimp (0.945 Tg/a, in terms of N) had the highest average EA among fish and crustacean cultures, respectively. The highest average EA from fish and crustacean were both Guangdong Province (fish: 0.248 Tg, crustacean: 0.547 Tg), and the highest sea area were both the South China Sea (fish: 0.316 Tg, crustacean: 1.082 Tg); the highest average EIA for fish and crustacean were Tianjin City [35.40 t/(hm2·a)] and Guangxi Zhuang Autonomous Region [19.83 t/(hm2·a)], respectively, and the highest sea areas were both the South China Sea (fish: 0.316 Tg, crustacean: 1.082 Tg). These analyses provide baseline data for a greenhouse gas emissions inventory for China, based on an interpretation of them, we provide recommendations for reducing N2O emissions in marine fish and crustacean culture.
Aquaculture, as the fastest-growing food production sector in the world, is becoming an increasingly nonnegligible source of greenhouse gas emissions. Despite this, there has been limited research on nitrous oxide (N2O) emission from marine aquaculture in China, where more marine aquaculture occurs than anywhere else, globally. We estimated N2O emissions (E) from marine mariculture of 10 fish and 6 crustacean species in China from 2003 to 2022 using production data from the China Fishery Statistical Yearbook (2004–2023), and data for feed conversion rates and types from the literature. From 2003, marine aquaculture production, the annual N2O emissions (EA), and the annual N2O emissions per unit of aquaculture area (EIA) trend upward. The EA of fish culture was lower than that of crustaceans, but the EIA of fish culture was generally higher. Sea bass (0.308 Tg/a, in terms of N) and white shrimp (0.945 Tg/a, in terms of N) had the highest average EA among fish and crustacean cultures, respectively. The highest average EA from fish and crustacean were both Guangdong Province (fish: 0.248 Tg, crustacean: 0.547 Tg), and the highest sea area were both the South China Sea (fish: 0.316 Tg, crustacean: 1.082 Tg); the highest average EIA for fish and crustacean were Tianjin City [35.40 t/(hm2·a)] and Guangxi Zhuang Autonomous Region [19.83 t/(hm2·a)], respectively, and the highest sea areas were both the South China Sea (fish: 0.316 Tg, crustacean: 1.082 Tg). These analyses provide baseline data for a greenhouse gas emissions inventory for China, based on an interpretation of them, we provide recommendations for reducing N2O emissions in marine fish and crustacean culture.
2024, 43(12): 66-74.
doi: 10.1007/s13131-024-2423-7
Abstract:
Particle size is an important characteristic of suspended matter, and it contains crucial information about the deposition process. Suspended particle samples in the water mixing zone of the Changjiang River Estuary were collected in December 2016. Untreated original grain size and the decentralized grain size of the suspended particles were measured via a laser particle size analyzer. Morphological characteristics and the chemical composition of the suspended particles were also studied systematically using a scanning electron microscope (SEM) with an energy dispersive X-ray spectrometer (EDS). Then, the flocculation and sedimentation of suspended matter in the water mixing zone were explored by combining them with the water mixing processes in the estuary. The average particle size of suspended matter in the mixing zone of the Changjiang River Estuary ranges from Ф5.73 to Ф7.98. The particle size distribution pattern is an abnormal model with a mainly unimodal pattern. In the freshwater area that was dominated by runoff, the suspended matter is mainly composed of fine particles, the settling velocity is slow, and the flocculation is weak. Floc particles were often seen in the mixing zone, with the flocs having a relatively large particle size, a low density and a loose structure appearing at the weak mixing zone; the flocs had a compacted structure in most areas of the mixing zone. The changes of suspended particle size in the estuarine mixing zone promote the settling and deposition of suspended matter, which has an important influence on the bed geomorphology and preservation of the fine suspended particles in the estuary.
Particle size is an important characteristic of suspended matter, and it contains crucial information about the deposition process. Suspended particle samples in the water mixing zone of the Changjiang River Estuary were collected in December 2016. Untreated original grain size and the decentralized grain size of the suspended particles were measured via a laser particle size analyzer. Morphological characteristics and the chemical composition of the suspended particles were also studied systematically using a scanning electron microscope (SEM) with an energy dispersive X-ray spectrometer (EDS). Then, the flocculation and sedimentation of suspended matter in the water mixing zone were explored by combining them with the water mixing processes in the estuary. The average particle size of suspended matter in the mixing zone of the Changjiang River Estuary ranges from Ф5.73 to Ф7.98. The particle size distribution pattern is an abnormal model with a mainly unimodal pattern. In the freshwater area that was dominated by runoff, the suspended matter is mainly composed of fine particles, the settling velocity is slow, and the flocculation is weak. Floc particles were often seen in the mixing zone, with the flocs having a relatively large particle size, a low density and a loose structure appearing at the weak mixing zone; the flocs had a compacted structure in most areas of the mixing zone. The changes of suspended particle size in the estuarine mixing zone promote the settling and deposition of suspended matter, which has an important influence on the bed geomorphology and preservation of the fine suspended particles in the estuary.
2024, 43(12): 75-84.
doi: 10.1007/s13131-024-2307-x
Abstract:
The lanternfishes are mesopelagic fish that are highly productive as common bycatch of deep-sea shrimp trawlers, but they are often neglected or discarded. Despite being one of the dominant lanternfish species in the Arabian Sea, little is known about the life history of Diaphus thiollierei and its role in marine ecosystems. In this study, 103 D. thiollierei were collected in the Arabian Sea during October-November 2020 to study population growth based on sagittal otolith daily ages; and 10 fish collected during April–May 2021 were subjected to otolith microchemistry analysis to reconstruct the vertical migration in their life history using LA-ICP-MS technique. The standard length–dry weight (SL-DW) relationships for D. thiollierei revealed both negative allometric growth and a significant difference between the sexes. Using daily growth annuli counts on the sagittal section of otoliths, the von Bertalanffy growth equation for D. thiollierei was determined. The pattern of four elemental ratios (Sr to Ca, Mg to Ca, Li to Ca, and Ba to Ca) in sagittal otolith suggested that, in general, D. thiollierei descended continually after hatching until the post-larval (PL) stage when they reached a depth of approximately 200 m. Subsequently, from the PL stage to the post-metamorphosis Ⅱ (PM Ⅱ) stage, D. thiollierei likely further sank from 200 m to a depth of approximately 300 m, and then in the daytime they were at a depth of approximately 300–800 m to take refuge from predators. This pilot study explored to unravel the vertical migration during life history in D. thiollierei from sagittal otoliths, whereas further investigation on otolith is needed to better delineate the population ecology in detail, and thus to provide basic information for the exploitation of the lanternfish resource and the understanding of their ecological roles.
The lanternfishes are mesopelagic fish that are highly productive as common bycatch of deep-sea shrimp trawlers, but they are often neglected or discarded. Despite being one of the dominant lanternfish species in the Arabian Sea, little is known about the life history of Diaphus thiollierei and its role in marine ecosystems. In this study, 103 D. thiollierei were collected in the Arabian Sea during October-November 2020 to study population growth based on sagittal otolith daily ages; and 10 fish collected during April–May 2021 were subjected to otolith microchemistry analysis to reconstruct the vertical migration in their life history using LA-ICP-MS technique. The standard length–dry weight (SL-DW) relationships for D. thiollierei revealed both negative allometric growth and a significant difference between the sexes. Using daily growth annuli counts on the sagittal section of otoliths, the von Bertalanffy growth equation for D. thiollierei was determined. The pattern of four elemental ratios (Sr to Ca, Mg to Ca, Li to Ca, and Ba to Ca) in sagittal otolith suggested that, in general, D. thiollierei descended continually after hatching until the post-larval (PL) stage when they reached a depth of approximately 200 m. Subsequently, from the PL stage to the post-metamorphosis Ⅱ (PM Ⅱ) stage, D. thiollierei likely further sank from 200 m to a depth of approximately 300 m, and then in the daytime they were at a depth of approximately 300–800 m to take refuge from predators. This pilot study explored to unravel the vertical migration during life history in D. thiollierei from sagittal otoliths, whereas further investigation on otolith is needed to better delineate the population ecology in detail, and thus to provide basic information for the exploitation of the lanternfish resource and the understanding of their ecological roles.
2024, 43(12): 85-101.
doi: 10.1007/s13131-024-2420-x
Abstract:
Phytoplankton blooms are complex environmental phenomena driven by multiple factors. Understanding their relationships with meteorological factors and climate oscillations is essential for advancing data-driven and hybrid statistical-dynamical models. However, these relationships have rarely been investigated across different temporal scales. This study employs wavelet transform coherence and multiple wavelet coherence to examine the multiscale and multivariate relationships between phytoplankton blooms, meteorological factors, and climate oscillations in eight large marine ecosystems of the western North Pacific. The results reveal that all phytoplankton blooms in the studied ecosystems exhibit significant annual oscillations, while seasonal climate patterns demonstrate either unimodal or bimodal distributions. A comparison of the wavelet transform coherence and multiple wavelet coherence results indicates that meteorological factors primarily drive short-period variations in phytoplankton blooms, whereas climate oscillations exert more influence on long-term changes. The explanation of phytoplankton blooms increases as the driver factors increase, but there are also some decreasing due to the collinearity between different factors. The sea-air temperature difference emerges as the most significant driving factor, with its mechanisms varying across marine ecosystems: one type influences mixed-layer depth, while the other arises from interspecific differences in temperature sensitivity. Furthermore, the results underscore the importance of integrating non-dominant large-scale circulation indices with predominant meteorological factors for a more comprehensive understanding.
Phytoplankton blooms are complex environmental phenomena driven by multiple factors. Understanding their relationships with meteorological factors and climate oscillations is essential for advancing data-driven and hybrid statistical-dynamical models. However, these relationships have rarely been investigated across different temporal scales. This study employs wavelet transform coherence and multiple wavelet coherence to examine the multiscale and multivariate relationships between phytoplankton blooms, meteorological factors, and climate oscillations in eight large marine ecosystems of the western North Pacific. The results reveal that all phytoplankton blooms in the studied ecosystems exhibit significant annual oscillations, while seasonal climate patterns demonstrate either unimodal or bimodal distributions. A comparison of the wavelet transform coherence and multiple wavelet coherence results indicates that meteorological factors primarily drive short-period variations in phytoplankton blooms, whereas climate oscillations exert more influence on long-term changes. The explanation of phytoplankton blooms increases as the driver factors increase, but there are also some decreasing due to the collinearity between different factors. The sea-air temperature difference emerges as the most significant driving factor, with its mechanisms varying across marine ecosystems: one type influences mixed-layer depth, while the other arises from interspecific differences in temperature sensitivity. Furthermore, the results underscore the importance of integrating non-dominant large-scale circulation indices with predominant meteorological factors for a more comprehensive understanding.
2024, 43(12): 102-112.
doi: 10.1007/s13131-024-2451-3
Abstract:
By establishing a distribution and environmental factor database of 21 typical harmful dinoflagellates in global waters, the MaxEnt model was used to predict shifts in the habitat of harmful dinoflagellates in Chinese waters under global climate change. The results revealed that offshore distance was the most important predictive factor and that surface seawater temperature (SST), primary productivity, and nitrate concentration were the key ecological factors influencing the distribution of harmful dinoflagellates. Under the low greenhouse gas emission scenario defined by the Intergovernmental Panel on Climate Change (IPCC), by approximately 2050, 17 of the 21 harmful dinoflagellate species in high-suitability areas (HSA) will migrate northward, six species will migrate eastward, and six species will expand their HSA. By 2100, approximately 18 of the 21 harmful dinoflagellate species in HSA will have migrated northward, seven species will have migrated eastward, and four species will have expanded their HSA. Notably, the HSA content of highly toxic Alexandrium minutum is expected to increase by 13.4% and 9.4% by 2050 and 2100, respectively. Under the high greenhouse gas emissions, there will be 17 species migrating northward, 6 species migrating eastward, and 4 species increasing in their size in HSA by 2050; moreover, there will be 16 species migrating northward, 2 migrating eastward, and 4 species according to their size of HSA by 2100. Specifically, the HSA of A. minutum is predicted to increase by 7.0% and 25.9% by 2050 and 2100, respectively. Notably, A. ostenfeldii, which is currently seldom present in the China seas, is predicted to exhibit an HSA in most coastal areas of the Yellow Sea, the Bohai Sea, the Hangzhou Bay, the Zhejiang Coast, and the Beibu Gulf of the South China Sea. Conversely, the HSA of Noctiluca scintillans, a typical red-tide species, will be reduced by 7%–90%. The northward migration of Karenia mikimotoi exceeded 100 km and 300 km under low and high greenhouse gas emission scenarios, respectively. These changes underscore the significant impact of climate change on the distribution and habitat suitability of harmful dinoflagellates, thus indicating a potential shift in their ecological dynamics and consequent effects on marine ecosystems.
By establishing a distribution and environmental factor database of 21 typical harmful dinoflagellates in global waters, the MaxEnt model was used to predict shifts in the habitat of harmful dinoflagellates in Chinese waters under global climate change. The results revealed that offshore distance was the most important predictive factor and that surface seawater temperature (SST), primary productivity, and nitrate concentration were the key ecological factors influencing the distribution of harmful dinoflagellates. Under the low greenhouse gas emission scenario defined by the Intergovernmental Panel on Climate Change (IPCC), by approximately 2050, 17 of the 21 harmful dinoflagellate species in high-suitability areas (HSA) will migrate northward, six species will migrate eastward, and six species will expand their HSA. By 2100, approximately 18 of the 21 harmful dinoflagellate species in HSA will have migrated northward, seven species will have migrated eastward, and four species will have expanded their HSA. Notably, the HSA content of highly toxic Alexandrium minutum is expected to increase by 13.4% and 9.4% by 2050 and 2100, respectively. Under the high greenhouse gas emissions, there will be 17 species migrating northward, 6 species migrating eastward, and 4 species increasing in their size in HSA by 2050; moreover, there will be 16 species migrating northward, 2 migrating eastward, and 4 species according to their size of HSA by 2100. Specifically, the HSA of A. minutum is predicted to increase by 7.0% and 25.9% by 2050 and 2100, respectively. Notably, A. ostenfeldii, which is currently seldom present in the China seas, is predicted to exhibit an HSA in most coastal areas of the Yellow Sea, the Bohai Sea, the Hangzhou Bay, the Zhejiang Coast, and the Beibu Gulf of the South China Sea. Conversely, the HSA of Noctiluca scintillans, a typical red-tide species, will be reduced by 7%–90%. The northward migration of Karenia mikimotoi exceeded 100 km and 300 km under low and high greenhouse gas emission scenarios, respectively. These changes underscore the significant impact of climate change on the distribution and habitat suitability of harmful dinoflagellates, thus indicating a potential shift in their ecological dynamics and consequent effects on marine ecosystems.
2024, 43(12): 113-122.
doi: 10.1007/s13131-024-2387-7
Abstract:
As our understanding of ecology deepens and modeling techniques advance, species distribution models have grown increasingly sophisticated, enhancing both their fitting and predictive capabilities. However, the dependability of predictive accuracy remains a critical issue, as the precision of these predictions largely hinges on the quality of the base data. We developed models using both field survey and remote sensing data from 2016 to 2020 to evaluate the impact of different data sources on the accuracy of predictions for Scomber japonicus distributions. Our research findings indicate that the variability of water temperature and salinity data from field suvery is significantly greater than that from remote sensing data. Within the same season, we found that the relationship between the abundance of S. japonicus and environmental factors varied significantly depending on the data source. Models using field survey data were able to more accurately reflect the complex relationships between resource distribution and environmental factors. Additionally, in terms of model predictive performance, models based on field survey data demonstrated greater accuracy in predicting the abundance of S. japonicus compared to those based on remote sensing data, allowing for more accurate mastery of their spatial distribution characteristics. This study highlights the significant impact of data sources on the accuracy of species distribution models and offers valuable insights for fisheries resources management.
As our understanding of ecology deepens and modeling techniques advance, species distribution models have grown increasingly sophisticated, enhancing both their fitting and predictive capabilities. However, the dependability of predictive accuracy remains a critical issue, as the precision of these predictions largely hinges on the quality of the base data. We developed models using both field survey and remote sensing data from 2016 to 2020 to evaluate the impact of different data sources on the accuracy of predictions for Scomber japonicus distributions. Our research findings indicate that the variability of water temperature and salinity data from field suvery is significantly greater than that from remote sensing data. Within the same season, we found that the relationship between the abundance of S. japonicus and environmental factors varied significantly depending on the data source. Models using field survey data were able to more accurately reflect the complex relationships between resource distribution and environmental factors. Additionally, in terms of model predictive performance, models based on field survey data demonstrated greater accuracy in predicting the abundance of S. japonicus compared to those based on remote sensing data, allowing for more accurate mastery of their spatial distribution characteristics. This study highlights the significant impact of data sources on the accuracy of species distribution models and offers valuable insights for fisheries resources management.
2024, 43(12): 123-140.
doi: 10.1007/s13131-024-2386-8
Abstract:
The proposed study focuses on the reported oil spill detection and assessments of oil impacts on marine ecosystems. Five selected oil spills, including those in East China Sea, Balikpapan Bay, Red Sea, Mauritius coast, and Colombo coast were detected using the Sentinel-1 satellite dataset. Sentinel-2/Landsat 8, and Sentinel-5 Precursor (S-5P) satellite datasets were utilized to observe the impacts of oil spills on vegetation cover and air quality respectively. Synthetic aperture radar-based oil spill detection techniques are effective in monitoring oil pollution. Impacts of oil spills on vegetation are monitored via different vegetation indices. The East China Sea spill moved around 190 km from the source point. The area of vegetation cover impacted by the Balikpapan Bay oil spill was 118 km2. Near real-time data of different toxic gases from S-5P were analyzed for Sri Lanka and the Red Sea using the Google Earth Engine. It is concluded that wind speed was between the range of 3 m/s to 9 m/s that is favorable for the oil spill detection, and it is also observed that wind direction had impacts on oil spill movement as well. Vegetation Indices provide highly reliable results for the four events but the Red Sea oil spill findings were not satisfactory due to low vegetation cover in this area.
The proposed study focuses on the reported oil spill detection and assessments of oil impacts on marine ecosystems. Five selected oil spills, including those in East China Sea, Balikpapan Bay, Red Sea, Mauritius coast, and Colombo coast were detected using the Sentinel-1 satellite dataset. Sentinel-2/Landsat 8, and Sentinel-5 Precursor (S-5P) satellite datasets were utilized to observe the impacts of oil spills on vegetation cover and air quality respectively. Synthetic aperture radar-based oil spill detection techniques are effective in monitoring oil pollution. Impacts of oil spills on vegetation are monitored via different vegetation indices. The East China Sea spill moved around 190 km from the source point. The area of vegetation cover impacted by the Balikpapan Bay oil spill was 118 km2. Near real-time data of different toxic gases from S-5P were analyzed for Sri Lanka and the Red Sea using the Google Earth Engine. It is concluded that wind speed was between the range of 3 m/s to 9 m/s that is favorable for the oil spill detection, and it is also observed that wind direction had impacts on oil spill movement as well. Vegetation Indices provide highly reliable results for the four events but the Red Sea oil spill findings were not satisfactory due to low vegetation cover in this area.
