黑臭水体遥感识别研究进展

doi:10.6046/gtzyyg.2020104

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摘要

黑臭水体对生态环境质量影响严重,加强黑臭水体整治是水环境治理的重要工作。黑臭水体的宏观监测是治理的前提,而遥感技术在宏观监测领域有巨大的优势。目前利用遥感技术识别黑臭水体已有少量研究,本文系统总结了目前黑臭水体遥感识别的研究现状,从反射光谱、水体颜色以及固有光学量3个识别特征分析黑臭水体的光学特性,并分别归纳其识别算法; 总结了这些算法可能存在的问题,包括算法通用性问题、大气校正问题导致遥感反射率不准确、对于不同类型水体识别特征出现重叠部分等; 最后从进一步挖掘识别特征、进行反射光谱分类、应用机器学习算法3方面对未来的发展趋势进行了展望。

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	陈帅
	赵文玉
	廖中平

关键词 ：黑臭水体, 光学特性, 遥感, 识别算法

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.

Key words： black-odor water bodies optical characteristics remote sensing recognition algorithms

收稿日期: 2020-04-13 出版日期: 2021-03-18

ZTFLH:

TP79

基金资助:湖南省重点研发计划项目“黑臭水体水质监控与黑臭预警关键技术”(2018SK2011);洞庭湖水环境治理与生态修复湖南省重点实验室开放基金项目“洞庭湖流域水污染现状及其对渔业影响研究”(2018DT02);湖南省水生资源食品加工工程技术研究中心开放基金项目“洞庭湖鱼类环境激素生物效应研究”(2018KJY11);湖南省研究生科研创新项目“基于遥感卫星影像的黑臭水体识别算法研究”共同资助(CX20190666)

通讯作者: 赵文玉

作者简介: 陈帅(1997-),男,硕士研究生,主要研究水色遥感。Email: csincs@stu.csust.edu.cn。

引用本文:

陈帅, 赵文玉, 廖中平. 黑臭水体遥感识别研究进展[J]. 国土资源遥感, 2021, 33(1): 20-29.
CHEN Shuai, ZHAO Wenyu, LIAO Zhongping. Remote sensing identification of black-odor water bodies: A review. Remote Sensing for Land & Resources, 2021, 33(1): 20-29.

链接本文:

https://www.gtzyyg.com/CN/10.6046/gtzyyg.2020104 或 https://www.gtzyyg.com/CN/Y2021/V33/I1/20

Fig.1 非黑臭水体和黑臭水体实测反射率^[15,37,39]

Fig.2 非黑臭水体和黑臭水体等效反射率^{[37, 39]}

Fig.3 非黑臭水体和黑臭水体影像反射率^[16,41]

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 基于反射光谱的阈值法

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