Comparing supervised classification algorithm–feature combinations for Spartina alterniflora extraction: a case study in Zhanjiang, China - Chen, Qiujie; Shen, Chunyan; Du, Hong; Tang, Danling (Frontiers in Remote Sensing, 2025)
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 5-year period, indicating a growth rate of 5.13%. By evaluating the accuracy of different classification methods and features, this research can facilitate S. alterniflora extraction and provide support for mangrove conservation efforts.