Effects of aerosol optical thickness on extracting cyanbacteria bloom

doi:10.6046/gtzyyg.2013.01.06

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Abstract

In this paper, the effect of Aerosol Optical Thickness (AOT) on cyanbacteria bloom in the Taihu Lake was investigated, which could lay a foundation for dynamically monitoring cyanbacteria bloom. Moreover, a study of the elimination of the effects of AOT on the extraction results was carried out. MODIS products (MOD 02, MOD 04 and MOD 09) were chosen. Single band (NIR) and ratio vegetation index (NIR/G) were used to extract the spatial distribution of cyanbacteria bloom in the Taihu Lake in 2006. The effect of AOT was explored. A quantitative analysis of the degree of the effect of AOT on both the net changes of the area of cyanbacteria bloom and threshold selection was conducted. The results show that the correlation in the case of fixed threshold is higher than that of the demand threshold when the correlation of the area differences of cyanbacteria bloom and AOT is studied with single band and ratio vegetation index. In these two methods, the correlation without atmospheric correction is higher than that with atmospheric correction in the case of the corresponding value. This shows that the atmosphere will have some impact on the extraction of cyanbacteria bloom. Therefore, if cyanbacteria bloom information needs to be extracted accurately and reliably, AOT cannot be ignored. Also, if it cannot be completely eliminated, appropriate methods could be used to minimize its effect.

Keywords land use generalization relative stable area-patches polygon merge

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Issue Date: 21 February 2013

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	YANG Bao-yao
	DU Zhen-hong
	LIU Ren-yi
	ZHANG Feng

Cite this article:

YANG Bao-yao,DU Zhen-hong,LIU Ren-yi, et al. Effects of aerosol optical thickness on extracting cyanbacteria bloom[J]. REMOTE SENSING FOR LAND & RESOURCES, 2013, 25(1): 33-38.

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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2013.01.06 OR https://www.gtzyyg.com/EN/Y2013/V25/I1/33

[1] Duan H,Zhang Y,Zhang B,et al.Estimation of chlorophyll-a concentration and trophic states for inland lakes in northeast China from landsat TM data and field spectral measurements[J].International Journal of Remote Sensing,2008,29(3):767-786.
[2] 齐峰,王学军.内陆水体水质监测与评价中的遥感应用[J].环境科学进展,1999,7(3):90-99. Qi F,Wang X J.Application of remote sensing techniques in monitoring and asscessing inland water quality[J].Advances in Environmental Science,1999,7(3):90-97.
[3] Forget P,Ouillon S,Lahet F,et al.Inversion of reflectance spectra of nonchlorophyllous turbid coastal waters[J].Remote Sensing of Environment,1999,68:261-272.
[4] Dekker A G,Peters S W M.The use of the thematic mapper for the analysis of Eutrophic lakes:A case study in the netherlands[J].International Journal of Remote Sensing,1993,14(5):799-822.
[5] Kloiber S M,Brezonik P L,Olmanson L G,et al.A procedure for regional lake water clarity assessment using Landsat multispectral data[J].Remote Sensing of Environment,2002,82(1):38-47.
[6] Schiebe F R,Harringington J R,Ritchie J C.Remote sensing of suspended sediments:The lake Chicot,Arkansas project[J].Internatinal Journal of Remote Sensing,1992,13(8):1487-1509.
[7] Dekker A G.The remote sensing Loosdrecht lakes project[J].International Journal of Remote Sensing,1988,9(10/11):1761-1773.
[8] Fraster R N.Hyperspectral remote sensing of turbidity and chlorophyll-a among nebrska sand hills lakes[J].International Journal of Remote Sensing,1998,19(8):1579-1589.
[9] 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. Duan H T,Zhang S X,Zhang Y Z.Cyanobacteria bloom monitoring with remote sensing in lake Taihu[J].Journal of Lake Science,2008,20(2):145-152.
[10] 中国科学院南京地理与湖泊研究所.太湖梅梁湾年蓝藻水华形成及取水口污水团成因分析与应急措施建议[J].湖泊科学,2007,19(4):357-358. Nanjing institute of geography and limnology,Chinese academy of Sciences.On the cause of cyanobacterial bloom and pollution in water intake area and emergency measures in Meiliang bay,lake Taihu in 2007[J].Journal of Lake Sciences,2007,19(4):357-358.
[11] 陈宇炜,秦伯强,高锡云.太湖梅梁湾藻类及相关环境因子逐步回归统计和蓝藻水华的初步预测[J].湖泊科学,2001,13(1):63-71. Chen Y W,Qin B Q,Gao X Y.Prediction of blue-green algae bloom using stepwise multiple regression between algae & related environmental factors in Meiliang gulf,lake Taihu[J].Journal of Lake Science,2001,13(1):63-71.
[12] 祝令亚.湖泊水质遥感监侧与评价方法研究[D].北京:中国科学院遥感应用研究,2006. Zhu L Y.Remote sensing monitoring and assessment of water quality for lakes[D].Beijing:Institute of Remote Sensing Application,Chinese Academy of Sciences,2006.
[13] 金相灿,刘明亮,中国湖泊富营养化[M].北京:中国环境科学出版社,1990. Jin X C,Liu X L.Eutrophication of Chinese lakes[M].Beijing:Chinese Environmental Science Press,1990.
[14] Kaufman Y J,Tanre D,Olivier B.A satellite view of aerosols in the climate system[J].Nature,2002,419:215-223.
[15] Kaufman Y J,Tanre D.Aerosol optical thickness and atmospheric path radiance[J].Journal of Geophysical Research,1993,98(D2):2677-2692.
[16] Thiemann S,Kaufmann H.Determination of chlorophyll content and trophic state of Lakes using field spectrometer and IRS-1C satellite data in the Mecklenburg Lake district,Germany[J].Remote Sensing of Environment,2000,73(2):227-235.
[17] 赵冬至.AVHRR遥感数据在海表赤潮细胞数探测中的应用[J].海洋环境科学,2003,22(1):10-14. Zhao D Z.Application of AVHRR data for detection of surface cell concentration during algal bloom[J].Marine Environmental Science,2003,22(1):10-14.
[18] 黄韦艮,毛显谋,张鸿翔,等.赤潮卫星遥感监测与实时预报[J].海洋预报,1998,15(3):111-115. Huang W G,Mao X M,Zhang H X,et al.Red tide in satellite remote sensing monitoring and real-time forecasting[J].Marine Forecasts,1998,15(3):111-115.
[19] Gower J F R.Observation of in situ fluorescence of chlorophyll in Seanich inlet[J].Boundary-layer Meteorology,1980,18(3):235-245.
[20] Gower J F R,Borstad G.Use of the in vivo fluorescence line at 685 nm for remote sensing surveys of surface chlorophyll-a[M]//Gower J F R.Oceanography from Space,New York:Plenum,1981,329-338.
[21] Gitelson A A,Schalles J F,Rundquist D C,et al.Comparative reflectance properties of algal cultures with manipulated densities[J].Journal of Applied Phycology,1999,11(4):345-354.
[22] Richardson L L.Remote sensing of algal bloom dynamics:New research fused remote sensing of aquatic ecosystems with algal accessory pigment analysis[J].BioScience,1996,46(7):492-501.
[23] 王海君,李云梅.用ASTER数据监测梅梁湖湖区蓝藻分布的方法[J].南京师大学报:自然科学版,2005,28(1):103-106. Wang H J,Li Y M.The method of monitoring the spatial distributions of blue algae in Meiliang bay using ASTER data[J].Journal of Nanjing Nornal University:Natural Science Edition, 2005,28(1):103-106.
[24] 徐京萍,张柏,李方,等.基于MODIS数据的太湖藻华水体识别模式[J].湖泊科学,2008,20(2):191-195. Xu J P,Zhang B,Li F,et al.Detecting modes of cyanobacteria bloom using MODIS data in lake Taihu[J].Journal of Lake Science,2008,20(2):191-195.
[25] 陈云,戴锦芳.基于遥感数据的太湖蓝藻水华信息识别方法[J].湖泊科学,2008,20(2):179-183. Chen Y,Dai J F.Extraction methods of cyanobacteria bloom in lake Taihu based on RS data[J].Journal of Lake Science,2008,20(2):179-183.

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