Xiaojian Liu

Storm surges are unanticipated natural disturbances to mangrove forests, but little is known about how they alter ecosystem-level carbon dioxide exchange and methane flux of natural and restored mangrove forests. In this study, we quantified the impact of storm surge on CO2 and CH4 fluxes in natural (HK site) and restored (PYR site) mangroves, and analyzed the differences in the impact of environmental variables on CO2 and CH4 fluxes before, during, and after storm surges. The results showed that the intensity of carbon dioxide absorption was temporarily weakened at PYR site, while CO2 absorption even turned into CO2 source at HK site during the storm surge. The emission of CH4 was suppressed at both sites during the storm surge landfall. GPP, Reco, and CH4 emissions were reduced by 29% and 59%, 20% and 10%, and 50% and 40% at restored and natural mangroves, respectively, due to the storm surge. For the NEE at both sites, the most correlated environmental variable before, during, and after the storm surge was PPFD, with negative correlation coefficients of -0.89, -0.74, and -0.81, respectively. For PYR site, the correlations between FCH4 and VPD and RH both increased compared with those before and after the storm surge. For HK site, the correlations between FCH4 and VPD, RH, and TS during the storm surge were not significant, while the correlations between FCH4 and TS, WS, and PPFD were higher than those before and after the storm surge. These findings indicated that storm surges weaken CO2 absorption and inhibit methane emissions, and the carbon fluxes of natural and restored mangroves respond differently to storm surges. The research results can provide a basis for the study of mangrove carbon flux under extreme weather events.

Synthetic Aperture Radar (SAR) is the primary equipment used to detect oil slicks on the ocean’s surface. On SAR images, oil spill regions, as well as other places impacted by atmospheric and oceanic phenomena such as rain cells, upwellings, and internal waves, appear as dark spots. Dark spot detection is typically the initial stage in the identification of oil spills. Because the identified dark spots are oil slick candidates, the quality of dark spot segmentation will eventually impact the accuracy of oil slick identification. Although certain sophisticated deep learning approaches employing pixels as primary processing units work well in remote sensing image semantic segmentation, finding some dark patches with weak boundaries and small regions from noisy SAR images remains a significant difficulty. In light of the foregoing, this paper proposes a dark spot detection method based on superpixels and deeper graph convolutional networks (SGDCNs), with superpixels serving as processing units. The contours of dark spots can be better detected after superpixel segmentation, and the noise in the SAR image can also be smoothed. Furthermore, features derived from superpixel regions are more robust than those derived from fixed pixel neighborhoods. Using the support vector machine recursive feature elimination (SVM-RFE) feature selection algorithm, we obtain an excellent subset of superpixel features for segmentation to reduce the learning task difficulty. After that, the SAR images are transformed into graphs with superpixels as nodes, which are fed into the deeper graph convolutional neural network for node classification. SGDCN leverages a differentiable aggregation function to aggregate the node and neighbor features to form more advanced features. To validate our method, we manually annotated six typical large-scale SAR images covering the Baltic Sea and constructed a dark spot detection dataset. The experimental results demonstrate that our proposed SGDCN is robust and effective compared with several competitive baselines. This dataset has been made publicly available along with this paper.

Kemp's ridley sea turtles were on the verge of extinction in the 1960s. While these sea turtles have slowly recovered, they are still critically endangered. In the last few years, the number of strandings on the beaches of Cape Cod, Massachusetts has increased by nearly an order of magnitude relative to preceding decades. This study uses a combination of ocean observations and a well-respected ocean model to investigate the causes and transport of cold-stunned sea turtles in Cape Cod Bay. After validating the model using satellite-tracked drifters and local temperature moorings, ocean currents were examined in Cape Cod Bay in an attempt to explain stranding locations as observed by volunteers and, for some years, backtracking was conducted to examine the potential source regions. The general finding of this study is that sub 10.5°C water temperatures in combination with persistently strong wind stress (>0.4 Pa), results in increased strandings along particular sections of the coast and are dependent on the wind direction. However, it is still uncertain where in the water column the majority of cold-stunned turtles reside and, if many of them are on the surface, considerable work will need to be done to incorporate the direct effects of wind and waves on the advective processes.

Kemp’s ridley turtles were on the verge of extinction in the 1960s. While they have slowly recovered, they are still endangered. In the last few years, the number of strandings on Cape Cod Massachusetts beaches has increased by nearly an order of magnitude relative to preceding decades. This study uses a combination of ocean observations and a well-respected ocean model to investigate the causes and transport of cold-stunned animals in Cape Cod Bay. After validating the model using satellite-tracked drifters and local temperature moorings, ocean currents were examined in the Cape Cod Bay in an attempt to explain stranding locations as observed by volunteers and, for some years, backtracking was conducted to examine the potential source regions. The general finding, as expected, is that sub 10.5°C water temperatures in combination with persistent strong wind stress (>0.4Pa) will result in increased strandings along particular sections of the coast dependent on the wind direction. However, it is still uncertain where in the water column the majority of cold stunned turtles reside and, if many of them are on the surface, considerable more work will need to be done to incorporate the direct effects of wind and waves on the advective processes.