A new deep learning approach based on bilateral semantic segmentation models for sustainable estuarine wetland ecosystem management.

Nowadays, estuarial areas have been strongly affected by the construction of electrical power dams from upstream, downstream urbanization and many types of hazards along the coastal regions. It has resulted in significant changes in estuarine wetland ecosystems between rainy and dry seasons. To avoid estuary vulnerability, monitoring and evaluation of the estuarine ecosystems are very critical tasks. The main goal of this research is to propose and implement a novel deep learning method in monitoring various ecosystems in estuarine regions. The processing speed and accuracy of common neural networks is improved more than ten times through spatial and context paths integrated into a novel Bilateral Segmentation Network (BiSeNet). The multi-sensor and multi-temporal satellite images (including Sentinel-2, ALOS-DEM, and NOAA-DEM images) served as input data. As a result, four BiSeNet models out of 20 trained models achieved a greater than 90% accuracy, especially for interpreting estuarine waters, intertidal forested wetlands, and aquacultural lands in subtidal regions. These models outperformed Random Forest and Support Vector Machine approaches. The best one was used to map estuarine ecosystems from 12 satellite images over a five-year period in the largest estuary in northern Vietnam. The ecosystem changes between dry and rainy seasons were analyzed in detail to assess the ecological succession in estuaries. Furthermore, this model can potentially update new estuarine ecosystem types in other estuarine areas across the world, making possible real-time monitoring and assessing estuarine ecological conditions for sustainable management of wetland ecosystem.

Polarity Control of ZnO Films Grown on Ferroelectric (0001) LiNbO3 Substrates without Buffer Layers by Pulsed-Laser Deposition.

For this study, polarity-controlled ZnO films were grown on lithium niobate (LiNbO3) substrates without buffer layers using the pulsed-laser deposition technique. The interfacial structure between the ZnO films and the LiNbO3 was inspected using high-resolution transmission electron microscopy (HR-TEM) measurements, and X-ray diffraction (XRD) measurements were performed to support these HR-TEM results. The polarity determination of the ZnO films was investigated using piezoresponse force microscopy (PFM) and a chemical-etching analysis. It was verified from the PFM and chemical-etching analyses that the ZnO film grown on the (+z) LiNbO3 was Zn-polar ZnO, while the O-polar ZnO occurred on the (-z) LiNbO3. Further, a possible mechanism of the interfacial atomic configuration between the ZnO on the (+z) LiNbO3 and that on the (-z) LiNbO3 was suggested. It appears that the electrostatic stability at the substrate surface determines the initial nucleation of the ZnO films, leading to the different polarities in the ZnO systems.