How to automate timely large-scale mangrove mapping with remote sensing

Abstract

Mangrove forests have witnessed significant changes resulted from both anthropogenic and natural disturbances in the last four decades. Although a few attempts have been reported, effective methods that can repeatedly generate large-scale mangrove maps on a timely basis are still lacking due to the difficulty in gathering sufficient training samples in large geographical areas. In this study, we have addressed three objectives in the following manner: (1) we aim to develop a method to automatically collect ample mangrove training samples; Correspondingly, we developed an automatic training sample collection method which extracted unchanged mangrove samples from a historical mangrove map; In addition, we employed a region growing method to include more diversified training samples; (2) we strive to foster compatible classifiers that can leverage the collected one-class training samples; To this end, we came up with two representative one-class classifiers: the Support Vector Data Description (SVDD), and the Positive and Unlabeled Learning algorithm (PUL); (3) we endeavor to compare the effectiveness of various combinations of training samples, classifiers, and input images; As a result, we developed 32 classification models by varying four different variables: training samples (unchanged vs. expanded), input data (Landsat 8, Sentinel-1, and Sentinel-2), classifiers (SVDD vs. PUL), and study sites (Florida, the United States and Guangxi, China). We found that our developed automatic training sample collection methods performed well (user's accuracy >97%). Inter-annual NDVI combined with geometric restrictions warranted the effective extraction of unchanged training samples while the region growing method further reduced the omission due to its addition of recently emerged mangroves. In addition, PUL performed better than SVDD. This is attributed to the fact that PUL draws upon not only mangrove samples, but also unlabeled ones unaccounted for in SVDD. Lastly, the combination of Sentinel-1 and Sentinel-2 is recommended among all the compared models. In summary, we developed an effective method to automatically extract mangrove training samples, based on which a one-class classification method for large-scale mangrove mapping is made possible. We envision our methods will contribute to a wide spectrum of timely large-scale mangrove mapping tasks.