| The phenomenon of water wars in China’s inland freshwater lakes is a serious threat to water quality safety,and their treatment is urgent.Therefore,by taking advantage of the fact that the planktonic algae in water blooms have spectral characteristics similar to those of plants,and by combining the advantages of remote sensing technology in a wide range,continuity and long time series,the spatial and temporal distribution characteristics and intensity of water blooms can be better monitored,thus providing macroscopic guidance for treatment and prevention.This paper takes Taihu Lake as an example to study the monitoring and risk early warning of cyanobacterial blooms in freshwater lakes in China.Through long time series of Taihu Lake cyanobacterial bloom multi-source remote sensing data monitoring results,the analysis summarises the spatial and temporal distribution pattern of cyanobacterial blooms in Taihu Lake,and then combines with meteorological data to establish an early warning model of cyanobacterial blooms in Taihu Lake,which provides a theoretical basis for the monitoring and management of cyanobacterial blooms in Taihu Lake,and for the management of cyanobacterial blooms in inland freshwater lakes in China The model will provide reference information.The main contents are as follows:(1)The accuracy of extraction algorithms and the consistency of identification results of multi-source optical remote sensing monitoring were evaluated.The accuracy of cyanobacterial bloom extraction algorithms and the consistency of their monitoring results were evaluated for the three types of optical remote sensing data,and it was found that the accuracy of cyanobacterial bloom identification was 95.83%for MODIS data using the normalized vegetation index algorithm;97.17%for Landsat 8 data using the planktonic algal vegetation index algorithm,for Sentinel-2 data using the accuracy of extracting the chlorophyll reflectance peak intensity algorithm combined with the normalized vegetation index algorithm reached 96.68%.the consistency analysis of the monitoring and identification results of MODIS,Landsat 8 and Sentinel-2 data showed that the R~2 of Landsat 8 and MODIS data was 0.9612,and the R~2 of Sentinel-2 and MODIS data was 0.83.The results of the monitoring and identification of cyanobacterial blooms are highly consistent across the different remote sensing imagery data.(2)The spatial and temporal distribution characteristics of cyanobacterial blooms in Taihu Lake by optical remote sensing were analysed.The following conclusions were obtained through quantitative analysis of the monitoring and identification results of cyanobacterial blooms in Taihu Lake from 2015 to 2020:in terms of temporal distribution,the summer and autumn seasons were the high outbreak periods of cyanobacterial blooms,May2017 was the largest month for cyanobacterial blooms in Taihu Lake with an area of 1080km~2,the largest annual average area of cyanobacterial blooms in Taihu Lake in 2017 and the smallest average area in 2015.In terms of spatial distribution,the largest area of outbreak was found in the central area of the lake,followed by the western coastal area and southern coastal area,with smaller areas in Meiwan Lake and Gong Lake,and the smallest area of cyanobacterial bloom in Zhushan Lake.(3)The identification method of cyanobacterial bloom monitoring by radar remote sensing was studied.The cyanobacterial bloom area has a low grey scale value in the radar image and appears as a black dark spot on the image.Establishing a dark spot classification model with C-SVM support vector machine as the core method for dark spot segmentation and dark spot feature extraction.The model recognition results showed that the recognition accuracy of the model reached 72.00%for the cyanobacteria water bloom in Taihu Lake,which proved the feasibility of using radar images for cyanobacteria water bloom monitoring and recognition.(4)A model for predicting the probability of occurrence of cyanobacterial blooms in Taihu Lake was constructed.Meteorological conditions such as temperature,light and wind speed can accelerate the growth,reproduction and other life activities of cyanobacteria.Using meteorological conditions as the independent variable and the probability of occurrence of cyanobacteria as the dependent variable,a risk prediction model for the occurrence of cyanobacteria in Taihu Lake was established with a logistic regression model as the core,and then the prediction of cyanobacteria in Taihu Lake was achieved.The prediction results show that the model has a prediction accuracy of 84.7%. |
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