Evidence map›Paper›PMID 28660506›Full record

ArticleEnvironmental science and pollution research international2017

Remote estimation of cyanobacterial blooms using the risky grade index (RGI) and coverage area index (CAI): a case study in the Three Gorges Reservoir, China.

Botian Zhou, Mingsheng Shang, Guoyin Wang, Li Feng, Kun Shan, Xiangnan Liu, Ling Wu, Xuerui Zhang

Abstract read
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In one paragraph

Article in Environmental science and pollution research international, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Extreme weather event may induce Microcystis blooms in the Qiantang River, Southeast China.Environmental science and pollution research international · 2018
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Botian ZhouChongqing Key Laboratory of Big Data and Intelligent Computing, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, 400714, China.
Mingsheng ShangChongqing Key Laboratory of Big Data and Intelligent Computing, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, 400714, China.
Guoyin WangChongqing Key Laboratory of Big Data and Intelligent Computing, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, 400714, China.
Li FengChongqing Collaborative Innovation Center of Big Data Application in Eco-Environmental Remote Sensing, Chongqing Academy of Environmental Science, Chongqing, 401147, China.
Kun ShanChongqing Key Laboratory of Big Data and Intelligent Computing, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, 400714, China.
Xiangnan LiuSchool of Information Engineering, China University of Geosciences, Beijing, 100083, China.
Ling WuSchool of Information Engineering, China University of Geosciences, Beijing, 100083, China.
Xuerui ZhangChongqing Key Laboratory of Big Data and Intelligent Computing, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, 400714, China. xrzhang722@163.com.ORCID http://orcid.org/0000-0003-3618-8319

Funding

Chongqing Science and Technology Innovation Special Project for Social Livelihood Y61Z030A10National Natural Science Foundation of China 51609229National Science and Technology Major Project 2014ZX07104-006
6 · The paper itself

Abstract

Harmful cyanobacterial blooms are exemplified as a major environmental concern due to producing toxin, and have generated a serious threat to public health. Knowledge on the spatial-temporal distribution of cyanobacterial blooms is therefore crucial for public health organizations and environmental agencies. In this study, field data and charge coupled device (CCD) image were collected in Lakes Gaoyang and Hanfeng of the Three Gorges Reservoir (TGR), China. We conducted the risky grade index (RGI) and coverage area index to develop a feasible estimation framework of cyanobacterial blooms. First, the close relationships between CCD reflectance spectral indices and water quality parameters were constructed based on water optical classification. Then, a regional algorithm for the RGI classification was established by density peaks. Finally, our proposed algorithm was applied to investigate dynamics of cyanobacterial blooms in the two lakes from 6-year series of CCD images. Encouraging results demonstrated that satellite remote sensing in conjunction with field observation can aid in the estimation of cyanobacterial blooms in the TGR.

Indexed as

CyanobacteriaEnvironmental MonitoringEutrophicationChinaLakesWater QualityCoverage area indexCyanobacterial bloomsDensity peaksRemote sensingRisky grade indexThree Gorges ReservoirWater optical classification

Identifiers

PMID28660506

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.