基于环境1号卫星的蓝藻水华提取方法研究
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摘要
随着经济高速发展、工业化水平的提高和人类生活的影响,湖泊污染和富营养化问题日益严重。分布在东部平原的一系列大型浅水湖泊发生了不同程度的水体富营养化,藻类大量繁殖,水华灾害频发,蓝藻水华的研究已成为当前二类水体研究的热点之一。目前绝大部分的研究是通过实测叶绿素a或藻蓝蛋白浓度进行建模估算,实现监测湖泊蓝藻水华的发生及其分布特征。模型的复杂性,适用性,及估算的精度是影响模型应用范围的重要因素。本文以太湖为实验区,利用环境一号卫星CCD的680nm可见光波段与790nm近红外波段,IRS的800nm近红外波段与1720nm短波红外波段,建立了基于蓝藻水华指数(CAI)的蓝藻水华提取方法。通过与叶绿素a定量估算模型和NDVI法提取蓝藻水华的结果比较分析,本文所构建的CAI方法可较好地去除大气和观测环境的影响,稳定的提取蓝藻水华。
通过本文的研究,可以得出以下结论：
(1)通过引入适用于一类水体的提取浮游藻类的FAI方法,对模型进行改进与修正,建立了适用于环境1号卫星的CAI蓝藻水华指数,应用于内陆湖泊-——太湖提取蓝藻水华,该方法可以较好的去除大气及观测环境的影响,稳定地提取蓝藻水华。CAI提取蓝藻水华的方法具有其物理意义与几何意义,是一种能应用于环境1号卫星的,适用于建立长时间数据序列的提取蓝藻水华的方法。
(2)环境1号卫星包含了790nm与800nm两个近红外波段,他们分别位于CCD与IRS的传感器,具有不同的空间、时间及光谱分辨率。而近红外波段是CAI方法当中最为重要的特征波段,通过分别对这两个波段及波段组合的比较分析可知,790nm的波段对于高浓度的蓝藻水华具有更高的敏感性,而800nm的波段对于较低浓度的蓝藻水华具有更高的敏感性,而两者对于水体具有非常近似的反应特征。790nm与800nm的波段组合方法能够较好综合这两个波段的特征,更为稳定而且可以较好的区分不同程度的蓝藻爆发。
(3)本文比较了定量估算叶绿素a浓度、NDVI与CAI等几种方法,分别提取太湖蓝藻水华,对几种方法进行稳定性、适用性进行评价,分析了他们各自的特点及适用的范围,CAI在稳定性方而都优于其它两种方法,且能很好的区分蓝藻水华与清洁水体,适用于长时间序列提取蓝藻水华。
(4)使用CAI的蓝藻水华提取方法,对太湖与巢湖分别进行验证,结果表明：CAI方法提取的蓝藻水华在空间分布或爆发程度上准确性较高,且该方法是不依赖于研究区的,对研究地域具有很好的普适性。最后使用CAI方法从不同的时间尺度研究水华空间分布特征、集聚面积、季节演化特征等,为动态、实时的监测太湖蓝藻水华的提供参考。
Along with the rapid economic development and the degree of industrialization and the influence of intensifying human life, Lakes pollution and eutrophication problem is getting worse. Distribution in the eastern plains by a series of large shallow lake eutrophication influence, Algal blooms that cyanobacteria disasters appears. The Cyanobacteria bloom for current research become one of the hot point int case 2 water research.Currently most of the research is to establish a model of chlorophyll a concentration or blue protein concentration algae are calculated to monitor cyanobacteria bloom in taihu distribution characteristics of the algae. The complexity of the prototype, applicability, and the accuracy of model was restricted by estimating the scope of application of important factors.Using the 680nm China Envirormental Satellite 1(HJ-1) CCD visual band and 790nm near-infrared wavelengths and IRS 800nm near-infrared wavelengths of 1720nm and short-wave infrared bands in tai lake area, for, a method of cyanobacteria bloom algae extracted CAI (cyanobacteria bloom algae index). Through quantitative extraction model, with such vegetation index NDVI extract cyanobacteria bloom method of comparative analysis, the result shows:CAI can better remove air, observation environment influence, stable extraction algae.
Through researchs of the paper, the following conclusions are concluded
(1) Through introducing the FAI extraction method of afloating algae which is application on case 1 water,inproved and fixed the model,constructedthe model of CAI(cyanobacteria algae index) based on HJ-1, applied in inland lake-taihu lake water to detect cyanobacteria blooms.This method can better purify air and observation environmental impact.the method detectting cyanobacteria blooms has physical meaning and geometric meaning,can applied on HJ-1,and is the extraction methods of cyanobacteria blooms whick is compliant for long time series
(2) HJ-1 included two Near-infrared wavelength of 790nm and 800nm.The two bands are on CCD and IRS remote sensor., With different time, space, radiation resolution. And near-infrared wavelengths are most important features bands of the CAI method.Through of the two bands respectively and band combination comparative analysis,the results showed 790nm band for high levels of cyanobacteria blooms has higher sensitivity,but 800nm band for low levels of cyanobacteria blooms has lower sensitivity. Both have very similar to water the response characteristics,800nm and 790nm bands combination method can better integrated these two band features, are More stable and can better distinguish different level of cyanobacteria bloom.
(3) This paper compared several methods such as the quantitative estimation chlorophyll-a concentration, NDVI and CAI, evaluated the stability and applicability of several methods,nalyzed their respective characteristics and applicable range. CAI is better than the others in the stability of the two methods, and can be a very good distinguish with clean water cyanobacteria algae, suitable for long time series cyanobacteria algal extracted.
(4) Using CAI cyanobacteria bloom extraction method,verified Taihu lake and Chaohu lake. Results show that:CAI method Cyanobacteria bloom extracted in spatial distribution or explosion extent accuracy is higher, and the method is not dependent on research to study, the regional has very good applicability. Finally using CAI method from different time scales the space distribution features of algal research, gathering area, season for dynamic evolution characteristics, such as real-time monitoring of cyanobacteria algae bloom in taihu to provide the reference.
通过本文的研究,可以得出以下结论：
(1)通过引入适用于一类水体的提取浮游藻类的FAI方法,对模型进行改进与修正,建立了适用于环境1号卫星的CAI蓝藻水华指数,应用于内陆湖泊-——太湖提取蓝藻水华,该方法可以较好的去除大气及观测环境的影响,稳定地提取蓝藻水华。CAI提取蓝藻水华的方法具有其物理意义与几何意义,是一种能应用于环境1号卫星的,适用于建立长时间数据序列的提取蓝藻水华的方法。
(2)环境1号卫星包含了790nm与800nm两个近红外波段,他们分别位于CCD与IRS的传感器,具有不同的空间、时间及光谱分辨率。而近红外波段是CAI方法当中最为重要的特征波段,通过分别对这两个波段及波段组合的比较分析可知,790nm的波段对于高浓度的蓝藻水华具有更高的敏感性,而800nm的波段对于较低浓度的蓝藻水华具有更高的敏感性,而两者对于水体具有非常近似的反应特征。790nm与800nm的波段组合方法能够较好综合这两个波段的特征,更为稳定而且可以较好的区分不同程度的蓝藻爆发。
(3)本文比较了定量估算叶绿素a浓度、NDVI与CAI等几种方法,分别提取太湖蓝藻水华,对几种方法进行稳定性、适用性进行评价,分析了他们各自的特点及适用的范围,CAI在稳定性方而都优于其它两种方法,且能很好的区分蓝藻水华与清洁水体,适用于长时间序列提取蓝藻水华。
(4)使用CAI的蓝藻水华提取方法,对太湖与巢湖分别进行验证,结果表明：CAI方法提取的蓝藻水华在空间分布或爆发程度上准确性较高,且该方法是不依赖于研究区的,对研究地域具有很好的普适性。最后使用CAI方法从不同的时间尺度研究水华空间分布特征、集聚面积、季节演化特征等,为动态、实时的监测太湖蓝藻水华的提供参考。
Along with the rapid economic development and the degree of industrialization and the influence of intensifying human life, Lakes pollution and eutrophication problem is getting worse. Distribution in the eastern plains by a series of large shallow lake eutrophication influence, Algal blooms that cyanobacteria disasters appears. The Cyanobacteria bloom for current research become one of the hot point int case 2 water research.Currently most of the research is to establish a model of chlorophyll a concentration or blue protein concentration algae are calculated to monitor cyanobacteria bloom in taihu distribution characteristics of the algae. The complexity of the prototype, applicability, and the accuracy of model was restricted by estimating the scope of application of important factors.Using the 680nm China Envirormental Satellite 1(HJ-1) CCD visual band and 790nm near-infrared wavelengths and IRS 800nm near-infrared wavelengths of 1720nm and short-wave infrared bands in tai lake area, for, a method of cyanobacteria bloom algae extracted CAI (cyanobacteria bloom algae index). Through quantitative extraction model, with such vegetation index NDVI extract cyanobacteria bloom method of comparative analysis, the result shows:CAI can better remove air, observation environment influence, stable extraction algae.
Through researchs of the paper, the following conclusions are concluded
(1) Through introducing the FAI extraction method of afloating algae which is application on case 1 water,inproved and fixed the model,constructedthe model of CAI(cyanobacteria algae index) based on HJ-1, applied in inland lake-taihu lake water to detect cyanobacteria blooms.This method can better purify air and observation environmental impact.the method detectting cyanobacteria blooms has physical meaning and geometric meaning,can applied on HJ-1,and is the extraction methods of cyanobacteria blooms whick is compliant for long time series
(2) HJ-1 included two Near-infrared wavelength of 790nm and 800nm.The two bands are on CCD and IRS remote sensor., With different time, space, radiation resolution. And near-infrared wavelengths are most important features bands of the CAI method.Through of the two bands respectively and band combination comparative analysis,the results showed 790nm band for high levels of cyanobacteria blooms has higher sensitivity,but 800nm band for low levels of cyanobacteria blooms has lower sensitivity. Both have very similar to water the response characteristics,800nm and 790nm bands combination method can better integrated these two band features, are More stable and can better distinguish different level of cyanobacteria bloom.
(3) This paper compared several methods such as the quantitative estimation chlorophyll-a concentration, NDVI and CAI, evaluated the stability and applicability of several methods,nalyzed their respective characteristics and applicable range. CAI is better than the others in the stability of the two methods, and can be a very good distinguish with clean water cyanobacteria algae, suitable for long time series cyanobacteria algal extracted.
(4) Using CAI cyanobacteria bloom extraction method,verified Taihu lake and Chaohu lake. Results show that:CAI method Cyanobacteria bloom extracted in spatial distribution or explosion extent accuracy is higher, and the method is not dependent on research to study, the regional has very good applicability. Finally using CAI method from different time scales the space distribution features of algal research, gathering area, season for dynamic evolution characteristics, such as real-time monitoring of cyanobacteria algae bloom in taihu to provide the reference.
引文
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[2]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,(2).179-183。
[3]冯伟,冯学智,马荣华.太湖水体叶绿素浓度与反射光谱特征关系的研究[J].遥感信息,2007,(1)：18-21.
[4]龚绍琦,黄家柱,李云梅,黎刚.应用时间序列分析法对太湖叶绿素-a含量的动态研究[J].海洋与湖沼,2008,39,(6)：591-598.
[5]胡雯,吴文玉,孔庆欣,荀尚培,王玉兰.用FY-1C/CAVHRR数据估算巢湖蓝藻叶绿素的含量.南京气象学院学报[J],2002,(1)：228-234.
[6]胡雯,杨世植,翟武全,程小泉NOAA卫星监测巢湖蓝藻水华的试验分析[J].环境科学与技术,2002,(1)：16-47.
[7]黄家柱,赵锐.卫星遥感监测太湖水域蓝藻暴发[J].遥感信息,1999,(4)：43-44.
[8]姜晟.基于MODIS数据的太湖蓝藻变化及其与水温关系研究,南京师范大学,2009。
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