2023 Vol. 45, No. 3
Display Method:
2023, 45(3): 1-3.
Abstract:
2023, 45(3): 1-13.
doi: 10.12284/hyxb2023036
Abstract:
Using the tropical cyclone (TC) best track data from the Shanghai Typhoon Research Institute of the China Meteorological Administration (CMA-STI) and the monthly mean reanalysis data of NCEP/NCAR, the interannual variability of the basin-scale large-scale environmental steering flow and the tropical cyclone activity in the western North Pacific (WNP) during peak season from July to September from 1979−2016 are investigated. The results show that: (1) There are two typical modes of summer large-scale environmental steering flow in the WNP at the inter-annual scale. The first typical mode is a dipole circulation with a meridional distribution, which is closely related to the eastern ENSO and the sea-air coupling mode in the WNP region. (2) The TC activity (generation location, tracks, intensity and duration) differs significantly between the two typical interannual mode anomaly years of the large-scale environment steering flow, but the differences have distinctly different characteristics for the two typical inter-annual modes. (3) The spatial distribution of TC generation location shows significant differences from north to south between the years of the first typical interannual mode anomalies of large-scale environment steering flow; the TC tracks, especially the northwestward and westward prevailing tracks, also have significant differences, and their average duration and intensity also show their corresponding significant differences. In the second major interannual mode anomaly years, the TC generation locations show significant east-west distribution especially in the southeast quadrant, and the differences in TC tracks are mainly in the northwestward and offshore steering prevailing tracks, and their mean durations and intensities also show significant differences.
Using the tropical cyclone (TC) best track data from the Shanghai Typhoon Research Institute of the China Meteorological Administration (CMA-STI) and the monthly mean reanalysis data of NCEP/NCAR, the interannual variability of the basin-scale large-scale environmental steering flow and the tropical cyclone activity in the western North Pacific (WNP) during peak season from July to September from 1979−2016 are investigated. The results show that: (1) There are two typical modes of summer large-scale environmental steering flow in the WNP at the inter-annual scale. The first typical mode is a dipole circulation with a meridional distribution, which is closely related to the eastern ENSO and the sea-air coupling mode in the WNP region. (2) The TC activity (generation location, tracks, intensity and duration) differs significantly between the two typical interannual mode anomaly years of the large-scale environment steering flow, but the differences have distinctly different characteristics for the two typical inter-annual modes. (3) The spatial distribution of TC generation location shows significant differences from north to south between the years of the first typical interannual mode anomalies of large-scale environment steering flow; the TC tracks, especially the northwestward and westward prevailing tracks, also have significant differences, and their average duration and intensity also show their corresponding significant differences. In the second major interannual mode anomaly years, the TC generation locations show significant east-west distribution especially in the southeast quadrant, and the differences in TC tracks are mainly in the northwestward and offshore steering prevailing tracks, and their mean durations and intensities also show significant differences.
2023, 45(3): 14-26.
doi: 10.12284/hyxb2023052
Abstract:
Diurnal observation is necessary for grasping the variability of carbonate system in coastal waters and sea-air CO2 exchange process and is helpful to reduce the uncertainty of assessments for carbon source/sink. Surface carbonate system and related parameters were obtained during twice 24 hours fixed sampling and observation conducted in April and August 2018 in the Yingluo Bay-Anpu Harbor, located in the northeastern Beibu Gulf. In this paper, we analyzed the hourly variations of partial pressure of CO2 in surface sea water (pCO2) and discussed the corresponding environment factors controlling pCO2 in both seasons. The pCO2 values ranged from 530−628 μatm in spring to 427−748 μatm in summer, with the average sea-air CO2 flux in spring and summer for (1.7±0.8) mmol/(m2·d) and (1.2±0.8) mmol/(m2·d), respectively. The study area acted as a weak CO2 source during both seasons. The hourly changes of pCO2 in spring were more significantly affected by temperature effects than in summer. During summertime, pCO2 had more sensibly response to tidal action, enhanced biological production and respiration with inflow of coastal freshwater such as rivers and submarine groundwater discharge. Water warming dominated the formation of high pCO2 in spring. The enhanced biological production during the physical mixing of saline and fresh water played a role in the drawdown of surface dissolved inorganic carbon (DIC) and the mangroves and salt marshes ecosystems along the bay had a certain contribution to the addition of DIC on the freshwater end-member in summer. The variations of the ratio of DIC concentration and total alkalinity (TA) in the water masses could imply the overall distribution pattern adjacent to the Yingluo Bay-Anpu Harbor that high values exists in the bay and lower values exists in offshore water.
Diurnal observation is necessary for grasping the variability of carbonate system in coastal waters and sea-air CO2 exchange process and is helpful to reduce the uncertainty of assessments for carbon source/sink. Surface carbonate system and related parameters were obtained during twice 24 hours fixed sampling and observation conducted in April and August 2018 in the Yingluo Bay-Anpu Harbor, located in the northeastern Beibu Gulf. In this paper, we analyzed the hourly variations of partial pressure of CO2 in surface sea water (pCO2) and discussed the corresponding environment factors controlling pCO2 in both seasons. The pCO2 values ranged from 530−628 μatm in spring to 427−748 μatm in summer, with the average sea-air CO2 flux in spring and summer for (1.7±0.8) mmol/(m2·d) and (1.2±0.8) mmol/(m2·d), respectively. The study area acted as a weak CO2 source during both seasons. The hourly changes of pCO2 in spring were more significantly affected by temperature effects than in summer. During summertime, pCO2 had more sensibly response to tidal action, enhanced biological production and respiration with inflow of coastal freshwater such as rivers and submarine groundwater discharge. Water warming dominated the formation of high pCO2 in spring. The enhanced biological production during the physical mixing of saline and fresh water played a role in the drawdown of surface dissolved inorganic carbon (DIC) and the mangroves and salt marshes ecosystems along the bay had a certain contribution to the addition of DIC on the freshwater end-member in summer. The variations of the ratio of DIC concentration and total alkalinity (TA) in the water masses could imply the overall distribution pattern adjacent to the Yingluo Bay-Anpu Harbor that high values exists in the bay and lower values exists in offshore water.
2023, 45(3): 27-39.
doi: 10.12284/hyxb2023046
Abstract:
The majority of global fish stocks lack adequate data for their stock statuses to be assessed using conventional stock assessment methods. Data-limited methods, such as CMSY, have been increasingly recommended as new solutions for stock assessment and fishery management. However, CMSY is highly dependent on data quality, and the reliability of the method is yet to be verified under circumstances of limited length of time series data and variable observational errors. In this study, we investigated effects of lengths of catch time series, stages of fishery development, and levels of observational errors in catches on stock assessment of three economically-important species in the Yellow Sea using CMSY method. The results show that chub mackerel (Scomber japonicus), hairtail (Trichiurus lepturus), and silver pomfret (Pampus argenteus), all have been overfished (B/BMSY<1 and F/FMSY>1), with their yields higher than estimated MSY since 2000, and although their fishing intensities have been reduced over the most recent decade, their biomasses remain at low levels (B/BMSY<1). The retrospective analysis show small differences in the results of stock assessment for the three species, indicating that the assessments are robust enough with long time series data. As to effects of lengths of catch time series, the assessments are more stable using time series data covering a period of both rise and fall in catches. The effect of observational errors in catches is also tested, showing that when the error is >20%, the model tend to overestimate MSY and BMSY, but the assessment remains robust enough. This study suggests that cautions should be undertaken in the application of CMSY by using longer time series of catch data and, in the presence of high uncertainty in the assessment, more conservative measures should be taken in fishery management.
The majority of global fish stocks lack adequate data for their stock statuses to be assessed using conventional stock assessment methods. Data-limited methods, such as CMSY, have been increasingly recommended as new solutions for stock assessment and fishery management. However, CMSY is highly dependent on data quality, and the reliability of the method is yet to be verified under circumstances of limited length of time series data and variable observational errors. In this study, we investigated effects of lengths of catch time series, stages of fishery development, and levels of observational errors in catches on stock assessment of three economically-important species in the Yellow Sea using CMSY method. The results show that chub mackerel (Scomber japonicus), hairtail (Trichiurus lepturus), and silver pomfret (Pampus argenteus), all have been overfished (B/BMSY<1 and F/FMSY>1), with their yields higher than estimated MSY since 2000, and although their fishing intensities have been reduced over the most recent decade, their biomasses remain at low levels (B/BMSY<1). The retrospective analysis show small differences in the results of stock assessment for the three species, indicating that the assessments are robust enough with long time series data. As to effects of lengths of catch time series, the assessments are more stable using time series data covering a period of both rise and fall in catches. The effect of observational errors in catches is also tested, showing that when the error is >20%, the model tend to overestimate MSY and BMSY, but the assessment remains robust enough. This study suggests that cautions should be undertaken in the application of CMSY by using longer time series of catch data and, in the presence of high uncertainty in the assessment, more conservative measures should be taken in fishery management.
2023, 45(3): 40-51.
doi: 10.12284/hyxb2023044
Abstract:
Yellowfin tuna (Thunnus albacares) is one of the most important fishes with great global economic and ecological value, and its conservation and management have received much concerns. The stock status of yellowfin tuna in the Indian Ocean based on the age-structured assessment program model is evaluated in this study, focusing on the uncertainties of its life history characteristics on the stock assessment results. The results show that the resources of yellowfin tuna in the Indian Ocean remained relatively stable from 1960 to 1985 and then declined gradually, while the fishing mortality coefficient F increased rapidly after 2010. This stock in 2020 may be overfished, since the estimated F2020 was greater than FMSY (F that could attain maximum sustainable yield MSY), while spawning stock biomass, SSB2020 was less than SSBMSY. Sensitivity analysis was also conducted to evaluate the uncertainties of stock assessment. Two important life history characteristics, natural mortality M and steepness of spawning-stock relationship h, were analyzed for their influence on the estimates of F, SSB and biological reference points. When h was set to 0.7, 0.8, and 0.9, SSBMSY and SSB0 (the unfished SSB) reduced by about 255 300 t and 340 400 t; and F2020/FMSY gradually decreased (from 2.88 to 2.21 and 1.73). When the M was set to M1 (0.963, 0.663, 0.548, 0.493, 0.463, 0.446) and M2 (1.068, 0.735, 0.608, 0.547, 0.514, 0.495) respectively, the larger M2 leads to lower SSB and F2020/FMSY. In summary, the conservation and management of Indian Ocean yellowfin tuna should be tightened in the future to achieve long-term sustainable development of this fishery. The life history characteristics of yellowfin tuna should be fully understood, especially M and h estimation should be improved, to provide more accurate information for stock assessment and fisheries management for Indian Ocean yellowfin tuna.
Yellowfin tuna (Thunnus albacares) is one of the most important fishes with great global economic and ecological value, and its conservation and management have received much concerns. The stock status of yellowfin tuna in the Indian Ocean based on the age-structured assessment program model is evaluated in this study, focusing on the uncertainties of its life history characteristics on the stock assessment results. The results show that the resources of yellowfin tuna in the Indian Ocean remained relatively stable from 1960 to 1985 and then declined gradually, while the fishing mortality coefficient F increased rapidly after 2010. This stock in 2020 may be overfished, since the estimated F2020 was greater than FMSY (F that could attain maximum sustainable yield MSY), while spawning stock biomass, SSB2020 was less than SSBMSY. Sensitivity analysis was also conducted to evaluate the uncertainties of stock assessment. Two important life history characteristics, natural mortality M and steepness of spawning-stock relationship h, were analyzed for their influence on the estimates of F, SSB and biological reference points. When h was set to 0.7, 0.8, and 0.9, SSBMSY and SSB0 (the unfished SSB) reduced by about 255 300 t and 340 400 t; and F2020/FMSY gradually decreased (from 2.88 to 2.21 and 1.73). When the M was set to M1 (0.963, 0.663, 0.548, 0.493, 0.463, 0.446) and M2 (1.068, 0.735, 0.608, 0.547, 0.514, 0.495) respectively, the larger M2 leads to lower SSB and F2020/FMSY. In summary, the conservation and management of Indian Ocean yellowfin tuna should be tightened in the future to achieve long-term sustainable development of this fishery. The life history characteristics of yellowfin tuna should be fully understood, especially M and h estimation should be improved, to provide more accurate information for stock assessment and fisheries management for Indian Ocean yellowfin tuna.
2023, 45(3): 52-65.
doi: 10.12284/hyxb2023032
Abstract:
In order to improve the success rate and accuracy of species identification of fish eggs and larvae, fish eggs and larvae samples collected from the Zhujiang River Estuary in spring were identified by DNA barcoding technology based on the mitochondrial COⅠand 12S rRNA genes. A total of 391 samples were amplified and 60 species in 7 orders, 25 families, 42 genera were successfully identified (2 species were not identified). Among them, Perciformes had the most species and quantity accounting for 51.6% and 47.91% respectively, followed by Clupeiformes with 25% and 34.56% respectively. There were 10 dominant species, among which Coilia mystus had the highest dominance of 0.071, Collichthys lucidus had the lowest dominance of 0.014. The amplification results of COⅠand 12S rRNA gene fragments showed that the success rate of 12S rRNA gene amplification in eggs and larvae (95.60%) was significantly higher than that of COⅠgene (43.22%). Genetic distance and ABGD analysis showed that the intraspecies genetic distance of COⅠ gene was 0−0.005 (average was 0.003), and the interspecific genetic distance was 0.061−0.376 (average was 0.253), and there was an obvious “barcode gap” between them. The intraspecific genetic distance of 12S rRNA gene was 0−0.011 (average was 0.007), and the interspecific genetic distance was 0.007−0.487(average was 0.283). which do not form a “barcode gap” between the interspecific and intraspecies genetic distances. The Bayesian phylogenetic tree based on COⅠ and 12S rRNA genes showed that all species could be clustered into independent branches and can be effectively distinguished. The above results show that both COⅠ gene and 12S rRNA gene can be used for the identification of most fish eggs and larvae, but the success rate of COⅠ gene amplification is low, and it is difficult to distinguish some closely related species of 12S rRNA gene. The combined use of the two genes can improve the success rate and accuracy of species identification of fish eggs and larvae.
In order to improve the success rate and accuracy of species identification of fish eggs and larvae, fish eggs and larvae samples collected from the Zhujiang River Estuary in spring were identified by DNA barcoding technology based on the mitochondrial COⅠand 12S rRNA genes. A total of 391 samples were amplified and 60 species in 7 orders, 25 families, 42 genera were successfully identified (2 species were not identified). Among them, Perciformes had the most species and quantity accounting for 51.6% and 47.91% respectively, followed by Clupeiformes with 25% and 34.56% respectively. There were 10 dominant species, among which Coilia mystus had the highest dominance of 0.071, Collichthys lucidus had the lowest dominance of 0.014. The amplification results of COⅠand 12S rRNA gene fragments showed that the success rate of 12S rRNA gene amplification in eggs and larvae (95.60%) was significantly higher than that of COⅠgene (43.22%). Genetic distance and ABGD analysis showed that the intraspecies genetic distance of COⅠ gene was 0−0.005 (average was 0.003), and the interspecific genetic distance was 0.061−0.376 (average was 0.253), and there was an obvious “barcode gap” between them. The intraspecific genetic distance of 12S rRNA gene was 0−0.011 (average was 0.007), and the interspecific genetic distance was 0.007−0.487(average was 0.283). which do not form a “barcode gap” between the interspecific and intraspecies genetic distances. The Bayesian phylogenetic tree based on COⅠ and 12S rRNA genes showed that all species could be clustered into independent branches and can be effectively distinguished. The above results show that both COⅠ gene and 12S rRNA gene can be used for the identification of most fish eggs and larvae, but the success rate of COⅠ gene amplification is low, and it is difficult to distinguish some closely related species of 12S rRNA gene. The combined use of the two genes can improve the success rate and accuracy of species identification of fish eggs and larvae.
2023, 45(3): 66-75.
doi: 10.12284/hyxb2023012
Abstract:
According to the data of four voyages of zooplankton survey in the southwest of Bohai Sea from August 2020 to May 2021, the community structure and seasonal variation of small medusae were analyzed, and the effects of environmental factors on the abundance of small medusae were discussed. There were seasonal variations in species composition and abundance distribution of small medusae in the southwest of Bohai Sea, a total of 13 species of small medusae were found throughout the year, including 11 species of hydromedusae and 2 species of ctenophores. The number of medusae species in spring, summer, autumn and winter were 4, 9, 7 and 2 species respectively. The mean abundance values were 30.74 ind./m3, 30.78 ind./m3, 12.08 ind./m3 and 0.57 ind./m3 respectively. The dominant species were Sugiura chengshanense, Rathkea octopunctata, Eirene ceylonensis, Clytia hemisphaerica, Pleurobrachia globosa. The average seasonal replacement rate of dominant species was 91.67%, showing obvious seasonal succession. Water temperature and salinity were main environmental factor affecting the seasonal variation of total abundance of small medusae in the southwest of Bohai Sea. The increase of water temperature and nutrients in spring promote the growth and reproduction of small medusae. In summer, copepods provided rich bait for small medusae to promote its growth, the community was mainly affected by salinity in autumn. According to the risk species of small medusae in the southwest of Bohai Sea, it was speculated that the peak of risk species in summer and autumn was a risk to the planned nuclear power.
According to the data of four voyages of zooplankton survey in the southwest of Bohai Sea from August 2020 to May 2021, the community structure and seasonal variation of small medusae were analyzed, and the effects of environmental factors on the abundance of small medusae were discussed. There were seasonal variations in species composition and abundance distribution of small medusae in the southwest of Bohai Sea, a total of 13 species of small medusae were found throughout the year, including 11 species of hydromedusae and 2 species of ctenophores. The number of medusae species in spring, summer, autumn and winter were 4, 9, 7 and 2 species respectively. The mean abundance values were 30.74 ind./m3, 30.78 ind./m3, 12.08 ind./m3 and 0.57 ind./m3 respectively. The dominant species were Sugiura chengshanense, Rathkea octopunctata, Eirene ceylonensis, Clytia hemisphaerica, Pleurobrachia globosa. The average seasonal replacement rate of dominant species was 91.67%, showing obvious seasonal succession. Water temperature and salinity were main environmental factor affecting the seasonal variation of total abundance of small medusae in the southwest of Bohai Sea. The increase of water temperature and nutrients in spring promote the growth and reproduction of small medusae. In summer, copepods provided rich bait for small medusae to promote its growth, the community was mainly affected by salinity in autumn. According to the risk species of small medusae in the southwest of Bohai Sea, it was speculated that the peak of risk species in summer and autumn was a risk to the planned nuclear power.
2023, 45(3): 76-83.
doi: 10.12284/hyxb2023038
Abstract:
Acanthaster planci, one of the predators of reef-building corals, has attracted much attention for its catastrophic damage to coral reef ecosystems. However, the spatial and temporal distribution characteristics of A. planci are still unclear in the coral reef ecosystem of the South China Sea. In this study, using environmental DNA and real-time quantitative PCR techniques, we analyzed the concentration variation of the mitochondrial cytochrome-c-oxidase subunits I (COTS-mtCOI) fragment of A. planci in the surface seawater of the Xisha Islands in September 2020, April 2021 and January 2022, and the correlations between the concentration variation with environmental factors such as seawater temperature, salinity, pH, chlorophyll content, nutrients content and other environmental factors. The results showed that COTS-mtCOI fragment concentration in the Xisha Islands varied from 0 copies/m3 to 4.13×107 copies/m3 during 2020−2022, and there were always higher concentrations in the Yongle Atoll. For Huaguang Reef, Jinqing Islands, Lingyang Reef, Quanfu Island and Zhaoshu Island, the average concentration of COTS-mtCOI fragment in September 2020 was significantly (p<0.05) higher than those in April 2021 and January 2022. In addition, COTS-mtCOI fragment concentration was significantly (p<0.05) positively correlated with surface seawater temperature. These results suggest that the population of A. planci is widely distributed in the seawater of Xisha Islands, and higher density of A. planci could appear in Yongle Atoll. Moreover, ocean warming may accelerate the outbreak of A. planci. This study is helpful to understand the population distribution characteristics of A. planci in the coral reef ecosystems of the South China Sea, and can provide a theoretical basis for the early warning and forecast of the A. planci outbreak.
Acanthaster planci, one of the predators of reef-building corals, has attracted much attention for its catastrophic damage to coral reef ecosystems. However, the spatial and temporal distribution characteristics of A. planci are still unclear in the coral reef ecosystem of the South China Sea. In this study, using environmental DNA and real-time quantitative PCR techniques, we analyzed the concentration variation of the mitochondrial cytochrome-c-oxidase subunits I (COTS-mtCOI) fragment of A. planci in the surface seawater of the Xisha Islands in September 2020, April 2021 and January 2022, and the correlations between the concentration variation with environmental factors such as seawater temperature, salinity, pH, chlorophyll content, nutrients content and other environmental factors. The results showed that COTS-mtCOI fragment concentration in the Xisha Islands varied from 0 copies/m3 to 4.13×107 copies/m3 during 2020−2022, and there were always higher concentrations in the Yongle Atoll. For Huaguang Reef, Jinqing Islands, Lingyang Reef, Quanfu Island and Zhaoshu Island, the average concentration of COTS-mtCOI fragment in September 2020 was significantly (p<0.05) higher than those in April 2021 and January 2022. In addition, COTS-mtCOI fragment concentration was significantly (p<0.05) positively correlated with surface seawater temperature. These results suggest that the population of A. planci is widely distributed in the seawater of Xisha Islands, and higher density of A. planci could appear in Yongle Atoll. Moreover, ocean warming may accelerate the outbreak of A. planci. This study is helpful to understand the population distribution characteristics of A. planci in the coral reef ecosystems of the South China Sea, and can provide a theoretical basis for the early warning and forecast of the A. planci outbreak.
2023, 45(3): 84-96.
doi: 10.12284/hyxb2023034
Abstract:
Bacteria play an important role in the aquatic ecosystem. In our work, bacterial community structure and assembly mechanisms were studied in Sansha Bay in Fujian Province based on DNA and RNA high-throughput sequencing. Our results revealed that: (1) A total of 1476 operational taxonomic units (OTUs) were detected, and γ-proteobacteria, α-proteobacteria, cyanobacteriia and bacteroidia were the most diverse bacterial groups. (2) γ-proteobacteria, α-proteobacteria, cyanobacteriia and bacteroidia were also the most abundant groups in Sansha Bay both in the DNA and RNA high-throughput sequencing results. The metabolic activities of these four dominant groups were different, and their metabolic activities were mainly regulated by salinity, total nitrogen, nitrite nitrogen and inorganic phosphate concentrations. (3) The bacterial community structure in Sansha Bay was different in spatial scale, and the closer the geographical location was, the more similar the bacterial community structure was. Neutral process was the main assembly mechanism affecting the construction of bacterial community in Sansha Bay. Our results were useful for the understanding the bacterial community structure and assembly mechanism in Sansha Bay, Fujian.
Bacteria play an important role in the aquatic ecosystem. In our work, bacterial community structure and assembly mechanisms were studied in Sansha Bay in Fujian Province based on DNA and RNA high-throughput sequencing. Our results revealed that: (1) A total of 1476 operational taxonomic units (OTUs) were detected, and γ-proteobacteria, α-proteobacteria, cyanobacteriia and bacteroidia were the most diverse bacterial groups. (2) γ-proteobacteria, α-proteobacteria, cyanobacteriia and bacteroidia were also the most abundant groups in Sansha Bay both in the DNA and RNA high-throughput sequencing results. The metabolic activities of these four dominant groups were different, and their metabolic activities were mainly regulated by salinity, total nitrogen, nitrite nitrogen and inorganic phosphate concentrations. (3) The bacterial community structure in Sansha Bay was different in spatial scale, and the closer the geographical location was, the more similar the bacterial community structure was. Neutral process was the main assembly mechanism affecting the construction of bacterial community in Sansha Bay. Our results were useful for the understanding the bacterial community structure and assembly mechanism in Sansha Bay, Fujian.
2023, 45(3): 113-124.
doi: 10.12284/hyxb2023042
Abstract:
Mangroves forests, as a coastal zone ecosystem dominated by mangrove plants in the tropics and subtropics, are one of the important coastal wetland types. In this paper, multi-source and multi-phase satellite data were used to form a data atlas of shoreline, reclamation, aquaculture area, mangrove distribution in the Guangdong-Hong Kong-Macao Greater Bay Area from 1969 to 2020, and the time series analysis of the evolution of mangroves in the Greater Bay Area was obtained by using the combine mangrove recognition index (CMRI). The results show that the existing mangrove forests data set can be obtained by interpreting the multi-source remote sensing data, and the CMRI time series data can establish the history of the existing mangrove forest change, and then effectively estimate the mangrove forest age. The temporal and spatial distribution of mangroves in the Guangdong-Hong Kong-Macao Greater Bay Area has undergone obvious changes, with the existing mangroves being about 3 316 hm2, and the existing forest age in various regions in the Greater Bay Area is quite different, and the overall average forest age is 20 a. In the past 50 years, the shoreline as a whole has moved towards the sea, and the changes in shoreline, reclamation, and breeding areas have significantly affected the area, spatial distribution, and age of mangroves. Artificial cultivation has been the main reason for the restoration of mangroves in the past 20 years.
Mangroves forests, as a coastal zone ecosystem dominated by mangrove plants in the tropics and subtropics, are one of the important coastal wetland types. In this paper, multi-source and multi-phase satellite data were used to form a data atlas of shoreline, reclamation, aquaculture area, mangrove distribution in the Guangdong-Hong Kong-Macao Greater Bay Area from 1969 to 2020, and the time series analysis of the evolution of mangroves in the Greater Bay Area was obtained by using the combine mangrove recognition index (CMRI). The results show that the existing mangrove forests data set can be obtained by interpreting the multi-source remote sensing data, and the CMRI time series data can establish the history of the existing mangrove forest change, and then effectively estimate the mangrove forest age. The temporal and spatial distribution of mangroves in the Guangdong-Hong Kong-Macao Greater Bay Area has undergone obvious changes, with the existing mangroves being about 3 316 hm2, and the existing forest age in various regions in the Greater Bay Area is quite different, and the overall average forest age is 20 a. In the past 50 years, the shoreline as a whole has moved towards the sea, and the changes in shoreline, reclamation, and breeding areas have significantly affected the area, spatial distribution, and age of mangroves. Artificial cultivation has been the main reason for the restoration of mangroves in the past 20 years.
2023, 45(3): 125-135.
doi: 10.12284/hyxb2023054
Abstract:
Mangroves are important for maintaining biodiversity as well as ecological balance. Therefore, it is necessary to extract mangrove vegetation information efficiently and accurately and to monitor it in real time. A deep learning method for pixel-level accurate extraction of mangroves from high-resolution remote sensing images is presented in this paper. For the problem of low accuracy of mangrove remote sensing classification, CU-Net model for mangrove identification is constructed by introducing CLoss loss function by strengthening image center information and weakening edge information, and adding Dropout and Batch Normalization layers. And a new prediction model is constructed by sliding overlap splicing method, which effectively solves the problem of insufficient edge information and splicing traces in the prediction results. The recognition results of the proposed method are compared with the prediction results of U-Net, SegNet and DenseNet models as well as the traditional SVM and RF methods. The results show that the proposed model has stronger generalization ability and better recognition effect compared with other deep learning models. In the two test areas, the average OA and MIoU reach 94.43% and 88.12%, respectively. The average F1-score in mangrove and ordinary trees reach 95.96% and 90.49%, respectively. The accuracy is significantly higher than that of traditional SVM and RF methods, as well as several other neural networks. The effectiveness of the model in the field of mangrove recognition is verified, which can provide a new idea for the field of high resolution remote sensing mangrove recognition.
Mangroves are important for maintaining biodiversity as well as ecological balance. Therefore, it is necessary to extract mangrove vegetation information efficiently and accurately and to monitor it in real time. A deep learning method for pixel-level accurate extraction of mangroves from high-resolution remote sensing images is presented in this paper. For the problem of low accuracy of mangrove remote sensing classification, CU-Net model for mangrove identification is constructed by introducing CLoss loss function by strengthening image center information and weakening edge information, and adding Dropout and Batch Normalization layers. And a new prediction model is constructed by sliding overlap splicing method, which effectively solves the problem of insufficient edge information and splicing traces in the prediction results. The recognition results of the proposed method are compared with the prediction results of U-Net, SegNet and DenseNet models as well as the traditional SVM and RF methods. The results show that the proposed model has stronger generalization ability and better recognition effect compared with other deep learning models. In the two test areas, the average OA and MIoU reach 94.43% and 88.12%, respectively. The average F1-score in mangrove and ordinary trees reach 95.96% and 90.49%, respectively. The accuracy is significantly higher than that of traditional SVM and RF methods, as well as several other neural networks. The effectiveness of the model in the field of mangrove recognition is verified, which can provide a new idea for the field of high resolution remote sensing mangrove recognition.
2023, 45(3): 136-146.
doi: 10.12284/hyxb2023026
Abstract:
Satellite derived bathymetric using multispectral imagery is an effective means to obtain shallow water depth information. However, its validity is limited to optical shallow water areas, but presents a “pseudo-shallow sea” distortion phenomenon in deep water areas. Therefore, accurately identifying the valid region of satellite derived bathymetry (SDB) data is crucial for its wide application. Based on high-spatial resolution remote sensing image, a data-driven method for evaluating the validity of SDB based on analysis of the differences in the statistical distribution of radiance in deep/shallow water regions is proposed in this paper. This method uses the local standard deviation of the radiance information of satellite images as a feature, optimizes the statistical characteristics of the optical deep water area based on the K-S test method, and uses the hypothesis test method to identify the SDB corresponding to the deep water invalid area. The experimental results in Ganquan Island region show that the method can effectively identify the invalid SDB associated with the optical deep water area by dividing the boundary between optical shallow and deep water area. After removing the invalid data, the mean absolute error (MAE) of SDB in the optical shallow region is 1.01, and the root mean square error (RMSE) is 1.52. The experimental results show that the proposed method can accurately identify the optical shallow region of SDB result, which benefits the interpretation and application of SDB results.
Satellite derived bathymetric using multispectral imagery is an effective means to obtain shallow water depth information. However, its validity is limited to optical shallow water areas, but presents a “pseudo-shallow sea” distortion phenomenon in deep water areas. Therefore, accurately identifying the valid region of satellite derived bathymetry (SDB) data is crucial for its wide application. Based on high-spatial resolution remote sensing image, a data-driven method for evaluating the validity of SDB based on analysis of the differences in the statistical distribution of radiance in deep/shallow water regions is proposed in this paper. This method uses the local standard deviation of the radiance information of satellite images as a feature, optimizes the statistical characteristics of the optical deep water area based on the K-S test method, and uses the hypothesis test method to identify the SDB corresponding to the deep water invalid area. The experimental results in Ganquan Island region show that the method can effectively identify the invalid SDB associated with the optical deep water area by dividing the boundary between optical shallow and deep water area. After removing the invalid data, the mean absolute error (MAE) of SDB in the optical shallow region is 1.01, and the root mean square error (RMSE) is 1.52. The experimental results show that the proposed method can accurately identify the optical shallow region of SDB result, which benefits the interpretation and application of SDB results.
2023, 45(3): 147-158.
doi: 10.12284/hyxb2023048
Abstract:
Ocean is an important carbon sink in nature. The sea-air carbon dioxide flux is usually estimated by the difference of partial pressure of carbon dioxide (pCO2) between the atmosphere and the sea surface. Due to the imbalance of observation data on temporal and spatial distribution and datasets used for prediction, there is still large room for improvement in spatial resolution for present reconstruction of pCO2 on sea surface. In order to fit the temporal and spatial variability under high spatial resolution better, based on the sea surface fugacity of carbon dioxide (fCO2) observations of the Surface Ocean CO2 Atlas (SOCAT) and other multi-source data including remote sensing data, the nonlinear relationship between sea surface pCO2 and physical, biological, optical factors was established by a XGBoost model and a weight model was built based on spatiotemporal frequency of samples. A 0.041 7°×0.041 7° monthly sea surface pCO2 dataset in Atlantic from 2000 to 2018 was finally constructed with correlation coefficient of 0.966, mean squared error of 8.087 μatm and mean error of 4.012 μatm on prediction dataset. The reconstruction is highly consistent to other similar reconstruction results on temporal and spatial trend and also gains advantage in spatial resolution.
Ocean is an important carbon sink in nature. The sea-air carbon dioxide flux is usually estimated by the difference of partial pressure of carbon dioxide (pCO2) between the atmosphere and the sea surface. Due to the imbalance of observation data on temporal and spatial distribution and datasets used for prediction, there is still large room for improvement in spatial resolution for present reconstruction of pCO2 on sea surface. In order to fit the temporal and spatial variability under high spatial resolution better, based on the sea surface fugacity of carbon dioxide (fCO2) observations of the Surface Ocean CO2 Atlas (SOCAT) and other multi-source data including remote sensing data, the nonlinear relationship between sea surface pCO2 and physical, biological, optical factors was established by a XGBoost model and a weight model was built based on spatiotemporal frequency of samples. A 0.041 7°×0.041 7° monthly sea surface pCO2 dataset in Atlantic from 2000 to 2018 was finally constructed with correlation coefficient of 0.966, mean squared error of 8.087 μatm and mean error of 4.012 μatm on prediction dataset. The reconstruction is highly consistent to other similar reconstruction results on temporal and spatial trend and also gains advantage in spatial resolution.
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