Abstract
The development of advanced and efficient methods for mapping and monitoring wetland regions is essential for wetland resources conservation, management, and sustainable development. Although remote sensing technology has been widely used for detecting wetlands information, it remains a challenge for wetlands classification due to the extremely complex spatial patterns and fuzzy boundaries. This study aims to implement a comprehensive and effective classification scheme for wetland land covers. To achieve this goal, a novel object-based multigrained cascade forest (OGCF) method with multisensor data (including Sentinel-2 and Radarsat-2 remote sensing imagery) was proposed to classify the wetlands and their adjacent land cover classes in the wetland National Natural Reserve. Moreover, a hybrid selection method (ReliefF-RF) was proposed to optimize the feature set in which the spectral and polarimetric decomposition features are contained. We obtained six spectral features from visible and shortwave infrared bands and 10 polarimetric decomposition features from the H/A/Alpha, Pauli, and Krogager decomposition methods. The experimental results showed that the OGCF method with multisource features for land cover classification in wetland regions achieved the overall accuracy and kappa coefficient of 88.20% and 0.86, respectively, which outperformed the support vector machine (SVM), extreme gradient boosting (XGBoost), random forest (RF), and deep neural network (DNN). The accuracy of the wetland classes ranged from 75.00% to 97.53%. The proposed OGCF method exhibits a good application potential for wetland land cover classification. The classification scheme in this study will make a positive contribution to wetland inventory and monitoring and be able to provide technical support for protecting and developing natural resources.
Highlights
Wetlands are regions that are shallow open waters and any land that is regularly or intermittently covered or saturated by water [1,2,3,4]
The hybrid feature selection algorithm (ReliefF-random forest (RF)) was proposed to optimize the feature set with 54 multisource features according to the ranking criterion of ReliefF and RF
A hybrid feature selection method (ReliefF-RF) was utilized to optimize the feature set extracted from Sentinel-2 and Radarsat-2 imagery
Summary
Wetlands are regions that are shallow open waters (lakes, ponds, rivers, and coastal fringes) and any land that is regularly or intermittently covered or saturated by water (marshes, bogs, swamps, and flood plains) [1,2,3,4]. Many precious plants and animals, dependent on wetlands, are disappearing with them [13,14,15]. Given these serious situations, several notable efforts to perform national and regional comprehensive wetland inventories have been successfully conducted, such as the Canadian Wetland Inventory (CWI) and the National Wetlands Inventory (NWI) by the U.S Fish and Wildlife Service [16,17,18,19]. The Chinese government always pays attention to wetlands and spares no efforts to take many protective measures, such as establishing nature conservation areas, regularly monitoring wetland environments and proposing many policies and regulations to restrict frequent human activities. To reinforce the conservation and management of wetland resources, the use of advanced technology for a regular status survey is significantly essential
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