无人机遥感和GWR结合的水华短时预测方法

A Short-term Prediction Method ofWater Bloom based on UAV Remote Sensing and GWR

目前水华已成为国内外一个重要的环境问题,单一的光学遥感方法难以实现精细化的水华预测。针对上述问题,本文以无人机多光谱影像、水质、水温及气象数据为数据源,首先通过像素匹配法(Matching Pixel-by-Pixel,MPP)反演水质参数及归一化植被指数(Normalized Difference Vegetation Index,NDVI)阈值法提取水华信息,然后基于地理加权回归(Geographically Weighted Regression,GWR)建立涵盖面积信息和位置信息的水华短时预测模型,并探讨了预测窗口尺寸对预测结果的影响。结果表明:①本文方法可以实现精细化的水华短时预测,在应用中对水华面积预测精度达到96.19%,在水华空间分布的预测上对水华和非水华的总体分类精度均大于0.97,生产者精度、Kappa系数均大于0.5;②MPP反演的总氮、总磷和溶解氧浓度与实测数据都有着较高的相关性,其决定系数R2分别达到0.89、0.85、0.89;③不同尺寸的预测窗口直接影响预测结果精度,相比8×8、12×12、14×14,选用10×10预测窗口得到的总体分类精度、生产者精度、Kappa系数最高,分别为0.98、0.77、0.77。该模型可为短时水华预测提供借鉴,有利于水华监测、预测工作的进一步深入与完善。

In recent years,the conflict between rapid growth of economy and protection of water resources has become increasingly prominent.Water bloom has also become an important environmental issue,both domestically and internationally.Efficient use of remote sensing to predict water bloom outbreaks is of great significance to promote the management and protection of the lake and reservoir water environment.Using multispectral images of Unmanned Aerial Vehicle and measured water quality parameters as data sources,the water quality parameters were inversed by the Matching Pixel-by-pixel(MPP)algorithm,and the water bloom information was extracted through the Normalized Difference Vegetation Index(NDVI)threshold method.Then,a short-term prediction model of water bloom based on geographical weighted regression was proposed.The characteristic of this model was that it can accurately estimate the area and spatial location of the water bloom within a short period.We also discussed the influence of window size on the prediction results.The results show that:(1)based on the proposed model,the prediction accuracy of bloom area reached 96.19%.For the spatial distribution of water bloom,the overall classification accuracy of water bloom and non-water bloom pixels was both greater than 0.969,and the producer accuracy and Kappa coefficient were both higher than 0.5;(2)the total nitrogen,total phosphorus,and dissolved oxygen concentrations inverted using the MPP algorithm showed high correlation with the measurements(R2 was 0.8886,0.8546,and 0.8969,respectively).The water bloom information extracted by NDVI threshold method had a high consistency with the true color orthophotobased visual results in terms of bloom outbreak density and spatial distribution;(3)the prediction window size was closely related to the spatial resolution of data.The prediction window of different sizes directly affected the accuracy of water bloom prediction results.Compared with the window size of 8×8,12×12,and 14×14,the highest accuracy(0.98),precision(0.77),and Kappa(0.77)were obtained with a window size of 10×10.Hence,the 10×10 prediction window was the most suitable for the water bloom prediction in this study area.The model developed here can effectively predict water bloom in lakes and reservoirs in advance,which provides a reference for improving short-term water bloom prediction and warning.