River–lake interaction is important for maintaining biodiversity, yet it is vulnerable to hydrological alteration. The connectivity of the channel connecting Poyang Lake and the Yangtze River not only ensures the regular migration of fish but also makes Poyang Lake a feeding and fattening ground for them. Unfortunately, human activities have dramatically changed the hydrodynamic conditions of Poyang Lake, which is experiencing severe drought due to the obvious decline in the water level in autumn and winter, especially since 2003. However, the possible impacts of the changes in the water level on the habitats of migratory fish remain unclear due to the limitation of traditional techniques in spatiotemporal analysis. Here, we combined a hydrodynamic model and habitat suitability model to simulate variations in the suitable habitat area and their connectivity under different water-level conditions. The conditions for the migration pathway of the target fish were obtained by a hydroacoustic survey using the Simrad EY60 echosounder. The results showed that the change in water level will significantly affect the spatiotemporal change in the suitable habitats and their connectivity. In particular, we found the existence of two thresholds that play a dominant role in illuminating the connectivity of effective suitable habitats (HC). Firstly, the maximum value of the weighted usable area (WUA) and HC can be achieved when the water level is more than 16 m. Secondly, when the water level is between 10 and 16 m, the changes in the HC are sensitive and rapid, and the area flooded at this stage is called the sensitive area. HC is a crucial element in fish migration and habitat conditions. Under the condition of continuous drought in the middle reaches of the Yangtze River, our research contributes to clarifying the influence of water level on key habitats for fish and optimizes the practice of river–lake ecological management.

After the completion of the Three Gorges Reservoir (TGR), there was a significant and drastic transformation of the original river habitat. These changes led to the loss of the original fish habitat and the emergence of a new habitat. To effectively classify and assess fish-spawning habitats in the TGR, a novel coastal complexity index (CCI) was developed. The CCI was formulated utilizing satellite remote sensing data and considering the river coastal line and river centerline on the river-reach scale. By integrating the CCI with river morphology, five river habitats were identified: the backwater bay, point bar, straight river channel, convex-bank point bar, and concave-bank deep pool. In order to evaluate the suitability of these habitats for sticky-egg-spawning fish, a single-factor habitat suitability curve was constructed using three key habitat factors: the CCI, slope, and vegetation coverage. This process involved the employment of two distinct methods: the habitat utilization method and the habitat preference method. The former only considered the survey data of spawning grounds, while the latter integrated the overall distribution of habitats in the TGR. Subsequently, a habitat suitability index (HSI) was established to assess the overall suitability of the identified habitats for sticky-egg-spawning fish. The results demonstrated a high classification accuracy, with the backwater bay representing the most prevalent habitat type, accounting for 43.31% of the total habitat types. When considering slope and vegetation coverage, the optimal ranges obtained through the two habitat suitability analysis methods were similar. However, for the CCI, there were variations in the optimal ranges obtained using the two methods. The habitat utilization method indicated an optimal interval of 2–4, while the habitat preference method provided an optimal interval of 4–8. Nonetheless, the assessment results for the spawning habitats’ suitability using both methods yielded essentially identical outcomes. Specifically, the backwater bay, convex-bank point bar, and concave-bank deep pool habitats exhibited higher suitability for spawning than point bar and straight river channel habitats. Further analysis revealed that approximately 75% of the 230 identified backwater bays were categorized as high-quality or higher-quality spawning habitats. In the time since this research was conducted, its findings have served as a theoretical foundation for the protection of aquatic biological resources and habitats.

Beijing-1 and ENVISAT ASAR images were used to classify wetland aquatic macrophytes in terms of their plant functional types (PFTs) over the Poyang Lake region, China. Speckle noise filtering, systematic sensor calibration within the same polarization or between different polarizations, and accurate geo-registration were applied to the time-series SAR data. As a result, time-series backscattering data, which is described as permittivity curves in this paper, were obtained. In addition, time-series indices, described as phenological curves, were derived from Beijing-1 time-series images in the classification experiment. Based on these two curves, a rule-based classification strategy was developed to extract wetland information from the combined SAR and optical data. In the rule-based wetland classification method, DEM data, submersion time index, temporal Beijing-1 images, time-series normalized difference vegetation index (TSNDVI) images, principal component analysis (PCA), and temporal ratio of ASAR time-series images were used. In addition, a decision tree-based method was used to map the wetlands. Conclusions include the following: (1) after the preprocessing of ASAR data, it was possible to satisfactorily separate different aquatic plant functional types; (2) hydrophytes from different PFTs exhibited distinct phenological, structural, moisture, and roughness characteristics due to the impact of the annual inundation of Poyang Lake wetland; and (3) more accurate results were obtained with the rule-based method than the decision tree (DT) method. Producer’s and user’s accuracy calculated from test samples in the classification results indicate that the DT method can potentially be used for mapping aquatic PFTs, with overall producer’s accuracy exceeding 80% and higher user’s accuracy for aquatic bed wetland PFTs. A comparison of producer’s and user’s accuracy from the rule-based classification increased from 3 to 12% and 7 to 26%, respectively, for different aquatic PFTs.