Abstract
Accurate large-area mangrove classification is a challenging task due to the complexity of mangroves, such as abundant species within the mangrove category, and various appearances resulting from a large latitudinal span and varied habitats. Existing studies have improved mangrove classifications by introducing time series images, constructing new indices sensitive to mangroves, and correcting classifications by empirical constraints and visual inspections. However, false positive misclassifications are still prevalent in current classification results before corrections, and the key reason for false positive misclassification in large-area mangrove classifications is unknown. To address this knowledge gap, a hypothesis that an inadequate classification scheme (i.e., the choice of categories) is the key reason for such false positive misclassification is proposed in this paper. To validate this hypothesis, new categories considering non-mangrove vegetation near water (i.e., within one pixel from water bodies) were introduced, which is inclined to be misclassified as mangroves, into a normally-used standard classification scheme, so as to form a new scheme. In controlled conditions, two experiments were conducted. The first experiment using the same total features to derive direct mangrove classification results in China for the year 2018 on the Google Earth Engine with the standard scheme and the new scheme respectively. The second experiment used the optimal features to balance the probability of a selected feature to be effective for the scheme. A comparison shows that the inclusion of the new categories reduced the false positive pixels with a rate of 71.3% in the first experiment, and a rate of 66.3% in the second experiment. Local characteristics of false positive pixels within 1 × 1 km cells, and direct classification results in two selected subset areas were also analyzed for quantitative and qualitative validation. All the validation results from the two experiments support the finding that the hypothesis is true. The validated hypothesis can be easily applied to other studies to alleviate the prevalence of false positive misclassifications.
Highlights
As the core of this study is to reveal the key reason for false positive misclassification, 2.6.2
The main maps (Figure 6a,d) show that the mangroves along the coast occupy areas too small to be seen at full scale, and the zoomed-in maps show a wide spread of mangrove patches
The improvement on the commission error when adopting the new scheme was consistent with visual judgement, that is, the classification result with the new scheme had a cleaner and tidier distribution of patches classified as mangroves than that with the standard scheme (Figure 6b,e)
Summary
Mangroves are a community of trees and shrubs adapted to tidal environments found in tropical and subtropical regions of the world [1,2]. Mangroves provide a wide range of ecosystem services such as coast protection and carbon sinks, as well as materials such as food and medicine [3,4,5,6,7]. Mangroves distribution is dynamic as a result of deforestation for agriculture, aquaculture, and urban expansion [8,9,10] or afforestation efforts [11,12]. Remote sensing (RS)-based approaches have been developed to efficiently map mangrove distribution over large areas, especially since Giri, et al [13] obtained a global mangrove distribution map using Landsat data. Due to the presence of different mangrove
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