Swin Transformer and Deep Convolutional Neural Networks for Coastal Wetland Classification Using Sentinel-1, Sentinel-2, and LiDAR Data

The use of machine learning algorithms to classify complex landscapes has been revolutionized by the introduction of deep learning techniques, particularly in remote sensing. Convolutional neural networks (CNNs) have shown great success in the classification of complex high-dimensional remote sensing imagery, specifically in wetland classification. On the other hand, the state-of-the-art natural language processing (NLP) algorithms are transformers. Although the transformers have been studied for a few remote sensing applications, the integration of deep CNNs and transformers has not been studied, particularly in wetland mapping. As such, in this study, we explore the potential and possible limitations to be overcome regarding the use of a multi-model deep learning network with the integration of a modified version of the well-known deep CNN network of VGG-16, a 3D CNN network, and Swin transformer for complex coastal wetland classification. Moreover, we discuss the potential and limitation of the proposed multi-model technique over several solo models, including a random forest (RF), support vector machine (SVM), VGG-16, 3D CNN, and Swin transformer in the pilot site of Saint John city located in New Brunswick, Canada. In terms of F-1 score, the multi-model network obtained values of 0.87, 0.88, 0.89, 0.91, 0.93, 0.93, and 0.93 for the recognition of shrub wetland, fen, bog, aquatic bed, coastal marsh, forested wetland, and freshwater marsh, respectively. The results suggest that the multi-model network is superior to other solo classifiers from 3.36% to 33.35% in terms of average accuracy. Results achieved in this study suggest the high potential for integrating and using CNN networks with the cutting-edge transformers for the classification of complex landscapes in remote sensing.

Environmental Filtering by pH and Salinity Jointly Drives Prokaryotic Community Assembly in Coastal Wetland Sediments

Understanding the microbial community assembly is an essential topic in microbial ecology. Coastal wetlands are an important blue carbon sink, where microbes play a key role in biogeochemical cycling of nutrients and energy transformation. However, the drivers controlling the distribution patterns and assembly of bacterial and archaeal communities in coastal wetland are unclear. Here we examined the diversity, co-occurrence network, assembly processes and environmental drivers of bacterial and archaeal communities from inshore to offshore sediments by the sequencing of 16S rRNA gene amplicons. The value of α- and β-diversity of bacterial and archaeal communities generally did not change significantly (P > 0.05) between offshore sites, but changed significantly (P < 0.05) among inshore sites. Sediment pH and salinity showed significant effects on the diversity and keystone taxa of bacterial and archaeal communities. The bacterial and archaeal co-occurrence networks were inextricably linked with pH and salinity to formed the large network nodes, suggesting that they were the key factors to drive the prokaryotic community. We also identified that heterogeneous and homogeneous selection drove the bacterial and archaeal community assembly, while the two selections became weaker from offshore sites to inshore sites, suggesting that deterministic processes were more important in offshore sites. Overall, these results suggested that the environmental filtering of pH and salinity jointly governed the assembly of prokaryotic community in offshore sediments. This study advances our understanding of microbial community assembly in coastal wetland ecosystems.

Swin Transformer for Complex Coastal Wetland Classification Using the Integration of Sentinel-1 and Sentinel-2 Imagery

The emergence of deep learning techniques has revolutionized the use of machine learning algorithms to classify complicated environments, notably in remote sensing. Convolutional Neural Networks (CNNs) have shown considerable promise in classifying challenging high-dimensional remote sensing data, particularly in the classification of wetlands. State-of-the-art Natural Language Processing (NLP) algorithms, on the other hand, are transformers. Despite the fact that transformers have been utilized for a few remote sensing applications, they have not been compared to other well-known CNN networks in complex wetland classification. As such, for the classification of complex coastal wetlands in the study area of Saint John city, located in New Brunswick, Canada, we modified and employed the Swin Transformer algorithm. Moreover, the developed transformer classifier results were compared with two well-known deep CNNs of AlexNet and VGG-16. In terms of average accuracy, the proposed Swin Transformer algorithm outperformed the AlexNet and VGG-16 techniques by 14.3% and 44.28%, respectively. The proposed Swin Transformer classifier obtained F-1 scores of 0.65, 0.71, 0.73, 0.78, 0.82, 0.84, and 0.84 for the recognition of coastal marsh, shrub, bog, fen, aquatic bed, forested wetland, and freshwater marsh, respectively. The results achieved in this study suggest the high capability of transformers over very deep CNN networks for the classification of complex landscapes in remote sensing.

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.