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自然资源遥感  2021, Vol. 33 Issue (3): 45-53    DOI: 10.6046/zrzyyg.2020377
  技术方法 本期目录 | 过刊浏览 | 高级检索 |
基于无人机高光谱遥感的河湖水环境探测
臧传凯1(), 沈芳1,2(), 杨正东3
1.华东师范大学河口海岸学国家重点实验室,上海 200062
2.上海市崇明生态研究院,上海 200062
3.上海市崇明区水文站,上海 200062
Aquatic environmental monitoring of inland waters based on UAV hyperspectral remote sensing
ZANG Chuankai1(), SHEN Fang1,2(), YANG Zhengdong3
1. State Key Laboratory of Estuarine and Coastal Research, East China Normal University, Shanghai 200062, China
2. Institute of Eco-Chongming(IEC), Shanghai 200062, China
3. Hydrological Station of Shanghai Chongming District, Shanghai 200062, China
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摘要 

本研究以上海市崇明岛内陆水体为研究区,通过无人机高光谱遥感影像对水体颜色变化及疑似污染水体识别进行研究。首先,对无人机搭载的高光谱传感器探测获得的辐亮度信号,进行了遥感反射率标定,通过与现场观测对比,该标定方法准确度较高,各波段平均无偏绝对百分比误差的均值为13.34%,决定系数R2均值为0.83。进一步,利用河湖水体高光谱遥感反射率根据CIE-XYZ颜色标准和加权调和平均法反演了色相角(Hue angle)、表观波长(apparent visible wavelength,AVW),根据实测数据构建水质参数反演模型。通过设定色相角阈值对研究区水体颜色进行分类,结果表明: 崇明区在枯水期的河湖黄棕色异常水体较多,且需加强主要航运河流的水环境监管和治理。最后综合水体颜色参量和水质参数结果对河湖疑似污染水体进行识别和分析。研究表明: 无人机高光谱获得的高时空连续性的水体颜色参量和水质参数反演结果,在节约成本的同时为河湖水环境调查提供了可靠的技术支持。

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臧传凯
沈芳
杨正东
关键词 无人机高光谱遥感水体颜色河湖水体水环境    
Abstract

With the inland waters in Chongming Island, Shanghai as the study area, this study researched the color changes of waters and the identification of suspected polluted waters using unmanned aerial vehicle (UAV) hyperspectral remote sensing images. First, reflectance calibration was carried out for the radiance signals detected by the hyperspectral sensor carried by UAVs. Compared with on-site observations, this calibration method was more accurate, the average unbiased absolute percentage differences of various bands were 13.34% on average and the average determination coefficient R2 was 0.83. Afterward, the inversion of hue angle and apparent visible wavelength (AVW) was conducted using the hyperspectral reflectance of the inland waters according to the CIE-XYZ color space and weighted harmonic mean. Then an inversion model of water quality parameters was constructed based on measured data, and the water colors in the study area were classified by setting the threshold of hue angle. As indicated by the results, there exist many abnormal yellowish-brown inland waters in the Chongming Island in dry seasons and it is necessary to strengthen the supervision and governance of the aquatic environment of major shipping rivers. Finally, the suspected polluted inland waters were identified and analyzed by integrating the inversion results of the parameters of water color and water quality. This study shows that UAV hyperspectral remote sensing can be used to obtain the inversion results with high temporal-spatial continuity of the parameters of water color and water quality, which will provide credible technical support for the aquatic environment investigations of inland waters while saving costs.

Key wordsUAV    hyperspectral remote sensing    water colour    inland waters    aquatic environment
收稿日期: 2020-12-01      出版日期: 2021-09-24
ZTFLH:  P231.1  
基金资助:国家自然科学基金项目“近海浮游植物分类的高光谱遥感探测机理与方法研究”(42076187);国家重点研发计划政府间国际科技创新合作重点专项“水环境的高光谱及多源高分辨率光学遥感研究”
通讯作者: 沈芳
作者简介: 臧传凯(1996-),男,硕士研究生,研究方向为无人机高光谱水环境遥感。Email: 51183904016@stu.ecnu.edu.cn
引用本文:   
臧传凯, 沈芳, 杨正东. 基于无人机高光谱遥感的河湖水环境探测[J]. 自然资源遥感, 2021, 33(3): 45-53.
ZANG Chuankai, SHEN Fang, YANG Zhengdong. Aquatic environmental monitoring of inland waters based on UAV hyperspectral remote sensing. Remote Sensing for Natural Resources, 2021, 33(3): 45-53.
链接本文:  
https://www.gtzyyg.com/CN/10.6046/zrzyyg.2020377      或      https://www.gtzyyg.com/CN/Y2021/V33/I3/45
Fig.1  研究区域及采样点
水质参数 最小值 最大值 平均值 标准差
Chl-a/(mg·m-3) 4.62 311.63 41.98 54.15
TSM/(mg·L-1) 4.50 281.67 45.71 42.26
CDOM/m 0.30 2.22 0.88 0.46
浊度/NTU 5.95 142.00 51.17 30.23
TN/(mg·L-1) 0.44 2.53 1.34 0.53
TP/(mg·L-1) 0.009 0.93 0.15 0.18
Tab.1  采样点水质参数浓度分布
Fig.2  无人机高光谱影像降噪流程及噪声评估
Fig.3  不同类型水体光谱曲线及对应Hue angle
水质
参数(y)
自变量(x) 反演模型
Chl-a/
(mg·m-3)
x=Rrs(636.91)/
Rrs(701.64)
y=38.27x-6.10
TSM/(mg·L-1) x=Rrs(733) y=251 886x2-7 300.8x+74.94
CDOM/m x1=Rrs(600)/Rrs(709), x2=Rrs(652)/Rrs(725) y=2.098x1+0.286x2+2.950
浊度/
NTU
x=(Rrs(651)-
Rrs(655))/Rrs(761)
y=140.58exp(18x)
TN/
(mg·L-1)
x=(Rrs(482)-
Rrs(677))/(Rrs(482)+Rrs(677))
y=751.27x3+201.79x2+3.60x+0.72
TP/
(mg·L-1)
x=(Rrs(618)-
Rrs(692))/(Rrs(618)+Rrs(692))
y=7 084.4x3-1 177.7x2+55.84x-0.48
Tab.2  水质参数反演模型
Fig.4  无人机-实测Rrs对比验证
Fig.5  无人机高光谱反演水体颜色参量、水质参数精度验证
Fig.6  上海市崇明岛河湖水体Hue angle分类
Fig.7  上海市崇明岛重点观察河道疑似污染水体识别
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