基于无人机多光谱影像的小微水域水质要素反演
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摘要
总磷(TP)、悬浮物浓度(SS)、浊度(TUB)3种水质参数可以直接通过遥感反演得到,常用于评价区域水环境的污染状况.以浙江农林大学东湖为研究对像,使用无人机携带多光谱传感器(Mica Sense Red Edge)获取多光谱影像,进而提取16个光谱参数,分别构建东湖水域TP、SS、TUB的反演模型.结果表明:光谱参数V5(NIR 0.770～0.890μm)与TP、SS相关性显著(r分别为0.470、-0.537,p<0.05),V4(0.670～0.760μm)与TUB相关性显著(r=0.486,p<0.05).在建立的TP反演模型中,指数函数模型精度最高,决定系数R~2为0.7829;在建立的SS、TUB反演模型中,多项式函数模型精度最高,决定系数R~2分别为0.7503、0.7334.经检验,TP、SS、TUB模型估测值与实测值线性拟合曲线的决定系数R~2分别为0.7374、0.8978、0.6726,满足水质要素反演的精度要求.最后利用建立的模型,结合多光谱影像数据,建立了东湖水域各参数的空间分布图,实现了水质参数的可视化,可为小微水域的污染防治提供技术支撑.
Total phosphorus(TP), suspended sediments concentration(SS), and turbidity(TUB) are important parameters that describe the water quality and have been proven to be promising tool for the regional water environment evaluation. This paper takes the Donghu Lake of Zhejiang Agriculture and Forestry University as the research object, obtains the multi-spectral images by using the multi-spectral sensor(Mica Sense Red Edge) carried by UAV, and then extracts 16 spectral parameters, which are used to construct the inversion models of TP, SS and TUB in the Donghu lake waters. The results show that the spectral parameters V5(NIR 0.770～0.890 μm) are highly correlated with TP and SS, which are 0.47 and-0.537, respectively, V4(0.670～0.760 μm)is highly correlated with TUB, which is 0.486. For all the three parameters, the significant differences were smaller than 0.05. In the established TP inversion model, the exponential function model has the highest precision, the determination coefficient R~2 is 0.7829; in the SS and TUB model, the polynomial function model has the highest precision, the determination coefficient R~2 are 0.7503 and 0.7334 respectively. The test results show that the determination coefficients R~2 of the estimated and measured values of linear fitting curves of TP, SS and TUB are 0.7374, 0.8978 and 0.6726 respectively, which meet the accuracy requirements of water quality factor inversion. At last, by using the established models, combined with multi-spectral image data, the spatial distribution map of the parameters concentration in the Donghu Lake waters has been established, and the visualization of water quality parameters has been realized, which provides technical support for the prevention and control of pollution in small and micro waters.
Total phosphorus(TP), suspended sediments concentration(SS), and turbidity(TUB) are important parameters that describe the water quality and have been proven to be promising tool for the regional water environment evaluation. This paper takes the Donghu Lake of Zhejiang Agriculture and Forestry University as the research object, obtains the multi-spectral images by using the multi-spectral sensor(Mica Sense Red Edge) carried by UAV, and then extracts 16 spectral parameters, which are used to construct the inversion models of TP, SS and TUB in the Donghu lake waters. The results show that the spectral parameters V5(NIR 0.770～0.890 μm) are highly correlated with TP and SS, which are 0.47 and-0.537, respectively, V4(0.670～0.760 μm)is highly correlated with TUB, which is 0.486. For all the three parameters, the significant differences were smaller than 0.05. In the established TP inversion model, the exponential function model has the highest precision, the determination coefficient R~2 is 0.7829; in the SS and TUB model, the polynomial function model has the highest precision, the determination coefficient R~2 are 0.7503 and 0.7334 respectively. The test results show that the determination coefficients R~2 of the estimated and measured values of linear fitting curves of TP, SS and TUB are 0.7374, 0.8978 and 0.6726 respectively, which meet the accuracy requirements of water quality factor inversion. At last, by using the established models, combined with multi-spectral image data, the spatial distribution map of the parameters concentration in the Donghu Lake waters has been established, and the visualization of water quality parameters has been realized, which provides technical support for the prevention and control of pollution in small and micro waters.
引文
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冼翠玲,张艳军,张明琴,等.2017.基于高分辨率多光谱影像的温瑞塘河水质反演模型研究[J].中国农村水利水电,(3):90-95
张毅博,张运林,査勇,等.2015.基于Landsat 8影像估算新安江水库总悬浮物浓度[J].环境科学,36(1):56-63
朱云芳,朱利,李家国,等.2017.基于GF-1 WFV影像和BP神经网络的太湖叶绿素a反演[J].环境科学学报,37(1):130-137
杜娟,杨国范,李佳奇.2014.基于环境小卫星的凌河叶绿素a浓度定量反演[J].节水灌溉,(9):50-53;56
冯海英,冯仲科,冯海霞.2017.一种基于无人机高光谱数据的植被盖度估算新方法[J].光谱学与光谱分析,37(11):3573-3578
冯奇,程学军,沈欣,等.2017.利用Landsat 8 OLI进行汉江下游水体浊度反演[J].武汉大学学报(信息科学版),42(5):643-647
Fabio Güttler N,Niculescu S,Gohin F.2013.Turbidity retrieval and monitoring of Danube Delta waters using multi-sensor optical remote sensing data: An integrated view from the delta plain lakes to the western-northwestern Black Sea coastal zone[J].Remote Sensing of Environment,132(6):86-101
李鑫,孙伟,李林.2017.基于小型无人机可见光遥感的蓝藻识别研究[J].测绘与空间地理信息,(4):153-156
马荣华,张玉超,段洪涛.2016.非传统湖泊水色遥感的现状与发展[J].湖泊科学,28(2):237-245
Mathews A J.2015.A Practical UAV remote sensing methodology to generate multispectral orthophotos for vineyards: Estimation of spectral reflectance using compact digital cameras[J]. International Journal of Applied Geospatial Research,6(4):65-87
Niethammer U,James M R,Rothmund S,et al.2012.UAV-based remote sensing of the super-sauze landslide: Evaluation and results[J].Engineering Geology 128(11):2-11
Peter K D,D′Oleire-Oltmanns S,Ries J B,et al.2014.Soil erosion in gully catchments affected by land-levelling measures in the Souss Basin,Morocco,analysed by rainfall simulation and UAV remote sensing data[J].Catena,113(2):24-40
Romero-Trigueros C,Nortes P A,Alarcón J J,et al.2017.Effects of saline reclaimed waters and deficit irrigation on Citrus physiology assessed by UAV remote sensing[J].Agricultural Water Management,183:60-69
邵全琴,郭兴健,李愈哲,等.2018.无人机遥感的大型野生食草动物种群数量及分布规律研究[J].遥感学报,22(3):497-507
史锐,张红,岳荣,等.2017.基于小波理论的干旱区内陆湖泊叶绿素a的TM影像遥感反演[J].生态学报,37(3):1043-1053
Su T C,Chou H T.2015.Application of multispectral sensors carried on unmanned aerial vehicle (UAV) to trophic state mapping of small reservoirs:A case study of Tain-Pu Reservoir in Kinmen,Taiwan[J].Remote Sensing,7(8):10078-10097
孙刚,黄文江,陈鹏飞,等.2018.轻小型无人机多光谱遥感技术应用进展[J].农业机械学报,49(3):1-17
田明璐,班松涛,常庆瑞,等.2016.基于无人机成像光谱仪数据的棉花叶绿素含量反演[J].农业机械学报, 47(11):285-293
田野,郭子琪,乔彦超,等.2015.基于遥感的官厅水库水质监测研究[J].生态学报,35(7):2217-2226
冼翠玲,张艳军,张明琴,等.2017.基于高分辨率多光谱影像的温瑞塘河水质反演模型研究[J].中国农村水利水电,(3):90-95
张毅博,张运林,査勇,等.2015.基于Landsat 8影像估算新安江水库总悬浮物浓度[J].环境科学,36(1):56-63
朱云芳,朱利,李家国,等.2017.基于GF-1 WFV影像和BP神经网络的太湖叶绿素a反演[J].环境科学学报,37(1):130-137