Remote sensing identification of black-odor water bodies: A review

doi:10.6046/gtzyyg.2020104

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Abstract

The equality of ecological environment has been severely affected by black-odor water bodies, and hence strengthening the treatment of black-odor water bodies is an important task for aquatic environment management. Macro-monitoring of black-odor water bodies is the prerequisite for governance, and remote sensing technology has huge advantages in the field of macro-monitoring. There have been very insufficient studies on black and odorous water bodies. This paper systematically summarizes the current status of identification and recognition of black-odor water bodies, mainly analyzes the optical characteristics of black-odor water bodies from the three identification characteristics of reflection spectrum, watercolor, and inherent optical quantity, summarizes the recognition algorithms and the problems of these algorithms, which include the low versatility of the algorithm, the inaccurate reflectance caused by the problem of atmospheric correction features, and the overlapping features of different types of water recognition features. The future development trends are predicted: ① mining recognition characteristics; ② performing classification of reflection spectrum; ③ applying of machine learning algorithms.

Keywords black-odor water bodies optical characteristics remote sensing recognition algorithms

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Corresponding Authors: ZHAO Wenyu E-mail: csincs@stu.csust.edu.cn;wenyuzh@csust.edu.cn

Issue Date: 18 March 2021

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	Shuai CHEN
	Wenyu ZHAO
	Zhongping LIAO

Cite this article:

Shuai CHEN,Wenyu ZHAO,Zhongping LIAO. Remote sensing identification of black-odor water bodies: A review[J]. Remote Sensing for Land & Resources, 2021, 33(1): 20-29.

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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2020104 OR https://www.gtzyyg.com/EN/Y2021/V33/I1/20

Fig.1 Measure reflectance of normal water body and black-odor water body

Fig.2 Equivalent reflectance of normal water body and black-odor water body

Fig.3 Image reflectance of normal water body and black-odor water body

反射率类型	模型	阈值(n)	正确率/%	实验地点	文献来源
实测	$345 (2 Rrs (555) - Rrs (485) - Rrs (660)) sum (Rrs (485) : Rrs (830)) ≤ n$	$0.80$	77.42	北京、杭州等	[15]
等效	$0 ≤ Green ≤ n$	$0.019$	50	南京	[37]
等效	$0 ≤ Green - Blue ≤ n$	$0.0036$	75	南京	[37]
等效	$n 1 ≤ Green + Red Green - Red ≤ n 2$	$n 1 = 0.06$	100	南京	[37]
等效	$n 1 ≤ Green + Red Green - Red ≤ n 2$	$n 2 = 0.115$	100	南京	[37]
等效	$Green - Blue Green - Red Δ λ 1 Δ λ 2 ≤ n$	$0.005$	62.5	南京	[42]
等效	$Green - Red Blue + Green + Red ≤ n$	$0.065$	100	沈阳	[39]
影像	$NIR + Red - Blue ≥ n$	$0.85$	92	北京	[16]
影像	$NIR + Red - Green ≥ n$	$0.87$	85	北京	[16]
影像	$NIR + Red - Blue NIR + Red + Blue ≥ n$	$0.4$	—	北京	[16]
影像	$NIR + Red - Green Blue + Green + Green ≥ n$	$0.45$	—	北京	[16]
影像	$n 1 ≤ (Green - Blue) / Δ λ 1 (Red - Green) / Δ λ 2 ≤ n 2$	$n 1 = 0$	92.86	太原	[41]
影像	$n 1 ≤ (Green - Blue) / Δ λ 1 (Red - Green) / Δ λ 2 ≤ n 2$	$n 2 = 1$	92.86	太原	[41]

Tab.1 Threshold method based on reflection spectrum

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