Advancing mangrove species mapping: An innovative approach using Google Earth images and a U-shaped network for individual-level Sonneratia apetala detection

The exotic mangrove species Sonneratia apetala has been colonizing coastal China for several decades, sparking attention and debates from the public and policy-makers about its reproduction, dispersal, and spread. Existing local-scale studies have relied on fine but expensive data sources to map mangrove species, limiting their applicability for detecting S. apetala in large areas due to cost constraints. A previous study utilized freely available Sentinel-2 images to construct a 10-m-resolution S. apetala map in China but did not capture small clusters of S. apetala due to resolution limitations. To precisely detect S. apetala in coastal China, we proposed an approach that integrates freely accessible submeter-resolution Google Earth images to control expenses, a 10-m-resolution S. apetala map to retrieve well-distributed samples, and several U-shaped networks to capture S. apetala in the form of clusters and individuals. Comparisons revealed that the lite U-squared network was most suitable for detecting S. apetala among the five U-shaped networks. The resulting map achieved an overall accuracy of 98.2 % using testing samples and an accuracy of 91.0 % using field sample plots. Statistics indicated that the total area covered by S. apetala in China was 4000.4 ha in 2022, which was 33.4 % greater than that of the 10-m-resolution map. The excessive area suggested the presence of a large number of small clusters beyond the discrimination capacity of medium-resolution images. Furthermore, the mechanism of the approach was interpreted using an example-based method that altered image color, shape, orientation, and textures. Comparisons showed that textures were the key feature for identifying S. apetala based on submeter-resolution Google Earth images. The detection accuracy rapidly decreased with the blurring of textures, and images at zoom levels of 20, 19, and 18 were applicable to the trained network. Utilizing the first individual-level map, we estimated the number of mature S. apetala trees to be approximately 2.35 million with a 95 % confidence interval between 2.30 and 2.40 million, providing a basis for managing this exotic mangrove species. This study deepens existing research on S. apetala by providing an approach with a clear mechanism, an individual-level distribution with a much larger area, and an estimation of the number of mature trees. This study advances mangrove species mapping by combining the advantages of freely accessible medium- and high-resolution images: the former provides abundant spectral information to integrate discrete local-scale maps to generate a large-scale map, while the latter offers textural information from submeter-resolution Google Earth images to detect mangrove species in detail.