Detection and Analysis of Spartina alterniflora in Chongming East Beach Using Sentinel-2 Imagery and Image Texture Features
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Abstract: 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.
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Key words:
- texture features /
- RFECV /
- SHAP /
- Sentinel 2 time-series imagery /
- Multi-model comparison
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Figure 3. The curve of cross-validated scores as the number of features changes, based on RFECV feature selection applied to the original 20-dimensional features. A line chart of (a) RF+RFECV, a line chart of (b) LightGBM+RFECV and a line chart of (c) XGBoost+RFECV are showed on the top.
Figure 4. Model accuracy with different features. (a) The feature importance of the RF model and the overall model accuracy as the feature combination changes. (b) The variation of Spartina alterniflora classification accuracy with different feature combinations.
Figure 5. Accuracy of various land covers under different models. (a) Comparison of different models under 20 feature combinations. (b) Comparison of optimal models.
Figure 6. The selected four Sentinel-2 images from (a) January 21, 2020, (b) April 23, 2020, (c) June 2, 2020, and (d) September 5, 2020, for land cover classification and Spartina alterniflora detection. Figure 6e represents the land cover interpretation results.
Figure 7. The SHAP summary plot for each land cover type. (a) SHAP summary plot for farmland classification, (b) SHAP summary plot for breed classification, (c) SHAP summary plot for Spartina alterniflora classification, (d) SHAP summary plot for Staggered bands classification, (e) SHAP summary plot for water classification, (f) SHAP summary plot for other classification.
Table 1. Spectral bands of Sentinel-2 MSI L2A (Segarra et al., 2020).
Bands Wavelength (nm) Resolution (m) B1-Coastal aerosol 433-453 60 B2-Blue 458-523 10 B3-Green 543-578 10 B4-Red 650-680 10 B5-Vegetation red edge 698-713 20 B6-Vegetation red edge 733-748 20 B7-Vegetation red edge 773-793 20 B8-NIR 785-900 10 B8A-Narrow NIR 855-875 20 B9-Water vapour 935-955 60 B11-SWIR 1565 -1655 20 B12-SWIR 2100 -2280 20 
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Table 2. Training and validation data of different land cover types.
Types Label Training
samplesValidation
samplesfarmland 1 22 9 reed 2 19 8 Spartina alterniflora 3 20 9 Staggered-bands 4 17 7 water 5 23 10 other (Bare ground,
building,Scirpus)6 21 9
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Table 3. The classification accuracy of wetland features in the experimental area using different algorithms.
type RF_20 RF_8 XGB_20 XGB_19 LGBM_20 LGBM_8 PA (%) UA (%) PA UA PA UA PA UA PA UA PA UA farmland 99.97 100 99.95 100 99.97 99.98 99.95 100 99.95 99.98 100 100 reed 99.49 98.99 99.77 99.33 99.77 99.55 99.71 99.54 99.72 99.83 99.72 99.94 Spartina 99.37 98.84 99.50 99.59 99.59 99.62 99.71 99.59 99.75 99.50 99.65 99.59 staggered 98.26 99.58 99.23 99.38 99.23 99.33 99.17 99.48 99.13 99.59 99.18 99.28 water 100 99.99 99.99 99.99 99.99 99.99 99.99 99.99 100 99.99 100 99.99 other 100 100 100 100 100 100 100 100 100 99.73 100 99.81 OA 99.91 99.94 99.95 99.95 99.95 99.95 1 Spartina stands for Spartina alterniflora; staggered stands for Spartina alterniflora-reed mixed staggered zone; type stands for land cover type.
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