基于高分二号的城市黑臭水体动态监测

doi:10.6046/gtzyyg.2020167

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摘要

目前,城市黑臭水体的遥感识别处于算法初探阶段,由于受到水深、阴影等因素的影响,在实际应用中精度较低,并且对于长时间的黑臭水体动态监测研究较少。以芜湖鸠江区为研究区,分析黑臭水体的成因及表观特征,针对单波段阈值法、波段差值法、归一化指数法和斜率指数法,基于GF-2影像进行阈值修正并评价精度,同时联合黑臭水体目视解译标志进行动态监测。结果表明: ①水体发生黑臭通常伴有颜色异常、河道淤塞、次生环境问题等特征; ②单一识别算法中差值法识别效果最好,总精度为87.5%; ③GF-2的高空间分辨率特性提升了目视解译的效率和精度,可以有效减少水深和建筑物阴影对其遥感识别的干扰,相比使用单一算法,进一步提高了动态监测的识别精度和可靠性; ④利用2014—2020年4景GF-2影像提取鸠江区主城区黑臭水体面积分别为0.313 km²,0.152 km²,0.069 km²和0.008 km²。结果显示,鸠江区水体黑臭现象正逐步得到改善,但神山公园水系黑臭现象依然严重。

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	胡国庆
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	李虎

关键词 ：高分二号, 黑臭水体, 动态监测, 芜湖

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.

Key words： GF-2 black-odor water bodies dynamic monitoring Wuhu

收稿日期: 2020-06-11 出版日期: 2021-03-18

ZTFLH:

TP79

基金资助:安徽省高校协同创新项目“国产高分辨率对地观测系统安徽区域综合应用示范”(GXXT-2019-047);芜湖市重点研发项目“长江芜湖段黑臭水体遥感监测”(2020ms1-3);安徽省科技重大专项项目“现代农业遥感监测系统构建与产业化应用”(202003a06020002);滁州市院士助滁行动项目“基于国产卫星数据的河(湖)长制遥感监测”共同资助(2019 1/S002)

通讯作者: 李虎

作者简介: 胡国庆(1995-),男,硕士研究生,主要研究方向为水环境遥感。Email: 2221001119@qq.com。

引用本文:

胡国庆, 陈冬花, 刘聪芳, 谢以梅, 刘赛赛, 李虎. 基于高分二号的城市黑臭水体动态监测[J]. 国土资源遥感, 2021, 33(1): 30-37.
HU Guoqing, CHEN Donghua, LIU Congfang, XIE Yimei, LIU Saisai, LI Hu. Dynamic monitoring of urban black-odor water bodies based on GF-2 image. Remote Sensing for Land & Resources, 2021, 33(1): 30-37.

链接本文:

https://www.gtzyyg.com/CN/10.6046/gtzyyg.2020167 或 https://www.gtzyyg.com/CN/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 载荷参数

Fig.1 融合前后遥感影像对比

Fig.2 黑臭水体与一般水体特征对比

Fig.3 样点分布

Fig.4 算法建模结果

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 公式及阈值选取

Tab.3 样点判别统计

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 精度评价公式

Tab.5 精度统计

Fig.5-1 单一算法识别结果

Fig.5-2 单一算法识别结果

Fig.6 东河整改前后对比

Fig.7 水深及阴影的干扰

Tab.6 黑臭水体动态监测结果

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