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

Mingli Zhou; Angying Xu; Chengming Yang; Lifeng Liang

doi:doi:10.11648/j.ajrs.20231102.12

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

Mingli Zhou, Angying Xu, Chengming Yang, Lifeng Liang

Published in American Journal of Remote Sensing (Volume 11, Issue 2)

Received: 24 July 2023 Accepted: 8 August 2023 Published: 28 August 2023

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Abstract

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.

Published in	American Journal of Remote Sensing (Volume 11, Issue 2)
DOI	10.11648/j.ajrs.20231102.12
Page(s)	36-43
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

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

References

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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. https://doi.org/10.11648/j.ajrs.20231102.12

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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

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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

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@article{10.11648/j.ajrs.20231102.12,
  author = {Mingli Zhou and Angying Xu and Chengming Yang and Lifeng Liang},
  title = {Mangrove Information Extraction and Precision Analysis Based on Multi-Feature Combination},
  journal = {American Journal of Remote Sensing},
  volume = {11},
  number = {2},
  pages = {36-43},
  doi = {10.11648/j.ajrs.20231102.12},
  url = {https://doi.org/10.11648/j.ajrs.20231102.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajrs.20231102.12},
  abstract = {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.},
 year = {2023}
}

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TY - JOUR
T1 - Mangrove Information Extraction and Precision Analysis Based on Multi-Feature Combination
AU - Mingli Zhou
AU - Angying Xu
AU - Chengming Yang
AU - Lifeng Liang
Y1 - 2023/08/28
PY - 2023
N1 - https://doi.org/10.11648/j.ajrs.20231102.12
DO - 10.11648/j.ajrs.20231102.12
T2 - American Journal of Remote Sensing
JF - American Journal of Remote Sensing
JO - American Journal of Remote Sensing
SP - 36
EP - 43
PB - Science Publishing Group
SN - 2328-580X
UR - https://doi.org/10.11648/j.ajrs.20231102.12
AB - 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.
VL - 11
IS - 2
ER -

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Author Information

Mingli Zhou

Department of Geography, Lingnan Normal University, Zhanjiang, China
Angying Xu

Department of Geography, Lingnan Normal University, Zhanjiang, China
Chengming Yang

Department of Geography, Lingnan Normal University, Zhanjiang, China
Lifeng Liang

Department of Geography, Lingnan Normal University, Zhanjiang, China

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Plain Text BibTeX RIS

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. https://doi.org/10.11648/j.ajrs.20231102.12

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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

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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

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@article{10.11648/j.ajrs.20231102.12,
  author = {Mingli Zhou and Angying Xu and Chengming Yang and Lifeng Liang},
  title = {Mangrove Information Extraction and Precision Analysis Based on Multi-Feature Combination},
  journal = {American Journal of Remote Sensing},
  volume = {11},
  number = {2},
  pages = {36-43},
  doi = {10.11648/j.ajrs.20231102.12},
  url = {https://doi.org/10.11648/j.ajrs.20231102.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajrs.20231102.12},
  abstract = {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.},
 year = {2023}
}

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