| 基于无人机高光谱遥感技术对内陆养殖池塘水质监测的研究 |
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| 引用本文： | 刘梅,马启良,原居林,倪蒙,练青平,郭爱环.基于无人机高光谱遥感技术对内陆养殖池塘水质监测的研究[J].海洋与湖沼,2022,53(1):195-205. |
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| 作者姓名： | 刘梅 马启良 原居林 倪蒙 练青平 郭爱环 |
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| 作者单位： | 农业农村部淡水渔业健康养殖重点实验室浙江省鱼类健康与营养重点实验室浙江省淡水水产研究所 浙江湖州313001;湖州师范学院信息技术中心 浙江湖州 313000 |
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| 基金项目： | 国家重点研发计划项目,2019YFD0900302号； |
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| 摘 要： | 为准确、快速、全面获取内陆养殖小区池塘及尾水处理池水体水质变化情况,建立养殖水环境实时预警调控及数字化管控机制,选择浙江湖州市集中连片的养殖小区为试验区,于2020年12月采用搭载GaiaSky-mini高光谱相机的无人机进行试验区近地遥感图像采集,并进行图像拼接、辐射校正和几何校正等预处理;然后对反射波段进行差值、比...
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| 关 键 词： | 无人机 高光谱 养殖池塘 机器算法 水质反演 |
| 收稿时间： | 2021/6/8 0:00:00 |
| 修稿时间： | 2021/7/27 0:00:00 |
WATER QUALITY MONITORING OF INLAND AQUACULTURE PONDS BASED ON UAV HYPERSPECTRAL REMOTE SENSING TECHNOLOGY |
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| LIU Mei,MA Qi-Liang,YUAN Ju-Lin,NI Meng,LIAN Qing-Ping,GUO Ai-Huan.WATER QUALITY MONITORING OF INLAND AQUACULTURE PONDS BASED ON UAV HYPERSPECTRAL REMOTE SENSING TECHNOLOGY[J].Oceanologia Et Limnologia Sinica,2022,53(1):195-205. |
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| Authors: | LIU Mei MA Qi-Liang YUAN Ju-Lin NI Meng LIAN Qing-Ping GUO Ai-Huan |
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| Institution: | (Agriculture Ministry Key Laboratory of Healthy Freshwater Aquaculture,Key Laboratory of Fish Health and Nutrition of Zhejiang Province,Zhejiang Institute of Freshwater Fisheries,Huzhou 313001,China;Center of Information Technology,Huzhou University,Huzhou 313000,China) |
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| Abstract: | To accurately,quickly,and comprehensively monitor the changes in water quality of inland aquaculture ponds and wastewater treatment ponds of a farming community,a real-time early warning and digital control system was established.In December 2020,a farming community in Huzhou City,Zhejiang Province in China was selected to conduct the experimental research.An unmanned aerial vehicle(UAV)equipped with a GaiaSky-mini hyperspectral camera was used to collect near-ground remote sensing images of the area,and these images were pre-processed in technologies including image stitching,radiation correction,and geometric correction.Next,the reflectance bands were transformed numerically using difference,ratio,and normalized difference index.The sensitive bands of different water quality parameters were determined based on correlation analysis results.Linear,exponential,and polynomial functions were used to construct quantitative inversion models for each water quality parameter.Partial least squares(PLS)regression,radial basis function(RBF)networks,and support vector machine(SVM)inversion models were constructed,verified,and evaluated in the full bands.Finally,the spatial distribution of water quality parameters in the study area was predicted and analyzed based on the best water quality model available.The results show that the maximum correlation coefficients of total suspended solids(TSS),total nitrogen(TN),total phosphorus(TP),potassium permanganate index(COD;),and ammonium-nitrogen(NH;-N)were 0.86,0.65,0.72,–0.85,and 0.75 respectively.Comparing the six modeling methods,the model constructed via polynomial functions had the highest accuracy for TSS inversion,the PLS model had the highest accuracy for NH;-N treatment inversion,the RBF model had the highest accuracy for TN and COD;inversion,and the SVM model had the highest accuracy for TP inversion.However,the accuracy of NH;-N inversion requires further optimization,while the other four water quality parameters all met the prediction accuracy requirements.Based on these results,a spatial distribution map of water environment in the farming area can be established via UAV hyperspectral technology,with which a rapid and accurate evaluation of water quality in farming and tailwater treatment ponds could be achieved. |
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| Keywords: | UAV hyperspectral technology inland ponds machine algorithm water quality inversion |
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