Harbor Aquaculture Area Extraction Aided with an Integration-Enhanced Gradient Descent Algorithm

In this study, the harbor aquaculture area tested is Zhanjiang coast, and for the remote sensing data, we use images from the GaoFen-1 satellite. In order to achieve a superior extraction performance, we propose the use of an integration-enhanced gradient descent (IEGD) algorithm. The key idea of this algorithm is to add an integration gradient term on the basis of the gradient descent (GD) algorithm to obtain high-precision extraction of the harbor aquaculture area. To evaluate the extraction performance of the proposed IEGD algorithm, comparative experiments were performed using three supervised classification methods: the neural network method, the support vector machine method, and the maximum likelihood method. From the results extracted, we found that the overall accuracy and F-score of the proposed IEGD algorithm for the overall performance were 0.9538 and 0.9541, meaning that the IEGD algorithm outperformed the three comparison algorithms. Both the visualized and quantitative results demonstrate the high precision of the proposed IEGD algorithm aided with the CEM scheme for the harbor aquaculture area extraction. These results confirm the effectiveness and practicality of the proposed IEGD algorithm in harbor aquaculture area extraction from GF-1 satellite data. Added to that, the proposed IEGD algorithm can improve the extraction accuracy of large-scale images and be employed for the extraction of various aquaculture areas. Given that the IEGD algorithm is a type of supervised classification algorithm, it relies heavily on the spectral feature information of the aquaculture object. For this reason, if the spectral feature information of the region of interest is not selected properly, the extraction performance of the overall aquaculture area will be extremely reduced.

Mapping Aquaculture Areas with Multi-Source Spectral and Texture Features: A Case Study in the Pearl River Basin (Guangdong), China

Aquaculture has grown rapidly in the field of food industry in recent years; however, it brought many environmental problems, such as water pollution and reclamations of lakes and coastal wetland areas. Thus, the evaluation and management of aquaculture industry are needed, in which accurate aquaculture mapping is an essential prerequisite. Due to the difference between inland and marine aquaculture areas and the difficulty in processing large amounts of remote sensing images, the accurate mapping of different aquaculture types is still challenging. In this study, a novel approach based on multi-source spectral and texture features was proposed to map simultaneously inland and marine aquaculture areas. Time series optical Sentinel-2 images were first employed to derive spectral indices for obtaining texture features. The backscattering and texture features derived from the synthetic aperture radar (SAR) images of Sentinel-1A were then used to distinguish aquaculture areas from other geographical entities. Finally, a supervised Random Forest classifier was applied for large scale aquaculture area mapping. To address the low efficiency in processing large amounts of remote sensing images, the proposed approach was implemented on the Google Earth Engine (GEE) platform. A case study in the Pearl River Basin (Guangdong Province) of China showed that the proposed approach obtained aquaculture map with an overall accuracy of 89.5%, and the implementation of proposed approach on GEE platform greatly improved the efficiency for large scale aquaculture area mapping. The derived aquaculture map may support decision-making services for the sustainable development of aquaculture areas and ecological protection in the study area, and the proposed approach holds great potential for mapping aquacultures on both national and global scales.

Physicochemical Factors Drive Bacterial Communities in an Aquaculture Environment

The role of microbial ecology in aquaculture is becoming increasingly significant; however, studies on the changes in microbial ecology driven by the culture environment are limited. In the present study, high-throughput sequencing and chemical analysis was used to explore changes in physicochemical factors, bacterial communities, and their relationships between a water source (Bay) and an aquaculture area located in a reclaimed area. Statistical analysis results revealed that operational taxonomic units levels in inlet water and pond water varied significantly (p < 0.05). Non-metric multidimensional scaling (NMDS) analysis revealed the distribution characteristics of bacterial communities with water properties. The abundance of Alphaproteobacteria, Actinobacteria, and Flavobacteria in pond water increased significantly when compared to inlet water. The abundance of heterotrophic bacteria, such as Candidatus Actinomarina, Candidatus Aquiluna, Marivita, and Vibrio genera in pond water was significantly higher (Welch’s t-tests, p < 0.05) than inlet water. Functional prediction analysis primarily revealed an increase in the function that was associated with carbon and nitrogen metabolism in the pond environment. Canonical correlation analysis revealed that the bacterial communities was predominantly influenced by inorganic nutrients. Nitrate-nitrogen (N), nitrite-N, ammonium-N, and phosphate-phosphorous (P) were the key factors influencing bacterial communities in pond environment. A significant correlation was observed between inorganic N and phosphorus (P), and dominant bacterial genera (p < 0.05), demonstrating the potential mechanism of regulation of nutrients in bacterial communities. The present study described the microbial ecology of aquaculture ponds in detail and provides a scientific basis for the management of aquacultural environments.

Extraction of Offshore Aquaculture Areas from Medium-Resolution Remote Sensing Images Based on Deep Learning

It is important for aquaculture monitoring, scientific planning, and management to extract offshore aquaculture areas from medium-resolution remote sensing images. However, in medium-resolution images, the spectral characteristics of offshore aquaculture areas are complex, and the offshore land and seawater seriously interfere with the extraction of offshore aquaculture areas. On the other hand, in medium-resolution images, due to the relatively low image resolution, the boundaries between breeding areas are relatively fuzzy and are more likely to ‘adhere’ to each other. An improved U-Net model, including, in particular, an atrous spatial pyramid pooling (ASPP) structure and an up-sampling structure, is proposed for offshore aquaculture area extraction in this paper. The improved ASPP structure and up-sampling structure can better mine semantic information and location information, overcome the interference of other information in the image, and reduce ‘adhesion’. Based on the northeast coast of Fujian Province Sentinel-2 Multispectral Scan Imaging (MSI) image data, the offshore aquaculture area extraction was studied. Based on the improved U-Net model, the F1 score and Mean Intersection over Union (MIoU) of the classification results were 83.75% and 73.75%, respectively. The results show that, compared with several common classification methods, the improved U-Net model has a better performance. This also shows that the improved U-Net model can significantly overcome the interference of irrelevant information, identify aquaculture areas, and significantly reduce edge adhesion of aquaculture areas.

Draft Genome Sequences of Putative Aerobic Anoxygenic Phototrophic Bacterial Strains Jannaschia sp. Strains AI_61 and AI_62, Isolated from Seawater around a Coastal Aquaculture Area

Here, we report the draft genome sequences of putative aerobic anoxygenic phototrophic bacterial strains Jannaschia sp. AI_61 and AI_62, isolated from seawater around a coastal aquaculture in Ainan, Ehime, Japan. These genome sequences could be useful for our understanding of the variation of aerobic anoxygenic phototrophs in the genus.

Quantification of metals in macrophytes in an aquaculture area of the itaipu binational reservoir

The macrophytes in natural conditions perform an important role in the maintenance and balance of aquatic environments with a capacity of absorbing the excess of nutrients and pollutants serving as bioindicators of water quality in aquatic ecosystems. The objective was to evaluate the levels of trace metals in three species of macrophytes (Egeria densa - submerged and Eichhornia crassipes and Salvinia auriculata - floating) collected around an aquaculture area of cages in the Itaipu Binational reservoir, during the four seasons of the year. The macrophyte samples were submitted of nitroperchloric digestion. Subsequently, the quantification of metals (Cu, Zn, Fe, Mn, Pb, Cd and Cr) was carried out by flame atomic absorption spectrometry analytical method. The concentration of Cu, Fe and Mn in E. densa and S. auriculata was higher (P<0.05) than in E. crassipes. The samples of S. auriculata and E. crassipes had the lowest concentrations (P<0.05) of Pb. The lowest metal pollution index (MPI) was determined in E. crassipes. There was greater bioaccumulation of metals in the root concerning the stem and leaves of E. crassipes (P<0.05). The results obtained in this study show the influence of seasonal variation in the levels of Fe and Zn and the species analyzed on the concentration of Fe, Zn and Mn accumulated in aquatic macrophytes. The macrophytes E. crassipes and E. densa can be considered efficient accumulators of metals, indicating the exposure of the concentration of trace metals around the aquaculture area intended for the fish production in cages.