Detection and Analysis of Spartina alterniflora in Chongming East Beach Using Sentinel-2 Imagery and Image Texture Features

Spartina alterniflora Loisel is now listed among the world’s 100 most dangerous invasive species, severely affecting the ecological balance of coastal wetlands. Remote sensing technologies based on deep learning enable large-scale monitoring of Spartina alterniflora, but they require large datasets and have poor interpretability. A new method is proposed to detect Spartina alterniflora Loisel from Sentinel-2 imagery. Firstly, to get the high canopy cover and dense community characteristics of Spartina alterniflora Loisel, multi-dimensional shallow features are extracted from the imagery. Secondly, to detect different objects from satellite imagery, index features are extracted, and the statistical features of the gray-level co-occurrence matrix (GLCM) are derived using Principal Component Analysis - Gray-Level Co-Occurrence Matrix (PCA-GLCM). Then, ensemble learning methods, including Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM) models, are employed for image classification. Meanwhile, Recursive Feature Elimination with Cross-Validation (RFECV) is used to select the best feature subset. Finally, to enhance the interpretability of the models, the best features are utilized to classify multi-temporal images and SHapley Additive exPlanations (SHAP) is combined with these classifications to explain the model prediction process. The method is validated by using Sentinel-2 imageries and previous observations of Spartina alterniflora Loisel in Chongming Island, it is found that the model combining image texture features such as GLCM covariance can significantly improve the detection accuracy of Spartina alterniflora by about 8% compared with the model without image texture features. Through multiple model comparisons and feature selection via RFECV, the selected model and eight features demonstrated good classification accuracy when applied to data from different time periods, proving that feature reduction can effectively enhance model generalization. Additionally, visualizing model decisions using SHAP revealed that the image texture feature component_1_GLCMVariance is particularly important for identifying each land cover type.