Dynamic monitoring of urban black-odor water bodies based on GF-2 image

doi:10.6046/gtzyyg.2020167

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Abstract

At present, the remote sensing identification of urban black-odor water bodies is in the preliminary stage of algorithm; due to the influence of water depth, shadow and other factors, the accuracy is low in practical applications, and there is little research on the long-term dynamic monitoring of black-odor water bodies. In this study, the Jiujiang District of Wuhu was chosen as a research area to analyze the causes and apparent characteristics of black-odor water bodies. For single-band threshold method, band difference method, normalized index method and slope index method, threshold correction was performed based on GF-2 images, the accuracy was evaluated, combined with the visual interpretation of the black-odor water bodies for dynamic monitoring at the same time. The results are as follows: ① The occurrence of black and odor in the water body is usually accompanied by features such as color abnormality, river siltation, and secondary environmental problems. ②The band difference method has the best recognition effect in the single recognition algorithm, and the total accuracy is 87.5%. ③ The high spatial resolution feature of GF-2 improves the efficiency and accuracy of visual interpretation, which can effectively reduce the interference of water depth and building shadows on its remote sensing recognition; compared with the use of a single algorithm, it further improves the recognition accuracy and reliability of dynamic monitoring. ④ The four GF-2 images from 2014 to 2020 were used to extract the areas of black-odor water bodies in the main urban area of Jiujiang District, which are 0.313 km², 0.152 km², 0.069 km², and 0.008 km² respectively. The results show that the black and odor phenomenon in the water body of Jiujiang District has been gradually improved, but the black and odor phenomenon in the water system of Shenshan Park is still serious.

Keywords GF-2 black-odor water bodies dynamic monitoring Wuhu

ZTFLH:

TP79

Corresponding Authors: LI Hu E-mail: 2221001119@qq.com;lihu2881@aliyun.com

Issue Date: 18 March 2021

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	Guoqing HU
	Donghua CHEN
	Congfang LIU
	Yimei XIE
	Saisai LIU
	Hu LI

Cite this article:

Guoqing HU,Donghua CHEN,Congfang LIU, et al. Dynamic monitoring of urban black-odor water bodies based on GF-2 image[J]. Remote Sensing for Land & Resources, 2021, 33(1): 30-37.

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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2020167 OR https://www.gtzyyg.com/EN/Y2021/V33/I1/30

载荷	谱段号	谱段范围/ $μm$	空间分辨率/m
全色多光谱相机	1	0.45~0.90	1
	2	0.45~0.52	4
	3	0.52~0.59
	4	0.63~0.69
	5	0.77~0.89

Tab.1 GF-2 load parameters

Fig.1 Comparision of remote sensing images before and after fusion

Fig.2 Comparison of characteristics between black and odorous water body and general water body

Fig.3 Sample distribution

Fig.4 Algorithm modeling results

算法	公式^①	阈值
算法	公式^①	修正前	修正后
单波段阈值法	$I 1 = Rrs (G) ≤ T 1$	0.019 0 $s r - 1$	0.144 3 $s r - 1$
波段差值法	$I 2 = Rrs (G) - Rrs (B) ≤ T 2$	0.003 6 $s r - 1$	0.002 2 $s r - 1$
归一化指数法	$I 3 = Rrs (G) - Rrs (R) Rrs (B) + Rrs (G) + Rrs (R) ≤ T 3$	0.065	0.064 2
斜率指数法	$I 4 = \| Rrs (G) - Rrs (B) \| Δ λ 1 · \| Rrs (G) - Rrs (R) \| Δ λ 2 ≤ T 4$	0.005 $s r - 2$	0.011 1 $s r - 2$

Tab.2 Formula and threshold selection

Tab.3 Sample discrimination statistics

指标	公式^①
用户精度	$U = n 正确识别数 n 总识别数 × 100 %$
产品精度	$P = n 正确识别数 n 实际识别数 × 100 %$
总精度	$P 0 = n 总正确识别数 n 总数 × 100 %$
Kappa系数	$Kappa = P 0 - P e 1 - P e$

Tab.4 Accuracy evaluation formula

Tab.5 Accuracy statistics

Fig.5-1 Single algorithm recognition results

Fig.5-2 Single algorithm recognition results

Fig.6 Comparison before and after Dong River rectification

Fig.7 Water depth and shadow interference

Tab.6 Dynamic monitoring results of black and odorous water

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