智能手机影像支持的水质监测算法与应用

doi:10.11947/j.AGCS.2022.20220049

测绘学报 ›› 2022, Vol. 51 ›› Issue (4): 568-576.doi: 10.11947/j.AGCS.2022.20220049

• 同济大学测绘学科创建90周年 • 上一篇下一篇

智能手机影像支持的水质监测算法与应用

李俊生^1,2,3, 高敏^1,4, 张兵^1,5, 张方方^1,2, 王胜蕾^1,2, 殷子瑶^1,5, 谢娅^1,4

1. 中国科学院空天信息创新研究院数字地球重点实验室, 北京 100094;
2. 可持续发展大数据国际研究中心, 北京 100094;
3. 中国科学院大学电子电气与通信工程学院, 北京 100049;
4. 中国地质大学(北京)地球科学与资源学院, 北京 100083;
5. 中国科学院大学资源与环境学院, 北京 100049

收稿日期:2022-01-20 修回日期:2022-03-10 发布日期:2022-04-24
通讯作者: 张兵 E-mail:zb@radi.ac.cn
作者简介:李俊生(1979-),男,博士,研究员,研究方向为水环境遥感。.E-mail:lijs@radi.ac.cn
基金资助:
国家自然科学基金（41971318）；中国科学院战略性先导科技专项（XDA19090109）

Smartphone photo based water quality monitoring algorithm and application

LI Junsheng^1,2,3, GAO Min^1,4, ZHANG Bing^1,5, ZHANG Fangfang^1,2, WANG Shenglei^1,2, YIN Ziyao^1,5, XIE Ya^1,4

1. Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China;
2. International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China;
3. School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China;
4. School of Earth Science and Resources, China University of Geosciences (Beijing), Beijing 100083, China;
5. College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China

Received:2022-01-20 Revised:2022-03-10 Published:2022-04-24
Supported by:
The National Natural Science Foundation of China (No. 41971318); The Strategic Priority Research Program of the Chinese Academy of Sciences (No. XDA19090109)

摘要/Abstract

摘要： 智能手机为基于公众科学的水质监测提供了新的手段,但是目前还缺乏基于智能手机拍摄的水体影像的水质参数反演的系统性研究。针对这种情况,本文首先构建了基于水面数码照片的典型水质参数反演和水体污染识别模型,包括水色指数、透明度、营养状态等级、蓝藻水华、黑臭水体;然后,基于这些模型开发了基于智能手机安卓系统的“观水色”app,并在一些典型研究区对“观水色”app进行了应用及评价,获得了较好的水质监测精度;最后,分析了白平衡、不同数码相机设备、照片存储格式对于基于智能手机的水质监测的影响及其应对策略。本文的研究成果将有利于推动智能手机在水质监测中的广泛应用。

关键词: 智能手机, 数码照片, 水质监测, 公众科学, 观水色, 应用程序

Abstract: Smartphones provide a new means for water quality monitoring based on citizen science, but there is still a lack of systematic research on water quality parameter inversion based on water surface photos taken by smartphones. In view of this situation, this study firstly developed models to retrieve typical water quality parameters and identify typical water pollution, including Forel-Ule index, water clarity, trophic state, cyanobacterial bloom, and black and odorous water. Then, based on these models, the “Water Color Watch” app based on smartphone Android system was developed. The app was applied and evaluated in several typical study areas, and obtained good accuracy in monitoring of water quality. Finally, the impacts of white balance, different digital cameras and photo storage formats on water quality monitoring based on smartphone and their coping strategies were analyzed. The results of this study will help to promote the wide application of smartphones in water quality monitoring.

Key words: Smartphone, digital image, water quality monitoring, citizen science, water color watch, app

中图分类号:

P227

李俊生, 高敏, 张兵, 张方方, 王胜蕾, 殷子瑶, 谢娅. 智能手机影像支持的水质监测算法与应用[J]. 测绘学报, 2022, 51(4): 568-576.

LI Junsheng, GAO Min, ZHANG Bing, ZHANG Fangfang, WANG Shenglei, YIN Ziyao, XIE Ya. Smartphone photo based water quality monitoring algorithm and application[J]. Acta Geodaetica et Cartographica Sinica, 2022, 51(4): 568-576.

参考文献

[1] 张兵, 李俊生, 申茜, 等. 长时序大范围内陆水体光学遥感研究进展[J]. 遥感学报, 2021, 25(1): 37-52. ZHANG Bing, LI Junsheng, SHEN Qian, et al. Recent research progress on long time series and large scale optical remote sensing of inland water[J]. National Remote Sensing Bulletin, 2021, 25(1): 37-52.
[2] BRIANT J, CLAIRE H, MATHIAS S, et al. Water resource monitoring exploiting Sentinel-2 satellite and Sentinel-2 satellite like time series; application in Yangtze River water bodies [J]. Journal of Geodesy and Geoinformation Science, 2020, 3(4): 41-49.
[3] GODDIJN-MURPHY L, DAILLOUX D, WHITE M, et al. Fundamentals of in situ digital camera methodology for water quality monitoring of coast and ocean[J]. Sensors (Basel, Switzerland), 2009, 9(7): 5825-5843.
[4] GALLAGHER J B, CHUAN C H. Chlorophyll a and turbidity distributions: applicability of using a smartphone “app” across two contrasting bays[J]. Journal of Coastal Research, 2018, 345: 1236-1243.
[5] GODDIJN L M, WHITE M. Using a digital camera for water quality measurements in Galway Bay[J]. Estuarine, Coastal and Shelf Science, 2006, 66(3-4): 429-436.
[6] 高敏. 基于水面数码照片的反射率校正和典型水质参数反演研究[D]. 北京:中国科学院大学,2021. GAO Min. Study on reflectance correction and typical water quality parameter inversion based on water surface digital photos [D]. Beijing:University of Chinese Academy of Sciences, 2021.
[7] NOVOA S, WERNAND M, VAN DER WOERD H J. WACODI: a generic algorithm to derive the intrinsic color of natural waters from digital images[J]. Limnology and Oceanography: Methods, 2015, 13(12): 697-711.
[8] LEEUW T, BOSS E. The HydroColor app: above water measurements of remote sensing reflectance and turbidity using a smartphone camera[J]. Sensors (Basel, Switzerland), 2018, 18(1): 256.
[9] GAO Min, LI Junsheng, ZHANG Fangfang, et al. Measurement of water leaving reflectance using a digital camera based on multiple reflectance reference cards[J]. Sensors (Basel, Switzerland), 2020, 20(22): 6580.
[10] KONDRATYEV K Y, POZDNYAKOV D V, PETTERSSON L H. Water quality remote sensing in the visible spectrum[J]. International Journal of Remote Sensing, 1998, 19(5): 957-979.
[11] WANG Shenglei, LI Junsheng, ZHANG Bing, et al. Trophic state assessment of global inland waters using a MODIS-derived Forel-Ule index[J]. Remote Sensing of Environment, 2018, 217: 444-460.
[12] LI Junsheng, WANG Shenglei, WU Yanhong, et al. MODIS observations of water color of the largest 10 lakes in China between 2000 and 2012[J]. International Journal of Digital Earth, 2016, 9(8): 788-805.
[13] WANG Shenglei, LI Junsheng, ZHANG Wenzhi, et al. A dataset of remote-sensed Forel-Ule index for global inland waters during 2000—2018[J]. Scientific Data, 2021, 8: 26.
[14] CIE. Commission internationale de i'eclairage proceedings 1931 [M].Cambridge:Cambridge University Press, 1932.
[15] WANG Shenglei, LI Junsheng, ZHANG Bing, et al. Changes of water clarity in large lakes and reservoirs across China observed from long-term MODIS[J]. Remote Sensing of Environment, 2020, 247: 111949.
[16] VOLLENWEIDER R A. Eutrophication- a global problem [J]. Water Qual Bull,1981, 6(3), 59-62.
[17] CARLSON R E. A trophic state index for lakes [J]. Limnology and Oceanography, 1977, 22(2), 361-369.
[18] PAERL H W, USTACH J F. Blue-green algal scums: an explanation for their occurrence during freshwater blooms[J]. Limnology and Oceanography, 1982, 27(2): 212-217.
[19] SHEN Qian, YAO Yue, LI Junsheng, et al. A CIE color purity algorithm to detect black and odorous water in urban rivers using high-resolution multispectral remote sensing images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(9): 6577-6590.
[20] MUELLER J L, FARGION G S, MCCLAIN C R, et al. Ocean optics protocols for satellite ocean color sensor validation: radiometric measurements and data analysis methods [M]. [S.l.]:Goddard Space Flight Center, 2003.
[21] 唐军武, 田国良, 汪小勇, 等. 水体光谱测量与分析Ⅰ: 水面以上测量法[J]. 遥感学报, 2004, 8(1): 37-44. TANG Junwu, TIAN Guoliang, WANG Xiaoyong, et al. The methods of water spectra measurement and analysis Ⅰ: above-water method[J]. Journal of Remote Sensing, 2004, 8(1): 37-44.
[22] LIU Yao, XIAO Chenchao, LI Junsheng, et al. Secchi disk depth estimation from China's new generation of GF-5 hyperspectral observations using a semi-analytical scheme[J]. Remote Sensing, 2020, 12(11): 1849.
[23] PRASAD D, NGUYEN R, BROWN M. Quick approximation of camera's spectral response from casual lighting [C]//Proceedings of 2013 IEEE International Conference on Computer Vision Workshops.Sydney, Australia:IEEE,2013.

智能手机影像支持的水质监测算法与应用

Smartphone photo based water quality monitoring algorithm and application

RichHTML

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 4

编辑推荐

Metrics

本文评价

[1]	陈锐志, 钱隆, 牛晓光, 徐诗豪, 陈亮, 裘超. 基于数据与模型双驱动的音频/惯性传感器耦合定位方法[J]. 测绘学报, 2022, 51(7): 1160-1171.
[2]	陈锐志, 郭光毅, 叶锋, 钱隆, 徐诗豪, 李正. 智能手机音频信号与MEMS传感器的紧耦合室内定位方法[J]. 测绘学报, 2021, 50(2): 143-152.
[3]	陈锐志, 陈亮. 基于智能手机的室内定位技术的发展现状和挑战[J]. 测绘学报, 2017, 46(10): 1316-1326.
[4]	单杰. 从专业遥感到大众遥感[J]. 测绘学报, 2017, 46(10): 1434-1446.