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智能手机影像支持的水质监测算法与应用EI北大核心CSCD
引用本文: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EI北大核心CSCD[J].测绘学报,2022,51(4):568-576.
作者姓名:李俊生  高敏  张兵  张方方  王胜蕾  殷子瑶  谢娅
作者单位: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
基金项目:国家自然科学基金(41971318);
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
收稿时间:2022-01-20
修稿时间:2022-03-10

Smartphone photo based water quality monitoring algorithm and application
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.
Authors:LI Junsheng  GAO Min  ZHANG Bing  ZHANG Fangfang  WANG Shenglei  YIN Ziyao  XIE Ya
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.
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