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Mangrove Information Extraction and Precision Analysis Based on Multi-Feature Combination

Extracting information of mangroves at different tide levels from remote sensing images is challenging. In this study, we investigated the use of multiple features for mangrove information extraction, including spectral features, vegetation indices (NDVI, NIMI), and texture features. The accuracy of the extraction was also analyzed. We collected remote sensing images covering mangrove areas at different tide levels and conducted a comprehensive analysis of these images and extracted the desired features. The collected data were then used to train and evaluate classification models for accurate mangrove identification. The results showed that: (1) The integration of NDVI, NIMI, and band features effectively enhanced the classification accuracy of mangroves. These features provided valuable information about the vegetation cover and health of mangroves, enabling better differentiation from other land cover types. (2) The introduction of texture features for classification resulted in a significant decrease in user classification accuracy of mangroves. This suggests that texture features may not be as reliable in distinguishing mangroves from other land cover types, possibly due to the complex and heterogeneous nature of mangrove ecosystems. (3) Feature selection methods played a crucial role in improving the accuracy of mangrove extraction. By selecting an appropriate number of relevant features, these methods helped to avoid data redundancy and reduce the influence of weak features. This was particularly beneficial for the extraction of submerged mangroves, which are often challenging to detect accurately. These findings contribute to the development of improved methods for monitoring and managing mangrove ecosystems, which are vital for their conservation and sustainable management.

Mangroves, Information Extraction, Sentinel-2 Imagery, Multiple Feature Combination, Random Forest

APA Style

Mingli Zhou, Angying Xu, Chengming Yang, Lifeng Liang. (2023). Mangrove Information Extraction and Precision Analysis Based on Multi-Feature Combination. American Journal of Remote Sensing, 11(2), 36-43.

ACS Style

Mingli Zhou; Angying Xu; Chengming Yang; Lifeng Liang. Mangrove Information Extraction and Precision Analysis Based on Multi-Feature Combination. Am. J. Remote Sens. 2023, 11(2), 36-43. doi: 10.11648/j.ajrs.20231102.12

AMA Style

Mingli Zhou, Angying Xu, Chengming Yang, Lifeng Liang. Mangrove Information Extraction and Precision Analysis Based on Multi-Feature Combination. Am J Remote Sens. 2023;11(2):36-43. doi: 10.11648/j.ajrs.20231102.12

Copyright © 2023 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License ( which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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