Identifying large-area mangrove distribution based on remote sensing: A binary classification approach considering subclasses of non-mangroves

Abstract

Mangroves have tremendous ecological value but are vulnerable to anthropogenic factors and sea level rise. Classification based on remote sensing is the first step for monitoring mangrove distribution in large areas but suffers from misclassifications due to the complexity of mangroves, such as having abundant species within the mangrove class, a large latitudinal span from tropical to subtropical, and mixing with nearby forests at the fringe. Binary classification approaches (i.e., mangroves and non-mangroves) have the potential to adjust the decision surface by adding subclasses similar to the mangrove class into the non-mangrove class in comparison with one-class classification approaches (i.e., separating mangroves from the background), and it demands fewer samples than multi-class classification (i.e., a mangrove class and multiple non-mangrove classes). However, the subclasses of binary classifications have rarely been considered to reduce the sample size with comparable performance to multi-class classification approaches. This paper first tested whether a conversion from either of two existing multi-class classifications to a binary classification could achieve comparable performance to corresponding multi-class classification for identifying mangroves in China in 2018 using the Google Earth Engine platform. The results showed that the conversion from multi-class classifications to binary classification achieved an overall accuracy value of 74.1% and 83.8%, respectively, the second of which was higher than that of the corresponding multi-class classification result with a reduction of sample size by a rate of 72.9%. Such an improved performance is due to the adjustment of subclasses during conversion when evaluated by the same validation dataset. Thus, this study provided a binary classification approach to identifying large-area mangrove distribution with a reduced sample size and comparable performance. This paper further discussed the impact of the feature selection procedure and that of subclass settings to reveal the margins of the proposed approach, the distribution and causes of false positives in binary classification results, and adaptations suitable for further generalizations to case areas outside of China. This study fills a knowledge gap and serves for accurate and consistent large-area mangrove identification based on remote sensing.