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Accurate monitoring of emergent aquatic vegetation (EAV) and floating-leave aquatic vegetation (FAV), is vital because vegetation provides a critical habitat for aquatic animals and plays a critical role in protecting biodiversity. However, owing to the interference of water spectrum signals, it is difficult to extract the emerging aquatic vegetation information from remote sensing images, especially to distinguish between EAV and FAV. This study first proposed an enhanced aquatic vegetation index based on the difference in two short-wave infrared (SWIR) bands to extract the aquatic vegetation above the water surface, and then, the EAV and FAV were further distinguished coupled with the phenological characteristics, subsequently, a case study of Taihu Lake for accurately extracting seasonal and annual distributions. The results demonstrate that the proposed aquatic vegetation index is highly sensitive to EAV and FAV, and can further distinguish EAV from them coupled with phenological difference. Additionally, the dramatic changes in EAV and FAV in time and space indicated that human purse seine culture has had a great influence on the succession of aquatic vegetation, which may lead to the deterioration of the water ecology environment. Using the SWIR bands coupled with phenological difference is a promising method for recognizing aquatic vegetation above the water surface owing that it can weaken and eliminate the impact of algal bloom at the same time.

Frequent geohazards have knock-on effects on ecological quality. Timely and dynamically monitoring the damage of geohazards to ecological quality is important to the geological hazards prevention, ecological restoration, and policy formulation. Existing studies mainly focused on the impacts of climate change, urbanization, and extreme weather on the ecological quality, largely ignoring the role of frequent geohazards in the highly susceptible area. At present, the impact mechanism of the high susceptibility of geohazards on ecological quality remains unknown. To fill this knowledge gap, we use the Remote Sensing Ecological Index (RSEI, a widely accepted ecological quality index) calculated on the Google Earth Engine (GEE) platform, geohazard density data, and the Landsat series of surface reflectance datasets to explore the mechanism that drives spatial–temporal variations of ecological quality. Taking the Danba County as the study area, our results indicate that the total number of geohazards is 944 during 1995–2019, and the number of geohazards fluctuates and rises every year (10 in 1995 and 82 in 2019). A conceptual framework was proposed to quantify the impact of the high susceptibility of geohazards on ecological quality by separately exploring its impact on the 4 ecological components of RSEI (i.e., greenness, wetness, dryness, and heat). We found that the density of geohazards is significantly negatively correlated with greenness (R = 0.48, Pearson Correlation Coefficient (PCC) = −0.528, p

Bloom occurrence probability prediction is a critical issue for freshwater resource management and protection. As the mechanism of algal blooms is not understood, the construction of prediction model mainly depends on statistical data. Therefore, knowledge on prior bloom occurrence derived from statistical data plays a significant role in establishing a prediction model. In this study, a naïve Bayesian model incorporating prior information was constructed to predict algal bloom occurrence probabilities 1–7 days in advance under different weather conditions in Dianchi Lake. The proposed model utilised the following data from the MODIS images, the floating algae index (FAI) for the previous 7 days and five meteorological variables (mean wind speed, air pressure, relative humidity on the prediction day, accumulated sunshine hours in the previous 3 days, and accumulated air temperature in the previous 7 days). The prior probabilities of each pixel were calculated on a monthly timescale to highlight the bloom’s temporal-spatial differences, and the 1–7 day posterior probabilities were calculated by combining the prior and conditional probabilities. The predictive effect was tested by the area under the receiver-operating characteristic curve (AUC), and the results showed the number of pixels with estimation predictions classified as ‘Good+’ and ‘Excellent’ were 91.3%, 91.4%, 82.7%, 83.3%, 89.0%, 86.6%, and 90.0% for the predictions on days 1–7, respectively. Additionally, the independent validation datasets showed that the percentage of correctly classified instances (CCI%) of approximately 90% of the pixels were greater than 50%. The proposed algal bloom prediction model based on the naïve Bayesian method has pixel-level prediction abilities, is applicable to other inland water systems, and can provide a reference for water environment risk forecasting and management.