Forel-Ule(FU)index of water color is an important parameter in traditional water quality investigations.We retrieved the FU index of the largest 10 lakes in China during 2000-2012 from MODerate-resolution Imaging Spec...Forel-Ule(FU)index of water color is an important parameter in traditional water quality investigations.We retrieved the FU index of the largest 10 lakes in China during 2000-2012 from MODerate-resolution Imaging Spectroradiometer surface reflectance product(MOD09)images.Since FU index is an optical parameter,it can be derived from optical remote sensing data by direct formulas,which is invariant with region and season.Based on validation by in situ measured reflectance data,the FU index products are reliable,with average relative error of 7.7%.FU index can be used to roughly assess water clarity:the clearer a water body is,and the bluer it is in color,the smaller its FU index is.FU index can also be used to roughly classify trophic state into three classes:oligotrophic,mesotrophic,and eutrophic.We analyzed the spatial,interannual,and seasonal variations of the FU index and its implications for water clarity and trophic state,and the findings are mostly consistent with the results from related literature.All in all,it might be a feasible way to roughly assess inland water quality by FU index in large region and over long time period.展开更多
Water color is a crucial optical indicator of water quality,polluted water bodies often show water color anomalies.To comprehensively understand the occurrence of water color anomalies in inland lakes,an integrated me...Water color is a crucial optical indicator of water quality,polluted water bodies often show water color anomalies.To comprehensively understand the occurrence of water color anomalies in inland lakes,an integrated method was designed using the hue angle based on the Forel-Ule Index(FUI)model,and other remote sensing indices,including the Turbid Water Index(TWI),Floating Algae Index(FAI),and Cyanobacteria and Macrophytes Index(CMI).Based on all available Landsat-8 OLI images from 2013 to 2020,continuous monitoring was conducted in three different lakes in the middle of the Yangtze River,China.The results demonstrated that:(1)The proposed method can accurately identify algal blooms,high sediment loads,and eutrophication from the abnormal water color areas;(2)The calculated hue angles of sediment-dominated water were significantly higher than those of algal blooms and aquatic vegetation,providing a noticeable visual discoloration of water;(3)These water color anomalies exhibited significant correlations with the water quality and environmental conditions.This study serves as an example for accurate and spatially continuous assessment of water color anomaly and supports practical information to facilitate local water environment conservation.展开更多
Coastal water environment is essentially enhanced by ocean color which is basically decided by substances concentration in water such as chlorophyll, suspended material and yellow substance. It is very difficult, even...Coastal water environment is essentially enhanced by ocean color which is basically decided by substances concentration in water such as chlorophyll, suspended material and yellow substance. It is very difficult, even not possible, to detect water color by expensive ship routing, because of its temporal and spatial variety of feature and scales in the very complicated dynamical system of coastal water. With the development of satellite technique in the last 20 a, space sensors can be applied to detect ocean color by measuring the spectra of water leaving radiance.It is proven that ocean color remote sensing is a powerful tool for understanding the process of oceanic biology and physics. Since the 1980s, great attention has been paid to the advanced remote sensing technique in China, especially to development of satellite programs for the coastal water environment. On 7 September 1988, China launched her first polar orbit satellite FY-1A for meteorological and oceanographic application (water color and temperature) and the second satellite FY-1B two years later. In May 1999, China launched her second generation environment satellite FY-1C with higher sensitivies, more channels and stable operation. The special ocean color satellite HY-1 is planned to be in the orbit in 2001, whose main purpose is to detect the coastal water environment of China seas. China is also developing a very advantageous sensor termed as Chinese moderate imaging spectra radiometer (CMODIS) with 91 channels, which will be a good candidate of the third generation satellite FY-3 in 2003. The technical system of ocean color remote sensing was developed by the Second Institute of Oceanography (SIO), State Oceanic Administration (SOA) in 1997. The system included data receiving, processing, distribution, calibration, validation and application units. The Hangzhou Station of SIO, SOA has the capability to receive FY-1 and AVHRR data since 1989. It was also a SeaWiFS scientific research station authorized by NASA,USA to free receive SeaWiFS data from 16 September 1997. In the recent years, the local algorithms of atmospheric correction and inversion of ocean color have been developed for FY-1C and SeaWiFS, to improve the accuracy of the measurement from satellites efficiently. The satellite data are being applied to monitor coastal water environment, such as the spatial distribution of chlorophyll, suspended material and yellow substance, red tide detection and coastal current study. The results show that the ocean color remote sensing has latent capacity in the detection of coastal water environment.In consideration of the update technique progress of ocean color remote sensing and its more important role in the detection of coastal water in the 2000s, some suggestions are set forth, which would be beneficial to the design of a cheaper but practical coastal water detection system for marine environment preservation.展开更多
This study aimed to investigate bacterial community in an urban drinking water distribution system （DWDS） during an occurrence of colored water. Variation in the bacterial community diversity and structure was obser...This study aimed to investigate bacterial community in an urban drinking water distribution system （DWDS） during an occurrence of colored water. Variation in the bacterial community diversity and structure was observed among the different waters, with the predominance of Proteobacteria. While Verrucomicrobia was also a major phylum group in colored water. Limnobacter was the major genus group in colored water, but Undibacterium predominated in normal tap water. The coexistence of Limnobacter as well as Sediminibacterium and Aquobocterium might contribute to the formation of colored water.展开更多
The River Nyong is situated in Akonolinga (central-south of Cameroon). In order to search for raw materials in ceramic use, samples of alluvium obtained from the deposits in the Nyong River clays have been studied. Th...The River Nyong is situated in Akonolinga (central-south of Cameroon). In order to search for raw materials in ceramic use, samples of alluvium obtained from the deposits in the Nyong River clays have been studied. The results obtained show that, the samples contain important quantity of organic matter and the soil is acidic .These two parameters are necessary to understand the origin of the characteristic color of alluvium and water of the River Nyong.展开更多
叶绿素含量是植物生长状况的重要指标。传统的测量叶绿素的方法费时费力,会对植物造成损伤。近年来,数字图像处理技术在估测植物叶绿素含量方向上取得了较好的进展,但针对银杏等经济林木的研究依旧偏少。以不同水氮互作条件下的2年生银...叶绿素含量是植物生长状况的重要指标。传统的测量叶绿素的方法费时费力,会对植物造成损伤。近年来,数字图像处理技术在估测植物叶绿素含量方向上取得了较好的进展,但针对银杏等经济林木的研究依旧偏少。以不同水氮互作条件下的2年生银杏幼苗为研究对象,使用数字扫描仪采集银杏幼苗叶片图像,利用数字图像技术构建颜色特征参数,结合相关性分析初筛出对叶绿素显著相关的颜色特征参数,并进一步基于高斯过程回归(gaussian process regression, GPR)和偏最小二乘回归(partial least squares regression, PLSR)优选建模中最为重要的颜色特征参数,建立基于银杏叶片颜色特征参数的叶绿素含量估测模型。结果表明,叶绿素含量随着施氮水平和水分处理水平的上升总体上呈现逐渐提高而后缓慢下降的趋势。基于单一颜色参数建立的单变量回归模型(R^(2)=0.01~0.72)预测精度总体上低于使用高斯过程回归(R^(2)=0.79~0.81)和偏最小二乘法(R^(2)=0.75~0.77)的模型。其中,GPR-BAT模型和PLSR-VIP模型都筛选出了对叶绿素敏感的R、G颜色特征参数;GPR模型的表现总体上优于PLSR模型,特别是在使用GPR-BAT优选颜色参数建模时表现最佳(R^(2)=0.81)。基于GPR-BAT优选颜色参数构建的GPR模型效果最佳,可准确估测银杏叶片叶绿素含量,为银杏生产的精确管理和监测银杏生长状况提供技术支撑。展开更多
基金This research has been jointly sponsored by the National Natural Science Foundation of China(Grant Numbers:41471308,41325004,41571361)Youth Innovation Promotion Association of Chinese Academy of Sciences,and China Scholarship Council.
文摘Forel-Ule(FU)index of water color is an important parameter in traditional water quality investigations.We retrieved the FU index of the largest 10 lakes in China during 2000-2012 from MODerate-resolution Imaging Spectroradiometer surface reflectance product(MOD09)images.Since FU index is an optical parameter,it can be derived from optical remote sensing data by direct formulas,which is invariant with region and season.Based on validation by in situ measured reflectance data,the FU index products are reliable,with average relative error of 7.7%.FU index can be used to roughly assess water clarity:the clearer a water body is,and the bluer it is in color,the smaller its FU index is.FU index can also be used to roughly classify trophic state into three classes:oligotrophic,mesotrophic,and eutrophic.We analyzed the spatial,interannual,and seasonal variations of the FU index and its implications for water clarity and trophic state,and the findings are mostly consistent with the results from related literature.All in all,it might be a feasible way to roughly assess inland water quality by FU index in large region and over long time period.
基金jointly supported by the National Key Research and Development Program of China[grant numbers 2018YFB0504900 and 2018YFB0504904]the National Natural Science Foundation of China[grant numbers 42071325,42171346,and 42176183]LIESMARS Special Research Funding,the‘985 Project’of Wuhan University,and Special funds of State Key Laboratory for equipment.
文摘Water color is a crucial optical indicator of water quality,polluted water bodies often show water color anomalies.To comprehensively understand the occurrence of water color anomalies in inland lakes,an integrated method was designed using the hue angle based on the Forel-Ule Index(FUI)model,and other remote sensing indices,including the Turbid Water Index(TWI),Floating Algae Index(FAI),and Cyanobacteria and Macrophytes Index(CMI).Based on all available Landsat-8 OLI images from 2013 to 2020,continuous monitoring was conducted in three different lakes in the middle of the Yangtze River,China.The results demonstrated that:(1)The proposed method can accurately identify algal blooms,high sediment loads,and eutrophication from the abnormal water color areas;(2)The calculated hue angles of sediment-dominated water were significantly higher than those of algal blooms and aquatic vegetation,providing a noticeable visual discoloration of water;(3)These water color anomalies exhibited significant correlations with the water quality and environmental conditions.This study serves as an example for accurate and spatially continuous assessment of water color anomaly and supports practical information to facilitate local water environment conservation.
文摘Coastal water environment is essentially enhanced by ocean color which is basically decided by substances concentration in water such as chlorophyll, suspended material and yellow substance. It is very difficult, even not possible, to detect water color by expensive ship routing, because of its temporal and spatial variety of feature and scales in the very complicated dynamical system of coastal water. With the development of satellite technique in the last 20 a, space sensors can be applied to detect ocean color by measuring the spectra of water leaving radiance.It is proven that ocean color remote sensing is a powerful tool for understanding the process of oceanic biology and physics. Since the 1980s, great attention has been paid to the advanced remote sensing technique in China, especially to development of satellite programs for the coastal water environment. On 7 September 1988, China launched her first polar orbit satellite FY-1A for meteorological and oceanographic application (water color and temperature) and the second satellite FY-1B two years later. In May 1999, China launched her second generation environment satellite FY-1C with higher sensitivies, more channels and stable operation. The special ocean color satellite HY-1 is planned to be in the orbit in 2001, whose main purpose is to detect the coastal water environment of China seas. China is also developing a very advantageous sensor termed as Chinese moderate imaging spectra radiometer (CMODIS) with 91 channels, which will be a good candidate of the third generation satellite FY-3 in 2003. The technical system of ocean color remote sensing was developed by the Second Institute of Oceanography (SIO), State Oceanic Administration (SOA) in 1997. The system included data receiving, processing, distribution, calibration, validation and application units. The Hangzhou Station of SIO, SOA has the capability to receive FY-1 and AVHRR data since 1989. It was also a SeaWiFS scientific research station authorized by NASA,USA to free receive SeaWiFS data from 16 September 1997. In the recent years, the local algorithms of atmospheric correction and inversion of ocean color have been developed for FY-1C and SeaWiFS, to improve the accuracy of the measurement from satellites efficiently. The satellite data are being applied to monitor coastal water environment, such as the spatial distribution of chlorophyll, suspended material and yellow substance, red tide detection and coastal current study. The results show that the ocean color remote sensing has latent capacity in the detection of coastal water environment.In consideration of the update technique progress of ocean color remote sensing and its more important role in the detection of coastal water in the 2000s, some suggestions are set forth, which would be beneficial to the design of a cheaper but practical coastal water detection system for marine environment preservation.
基金financially supported by State Environmental Protection Key Laboratory of Microorganism Application and Risk Control(No.MARC2012D010)National Water Special Program(No.2012ZX07404-002)International Science&Technology Cooperation Program of China(No.2010DFA91830)
文摘This study aimed to investigate bacterial community in an urban drinking water distribution system （DWDS） during an occurrence of colored water. Variation in the bacterial community diversity and structure was observed among the different waters, with the predominance of Proteobacteria. While Verrucomicrobia was also a major phylum group in colored water. Limnobacter was the major genus group in colored water, but Undibacterium predominated in normal tap water. The coexistence of Limnobacter as well as Sediminibacterium and Aquobocterium might contribute to the formation of colored water.
文摘The River Nyong is situated in Akonolinga (central-south of Cameroon). In order to search for raw materials in ceramic use, samples of alluvium obtained from the deposits in the Nyong River clays have been studied. The results obtained show that, the samples contain important quantity of organic matter and the soil is acidic .These two parameters are necessary to understand the origin of the characteristic color of alluvium and water of the River Nyong.
文摘叶绿素含量是植物生长状况的重要指标。传统的测量叶绿素的方法费时费力,会对植物造成损伤。近年来,数字图像处理技术在估测植物叶绿素含量方向上取得了较好的进展,但针对银杏等经济林木的研究依旧偏少。以不同水氮互作条件下的2年生银杏幼苗为研究对象,使用数字扫描仪采集银杏幼苗叶片图像,利用数字图像技术构建颜色特征参数,结合相关性分析初筛出对叶绿素显著相关的颜色特征参数,并进一步基于高斯过程回归(gaussian process regression, GPR)和偏最小二乘回归(partial least squares regression, PLSR)优选建模中最为重要的颜色特征参数,建立基于银杏叶片颜色特征参数的叶绿素含量估测模型。结果表明,叶绿素含量随着施氮水平和水分处理水平的上升总体上呈现逐渐提高而后缓慢下降的趋势。基于单一颜色参数建立的单变量回归模型(R^(2)=0.01~0.72)预测精度总体上低于使用高斯过程回归(R^(2)=0.79~0.81)和偏最小二乘法(R^(2)=0.75~0.77)的模型。其中,GPR-BAT模型和PLSR-VIP模型都筛选出了对叶绿素敏感的R、G颜色特征参数;GPR模型的表现总体上优于PLSR模型,特别是在使用GPR-BAT优选颜色参数建模时表现最佳(R^(2)=0.81)。基于GPR-BAT优选颜色参数构建的GPR模型效果最佳,可准确估测银杏叶片叶绿素含量,为银杏生产的精确管理和监测银杏生长状况提供技术支撑。
