Prediction of algal bloom occurrence based on the naive Bayesian model considering satellite image pixel differences-Reference-Cited by-同舟云学术

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Prediction of algal bloom occurrence based on the naive Bayesian model considering satellite image pixel differences

Published:2021-05 Issue: Volume:124 Page:107416
ISSN:1470-160X
Container-title:Ecological Indicators
language:en
Short-container-title:Ecological Indicators

Author:

Mu Meng,Li Yunmei,Bi Shun,Lyu Heng,Xu Jie,Lei Shaohua,Miao Song,Zeng Shuai,Zheng Zhubin,Du Chenggong

Funder

National Natural Science Foundation of China

Natural Science Foundation of Jiangsu Province

Major Science and Technology Program for Water Pollution Control and Treatment

Natural Science Foundation of Jiangxi Province

Publisher

Elsevier BV

Subject

Ecology,Ecology, Evolution, Behavior and Systematics,General Decision Sciences

Reference64 articles.

1. Quantifying spatiotemporal dynamics of the column-integrated algal biomass in nonbloom conditions based on OLCI Data: A case study of lake Dianchi, China;Bi;IEEE Trans. Geosci. Remote Sensing,2019

2. Resilience to blooms;Brookes;Science,2011

3. Ecological forecasting in Chesapeake Bay: using a mechanistic–empirical modeling approach;Brown;J. Mar. Syst.,2013

4. Remote detection of cyanobacteria blooms in an optically shallow subtropical lagoonal estuary using MODIS data;Cannizzaro;Remote Sens. Environ.,2019

5. Health impacts from cyanobacteria harmful algae blooms: Implications for the North American Great Lakes;Carmichael;Harmful Algae,2016

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