Search Results (2)

Estimating the distribution, extent and change of coastal ecosystems is essential for monitoring global change. However, spatial models developed to estimate the distribution of land cover types require accurate and up-to-date reference data to support model development, model training and data validations. Owing to the labor-intensive tasks required to develop reference datasets, often requiring intensive campaigns of image interpretation and/or field work, the availability of sufficiently large quality and well distributed reference datasets has emerged as a major bottleneck hindering advances in the field of continental to global-scale ecosystem mapping. To enhance our ability to model coastal ecosystem distributions globally, we developed a global reference dataset of 193,105 occurrence records of seven coastal ecosystem types—muddy shorelines, mangroves, coral reefs, coastal saltmarshes, seagrass meadows, rocky shoreline, and kelp forests—suitable for supporting current and next-generation remote sensing classification models. coastTrain version 1.0 contains curated occurrence records collected by several global mapping initiatives, including the Allen Coral Atlas, Global Tidal Flats, Global Mangrove Watch and Global Tidal Wetlands Change. To facilitate use and support consistency across studies, coastTrain has been harmonized to the International Union for the Conservation of Nature’s (IUCN) Global Ecosystem Typology. coastTrain is an ongoing collaborative initiative designed to support sharing of reference data for coastal ecosystems, and is expected to support novel global mapping initiatives, promote validations of independently developed data products and to enable improved monitoring of rapidly changing coastal environments worldwide. Full article

Mangroves are a globally important ecosystem that provides a wide range of ecosystem system services, such as carbon capture and storage, coastal protection and fisheries enhancement. Mangroves have significantly reduced in global extent over the last 50 years, primarily as a result of deforestation caused by the expansion of agriculture and aquaculture in coastal environments. However, a limited number of studies have attempted to estimate changes in global mangrove extent, particularly into the 1990s, despite much of the loss in mangrove extent occurring pre-2000. This study has used L-band Synthetic Aperture Radar (SAR) global mosaic datasets from the Japan Aerospace Exploration Agency (JAXA) for 11 epochs from 1996 to 2020 to develop a long-term time-series of global mangrove extent and change. The study used a map-to-image approach to change detection where the baseline map (GMW v2.5) was updated using thresholding and a contextual mangrove change mask. This approach was applied between all image-date pairs producing 10 maps for each epoch, which were summarised to produce the global mangrove time-series. The resulting mangrove extent maps had an estimated accuracy of 87.4% (95th conf. int.: 86.2–88.6%), although the accuracies of the individual gain and loss change classes were lower at 58.1% (52.4–63.9%) and 60.6% (56.1–64.8%), respectively. Sources of error included misregistration in the SAR mosaic datasets, which could only be partially corrected for, but also confusion in fragmented areas of mangroves, such as around aquaculture ponds. Overall, 152,604 km² (133,996–176,910) of mangroves were identified for 1996, with this decreasing by −5245 km² (−13,587–1444) resulting in a total extent of 147,359 km² (127,925–168,895) in 2020, and representing an estimated loss of 3.4% over the 24-year time period. The Global Mangrove Watch Version 3.0 represents the most comprehensive record of global mangrove change achieved to date and is expected to support a wide range of activities, including the ongoing monitoring of the global coastal environment, defining and assessments of progress toward conservation targets, protected area planning and risk assessments of mangrove ecosystems worldwide. Full article