ORIGINAL RESEARCH article
Front. Remote Sens.
Sec. Image Analysis and Classification
Volume 6 - 2025 | doi: 10.3389/frsen.2025.1606549
Comparing Supervised Classification Algorithm-Feature Combinations for Spartina alterniflora Extraction: A Case Study in Zhanjiang, China
Provisionally accepted
- 1Guangdong Provincial Key Laboratory of the Marine Disaster Prediction and Prevention, Shantou University, Shantou, Guangdong Province, China
- 2Guangdong Remote Sensing Center for Marine Ecology and Environment, Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Guangzhou, China
- 3College of Fisheries, Guangdong Ocean University, Zhanjiang, Guangdong Province, China
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Mangrove forests are vital blue carbon ecosystems whose security is increasingly threatened by the non-native species Spartina alterniflora. Accurate remote sensing-based identification and monitoring are crucial for invasive species management; however, such methods have rarely been applied to determine the distribution of S. alterniflora in Zhanjiang, China. Here, we combined five supervised classification algorithms-random forest (RF), support vector machine, maximum likelihood classification (MLC), minimum distance classification, and Mahalanobis distance classification-with spectral bands, spectral indices, and the gray-level co-occurrence matrix (GLCM) derived from Sentinel-2 imagery to identify the optimal combination for monitoring the spatial distribution of S. alterniflora on Donghai Island, Zhanjiang. The sample dataset was divided into training and validation sets at a ratio of 7:3, yielding a sub-dataset with Jeffries-Matusita distances of 1.893-2.000, which satisfied classification requirements. The most accurate algorithm-feature combination was MLC plus spectral features, which achieved a kappa coefficient of 0.9061, an overall accuracy of 95.32%, and a similar extracted area (72.51 ha) to that derived from visual interpretation (68.7 ha). The next most accurate combinations were RF plus spectral bands+GLCM and RF plus spectral bands+spectral indices+GLCM, with kappa coefficients of 0.8991, overall accuracy of 94.96%, and extraction areas of 74.76 ha and 75.31 ha, respectively. RF showed superior adaptability across different feature scenarios, resulting in stable accuracy and minimal area error. According to visual interpretation, the area of S. alterniflora increased by 3.35 ha over a
Keywords: random forest (RF)1, maximum likelihood classification (MLC)2, Sentinel-23, Spartina alterniflora4, machine learning5
Received: 05 Apr 2025; Accepted: 09 Jun 2025.
Copyright: © 2025 Chen, Shen, Hong and Tang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Du Hong, Guangdong Provincial Key Laboratory of the Marine Disaster Prediction and Prevention, Shantou University, Shantou, 515063, Guangdong Province, China
DanLing Tang, Guangdong Remote Sensing Center for Marine Ecology and Environment, Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Guangzhou, China
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