## 1 Introduction  
Aquatic macrophytes play an important role in maintaining the balance of lake ecosystems and supporting socioeconomic functions [1]. For example, macrophytes stabilize sediments, regulate the nutrient cycle [2], slow water currents and maintain fishery production [3]. Studies have indicated that aquatic vegetation has a significant and positive effect on pollutant removal and thereby purifies water, which makes aquatic ecosystems shift from a turbid algal-dominated state to a clear-water, plant-dominated state [4, 5]. In the eutrophic Taihu Lake of China (30\({}^{\circ}\)55'40''-31\({}^{\circ}\)32'58''N, 119\({}^{\circ}\)52'32''-120\({}^{\circ}\)36'10''E), it therefore is important to accurately map the distribution of and detect changes in aquatic vegetation for lake management. At a large scale or across the whole lake, it is laborious and difficult to survey types and distribution of aquatic vegetation by traditional and conventional methods. Developing efficient, large-scale monitoring strategies is helpful for effectively tracking and mitigating harmful changes in the lake. Remote sensing has proven to be an effective tool for mapping the distribution of aquatic vegetation over such a large area. Aerial photographs commonly were used in the past to map aquatic vegetation [6, 7]. With the rapid development of satellite sensors, multispectral and hyperspectral remote sensing have become more feasible for mapping and monitoring aquatic vegetation. For example, researchers have used high spatial resolution satellite images (e.g., IKONOS and QuickBird images) to illustrate fine-level habitat discrimination [8, 9] and to identify submerged vegetation [10]. High resolution imagery also has been used to identify and detect plant communities in wetland settings [11]. Considering availability and accessibility, moderate spatial resolution multispectral satellite data, such as from the Landsat Multispectral Scanner (MSS), Thematic Mapper (TM) and Enhanced Thematic Mapper (ETM+) [12, 13], and Satellite Pour l'Observation de la Terre (SPOT) imagery [14], have been widely used for mapping distribution of aquatic macrophytes and assessing spatial and temporal dynamics of aquatic vegetation [15, 16]. Meanwhile, a variety of methods and algorithms based on remote sensing data have been applied to automatically identify aquatic vegetation, such as unsupervised isoclustering, supervised maximum likelihood classifiers [17], and tasseled-cap classification [18]. Supervised classification and classification trees have been the most widely and successfully used for classifying aquatic vegetation. For example, [PERSON] _et al._[19] developed a transparency-assisted classification tree to classify the aquatic vegetation in Taihu Lake and [PERSON] _et al._[20] developed a CT model for mapping aquatic vegetation in Taihu Lake based on spectral indices. These studies have demonstrated that an accurate CT model for mapping aquatic vegetation could probably be developed using remote sensing imagery for a specific time, given enough ground-truth samples [21, 22]. Ground-based data are expensive and often difficult to acquire. Thus, an important question is if aquatic vegetation can be mapped well without ground-truth samples, such as for mapping past conditions in lakes using historical archived satellite data for which corresponding ground-truth samples are unavailable. It has been shown that the classification trees developed for one time period or image might not be adequate when applied to other periods or dates due to influences of various intrinsic and extrinsic factors [23]. To investigate this, we developed traditional CT models for mapping aquatic vegetation using ground-truth samples and corresponding HJ-CCD images from China's HJ-1A/1B of the Environment and Disasters Monitoring Microsatellite Constellation Charge-Coupled Device (CCD) camera.