Search Results (4)

Eutrophication due to aquaculture can cause the decline of seagrasses and impact their carbon storage capacity. This study explored the effects of aquaculture on the sediment organic carbon (SOC) in Thalassia testudinum seagrass beds using enzyme activity and microorganisms as indicators. Our results showed that the distance to aquaculture significantly increased the SOC and TN of sediments; the C/N ratio of sediments was reduced by the distance to aquaculture. Distance to aquaculture and seagrasses significantly impacted the δ¹³C of sediments, and their significant interactive effects on the δ¹³C of sediments were found. Distance to aquaculture and seagrasses had significantly interactive effects on the cellulase activity of sediments. Distance to aquaculture and seagrasses separately reduced the invertase activity of sediments. SOC in the seagrass bed was significantly positively impacted by cellulase activity and polyphenol oxidase activity in sediments. Firmicutes, Desulfobacterota and Chloroflexi were the dominant taxa in the S1 and S2 locations. From the S1 location to the S2 location, the relative abundances of Firmicutes and Desulfobacterota increased. The functional profiles of COG were relatively similar between the S1 and S2 locations. BugBase phenotype predictions indicated that the microbial phenotypes of all the seagrass sediment samples were dominated by anaerobic bacteria in terms of oxygen utilizing phenotypes. FAPROTAX functional predictions indicated that aquaculture affects functions associated with seagrass bed sediment bacteria, particularly those related to carbon and nitrogen cycling. This study can provide an important basis for understanding the response mechanism of global carbon sink changes to human activities such as aquaculture and supply more scientific data for promoting the conservation and management of seagrass beds. Full article

The bottom depth of coastal benthic habitats plays a vital role in the coastal ecological environment and navigation. In optically shallow waters (OSWs), seafloor reflectance has an impact on the remotely sensed data, and thus, water depth can be retrieved from the remote sensing reflectance ($R_{rs}\left(\lambda \right)$) values provided by satellite imagery. Empirical methods for depth estimation are mainly limited by field measurements coverage. In addition, owing to the diverse range of water bio-optical properties in coastal regions, the high-precision models that could be applied to all OSWs are insufficient. In this study, we developed a novel bottom-depth retrieval method based on Hydrolight simulated datasets, in which $R_{rs}\left(\lambda \right)$ were generated from radiative transfer theory instead of actual satellite data. Additionally, this method takes into consideration the variable conditions of water depth, chlorophyll concentrations, and bottom reflectance. The bottom depth can be derived from $R_{rs}\left(\lambda \right)$ using a data-driven machine learning method based on the random forest (RF) model. The determination coefficient (R²) was greater than 0.98, and the root mean squared error ($RMSE$) was less than 0.4 m for the training and validation datasets. This model shows promise for use in different coastal regions while also broadening the applications that utilize satellite data. Specifically, we derived the bottom depth in three areas in the South China Sea, i.e., the coastal regions of Wenchang city, Xincun Bay, and Huaguang Reef, based on Sentinel-2 imagery. The derived depths were validated by the bathymetric data acquired by spaceborne photon-counting lidar ICESat-2, which was able to penetrate clean shallow waters for sufficient bottom detection. The predicted bottom depth showed good agreement with the true depth, and large-scale mapping compensated for the limitations resulting from along-track ICESat-2 data. Under a variety of circumstances, this general-purpose depth retrieval model can be effectively applied to high spatial resolution imagery (such as that from Sentinel-2) for bottom depth mapping in optically shallow waters. Full article

Seagrass is an important structural and functional component of the global marine ecosystem and is of high value for its ecological services. This paper took Xincun Bay (including Xincun Harbor and Li’an Harbor) of Hainan Province as the study area, combined ground truth data, and adopted two methods to map seagrass in 2020 using Chinese GF2 satellite images: maximum-likelihood and object-oriented classification. Sentinel-2 images from 2016 to 2020 were used to extract information on seagrass distribution changes. The following conclusions were obtained. (1) Based on GF2 imagery, both the classical maximum likelihood classification (MLC) method and the object-based image analysis (OBIA) method can effectively extract seagrass information, and OBIA can also portray the overall condition of seagrass patches. (2) The total seagrass area in the study area in 2020 was about 395 hectares, most of which was distributed in Xincun Harbor. The southern coast of Xincun Harbor is an important area where seagrass is concentrated over about 228 hectares in a strip-like continuous distribution along the coastline. (3) The distribution of seagrasses in the study area showed a significant decaying trend from 2016 to 2020. The total area of seagrass decreased by 79.224 ha during the five years from 2016 to 2020, with a decay rate of 16.458%. This study is the first on the comprehensive monitoring of seagrass in Xincun Bay using satellite remote sensing images, and comprises the first use of GF2 data in seagrass research, aiming to provide a reference for remote sensing monitoring of seagrass in the South China Sea. Full article

Seagrass distribution is a very important index for costal management and protection. Seagrass distribution changes can be used as indexes to analyze the reasons for the changes. In this paper, in situ hyperspectral observation and satellite images of QuickBird, CBERS (China Brazil Earth Resources Satellite data) and Landsat data were used to retrieve bio-optical models and seagrass (Enhalus acoroides,Thalassia hemperichii) distribution in Xincun Bay, Hainan province, and seagrass distribution changes from 1991 to 2006 were analyzed. Hyperspectral results showed that the spectral bands at 555, 635, 650 and 675 nm are sensitive to leaf area index (LAI). Seagrass detection with QuickBird was more accurate than that with Landsat TM and CBERS; five classes could be classified clearly and used as correction for seagrass remote sensing data from Landsat TM and CBERS. In order to better describe seagrass distribution changes, the seagrass distribution area was divided as three regions: region A connected with region B in 1991, however it separated in 1999 and was wholly separated in 2001; seagrass in region C shrank gradually and could not be detected in 2006. Analysis of the reasons for seagrass reduction indicated it was mainly affected by aquaculture and typhoons and in recent years, by land use changes. Full article