## 1 Introduction  
Coastal wetlands, which are located in the interactive zone between terrestrial and aquatic ecosystems, are dynamic and fragile ecosystems [1]. They play important roles in water conservation, regional climate regulation, flood control, biodiversity protection, and so on [2, 3]. Over the past century, coastal wetlands have experienced obvious degradation or even disappearance, as a result of environmental changes and human activities, such as climate change, urban expansion, livestock grazing, and agricultural development [4]. Change detection (CD) is the appropriate way to observe wetland changes, therefore accurate and timely CD is fundamental to reliable wetland management and successful wetland protection. Optical images [5, 6, 7] and synthetic aperture radar (SAR) images [8, 9, 10] have been effectively utilized for dynamic monitoring in wetlands. Spectral-enhanced features [11], textural features [12], spectral-spatial features [13], spectral-textural features [14], and spectral-spatial-textural features [15; 16] have been utilized for change detection, however, there is inevitably redundancy among these features. If all of these features are input into the image classifier, on the one hand, it is easy to cause the \"dimension disaster\" problem, and computer operation becomes slow. On the other hand, the addition of some features is not conducive to the improvement of image classification and change detection. Therefore, it is necessary to select some features to obtain optimal feature subsets. Feature selection methods select optimal feature subsets from the available image features, which are divided into filter methods and wrapper methods [17]. Recently, principal component analysis [18], random forest-based [19], genetic particle swarm optimization [17], sequential forward selection [20], ensemble learning [21], and adaptive feature selection network [22] have been proposed for feature selection. Random forest is one of the wrapper methods and it removes irrelevant features one by one in order to obtain optimal feature subsets. When there are hundreds or thousands of features, it will take more time to remove irrelevant features one by one, therefore, it is necessary to improve the efficiency of feature selection using random forest. In addition, due to the large-scale and complex geographical conditions of coastal wetlands [23], it is time-consuming and laborious to collect training samples. According to the availability of training samples, CD methods can be divided into two categories: supervised CD and unsupervised CD. In addition, according to the final detailed requirements for the changed regions, CD can be divided into two categories: binary CD and multiple CD. Binary CD only separates changed areas from unchanged areas, while multiple CD can not only extract the changed areas but also obtain the \"from-to\" change information. A number of binary CD methods have been proposed, such as change vector analysis [24], clustering method [25], threshold method [26], and deep learning [27]. The development of binary CD methods is summarized in references [28; 29; 30; 31]. Compared with binary CD, multiple CD is more challenging. Some approaches have been developed, such as post classification comparison [32], CD in change vector analysis polar domain [24], and slow feature analysis [33]. However, these methods have been developed based on the assumption that there are either some or no ground reference samples at both pre- and post-changed times. Only small amounts of ground reference data are available at only one of the pre- and post-changed times [34], therefore, how to utilize limited ground reference samples for multiple CD is another important consideration. The contribution of this article is to propose a random forest-based feature hierarchical selection method in order to obtain optimal feature subsets, and these optimal feature subsets could then be utilized for coastal wetlands classification.