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This study summarizes the advances in mangrove species mapping based on multispectral and hyperspectral imagery achieved over the last decade. The influence of species diversity and sensor specifications on the performances of various classification approaches are discussed. Based on the limitations of previous approaches, we propose a novel framework to map mangrove species at medium, high, and very-high spatial resolution using multispectral and hyperspectral images. The framework relies on a multitask convolutional neural network to achieve accurate classification at pixel and object (i.e. individual tree crown) level. It was successfully applied to the most comprehensive dataset of optical imagery ever collected over a mangrove forest, achieving accurate mapping of species on airborne (OA = 95 %, Kappa = 0.93) and spaceborne imagery (OA and Kappa up to 97 % and 0.95, respectively), including the newly-operating hyperspectral DESIS and PRISMA instruments. This study brings new insights into the role of spatial and spectral resolutions in mangrove species classification, particularly the importance of short-wave infrared bands. Multispectral imagery performs well at very-high to high resolutions (up to 10–20 m) but suffers from the lack of spectral information at medium resolution (30 m). Hyperspectral imagery provided the best results at sub-metric resolution (0.5 m) and satisfactory results at 30-m resolution (OA ≥ 85), and could benefit from implementing spectral unmixing in the latter case. Given the increasing attention accorded to mangroves, remote sensing will undoubtedly become an essential tool to monitor the diversity of these endangered ecosystems in the future. In that respect, efforts should concentrate on evaluating the capabilities of new and upcoming instruments and multisource data combination to improve mangrove species mapping and, more broadly, to meet conservation goals.
Lassalle et al. (Mon,) studied this question.