基于Landsat8 影像的蓝藻水华提取方法对比研究

大气与环境光学学报 ›› 2021, Vol. 16 ›› Issue (6): 520-528.

基于Landsat8 影像的蓝藻水华提取方法对比研究

晁明灿1∗, 赵强2, 杨铁利1, 谢发之2

1 辽宁科技大学土木工程学院, 辽宁鞍山 114051; 2 安徽建筑大学环境与能源工程学院, 安徽合肥 230601

收稿日期:2020-07-29 修回日期:2021-02-26 出版日期:2021-11-28 发布日期:2021-11-28
通讯作者: E-mail: 262439340@qq.com E-mail:262439340@qq.com
作者简介:晁明灿 (1995 - ), 安徽六安人, 硕士研究生, 主要从事水质遥感方面的研究。 E-mail: cmc2877160956@163.com
基金资助:
Supported by National Natural Science Foundation of China (国家自然科学基金项目, 21777001)

Comparative Research of Cyanobacteria Blooms Extraction Methods Based on Landsat8 Images

CHAO Mingcan1∗, ZHAO Qiang2, YANG Tieli1, XIE Fazhi2

1 School of Civil Engineering, University of Science and Technology Liaoning, Anshan 114051, China; 2 School of Environment and Energy Engineering, Anhui Jianzhu University, Hefei 230601, China

Received:2020-07-29 Revised:2021-02-26 Published:2021-11-28 Online:2021-11-28

摘要/Abstract

摘要： 对湖泊蓝藻水华实现精准的遥感监测, 可为湖泊的污染防控和治理提供科学依据。基于 Landsat8 多光谱数据, 以巢湖为研究对象, 利用归一化植被指数 (NDVI) 和浮游藻类指数 (FAI) 分别对蓝藻水华进行提取, 并将提取的蓝藻水华强度划分为三级, 进一步对这两种提取方法进行对比, 分析了巢湖的蓝藻水华分布。研究结果表明: (1) 针对 NDVI 和 FAI 两种不同提取方法确定的阈值及分级阈值可以有效地提取巢湖的蓝藻水华; (2) 相对于 NDVI 方法, FAI 方法可以降低云对提取精度的影响; (3) 巢湖西半湖及沿岸水体富营养化一直比较严重, 东半湖的富营养化也日趋严重。研究确定的蓝藻水华提取阈值及分级阈值不仅为巢湖的蓝藻水华污染治理和预警提供方法支持, 还为其它湖泊蓝藻水华的遥感监测提供参考依据。

关键词: 蓝藻水华, 浮游藻类指数, 归一化植被指数, 巢湖, 阈值

Abstract: Accurate remote sensing monitoring of cyanobacteria blooms in lakes can provide scientific basis for prevention and control of lake pollution. Based on Landsat8 multispectral data, Chaohu Lake, China, was taken as research objects, and normalized difference vegetation index (NDVI) model and floating algae index (FAI) model were used to extract the cyanobacteria blooms, then the intensity of cyanobacteria blooms extracted was divided into three levels. Furthermore, the two extraction methods were compared, and the distribution of cyanobacteria blooms in Chaohu Lake were analyzed. The research results show that: (1) the thresholds and grading thresholds determined for NDVI method and FAI method in this workcan effectively extract the cyanobacteria blooms in Chaohu Lake, (2) compared with NDVI method, FAI method can reduce the influence of clouds on the extraction accuracy, (3) the eutrophication in the western half of Chaohu Lake and its coastal waters has been serious, and the eutrophication in the eastern half of the Chaohu Lake is becoming serious. The extraction thresholds and classification thresholds of cyanobacteria blooms determined in this paper not only provide method support for the treatment and early warning of cyanobacteria blooms in Chaohu Lake, but also provide reference for remote sensing monitoring of cyanobacteria blooms in other lakes.

Key words: cyanobacteria bloom, floating algae index, normalized difference vegetation index, Chaohu Lake; threshold

中图分类号:

P237

晁明灿∗, 赵强, 杨铁利, 谢发之. 基于Landsat8 影像的蓝藻水华提取方法对比研究[J]. 大气与环境光学学报, 2021, 16(6): 520-528.

CHAO Mingcan∗, ZHAO Qiang, YANG Tieli, XIE Fazhi. Comparative Research of Cyanobacteria Blooms Extraction Methods Based on Landsat8 Images[J]. Journal of Atmospheric and Environmental Optics, 2021, 16(6): 520-528.

参考文献 20

[1]	Xie Guoqing, Li Meng, Lu Weikun, et al. Spectral features, remote sensing identification and breaking-out meteorological
	conditions of algal bloom in Lake Dinachi [J]. Journal of Lake Sciences, 2010, 22(3): 327-336.
	谢国清, 李蒙, 鲁韦坤, 等. 滇池蓝藻水华光谱特征、遥感识别及爆发气象条件 [J]. 湖泊科学, 2010, 22(3): 327-336.
[2]	Wen Xinlong, Jing Yuanshu, Zhang Di. Review on application of common satellite sensors in monitoring algal blooms [J].
	Environmental Science & Technology, 2014, 37(1): 81-87.
	温新龙, 景元书, 张娣. 常见卫星传感器在蓝藻水华监测中的应用进展 [J]. 环境科学与技术, 2014, 37(1): 81-87.
[3]	Gower J, Hu C, Borstad G, et al. Ocean color satellites show extensive lines of floating sargassum in the gulf of Mexico [J].
	IEEE Transactions on Geoscience & Remote Sensing, 2006, 44(12): 3619-3625.
[4]	Duan Hongtao, Zhang Shouxuan, Zhang Yuanzhi. Cyanobacteria bloom monitoring with remote sensing in Lake Taihu [J].
	Journal of Lake Sciences, 2008, 20(2): 145-152.
	段洪涛, 张寿选, 张渊智. 太湖蓝藻水华遥感监测方法 [J]. 湖泊科学, 2008, 20(2): 145-152.
[5]	Tang Xiaoxian, Shen Ming, Duan Hongtao. Temporal and spatial distribution of algal blooms in Lake Chaohu, 2000-2015 [J].
	Journal of Lake Sciences, 2017, 29(2): 276-284.
	唐晓先, 沈明, 段洪涛. 巢湖蓝藻水华时空分布 (2000-2015年) [J]. 湖泊科学, 2017, 29(2): 276-284.
[6]	Zhang Jiao, Chen Liqiong, Chen Xiaoling. Monitoring the cyanobacterial blooms based on remote sensing in Lake Erhai by
	FAI [J]. Journal of Lake Sciences, 2016, 28(4): 718-725.
	张娇, 陈莉琼, 陈晓玲. 基于 FAI 方法的洱海蓝藻水华遥感监测 [J]. 湖泊科学, 2016, 28(4): 718-725.
[7]	Zhang Dongyan, Yin Xun, She Bao, et al. Using multi-source satellite imagery data to monitor cyanobacterial blooms of
	Chaohu Lake [J]. Infrared and Laser Engineering, 2019, 48(7): 0726004.
	张东彦, 尹勋, 佘宝, 等. 多源卫星遥感数据监测巢湖蓝藻水华爆发研究 [J]. 红外与激光工程, 2019, 48(7): 0726004.
[8]	Feng Honge, Li Jiaguo, Zhu Yunfang, et al. Synergistic inversion method of chlorophyll a concentration in GF-1 and Landsat8
	imagery: A case study of the Taihu Lake [J]. Remote Sensing for Land & Resources, 2019, 31(4): 182-189.
	封红娥, 李家国, 朱云芳, 等. GF-1 与 Landsat8 水体叶绿素 a 浓度协同反演—以太湖为例 [J]. 国土资源遥感, 2019, 31(4):
18	2-189.
[9]	McFeeters S K. The use of the normalized difference water index (NDWI) in the delineation of open water features [J].
	International Journal of Remote Sensing, 1996, 17(7): 1425-1432.
[10]	Xu Hanqiu. A study on information extraction of water body with the modified normalized difference water index (MNDWI)
[J]	Journal of Remote Sensing, 2005, (5): 589-595.
	徐涵秋. 利用改进的归一化差异水体指数 (MNDWI) 提取水体信息的研究 [J]. 遥感学报, 2005, (5): 589-595.
[11]	Huete A, Didan K, Miura T, et al. Overview of the radiometric and biophysical performance of the MODIS vegetation indices
[J]	Remote sensing of Environment, 2002, 83(1-2): 195-213.
[12]	Hu C. A novel ocean color index to detect floating algae in the global oceans [J]. Remote Sensing of Environment, 2009,
11	3(10): 2118-2129.
[13]	Fang Xu, Duan Hongtao, Cao Zhigang, et al. Remote monitoring of cyanobacterial blooms using multi-source satellite data:
	A case of Yuqiao Reservoir Tianjin [J]. Journal of Lake Sciences, 2018, 30(4): 967-978.
	房旭, 段洪涛, 曹志刚, 等. 基于多源卫星数据的小型水体蓝藻水华联合监测—以天津于桥水库为例 [J]. 湖泊科学,
20	18, 30(4): 967-978.

[1]	杨可意, 韩舸∗, 毛慧琴, 董燕妮, 马昕, 李四维, 龚威. 利用高分五号AHSI 载荷的高分辨率XCH4 异常探测方法研究[J]. 大气与环境光学学报, 2022, 17(6): 670-678.
[2]	肖渝, 黄丽雯, 汤斌∗, 肖棋森, 赵明富, 李奉笑. 遗传算法优化小波阈值对水质光谱数据的去噪研究[J]. 大气与环境光学学报, 2021, 16(5): 432-442.
[3]	王佳佳, 孙晓兵∗, 提汝芳, 余海啸, . 海洋上空云多角度偏振辐射阈值检测方法研究[J]. 大气与环境光学学报, 2021, 16(3): 247-255.
[4]	晁明灿, 赵强, 杨铁利∗, 李华富, 谢发之, 吴蕾. 基于GF-1影像的巢湖浊度遥感监测及时空变化研究[J]. 大气与环境光学学报, 2021, 16(2): 149-157.
[5]	梁渠张亚婷张强领邹雪陈美玲王鸿梅黄超群沈成银储焰南. 质子转移反应飞行时间质谱的建立与性能研究[J]. 大气与环境光学学报, 2019, 14(4): 289-295.
[6]	张若楠沐超张固刘小勤. QFN芯片表面划痕检测定位方法设计[J]. 大气与环境光学学报, 2019, 14(4): 313-320.
[7]	张培1,吴东1,2. 基于Himawari-8数据的日间海雾检测方法[J]. 大气与环境光学学报, 2019, 14(3): 211-220.
[8]	王行芳金施群侯少阳. 基于小波阈值的激光探测声音信号去噪研究[J]. 大气与环境光学学报, 2018, 13(5): 388-394.
[9]	谢欣欣江伦张雷佟首峰李响. 天空背景光条件下空间激光通信系统粗跟踪光斑提取方法的研究[J]. 大气与环境光学学报, 2017, 12(3): 236-240.
[10]	范书广朱灵赵树弥张悦龙张龙邓国庆王安刘勇. 用于微流控荧光PCR的高分辨率温度控制系统[J]. 大气与环境光学学报, 2015, 10(5): 417-424.
[11]	吴海滨周后伟张铁译陈新兵周雨润王哲. 基于OTSU的动态结合全局阈值的图像分割[J]. 大气与环境光学学报, 2012, (6): 463-468.
[12]	陈臻懿刘文清张玉钧何俊峰阮俊崔益本李胜. 自适应分层阈值小波去噪在雷达信号中的运用研究[J]. 大气与环境光学学报, 2012, (2): 147-153.
[13]	杨成武，刘文清，张玉钧. 半导体激光云高仪小波去噪算法研究[J]. 大气与环境光学学报, 2011, 6(6): 463-469.
[14]	麻金继陈浩江新华. 基于MODIS图像I类水和II类水的识别方法研究[J]. 大气与环境光学学报, 2009, 4(3): 201-205.