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Article

The Key Reason of False Positive Misclassification for Accurate Large-Area Mangrove Classifications

by 1,2,3 and 2,3,4,*
1
Key Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China
2
State Key Laboratory of Resources & Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
3
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
4
Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
*
Author to whom correspondence should be addressed.
Academic Editor: Arturo Sanchez-Azofeifa
Remote Sens. 2021, 13(15), 2909; https://doi.org/10.3390/rs13152909
Received: 14 June 2021 / Revised: 13 July 2021 / Accepted: 21 July 2021 / Published: 24 July 2021
(This article belongs to the Special Issue GIS and RS in Ocean, Island and Coastal Zone)
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. View Full-Text
Keywords: classification scheme; false positive misclassification; non-mangrove vegetation near water; mangrove map; Google Earth Engine classification scheme; false positive misclassification; non-mangrove vegetation near water; mangrove map; Google Earth Engine
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MDPI and ACS Style

Zhao, C.; Qin, C.-Z. The Key Reason of False Positive Misclassification for Accurate Large-Area Mangrove Classifications. Remote Sens. 2021, 13, 2909. https://doi.org/10.3390/rs13152909

AMA Style

Zhao C, Qin C-Z. The Key Reason of False Positive Misclassification for Accurate Large-Area Mangrove Classifications. Remote Sensing. 2021; 13(15):2909. https://doi.org/10.3390/rs13152909

Chicago/Turabian Style

Zhao, Chuanpeng, and Cheng-Zhi Qin. 2021. "The Key Reason of False Positive Misclassification for Accurate Large-Area Mangrove Classifications" Remote Sensing 13, no. 15: 2909. https://doi.org/10.3390/rs13152909

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