Comparison of Classification Algorithms for Detecting Typical Coastal Reclamation in Guangdong Province with Landsat 8 and Sentinel 2 Images

Coastal reclamation in Guangdong Province is highly concentrated and is growing rapidly. However, intensive reclamation use has resulted in serious influence on the coastal ecosystem, directly and indirectly. The current conditions and spatial distribution of reclamations must be detected for coastal preservation and management using efficient technology. This study aims to find a suitable method and data to map reclamations accurately at a large scale. Pixel-based and object-oriented classification methods were applied in extracting the three typical types of coastal reclamation, namely, ports, aquaculture ponds, and salt pans, in Guangdong Province from Landsat 8 and Sentinel 2 images. The algorithms of a support vector machine, random forest, decision tree, and rule-based algorithm were performed. Classification results were compared with statistical measures to assess the performance of different algorithms. The results indicated that all of the algorithms could obtain classification results with high accuracy, whereas the object-oriented algorithm showed less efficiency than other algorithms in classifying ports with complicated features. High-resolution data were not always superior to lower-resolution data in the reclamation classification. Generally speaking, applying the rule-based object-oriented algorithm in Sentinel 2A MSI images is relatively efficient at detecting the reclamation use in coastal Guangdong considering its actual situation. The mapping of reclamations in the whole of coastal Guangdong shows that they present obvious agglomeration characteristics in the space. The aquaculture ponds are mainly distributed in the coastal zones of western Guangdong and eastern Guangdong, with the largest area of 77,963 ha. The other types of ports are mainly distributed in the coastal zones of the Pearl River Delta, with an area of 8146 ha, while salt pans are mainly distributed in the coastal zones of Jiangmen, Zhuhai, and Zhongshan, with a total area of 4072 ha. The results can provide key supporting data for decision making in coastal management and preservation.

Water Turbidity Retrieval Based on UAV Hyperspectral Remote Sensing

The water components affecting turbidity are complex and changeable, and the spectral response mechanism of each water quality parameter is different. Therefore, this study mainly aimed at the turbidity monitoring by unmanned aerial vehicle (UAV) hyperspectral technology, and establishes a set of turbidity retrieval models through the artificial control experiment, and verifies the model’s accuracy through UAV flight and water sample data in the same period. The results of this experiment can also be extended to different inland waters for turbidity retrieval. Retrieval of turbidity values of small inland water bodies can provide support for the study of the degree of water pollution. We collected the images and data of aquaculture ponds and irrigation ditches in Dawa District, Panjin City, Liaoning Province. Twenty-nine standard turbidity solutions with different concentration gradients (concentration from 0 to 360 NTU—the abbreviation of Nephelometric Turbidity Unit, which stands for scattered turbidity.) were established through manual control and we simultaneously collected hyperspectral data from the spectral values of standard solutions. The sensitive band to turbidity was obtained after analyzing the spectral information. We established four kinds of retrieval, including the single band, band ratio, normalized ratio, and the partial least squares (PLS) models. We selected the two models with the highest R2 for accuracy verification. The band ratio model and PLS model had the highest accuracy, and R2 was, respectively, 0.65 and 0.72. The hyperspectral image data obtained by UAV were combined with the PLS model, which had the highest R2 to estimate the spatial distribution of water turbidity. The turbidity of the water areas in the study area was 5–300 NTU, and most of which are 5–80 NTU. It shows that the PLS models can retrieve the turbidity with high accuracy of aquaculture ponds, irrigation canals, and reservoirs in Dawa District of Panjin City, Liaoning Province. The experimental results are consistent with the conclusions of the field investigation.

Assessing the Ecosystem Health of Coastal Wetland Vegetation (Suaeda salsa) Using the Pressure State Response Model, a Case of the Liao River Estuary in China

Suaeda salsa (S. salsa) is an important ecological barrier and tourism resource in coastal wetland resources, and assessing changes in its health is beneficial for protecting the ecological health of wetlands and increasing finances. The aim was to explore improvements in the degradation of S. salsa communities in the Liao River Estuary National Nature Reserve since a wetland restoration project was carried out in Panjin, Liaoning Province, China, in 2015. In this study, landscape changes in the reserve were assessed based on Sentinel-2 images classification results from 2016 to 2019. A pressure-state-response framework was constructed to assess the annual degradation of S. salsa communities within the wetlands. The assessment results show that the area of S. salsa communities and water bodies decreased annually from 2016 to 2019, and the increased degradation indicators indicate a state of continued degradation. The area of types such as aquaculture ponds and Phragmites australis communities did not change much, while the estuarine mudflats increased year by year. The causes of S. salsa community degradation include anthropogenic impacts from abandoned aquaculture ponds and sluice control systems but also natural impacts from changes in the tidal amplitude and soil properties of the mudflats. The results also indicate that the living conditions of S. salsa in the Liao River estuary wetlands are poor and that anthropogenic disturbance is necessary to restore the original vegetation abundance.

A Risk-Based Approach for Managing Aquaculture Used Oxytetracycline-Induced TetR in Surface Water Across Taiwan Regions

Oxytetracycline (OTC), one of the most important antibiotics in aquaculture industry, has been linked to emergence of antibiotic resistant genes in the aquatic environment. Given rapid growth of the aquaculture industry and unregulated use of antibiotics, it is necessary to implement measures to mitigate the impact of antibiotic resistance risk on environmental and human health. However, there is a lack of quantitative models to properly assess risk of antibiotic resistance associated with environmentally relevant antibiotic residues. To address this issue, here we developed a computational framework to assess antibiotic resistance risk posed by low-concentration OTC in aquaculture ponds and rivers across Taiwan regions. To this end, estimated amount of aquaculture used OTC as a crucial input parameter was incorporated into a multimedia fugacity model to predict environmental concentrations of OTC in surface water/sediment. A pharmacodynamic-based dose–response model was used to characterize the OTC concentration–antibiotic resistance relationships. The risk of antibiotic resistance selection in an aquatic environment could be assessed based on a probabilistic risk model. We also established a control measure model to manage the risks of substantial OTC-induced antibiotic resistance impacts. We found that OTC residues were likely to pose a high risk of tetracycline resistance (tetR) genes selection in aquaculture ponds among all the study basins, whereas risk of tetR genes selection in rivers experienced a variably changing fashion. We also showed that it was extremely difficult to moderate the tetR genes selection rates to less than 10% increase in aquaculture ponds situated at northeastern river basins in that the minimum reductions on OTC emission rates during spring, summer, and autumn were greater than 90%. On the other hand, water concentrations of OTC during spring and summer in southwestern rivers should be prioritized to be severely limited by reducing 67 and 25% of OTC emission rate, respectively. Overall, incorporating a computational fugacity model into a risk assessment framework can identify relative higher risk regions to provide the risk-based control strategies for public health decision-making and development of robust quantitative methods to zero-in on environment with high risk of tetR genes selection in relation to aquaculture-used pharmaceutical residues.

Relationships between pond water and tilapia skin microbiomes in aquaculture ponds in Malawi

Intensification of fish farming practices is being driven by the demand for increased food production to support a rapidly growing global human population, particularly in lower-middle income countries. Intensification of production, however, increases the risk of disease outbreaks and thus likelihood for crop losses. The microbial communities that colonise the skin mucosal surface of fish are poorly understood, but are important in maintaining fish health and resistance against disease. This skin microbial community is susceptible to disruption through stressors associated with transport, handling and the environment of intensive practices, and this risks the propagation of disease-causing pathogens. In this study, we characterised the microbial assemblages found on tilapia skin — the most widely farmed finfish globally — and in the surrounding water of seven earthen aquaculture ponds from two pond systems in distinct geographic regions in Malawi. Metabarcoding approaches were used to sequence the prokaryotic and microeukaryotic communities. We found 92% of prokaryotic amplicon sequence variants were common to both skin and water samples. Differentially enriched and core taxa, however, differed between the skin and water samples. In tilapia skin, Cetobacterium, Paucibacter, Pseudomonas and Comamonadaceae were enriched, whereas, the cyanobacteria Cyanobium, Microcystis and/or Synechocystis, and the diatom Cyclotella, were most prevalent in pond water. Ponds that clustered together according to their water prokaryotic communities also had similar microeukaryotic communities indicating strong environmental influences on prokaryotic and microeukaryotic community structures. While strong site-specific clustering was observed in pond water, the grouping of tilapia skin prokaryotes by pond site was less distinct, suggesting fish microbiota have a greater buffering capacity against environmental influences. The characterised diversity, structure and variance of microbial communities associated with tilapia culture in Malawi provides the baseline for studies on how future intensification practices may lead to microbial dysbiosis and disease onset.HighlightsTilapia skin communities vary in different ponds but share some common taxaPond sites have a stronger influence on water microbiome community structure than in fish skinSelected skin-associated taxa could be used to monitor dysbiotic eventsThe fish skin microeukaryotic community is complex and poorly characterised

State of the Fishing and the Aquaculture in the southern Seas of Russia in XXI century

The analyze of dynamic of quantity and composition of the Russian catches in the Sea of Azov, Black and Caspian Seas in the 21st century was made. It has been shown that catches decrease with a simultaneous increase in the proportion of marine fish proper. The analyze of aquaculture development in southern regions is provided and also identified the prospects for its development in the near future. Currently, there is a positive dynamics of growth in the production of commercial fish farming products. Since 2000 this parameter has decreased from 77 to 328 thousand tons in 2020. The volume of aquaculture in the southern regions of the country in 2020 amounted to more than 78 thousand tons, which is 24% of the total Russian. In Rostov and Astrakhan region, Krasnodar territory obtained 90.6% aquaculture production. The perspective ways are pasture aquaculture, ponds and industrial fish farming, recreational aquaculture, mariculture. The expansion of the species diversity of farmed fish is possible both due to the aboriginal ichthyofauna and the use of previously acclimatized species.

The ‘Perfect’ Conversion: Dramatic Increase in CO2 Efflux from Shellfish Ponds and Mangrove Conversion in China

Aquaculture, particularly shellfish ponds, has expanded dramatically and become a major cause of mangrove deforestation and “blue carbon” loss in China. We present the first study to examine CO2 efflux from marine aquaculture/shellfish ponds and in relation to land-use change from mangrove forests in China. Light and dark sediment CO2 efflux from shellfish ponds averaged at 0.61 ± 0.07 and 0.90 ± 0.12 kg CO2 m−2 yr−1 (= 37.67 ± 4.89 and 56.0 ± 6.13 mmol m−2 d−1), respectively. The corresponding rates (−4.21 ± 4.54 and 41.01 ± 4.15 mmol m−2 d−1) from the adjacent mangrove forests that were devoid of aquaculture wastewater were lower, while those from the adjacent mangrove forests (3.48 ± 7.83 and 73.02 ± 5.76 mmol m−2 d−1)) receiving aquaculture wastewater markedly increased. These effluxes are significantly higher than those reported for mangrove forests to date, which is attributable to the high nutrient levels and the physical disturbance of the substrate associated with the aquaculture operation. A rise of 1 °C in the sediment temperature resulted in a 6.56% rise in CO2 released from the shellfish ponds. Combined with pond area data, the total CO2 released from shellfish ponds in 2019 was estimated to be ~12 times that in 1983. The total annual CO2 emission from shellfish ponds associated with mangrove conversion reached 2–5 Tg, offsetting the C storage by mangrove forests in China. These are significant environmental consequences rather than just a simple shift of land use. Around 30% higher CO2 emissions are expected from aquaculture ponds (including shellfish ponds) compared to shellfish ponds alone. Total annual CO2 emission from shellfish ponds will likely decrease to the level reported in early 1980 under the pond area-shrinking scenario, but it will be more than doubled under the business-as-usual scenario projected for 2050. This study highlights the necessity of curbing the expansion of aquaculture ponds in valuable coastal wetlands and increasing mangrove restoration to abandoned ponds.