ALBERT

Description: The monitoring of Global Aquatic Land Cover (GALC) plays an essential role in protecting and restoring water-related ecosystems. Although many GALC datasets have been created before, a uniform and comprehensive GALC dataset is lacking to meet multiple user needs. This study aims to assess the effectiveness of using existing global datasets to develop a comprehensive and user-oriented GALC database and identify the gaps of current datasets in GALC mapping. Eight global datasets were reframed to construct a three-level (i.e., from general to detailed) prototype database for 2015, conforming with the United Nations Land Cover Classification System (LCCS)-based GALC characterization framework. An independent validation was done, and the overall results show some limitations of current datasets in comprehensive GALC mapping. The Level-1 map had considerable commission errors in delineating the general GALC distribution. The Level-2 maps were good at characterizing permanently flooded areas and natural aquatic types, while accuracies were poor in the mapping of temporarily flooded and waterlogged areas as well as artificial aquatic types; vegetated aquatic areas were also underestimated. The Level-3 maps were not sufficient in characterizing the detailed life form types (e.g., trees, shrubs) for aquatic land cover. However, the prototype GALC database is flexible to derive user-specific maps and has important values to aquatic ecosystem management. With the evolving earth observation opportunities, limitations in the current GALC characterization can be addressed in the future

Description: The sustainable management of aquatic resources requires spatially explicit information on the water and vegetation presence of aquatic ecosystems. Previous Global Aquatic Land Cover (GALC) mapping has been focused on water bodies while lacking information on vegetation, and aquatic types have always been characterized by low accuracies in global land cover products, calling for specific attention to improve GALC mapping. The availability of a wealth of open Earth Observation (EO) data on cloud-computing platforms provides opportunities to map aquatic land cover globally. This study aims to evaluate the potential of multi-source freely available EO data, including optical, Synthetic Aperture Radar (SAR), and various ancillary datasets, for improving the characterization of aquatic land cover comprising both water and vegetation types on a global scale. Using different combinations of features derived from these data, the classification performance of five land cover classes (i.e., trees, shrubs, herbaceous cover, bare/sparsely vegetated lands, and water bodies) in aquatic areas was cross-validated. Results showed that Sentinel-2 data alone achieved similarly good overall accuracy as those combining multi-source data. However, the single-sensor Sentinel-2 data cannot discriminate highly mixed and spectrally similar types, such as shrubs, trees, and herbaceous vegetation. Integrating SAR features from the ALOS/PALSAR mosaic and Sentinel-1 data with optical features provided by Sentinel-2 data could help address this limitation to some extent. Although with a lower spatial and temporal resolution, the ALOS/PALSAR mosaic had a stronger impact on GALC classification than Sentinel-1 data when they were synergistically used. Features provided by ancillary datasets did not lead to significant improvement in the overall GALC classification. At class-level, topographic and soil features helped to reduce the commission error of shrubs, and climate variables were useful to improve the characterization of bare aquatic lands. The Global Ecosystem Dynamics Investigation (GEDI) forest canopy height dataset helped to characterize trees but also resulted in a decrease in accuracies of shrubs. By assessing multi-source earth observation data, this research represents an important step forward in the global mapping of comprehensive aquatic land cover types at high spatial resolution (i.e., 10 m).