Abstract
The spatial distribution of coastal wetlands affects their ecological functions. Wetland classification is a challenging task for remote sensing research due to the similarity of different wetlands. In this study, a synergetic classification method developed by fusing the 10 m Zhuhai-1 Constellation Orbita Hyperspectral Satellite (OHS) imagery with 8 m C-band Gaofen-3 (GF-3) full-polarization Synthetic Aperture Radar (SAR) imagery was proposed to offer an updated and reliable quantitative description of the spatial distribution for the entire Yellow River Delta coastal wetlands. Three classical machine learning algorithms, namely, the maximum likelihood (ML), Mahalanobis distance (MD), and support vector machine (SVM), were used for the synergetic classification of 18 spectral, index, polarization, and texture features. The results showed that the overall synergetic classification accuracy of 97% is significantly higher than that of single GF-3 or OHS classification, proving the performance of the fusion of full-polarization SAR data and hyperspectral data in wetland mapping. The synergy of polarimetric SAR (PolSAR) and hyperspectral imagery enables high-resolution classification of wetlands by capturing images throughout the year, regardless of cloud cover. The proposed method has the potential to provide wetland classification results with high accuracy and better temporal resolution in different regions. Detailed and reliable wetland classification results would provide important wetlands information for better understanding the habitat area of species, migration corridors, and the habitat change caused by natural and anthropogenic disturbances.
Highlights
Introduction conditions of the Creative CommonsCoastal wetlands play a pivotal role in providing many ecological services, including storing runoff, reducing seawater erosion, providing food, and sheltering many organisms, including plants and animals [1]
A larger amount of noise deteriorates the quality of GF-3 classification results, and many pixels belonging to the river are misclassified as saltwater (Figure 8a,d,g), indicating that the GF-3 fails to separate different water bodies
The complete classification results generated by the synergetic classification are clearer than those of GF-3 and Orbita Hyperspectral Satellite (OHS) data separately (Figure 8c,f,i)
Summary
Introduction conditions of the Creative CommonsCoastal wetlands play a pivotal role in providing many ecological services, including storing runoff, reducing seawater erosion, providing food, and sheltering many organisms, including plants and animals [1]. The mudflats [4], mangroves, and vegetation (e.g., Tamarix chinensis, Suaeda salsa, and Spartina alterniflora) [5] in coastal wetlands have strong carbon sequestration ability. Intense anthropogenic activities in recent decades, such as dam building, agricultural irrigation, groundwater pumping, hydrocarbon extraction, and the artificial diversion of the estuary, have posed serious threats to the coastal wetlands of YRD [9,10,11,12,13]. It is of great significance to carry out dynamic monitoring and obtain a reliable and up-to-date classification of coastal wetlands over the YRD for studying the impact of human activities on habitat area [14]
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