Classification of mangrove species using satellite images is important for investigating the spatial distribution of mangroves at community and species levels on local, regional and global scales. Hence, studies of mangrove deforestation and reforestation are imperative to support the conservation of mangrove forests. However, accurate discrimination of mangrove species remains challenging due to many factors such as data resolution, species number and spectral confusion between species. In this study, three different combinations of datasets were designed from Worldview-3 and Radarsat-2 data to classify four mangrove species, Kandelia obovate (KO), Avicennia marina (AM), Acanthus ilicifolius (AI) and Aegiceras corniculatum (AC). Then, the Rotation Forest (RoF) method was employed to classify the four mangrove species. Results indicated the benefits of dual polarimetric SAR data with an improvement of accuracy by 2–3%, which can be useful for more accurate large-scale mapping of mangrove species. Moreover, the difficulty of classifying different mangrove species, in order of increasing difficulty, was identified as KO < AM < AI < AC. Dual polarimetric SAR data are recognized to improve the classification of AI and AC species. Although this improvement is not remarkable, it is consistent for all three methods. The improvement can be particularly important for large-scale mapping of mangrove forest at the species level. These findings also provide useful guidance for future studies using multi-source satellite data for mangrove monitoring and conservation.

Hyperspectral data has been widely used in species discrimination of plants with rich spectral information in hundreds of spectral bands, while the availability of hyperspectral data has hindered its applications in many specific cases. The successful operation of the Chinese satellite, Gaofen-5 (GF-5), provides potentially promising new hyperspectral dataset with 330 spectral bands in visible and near infrared range. Therefore, there is much demand for assessing the effectiveness and superiority of GF-5 hyperspectral data in plants species mapping, particularly mangrove species mapping, to better support the efficient mangrove management. In this study, mangrove forest in Mai Po Nature Reserve (MPNR), Hong Kong was selected as the study area. Four dominant native mangrove species were investigated in this study according to the field surveys. Two machine learning methods, Random Forests and Support Vector Machines, were employed to classify mangrove species with Landsat 8, Simulated Hyperion and GF-5 data sets. The results showed that 97 more bands of GF-5 over Hyperion brought a higher over accuracy of 87.12%, in comparison with 86.82% from Hyperion and 73.89% from Landsat 8. The higher spectral resolution of 5 nm in GF-5 was identified as making the major contribution, especially for the mapping of Aegiceras corniculatum. Therefore, GF-5 is likely to improve the classification accuracy of mangrove species mapping via enhancing spectral resolution and thus has promising potential to improve mangrove monitoring at species level to support mangrove management.

Reconstructive phase transitions involving breaking and reconstruction of primary chemical bonds are ubiquitous and important for many technological applications. In contrast to displacive phase transitions, the dynamics of reconstructive phase transitions are usually slow due to the large energy barrier. Nevertheless, the reconstructive phase transformation from β- to λ-Ti₃O₅ exhibits an ultrafast and reversible behavior. Despite extensive studies, the underlying microscopic mechanism remains unclear. Here, we discover a kinetically favorable in-plane nucleated layer-by-layer transformation mechanism through metadynamics and large-scale molecular dynamics simulations. This is enabled by developing an efficient machine learning potential with near first-principles accuracy through an on-the-fly active learning method and an advanced sampling technique. Our results reveal that the β−λ phase transformation initiates with the formation of two-dimensional nuclei in the a b-plane and then proceeds layer-by-layer through a multistep barrier-lowering kinetic process via intermediate metastable phases. Our work not only provides important insight into the ultrafast and reversible nature of the β−λ transition, but also presents useful strategies and methods for tackling other complex structural phase transitions.

Mangrove deforestation is occurring globally at a rapid rate, and is causing serious ecological and economic losses on all scales. Monitoring mangrove changes is the first important step for mangrove management and conservation. Zonation of mangrove species (ZMS) is the predictable and discrete ordering of mangrove species caused by a unique, intertidal environment. Mapping the ZMS is critical to understanding the mangrove community at a species level. In this paper, the Standard Deviational Ellipse (SDE) was proposed as a method to evaluate mangrove species from a new dimension of directional changes. Three dominant mangrove species, Kandelia obovata (KO), Avicennia marina (AM), and Acanthus ilicifolius (AI), in Mai Po, Hong Kong were analyzed using SDEs based on the time series Système Pour l’Observation de la Terre (SPOT) and Gaofen-1 (GF-1) satellite images. The SDE results demonstrated that in the past 25 years: (1) The overall spatial extent of the mangroves in Mai Po expanded significantly, approximately from 150 to 350 Ha, and show a zonation pattern with a clear sequence of species perpendicular to the shoreline; (2) KO was the dominant species in most years, showing the strongest directional characteristic; (3) All three species zones have moved toward the north and west, as observed by the SDE centers. The SDE was proved to be a useful tool for understanding the temporal and spatial changes of mangrove zonation.

Coastal habitat dynamics and ecosystem function in response to human-induced disturbance, especially urbanization, are of increasing concern. However, how changes in landscape composition as well as habitat quantity and quality may affect the long-term sustainability of rapidly urbanizing coasts remains unclear. This study aimed to quantify the extent, change rate, patterns, change process and interrelationships of mangrove habitats, impervious surfaces, and other land cover types in Deep Bay in the Guangdong-Hong Kong-Macau Greater Bay Area (GBA), China, the world’s largest megalopolis, from 1924 to 2020. We processed historical aerial photos (1924–2020) and multiple sources of satellite data (1973–2020) for different types of land cover mapping. Post-classification analysis, including correlation analysis and change detection analysis, was conducted based on the long time-series land cover classification results. Mangrove habitats increased in Deep Bay from 1924 to 2020, except for a large area decrease from 1954 to 1964 due to the construction of tidal aquaculture ponds. Mudflat areas contributed most to the expansion of mangrove habitats of about 275 ha from 1987 to 2020. During this period, reclamation and urbanization for the construction of the megacity of Shenzhen turned large areas of water and mudflat (about 4000 ha) on the northern shore into impervious surface and urban vegetation. Overall, the landscape pattern of mangrove habitats in Deep Bay showed increasing connectivity and decreasing degree of fragmentation from 1987 to 2020. These changes have significant implications for the ecosystem services, e.g., supporting migratory waterbirds, supported by these wetlands.

Sonneratia have posed a threat to native mangrove species in Hong Kong. Early detection of individual Sonneratia when they are introduced and naturalized before invasion is essential for native mangrove species protection, especially for Sonneratia with a strong ability of propagation. This letter aims to provide an effective way to the accurate detection of individual Sonneratia. Specifically, using very high spatial resolution remotely sensed data, we adapt the RetinaNet, incorporating multiscale features for sapling detection and convolutional neural networks for detecting the Sonneratia distributed scatteredly among native species. The Sonneratia were detected with a higher mean average precision (mAP) 0.50 of 0.3891 with a precision of 0.5465 than that from the deformable part model. In addition, 3678 Sonneratia were detected at early stage. This letter can support the government for mangrove forest management and offer a scientific guidance for adequate response to the species invasion, like annual removal of Sonneratia, and then reduce the consumption of labor and time over a large scale. In addition, it can provide a quantitative survey for Sonneratia management.