DOI: 10.57760/sciencedb.13298
title: Toward a better understanding of coastal salt marsh mapping: A case from China using dual-temporal images
title: 基于双时相影像的中国滨海盐沼植被分布

description: Coastal salt marsh vegetation is an assembly of herbs, grasses and low shrubs, playing vital ecological roles in promoting sedimentation, providing habitat, and carbon sequestration. Remote sensing-based mapping of coastal salt marsh vegetation has been enhanced through the application of phenological information and various black-box algorithms. However, existing research based on dual-temporal images faces the following challenges: 1) low efficiency in constructing dual-temporal images, 2) dependence on post-processing rather than reducing misclassifications at the source, and 3) failure to reveal implicit knowledge implied in a trained model. This study proposed a comprehensive approach addressing these issues. It efficiently constructed dual-temporal images by utilizing a latitude gradient-based common time period of the growing season in the north and that in the south (the same common time period for the senescence season), iteratively optimized samples to reduce misclassifications, and approximated the trained model through a decision rule to reveal implicit knowledge. Using independently collected samples, the produced 10-me resolution map in China for 2020 achieved an overall accuracy of 92.5%. The study elaborated on the potential mechanism for identifying coastal salt marsh vegetation based on dual-temporal images, indicating that water signals during the growing season are obscured by flourishing vegetation, while these signals are observable during the senescence season when vegetation withers. This research provides a novel approach for fine-scale mapping of coastal wetland land covers based on remote sensing, facilitating coastal conservation and blue carbon estimation studies.
description: Coastal salt marsh vegetation is an assembly of herbs, grasses and low shrubs, playing vital ecological roles in promoting sedimentation, providing habitat, and carbon sequestration. Remote sensing-based mapping of coastal salt marsh vegetation has been enhanced through the application of phenological information and various black-box algorithms. However, existing research based on dual-temporal images faces the following challenges: 1) low efficiency in constructing dual-temporal images, 2) dependence on post-processing rather than reducing misclassifications at the source, and 3) failure to reveal implicit knowledge implied in a trained model. This study proposed a comprehensive approach addressing these issues. It efficiently constructed dual-temporal images by utilizing a latitude gradient-based common time period of the growing season in the north and that in the south (the same common time period for the senescence season), iteratively optimized samples to reduce misclassifications, and approximated the trained model through a decision rule to reveal implicit knowledge. Using independently collected samples, the produced 10-me resolution map in China for 2020 achieved an overall accuracy of 92.5%. The study elaborated on the potential mechanism for identifying coastal salt marsh vegetation based on dual-temporal images, indicating that water signals during the growing season are obscured by flourishing vegetation, while these signals are observable during the senescence season when vegetation withers. This research provides a novel approach for fine-scale mapping of coastal wetland land covers based on remote sensing, facilitating coastal conservation and blue carbon estimation studies.