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Multispectral Remote Sensing of Turbidity Among Nebraska Sand Hill Lakes
International Journal of Remote Sensing
Abstract
This study shows robust correlation of multispectral reflectance data (from Landsat Thematic Mapper) with turbidity among 21 lakes in Nebraska, USA, sampled in June, 1994. Mean lake reflectance percentages ranged from 5-12 (TM1), 4-18 (TM2), 2-12 (TM3) and 1-5 (TM4). Turbidity ranged from 2.7-82.3 nephelometric turbidity units. Correlations were highly significant (r 0.68; P 0.001) between each of the TM bands and turbidity. Linear models were useful for measurement among lakes in the region, despite potential bottom effects or variation in turbidity components due to a range of water quality.
... Remote sensing basis (radiative transfer) for water pollution are widely reported in literature and are based on concepts as Inherent and Apparent Optical Properties of the Water, water coefficients of absorption and of scatting, water leaving radiance, surface reflectance, under-water reflectance [1,2]. Satellite images have been widely used to monitor the water quality [3][4][5][6][7][8][9][10]. Satellite and aircraft remote sensing systems, were used in the assessment of water quality parameters, such as temperature, chlorophyll-a, turbidity, total suspended solids, and secchi disk depth for Lakes and reservoirs [11][12][13]. ...
... A total of eleven sampling stations (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11) are identified for the AV Lake covering both Akkulam with seven stations numbering from 1 to 7 (n=7) and Veli with 4 stations numbering from 8 to 11(n=4). The station 1 is located in the upstream portion of the Akkulam Lake where the Kannamoola stream joins it. ...
... Water samples were taken from eleven sampling stations from the AV Lake. Due to the interference of aquatic vegetation, the water-quality data of eight sampling stations (Stations 2, 5,6,7,8,9,10, and 11) have only been 4,5,6,7,8,9,10, and 11 can only be used for the validation purpose. ERDAS-Imagine 9.1 software is used for the image processing of the satellite imagery. ...
Water pollution is a major threat to the existence of living beings. The restoration measures can be taken by assessing the extent of pollution in water bodies using various water quality indices. National Sanitation Federation Water Quality Index (NSFWQI) is the most widely used index. Usually NSFWQI is determined by collecting and analyzing water samples from various locations and it is a tedious and expensive process. Trophic status is usually ascertained from satellite imagery of Landsat TM. Here attempt has been made to quickly assess the pollution status in a vast area (Akkulam-Veli Lake, Kerala, India) directly from the satellite imagery (IRS P6- LISSIII) using NSFWQI. It is also attempted to calculate the pH, dissolved oxygen (DO), biochemical oxygen demand (BOD) and fecal coliforms (FC) in the Lake system. Regression equations for the prediction of NSFWQI, pH, DO, BOD and FC from radiance values from green, red, NIR and SWIR bands of satellite imagery were developed. The study reveals that the simple regression equation formed by the ratio of radiance in the green and the red bands, which yields a strong correlation coefficient for the prediction of NSFWQI. For the prediction of DO, the best equation is the simple regression equation formed by the ratio of radiance in green and red bands with a strong correlation. For BOD, multiple regression equation was formed by the radiance in the red and SWIR bands with a strong correlation. The best equation for predicting pH is the regression equation with the ratio of green and red bands with a strong correlation. But for fecal coliform, multiple regression equation is the best equation formed by the ratio of radiance in the green and SWIR bands with a low correlation coefficient. The performance of this model can be improved by using a large set of data. The spatial variation of these utmost important water quality characteristics is derived from imagery using remote sensing techniques. It is also found out whether the water quality is conforming to the standards or not for envisaging control measures. IRS P6-LISSIII imagery can give a quick assessment of the pollution status of the Lake system using water quality index (NSFWQI). Control measures can accordingly be adopted on priority basis. Satellite imagery can be used for the quick assessment of urban pollution status of water bodies all over the world.
... Concept of applying remote sensing techniques for water quality monitoring have been addressed in a number of early researches (e.g. Lathrop, 1992;Brezonik et al., 2007;Islam et al., 2007;Wu et al., 2015;Hu et al., 2016;Fraser, 1998;Liu et al., 2003), and proved as a promising method to cover more frequent, cost-effective, hard to reach areas, with acceptable accuracy. ...
... Liu et al., 2003;Brezonik et al., 2005;Islam et al., 2007;Wu et al ., 2008;Miller et al., 2015;Lihan et al., 2008). while certain similarity can be detected in the water quality-reflectance relationships (Lathrop, 1992), yet correlations between remotely sensed data and water quality parameters have mostly been acknowledging site-specific nature (Fraser, 1998;Liu et al., 2003;Sravanthi et al., 2013) In Egypt, the growing concerns about lakes' environmental condition triggered researches addressing remote sensing role in the monitoring process (Dewidar and Khedr, 2005;Hereher et al., 2010;Abayazid, 2015;El-Kafrawy et al., 2015). Also, Saad El-Din et al. (2013) used high resolution multi-spectral satellite image (WorldView-2) in a comparative study for water quality monitoring in Lake Timsah. ...
Coastal systems of the Nile Delta experience active interactions and continuous alteration. Beside anthropogenic activities, the delta coast is the end point of the Egyptian widely distributed irrigation-drainage network, and directly connected with coastal lakes. Concerns about water quality conditions and compromised environmental health, and consequently beneficial uses have triggered regular monitoring campaigns. Yet, field sampling and in-situ measurements of water quality indicators for the coastline of hundreds of kilometers are laborious, costly, time-consuming, and frequently faced with inaccessibility. This research investigates the usefulness of using satellite-based techniques in deriving water clarity trends, with wider spatial coverage and more frequent data acquisition. The research study uses the Geographical Information System (GIS) ArcView, ERDAS Imagine image 2010, and Landsat 7 satellite-Enhanced Thematic Mapper Plus (ETM+) imageries in the nearest corresponding overpass dates of ground truth reference data. Distributed ground control points (GCPs) of turbidity and suspended solids for years 2008 to 2011 were used for calibration (R 2 were 0.92 and 0.70, respectively). Validation of developed algorithm, using data from years 2012 and 2013, proved successful estimations (R 2 were 0.78 and 0.65 for turbidity and suspended solids, respectively). The study establishes a predictive relationship with acceptable accuracy results to follow changes in clarity indications along the delta coastline, allowing development of wide spatial and temporal database.
... Concept of applying remote sensing techniques for water quality monitoring have been addressed in a number of early researches (e.g. Lathrop, 1992;Brezonik et al., 2007;Islam et al., 2007;Wu et al., 2015;Hu et al., 2016;Fraser, 1998;Liu et al., 2003), and proved as a promising method to cover more frequent, cost-effective, hard to reach areas, with acceptable accuracy. ...
... Liu et al., 2003;Brezonik et al., 2005;Islam et al., 2007;Wu et al ., 2008;Miller et al., 2015;Lihan et al., 2008). while certain similarity can be detected in the water quality-reflectance relationships (Lathrop, 1992), yet correlations between remotely sensed data and water quality parameters have mostly been acknowledging site-specific nature (Fraser, 1998;Liu et al., 2003;Sravanthi et al., 2013) In Egypt, the growing concerns about lakes' environmental condition triggered researches addressing remote sensing role in the monitoring process (Dewidar and Khedr, 2005;Hereher et al., 2010;Abayazid, 2015;El-Kafrawy et al., 2015). Also, Saad El-Din et al. (2013) used high resolution multi-spectral satellite image (WorldView-2) in a comparative study for water quality monitoring in Lake Timsah. ...
Coastal systems of the Nile Delta experience active interactions and continuous alteration. Beside anthropogenic activities, the delta coast is the end point of the Egyptian widely distributed irrigation-drainage network, and directly connected with coastal lakes. Concerns about water quality conditions and compromised environmental health, and consequently beneficial uses have triggered regular monitoring campaigns. Yet, field sampling and in-situ measurements of water quality indicators for the coastline of hundreds of kilometers are laborious, costly, time-consuming, and frequently faced with inaccessibility. This research investigates the usefulness of using satellite-based techniques in deriving water clarity trends, with wider spatial coverage and more frequent data acquisition. The research study uses the Geographical Information System (GIS) ArcView, ERDAS Imagine image 2010, and Landsat 7 satellite-Enhanced Thematic Mapper Plus (ETM+) imageries in the nearest corresponding overpass dates of ground truth reference data. Distributed ground control points (GCPs) of turbidity and suspended solids for years 2008 to 2011 were used for calibration (R 2 were 0.92 and 0.70, respectively). Validation of developed algorithm, using data from years 2012 and 2013, proved successful estimations (R 2 were 0.78 and 0.65 for turbidity and suspended solids, respectively). The study establishes a predictive relationship with acceptable accuracy results to follow changes in clarity indications along the delta coastline, allowing development of wide spatial and temporal database.
... Optical remote sensing techniques are used to estimate water quality parameters such as turbidity, chlorophyll, temperature, suspended inorganic materials like sand, dust and clay, coloured dissolved organic matter etc. (Allee and Johnson, 1999;Fraser, 1998;Kondratyev et al., 1998;Pattiaratchi et al., 1994). The optical sensors on satellites provide spatial and temporal information to understand changes in the water quality parameters necessary for developing better management practices (Ellis, 1999;Jensen 2000). ...
... The best correlation was found in blue reflectance which is inconsistent with the results of Fraser, (1998), where an equal correlation was found for red, green and blue bands. However, Lathrop and Lillesand (1986) found it for the visible red band only. ...
The water quality of Malad Creek in Mumbai is deteriorating due to heavy discharge of partially treated wastewater and sewage from point and non-point sources. Assessing environmental condition on a large scale requires lot of efforts, is more time consuming and can sometimes also be uneconomical. In the present study, an attempt has been made to retrieve a water quality parameter and assess the extent of sewage pollution through remote sensing image of IKONOS. For in-situ measurement, locations were identified using global positioning system and water samples from creek and drains were collected and analysed for turbidity and BOD. To study the influence of sewage and wastewater on creek, a linear correlation was established between turbidity and reflectance of visible bands of the image. A strong negative correlation, from 0.72 to 0.98, was observed between turbidity and reflectance values at creek and drain locations. This was due to high organic content, observed as BOD, in the water. Further, equations were formulated based on least square method for estimating turbidity at unknown locations to know the extent of pollution in the creek environment.
... 193 and 194.), providing Landsat TM images on a 7_9_7-day cycle and very useful data for monitoring the water quality [14]. As the Landsat TM sensor is suitable for investigation of inland waters such as Evros river, TM data have been used by several groups [9]; [13]; [17]; [24]; [34]; [35] for chlorophyll-a (Chl-a) and turbidity estimations in inland waters due to their high spatial and temporal resolution. The general approach used to investigate inland water is based on the use of optical bands ranging from blue (0.45-0.52 μm, TM1) to near-infrared (0.76-0.9 μm, TM4), in order to explore the relationships between the subsurface irradiance o reflectance (volume reflectance) and the water bio-physical parameters, such as in situ concentrations of chlorophyll-a [4] and turbidity values. ...
... Statistical techniques for the derivation of the correlation between appropriate spectral bands, in situ Chl-a concentration and turbidity values have been a common approach. Though limited in their universal application, empirically derived algorithms can provide adequate estimation of Chl-a concentration [31] and turbidity [13]. Such techniques were also adopted in this work which aims to combine ground truth with satellite data for estimating Chl-a and turbidity concentrations in an freshwater body in Greece and additionally contribute to the enhancement of monitoring inland water quality taking advantage of remote sensing capabilities. ...
Satellite products are utilized for numerous environmental applications
nowadays including water quality monitoring and assessment. Various
techniques have been developed during the last two decades for
estimating environmental parameters such as chlorophyll-a and turbidity.
In the context of this research effort, various algorithms have been
developed to retrieve chlorophyll-a and turbidity values in Evros river,
Greece, using satellite imageries of Landsat 5 TM. These imageries were
obtained from United States Geological Survey (USGS) and covered the
summer periods of 2008-2009 for chlorophyll-a and 2008-2011 for
turbidity, respectively. Field data of relevant dates have been used for
the development and validation of the remote sensing retrieval
algorithms (separate datasets for the algorithm development and the
validation process). The best applicable algorithms, using both the
point sampling values and the remotely sensed data under different
regression models, proved to be the ratio of TM4 /TM3 bands and
logarithmical ratio of TM1/TM2 bands for the estimation of chlorophyll-a
concentration and turbidity values, respectively. This process resulted
in the generation of maps showing the distribution of chlorophyll-a and
turbidity along the river. Validation of the selected algorithms was
also conducted by comparing the estimated chl-a concentrations and
turbidity values with the corresponding in-situ measurements of the
validation datasets. The results indicated a relatively high coefficient
of determination (R2), fact that characterizes the satellite developed
algorithms reliable and efficient to monitor the chlorophyll-a
concentration and turbidity in the particular riverine system.
... On the other hand, the relationship between hyperspectral radiance and SSC in the open channel flows is complicated since the radiance of SSC is influenced by many factors, such as the existence of various types of substrate in the water, varying water depth according to discharge, and surface scattering effect of glinting sunlight. Thus, in this study, nonlinear regression was additionally employed to represent the optical complexity in shallow open channel waters Baek et al., 2019;Binding et al., 2005;Fraser, 1998;Legleiter and Harrison, 2019;Schiebe et al., 1992). SR was chosen from various nonlinear regression approaches. ...
Since conventional in-situ measurements of suspended sediments in the river system are labor-intensive and time-consuming, remote sensing approaches using multi- or hyper-spectral cameras have widely been applied to obtain high-resolution suspended sediment concentration (SSC) distributions in rivers and streams. However, in nature, the properties of heterogeneous sediment, such as the mineral content and particle size distribution, induce a strong variability in the optical images of the suspended sediments. For this reason, the robust estimation of the suspended sediment using the remote sensing technique is challenging due to the optical variability of the suspended sediment. Thus, it is necessary to deal with this variability of the optical images to improve the accuracy of remote sensing-based SSC measurements and extend them to the global estimator. In this study, a robust Machine Learning (ML) model for SSC estimation based on hyperspectral images was developed by considering the optical variability of the suspended sediment in water bodies. A series of field-scale tracer experiments were conducted in open channels with three different sediment types in order to obtain both the SSC using laser diffraction sensors and hyperspectral images using a UAV camera. The experimental results showed that the optical characteristics of SSC were critically heterogeneous due to the properties of the sediment. Using these experimental dataset, four explicit regression models and two implicit ML regression models were developed and compared to select an optimal estimator. Consequently, a Support Vector Regression (SVR) model using relevant spectral bands in a wide wavelength range yielded the most accurate results, with an R² of 0.90 for the whole dataset. However, linear regression models, which could not consider various spectral bands and the nonlinear effect of the optical variability of SSC, were limited in their ability to retrieve SSC from hyperspectral images. Furthermore, the SVR model accurately reproduced the spatio-temporal SSC distributions in all study cases, including low-visibility suspended sediments, thus successfully resolved the optical variability of SSC with widely selected spectral bands from recursive feature elimination (RFE). The SVR model also successfully retrieved the SSC distribution in uncalibrated rivers. The results of this study demonstrated that the proposed ML regression models based on the hyperspectral imagery achieved a significant improvement in SSC estimation in terms of accuracy and global applicability.
... Hence, these restraints and drawbacks make the conventional methods challenging for continuous water quality prediction at spatial scales (Panwar et al., 2015;Chabuk et al., 2017). For observing and analyzing water quality parameters, such as turbidity, chlorophyll, temperature, and suspended inorganic materials, techniques, such as optical remote sensing, are being used (Pattiaratchi et al., 1994;Fraser, 1998;Kondratyev et al., 1998). To calculate the measure of solar irradiance at varied wavelength bands reflected by the surface water, remote sensing satellite sensors are used (Zhang et al., 2003;Dwivedi and Pathak, 2007;Girgin et al., 2010;Ronghang et al., 2019). ...
The Yamuna river has become one of the most polluted rivers in India as well as in the world because of the high-density population growth and speedy industrialization. The Yamuna river is severely polluted and needs urgent revival. The Yamuna river in Dehradun is polluted due to exceptional tourist activity, poor sewage facilities, and insufficient wastewater management amenities. The measurement of the quality can be done by water quality assessment. In this study, the water quality index has been calculated for the Yamuna river at Dehradun using monthly measurements of 12 physicochemical parameters. Trend forecasting for river water pollution has been performed using different parameters for the years 2020–2024 at Dehradun. The study shows that the values of four parameters namely, Temperature, Total Coliform, TDS, and Hardness are increasing yearly, whereas the values of pH and DO are not rising heavily. The considered physicochemical parameters for the study are TDS, Chlorides, Alkalinity, DO, Temperature, COD, BOD, pH, Magnesium, Hardness, Total Coliform, and Calcium. As per the results and trend analysis, the value of total coliform, temperature, and hardness are rising year by year, which is a matter of concern. The values of the considered physicochemical parameters have been monitored using various monitoring stations installed by the Central Pollution Control Board (CPCB), India.
... Prediction models were then developed using the linear regression analysis. The regression relationship between water quality parameters and the corresponding reflectance values of remotely sensed imagery can be simple linear, multiple linear or nonlinear (Lopez-Garcia and Caselles, 1987;Lathrop and Lillesand, 1989;Lathrop, 1992;Aguirre-Gomez, 2000), however, linear type of regression is effective in inferring turbidity despite possible variation in water constituents and impact of bottom reflectance (Fraser, 1998). ...
Landsat operational land imager (OLI) data and consequent laboratory measurements were used to predict water clarity for an inland lake within the East Kolkata Wetland, India (a Ramsar site). Total suspended sediment (TSS) and turbidity was considered as responsible parameters for assessment of lake clarity. The most suitable band ratio was identified by performing Pearson correlation analysis between water clarity concentrations and possible OLI band and band ratios from the 'study points'. The OLI 4 band (636-673 nm) showed the best 'r' value, 0.96 and 0.89 in case of TSS and turbidity respectively. The two separate prediction models (using non-transformed and logarithmically transformed water clarity data) was developed by applying regression analysis between the band reflectance value of OLI4 and water clarity concentrations of the study points. The band reflectance value of the 'validation points' was given as input in the prediction model and model predicted dataset was considered as predicted water clarity parameters. The model predicted dataset exhibit lower standard error of estimates (SEE) with contemporaneous in situ measurements. The validation of the multi-temporal competence of the best models indicated that it is feasible to apply the linear regression model using OLI 4 band to estimate water clarity concentrations across the seasons in Nalban Lake without any in situ data. The water clarity mapping of the lake was then developed using the predicted dataset. This empirical study showed that Landsat 8 OLI imagery could be effectively applied for the mapping of TSS and turbidity for inland lakes.
... Prediction models were then developed using the linear regression analysis. The regression relationship between water quality parameters and the corresponding reflectance values of remotely sensed imagery can be simple linear, multiple linear or nonlinear (Lopez-Garcia and Caselles, 1987;Lathrop and Lillesand, 1989;Lathrop, 1992;Aguirre-Gomez, 2000), however, linear type of regression is effective in inferring turbidity despite possible variation in water constituents and impact of bottom reflectance (Fraser, 1998). ...
Landsat operational land imager (OLI) data and consequent laboratory measurements were used to predict water clarity for an inland lake within the East Kolkata Wetland, India (a Ramsar site). Total suspended sediment (TSS) and turbidity was considered as responsible parameters for assessment of lake clarity. The most suitable band ratio was identified by performing Pearson correlation analysis between water clarity concentrations and possible OLI band and band ratios from the 'study points'. The OLI 4 band (636-673 nm) showed the best 'r' value, 0.96 and 0.89 in case of TSS and turbidity respectively. The two separate prediction models (using non-transformed and logarithmically transformed water clarity data) was developed by applying regression analysis between the band reflectance value of OLI4 and water clarity concentrations of the study points. The band reflectance value of the 'validation points' was given as input in the prediction model and model predicted dataset was considered as predicted water clarity parameters. The model predicted dataset exhibit lower standard error of estimates (SEE) with contemporaneous in situ measurements. The validation of the multi-temporal competence of the best models indicated that it is feasible to apply the linear regression model using OLI 4 band to estimate water clarity concentrations across the seasons in Nalban Lake without any in situ data. The water clarity mapping of the lake was then developed using the predicted dataset. This empirical study showed that Landsat 8 OLI imagery could be effectively applied for the mapping of TSS and turbidity for inland lakes.
... The model for the dark gray soil produced an RMSE value of 105 mg/l and MAE of 88 mg/l for the WFT River; the model for the light gray soil produced an RMSE of 22 mg/l and MAE of 18 mg/l. The coefficients of determination, RMSE, and MAE for these two sites were consistent with the values of the previously developed reflectance-SSC models (Fraser, 1998;Smith et al., 2009;Song, 2011). ...
Satellite remote sensing has been extensively used for estimating the suspended sediment concentration (SSC) in
coastal and inland waters. The application of this method to smaller waterbodies, however, encounters several
limitations, such as the coarse temporal and spatial resolution of satellite images, cloud coverage intercession,
and inaccessibility during emergency periods. Conventional digital cameras are used for monitoring the water
quality parameters in inland waters, but their application for remote sensing of SSC in inland waters is still under
development. The empirical models developed to estimate SSC from digital imagery are usually site-specific and
do not consider the effect of the sediment properties such as grain size and shape, sediment color, and types of
minerals. The performance of digital cameras for estimating SSC is assessed in this study, and the effect of
sediment color on the accuracy of this method is investigated. A series of laboratory experiments were conducted
using four different colors of sediment: light gray, dark gray, light brown, and dark brown. The results showed
that the red waveband reflectance of digital imagery was more sensitive to the variation of SSC than that of blue
and green waveband. The sediment color evidently affected the correlation between the measured SSC and
waveband reflectance, even with the same particle size distribution. Sediment with darker color showed the
lowest reflectance values, whereas lighter-color sediment has the highest reflectance values. The performance of
the developed SSC-reflectance regression models for all sediment colors was assessed at two river sites, i.e.,
Village Creek, and the West Fork Trinity River. The correlation between measured and predicted SSC for Village
Creek, that had the same sediment size and color as the light gray soil, was high (R2 = 0.98). The correlation was
relatively low (R2 = 0.55) for the West Fork Trinity River and dark gray soil water samples which had a similar
color and different sediment sizes. The difference in sediment sizes could explain why the SSC-reflectance model
derived from the dark gray soil exhibited poor performance in predicting the SSC for this site. This study demonstrated
that consideration of sediment color effect in developing a remote sensing algorithm from digital
imagery would result in more accurate estimations of SSC in riverine environments.
... In several studies, Eq. (1) has been physically extended based on the radiative transfer model to extract WQPs, bathymetry, and types of substrates from spectral image data ( Philpot, 1987;Estep, 1994;Mobley, 1994;Maritorena et al., 1994;Lee et al., 1999 ). However, in most previous studies, the empirical calibration approaches with in-situ measurements have generally been employed based on the regression analysis because it was challenging to estimate the parameters in the physicalbased model due to the optical complexity of the shallow waters ( Ritchie et al., 1975;Rimmer et al., 1987;Lathrop et al., 1991 ;Harrington et al., 1992;Lathrop, 1992;Dierberg and Carriker, 1994;Fraser, 1998;Aguirre-Gomez, 2000 ). The relationship between images and the WQPs of interest have been found by using simple linear, multivariate linear or nonlinear regression analyses ( Liu et al., 2003 ). ...
... Furthermore, according to Vargo and Husseneder (2011), ecological factors could shape the colony breeding structure, especially factors that select against inbreeding. A study in the region of southeastern Nebraska with the Dissected Till Plains and the Great Plains (Fraser, 1998) demonstrated that the lands are flat, and lack of distinct geographic factors such as big mountains and lakes to impact the dispersal of R. flavipes. ...
Reticulitermes flavipes (Kollar) has become the most destructive subterranean termite pest, on urban structures in Nebraska. In this study, we used seven microsatellite loci to infer the colony breeding system and population genetic structure among 20 infested urban structures in Nebraska. Our data revealed that 17 structures were infested by simple family colonies of R. flavipes, while, the remaining three were infested with mixed family colonies. The measure of population differentiation, FCT value (0.459) indicated that all the 20 urban colonies (10 - 410 km apart) represented pronounced levels of genetic differentiation. The Mantel test disclosed a weak and significantly-positive correlation between genetic and geographic distance (slope = 0.0009, P = 0.001).The urban populations of R. flavipes in Nebraska possessed a breeding system characterized by monogamous pairs of outbred reproductives with excessive heterozygosity.
... Such statistically valid models have been developed for a number of water quality parameters in many geographic regions and water body types throughout the United States. (Fraser, 1998;Kloiber et al., 2002;Chipman et al., 2004 andBrezonik et al., 2005) reviewed the advantages and disadvantages of several remote sensing platforms such as Landsat, MODIS, and high-resolution commercial imagery (i.e. ...
Satellite based prediction of inland water quality
... The rapid degree of change in TSS patterns can also be observed from geostationary platforms, for example the Spinning Enhanced Visible and InfraRed ImagerÀMeteosat Second Generation (SEVIRI-MSG) meteorological sensor (Neukermans et al., 2009(Neukermans et al., , 2012 or Geostationary Ocean Color Imager (GOCI) (Lyu et al., 2015). In addition, sensors developed for land applications, such as Landsat with a pixel size of 30 m and with an historical archive of almost 40 years, provide useful imagery to investigate TSS patterns at the finer spatial scale, suitable for inland water bodies (Schiebe et al., 1992;Baban, 1993;Fraser, 1998;Dekker et al., 2001;Brando et al., 2015). In the next section, a selection of these sensors is presented to provide TSS mapping of a couple of inland waters present in the drainage basin of the Po River. ...
Total suspended solids (TSS) play a fundamental role in inland waters as different materials including contaminants and pollutants can aggregate to these solids and brought in suspension. This can alter the state of the aquatic ecosystem and the use of freshwater resources. For instance, excessive suspended sediment might condition primary productivity and can hinder water use in agriculture. Suspended solids are one of the most successful parameters that can be measured by means of remote sensing due to the effect of TSS on backscattering and water leaving radiance. Consequently, a variety of applications have been developed since the eighties; they have generally been build on empirical or semi-empirical methods which use reflectance at appropriate wavebands as correlates, or semi-analytical and quasi-analytical approaches such as the spectral inversion procedures which relies on the matching of spectral data to bio-optical forward models. Forward bio-optical modeling is used to show the response of water leaving reflectance depending on inherent optical properties of particles and TSS concentrations. Then, remotely sensed data acquired by different optical sensors are presented to show the performance of state-of-art algorithms for mapping TSS and turbidity in different aquatic systems located in Northern Italy, which include deep clear lakes, a system of fluvial lakes characterized by highly productive waters and a segment of the longest Italian river prior reaching the delta. Overall, the conclusions presented in this chapter encourage the use of remote sensing technology to improve inland water management, although new research efforts remain open to adapt bio-optical modeling to TSS to the variety of sensors used in inland water applications.
... La teledetección puede proporcionar los medios adecuados para estimar algunos de los parámetros relacionados con la calidad del agua, que generalmente son determinados por mediciones tradicionales. Desde la década de 1980, con la mejora de los sensores satelitales (Resolución espacial y espectral), las imágenes multiespectrales se han utilizado para vigilar las aguas continentales, mediante el uso de correlaciones entre la reflectancia medida por cada una de las bandas y las propiedades de superficiales del agua, incluyendo: i) profundidad del disco Secchi; ii) concentraciones de clorofila; iii) total de sedimentos en suspensión, iv) temperatura; y, v) datos de calidad del agua analizadas en un laboratorio (Schiebe et al., 1992;Dekker y Peters, 1993;Schneider and Mauser, 1996;Zilioli y Brivio, 1997;Fraser, 1998;Giardiano et al., 2001;Kloiber et al., 2000Kloiber et al., , 2002. El satélite Landsat-5 TM (Thematic Mapper) ha sido utilizado para adquirir información espectral sobre los cuerpos de agua. ...
... La teledetección puede proporcionar los medios adecuados para estimar algunos de los parámetros relacionados con la calidad del agua que, generalmente, son determinados por mediciones tradicionales. Desde la década de 1980, con la progresiva mejora de la teledetección basada en la utilización de sensores remotos montados en plataformas espaciales, las imágenes multiespectrales se han utilizado para vigilar las aguas continentales mediante el uso de correlaciones entre la reflectancia medida por cada una de las bandas y las propiedades físico químicas superficiales del agua, incluyendo: i) profundidad del disco Secchi; ii) concentraciones de clorofila; iii) total de sedimentos en suspensión; iv) temperatura; y v) datos de la calidad del agua analizadas en un laboratorio (Schiebe et al., 1992;Dekker & Peters, 1993;Schneider & Mauser, 1996;Zilioli & Brivio, 1997;Fraser, 1998;Giardiano et al., 2001;Kloiber et al., 2000Kloiber et al., , 2002. ...
... The Landsat sensors has been used to a great extent to establish the relationships between water quality parameters of inland water bodies and spectral reflectance, as well as to assess the spatial distribution of some water quality parameters such as chlorophyll-a, turbidity, Secchi disc depth and total suspended solids (TSS) (Dwivedi & Narain 1987;Ritchie et al. 1990;Lathrop 1992;Dekker & Peters 1993;Tassan & Ribera d'Alcalá 1993;Yang et al. 1996;Fraser 1998;Tripathi & Singh 2000;Dewidar & Khedr 2008;Singh et al. 2014). ...
The overarching aim of this study was to derive simple and accurate algorithms for the retrieval of water quality parameters for Wular Lake using Landsat 8 OLI satellite data. The water quality parameters include pH, COD, DO, alkalinity, hardness, chloride, TDS, TSS, turbidity, electric conductivity and phosphate. Regression analysis was performed using atmospherically corrected true reflectance values of original OLI bands, images after applying enhancement techniques (NDVI, principal components) and the values of the water quality parameters at different sample locations to obtain the empirical relationship. Most of the parameters were well correlated with single OLI bands with R2 greater than 0.5 whereas phosphate showed a good correlation with NDVI image. The parameters like pH and DO showed a good relation with the principal component I and IV having respectively. The high concentration of pH, COD, turbidity and TSS and low concentration of DO infers the anthropogenic impact on lake.
... Today, many highresolution satellites are available for monitoring water quality studies and prove to be suitable for water quality mapping (Ritchie et al. 1990;Li and Li 2004;Satapathy et al. 2010;Vijay et al. 2015). The water quality parameters that can be estimated with optical remote sensing methods include phytoplankton (chlorophyll), turbidity, water temperature, suspended inorganic material (sand, dust and clay), colloidal matter and coloured dissolved organics (Pattiaratchi et al. 1994;Fraser 1998;Kondratyev et al. 1998;Parada and Canton 1998;Tassan 1998;Allee and Johnson 1999;Satapathy et al. 2010). ...
Today object-based image analysis provides an option for integrating spatial information beyond the conventional pixel based classifications for high resolution imagery. Due to its rare applicability in pollution assessment, an attempt has been made to assess the spatial extent of sewage pollution in Malad Creek, Mumbai, India. Based on multi-resolution segmentation of IRS P6 (LISS IV) image and Normalized Difference Turbidity Index (NDTI), the various water quality regions in the creek were classified. The existing literature implies that the reflectance of turbid water is similar to that of bare soil which gives positive NDTI values. In contrast to this, negative values of NDTI are observed in the present study due to the presence of organic matter which absorbs light and imparts turbidity; which is supported by the significant correlation between NDTI and turbidity. A strong relationship is observed between turbidity and water quality parameters implying the impact of organic matter through discharges of sewage in the creek. Based on the classified regions and the water quality parameters, the extent of pollution was ranked as high, moderate, low and least. The methodology developed in the present study was successfully applied on an IKONOS image for the same study area but a different time frame. The approach will help in impact assessment of sewage pollution and its spatial extent in other waterbodies.
... Satellite remote sensing may provide suitable ways to integrate limnological data collected from traditional in situ measurements. Since the 1980s, with improvement of sensor spatial and spectral resolution, satellite remote sensing has been used to monitor inland water by using correlation between broad-band reflectance and other properties of the water column, including Secchi disk depth, chlorophyll concentrations, pigment load, total suspended sediments, temperature and water quality data analyzed in a laboratory (Schiebe et al., 1992;Dekker and Peters, 1993;Schneider and Mauser, 1996;Zilioli and Brivio, 1997;Fraser, 1998;Giardiano et al., 2001;Kloiber et al., 2000Kloiber et al., , 2002. ...
The objective of this research is to explore a precise and fast way of monitoring water chemical and biochemical quality in the reservoirs of Shenzhen, China. Water quality change in 1988 and 1996 are detected by synthesizing satellite data and ground-based data. One scene Thematic Mapper (TM) image in winter of 1996 was acquired and the simultaneous in situ measurement, sampling and analysis were performed. Main methods include radiometric calibration of TM remote sensor, atmospheric correction to image data and statistical model construction. The results indicate that satellite-based estimates and in situ measured water reflectance have very high correlation, and the root mean square differences between two kinds of indices are close to 0.02–0.03 for each TM band in Visible-Near Infrared (VI-NIR) range. Statistical relationship between calibrated image data (average of 5×5 pixels) of TM bands and laboratory analyzed data of water samples indicated reflectance of TM band 1 to band 4 and organic pollution measurements such as TOC, BOD and COD had higher correlation. The same scene TM data in the winter of 1988 was processed in the same procedure. Results indicate that water quality of most reservoirs have become worse. Water of eastern reservoirs near Dongjiang River is characterized with higher TOC and TSS, and water of western reservoirs is characterized with higher BOD and COD.
... Although stream gages are the most accurate method for obtaining sediment load, turbidity, and temperature, the gages provide only point-specific data. Multispectral, visible-wavelength image data have been used to estimate turbidity and total suspended sediment load, but all techniques to date require ground-truth calibration data (i.e., in water measurements) to relate spectral response to absolute water values (e.g., Whitlock et al., 1978;Goodin et al., 1993;Jerome et al., 1996;Sathyendranath et al., 1997;Fraser, 1998aFraser, , 1998bPozdoyakov et al., 1998;Tassan, 1998). This is by no means a serious limitation because the water gages can provide such groundtruth data and because remotely sensed image data has the potential for extrapolating point-specific data to map the distribution of a water parameter over entire river reaches. ...
... Choubey, 1994;Ritchie et al., 1994;Yacobi et al., 1995;Fraser, 1998;Allee and Johnson, 1999;Yang et al., 1999;Baruah, 2000;Brivio et al., 2001;Kloiber et al., 2002;Chipman et al., 2004;Panda et al., 2004). However, major drawbacks of these models are variation of model input parameters from one model to other because they are specific to water bodies for which they were developed, and a need for ground-truth data to calibrate and validate the underlying statistical relationships (Marcus and Fonstad, 2007;Sudheer et al., 2006). ...
A physical hyperspectral optical-Monte Carlo (PHO-MC) model for computing reflectance from a body of water is evaluated in a water tank experiment. Reflectance calculation in the model is simulated based on Monte Carlo simulations of photon packet paths in three dimensions. Simulated reflectance was compared with the measured reflectance. The model was evaluated for varied depths of water and different characteristics of the tank bottom. Significant differences were found in the measured reflectance even for clear water due to change in water depth and bottom reflectance characteristics of the water tank. The model reproduced measured reflectance over the entire spectral region extending from 400 to 800 nm for these varied conditions. Model calculation showed that the effect of diffuse radiation and its specular reflection from the air-water interface contribute significantly to reflectance. The relative error in predicted reflectance for all 41 wavelengths evaluated in this study increased from 5.2% to 25.4% when diffuse radiation and its specular reflections were ignored in the model formulation.
... The advantages of using the synoptic coverage provided by sensors on satellite platforms for water quality assessment, such as costeffectiveness, timeliness, and the ability for quantitative comparison of numerous water bodies, are well documented (Ritchie et al. l987). Spectral data allows for semi empirical methods based on knowledge of the specific absorption characteristics of water constituents in connection with regression approach (Thiemann, 2002;Fraser, 1998;Dekker, 1993;Doeffer, 1992). ...
Remote sensing techniques have been used extensively to estimate optically active water quality parameters. Suspended sediment (SS) is the most common type of pollutant both in terms of weight and volume in inland waters. SS are helpful in determining water dynamics and spread of other pollutants. Laboratory studies done in past have developed regression between reflectance and uniform concentration of SS in water tank. However, depth distribution of SS is not uniform in inland waters and therefore, algorithms developed may be of limited applicability and accuracy. In this study reflectance was measured with time as SS settled in a water tank-giving rise to variation in SS concentration along the depth. A dual sensor Spectro-radiometer was used to measure relative reflectance in the electromagnetic spectrum region of 346 nm to 1000 nm (456 channel), with bandwidth of approximately of 1.438 nm. Reflectance values significantly changed with time even though overall SS volume remained same in water tank. It suggests that same SS volume with different depth distribution can give different reflectance values. Higher variation in reflectance was observed near 403, 576, and 807 nm spectral regions. Analysis is currently underway to regress the reflectance values with the volume of SS within the penetration depth of spectral region of reflectance. This analysis may provide us methodology to find SS volume in surface layer of water body.
... Para el sensor TM la definición del NDWI es (Fraser, 1998): ...
... Satellite imagery has been widely used to monitor water quality (Dekker & Peters 1993;Zilioli & Brivio 1997;Fraser 1998a;Giardino et al. 2001;Brezonik et al. 2002;Olmanson et al. 2008). Phytoplankton are usually the predominant constituent in deep ocean waters, with the concentrations of other constituents covarying with the chlorophyll concentration (Moses et al. 2009). ...
Lakes are versatile ecosystems, with eutrophication being a serious problem affecting their condition and trophic status. Eutrophication can lead to an over-abundance of macrophytes in lakes, producing favourable conditions for mosquito larvae. Increased eutrophication is attributed in most to excessive phosphorus concentrations in lake water. Satellite imagery analysis now plays a prominent role for quickly assessing water quality over a large area. The present study is an attempt to illustrate the variation of phosphate and total phosphorus concentrations in Akkulam–Veli Lake, Kerala, India, using Indian Remote Sensing satellite (IRS P6- LISS III) imagery. A multiple regression equation derived using radiance in the red and MIR bands in the imagery was found to yield superior results for predicting the phosphate concentration, whereas a simple regression equation using radiance in red band was found to yield good results for the total phosphorus concentration in lake water. Accordingly, the trophic status of the lake system can be determined easily from satellite imagery in this manner.
... The factors of water body water quality remote sensing include water temperature, total suspended solid particulate matter, chlorophyll, transparency (turbidity), thermal pollution, and other information (Schiebe et al.,1992;Dekker and Peters, 1993;Fraser, 1998 . These water quality parameters in remote sensing images need remote sensing data inversion model for pre-processing, extracting the intuitively thresholds to reflect the lake water quality. ...
It is of great significance to establish a space-earth based integrated monitoring system to enhance the ability for water environmental risk prevention. Modern remote sensing technology has been used in surveying and providing urban water environment information combined with GIS spatial analysis and information management capabilities of water bodies such as lakes and reservoirs. In this paper, three monitoring methods are argued. With the use of satellite image, aerial photography and other means providing all-weather, omni-directional, multi-bands, multi-temporal remote sensing image information, the water temperature, total suspended solids, particulate matter, chlorophyll, transparency (turbidity), thermal pollution and other information could be obtained; secondly, with integrated methods of laboratory testing, water quality monitoring sites, the cross-section of the routine monitoring of benzene, phenol, COD, ammonia nitrogen, total phosphorus concentrations could be assessed; thirdly, with the use of online monitoring of toxic chemical substances, the arsenic, mercury concentration could be monitored. And the construction framework of the space-earth based integrated monitoring system for water environment was discussed. A whole scheme integrated online monitoring device, communication system, and software support system was proposed. Meanwhile, the key techniques of this system were addressed.
... Several methods of water body recognition with National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) data were suggested including a channel 2 model, a temperature model, a difference and a ratio model between channels 2 and 1 (Sheng et al. 2001). In fact, these methods have also been applied to Landsat, Indian Remote Sensing (IRS) and Satellite pour l'Observation de la Terre (SPOT) images (McFeeters 1996, Fraser 1998, Yang et al. 1998, Du et al. 2001, Yang and Zhou 2001, Rogers and Kearney 2004, Chatterjee et al. 2005, Deng et al. 2005, Jain et al. 2005, Overton 2005, Xu 2005. A water body can be distinguished by these methods to determine its extent and flooding over large areas, though there are obvious disadvantages, such as individual pixel misclassification, mixed pixels, high turbidity or shallow depth and confusion of dark shadow. ...
Poyang Lake is a seasonal lake, exchanging water with the lower branch of the Yangtze River. During the spring and summer flooding season it inundates a large area while in the winter it shrinks considerably, creating a large tract of marshland for wild migratory birds. A better knowledge of the water coverage duration and the beginning and ending dates for the vast range of marshlands surrounding the lake is important for the measurement, modelling and management of marshland ecosystems. In addition, the abundance of a special type of snail (Oncomelania hupensis), the intermediate host of parasite schistosome (Schistosoma japonicum) in this region, is also heavily dependent on the water coverage information. However, there is no accurate digital elevation model (DEM) for the lake bottom and the inundated marshland, nor is there sufficient water level information over this area. In this study, we assess the feasibility of the use of multitemporal Landsat images for mapping the spatial‐temporal change of Poyang Lake water body and the temporal process of water inundation of marshlands. Eight cloud‐free Landsat Thematic Mapper images taken during a period of one year were used in this study. We used the normalized difference water index (NDWI) and the modified normalized difference water index (MNDWI) methods to map water bodies. We then examined the annual spatial‐temporal change of the Poyang Lake water body. Finally we attempted to obtain the duration of water inundation of marshlands based on the temporal sequence of water extent determined from the Landsat images. The results showed that although the images can be used to capture the snapshots of water coverage in this area, they are insufficient to provide accurate estimation of the spatial‐temporal process of water inundation over the marshlands through linear interpolation.
... monitor inland water quality (Lathrop and Lillesand 1986, Schiebe et al. 1992, Dekker and Peters 1993, Schneider and Mauser 1996. Currently, the most popular operational spacecraft remote sensing system with a potential applicability to study lake water quality is Landsat Thematic Mapper (TM) (Ritchie et al. 1990, Gitelson et al. 1993, Tassan 1997, Zilioli and Brivio 1997, Fraser 1998. TM sensors acquire spectral data in the optical bands and in the thermal region of the electromagnetic spectrum and provide su cient spatial resolution to monitor small inland water bodies. ...
The distribution of phytoplankton chlorophyll concentration in Lake Garda (Italy) was estimated using Landsat Thematic Mapper (TM) data acquired at two different times, February 1992 and March 1993. To investigate the waterleaving radiance adequately, the contribution of the atmospheric path radiance reaching the sensor should be removed. In this work a completely image-based atmospheric correction method was applied by means of an inversion technique based on a simplified radiative transfer code (RTC). A semi-empirical approach of relating atmospherically corrected TM spectral reflectances to in situ measurements through regression analysis was used. Limnological parameters were measured near to the TM images dates; some of the in situ measurements were used to define algorithms relating chlorophyll concentration measurements to water surface reflectance and the others too were used to validate the results of the predictive model. The models developed, which performed better (r = 0.818) when concentrations were higher than > 3.0 mg m, were used to map chlorophyll concentration throughout the lake. Spatial distribution maps of chlorophyll concentration and concentration changes were produced with contour intervals of 1 mg m.
Enhanced and effective hydrological monitoring plays a crucial role in understanding water-related processes in a rapidly changing world. Within this context, image-based river monitoring has shown to significantly enhance data collection, improve analysis and accuracy, and support effective and timely decision-making. The integration of remote and proximal sensing technologies, with citizen science, and artificial intelligence may revolutionize monitoring practices. Therefore, it is crucial to quantify the quality of current research and ongoing initiatives to envision the potential trajectories for research activities within this specific field. The evolution of monitoring strategies is progressing in multiple directions that should converge to build critical mass around relevant challenges to meet the need for innovative solutions to overcome limitations of traditional approaches. The present study reviews showcases and good practices of enhanced hydrological monitoring in different applications, reflecting the strengths and limitations of new approaches.
The Ganga River basin is a lifeline to the millions inhabiting the Indian subcontinent. Pollution and deteriorating water quality in this ecosystem have been linked to various anthropogenic activities such as habitation, industrialization, agriculture, etc. The estimation and evaluation of water quality levels are essential for societal and economic development. In recent times, satellite imaging approach is widely used in diverse environmental applications, including water quality monitoring. Turbidity is an indicator of water transparency that is associated with total suspended sediment concentration and other impurities in the water through the process of light attenuation. The present study envisaged surface reflectance values to estimate the water turbidity across the Ganga River system, which is spatially classified into four different river sections. The Modified Normalized Difference Water Index (MNDWI) was used to delineate water pixels from the multispectral satellite datasets, while the turbidity was assessed spatially for different river sections using widely used Turbidity retrieval algorithms. The relative consistency among the selected algorithms was evaluated using collocated in-situ measurements during the period 2013–2016. Analysis of turbidity values showed a steady decrease from upstream to downstream, with turbidity values of >115 NTU and 60–85 NTU in the upper and lower sections, respectively. The results indicated that remote sensing provides a robust alternative for monitoring surface water turbidity.KeywordsGanga RiverTurbidityEmpirical modelMNDWINTU
Turbidity is an important indicator of riverine conditions, especially in a fragile environment such as the Tibetan Plateau. Remote sensing, with the advantages of large-scale observations, has been widely applied to monitor turbidity change in lakes and rivers; however, few studies have focused on turbidity change of rivers on the Tibetan Plateau. We investigated the pattern of turbidity change in the middle reaches of the Yarlung Zangbo River, southern Tibetan Plateau, based on multispectral satellite imagery and in situ measurements. We developed empirical models from in situ measured water leaving reflectance and turbidity, and applied the best performed s-curve models on satellite imagery from Sentinel-2, Landsat 8, and Landsat 5 to derive turbidity change in 2007–2017. Our results revealed an overall decreasing spatial trend from the upper to lower streams. Seasonal variations were observed with high turbidity from July to September and low turbidity from October to May. Annual turbidity showed a temporally slightly declining trend from 2007 to 2017. The pattern of turbidity change is affected by the confluence of tributaries and the changes in precipitation and vegetation along the river. These findings provide important insights into the responses of riverine turbidity to climate and environmental changes on the Tibetan Plateau.
River Yamuna is the largest tributary of river Ganges and has been acclaimed as a heavenly waterway in Indian mythology. However, 22-km segment of river Yamuna passing through Delhi from downstream of Wazirabad barrage up to Okhla barrage is considered as the filthiest stretch having been rendered into a sewer drain. The present study employs high-resolution GeoEye-2 imagery for mapping and monitoring pollution levels within the river segment by testing correlation between water quality parameters (WQPs) and the corresponding spectral reflectance values of the image. A total of 100 water samples collected from random sampling locations along the river segment were analyzed for 12 WQPs in the laboratory and grouped into two classes, namely (WQP)organic and (WQP)inorganic. Several spectral band combinations as well as single bands were tested for any significant correlation with the two formulated WQP classes by performing multiple linear regression analysis. Results reveal that spectral band combination, i.e., \(\left\{ {\overline{{\left( {RGB} \right)}} \times \sqrt {B/R} } \right\},\) and the two formulated WQP classes exhibit strong positive correlation with R = 0.92 and 0.91 (R² ~ 0.85 and 0.82; RMSE ~ 1.03 and 1.12) for calibration data and 0.85 and 0.84 (i.e., R² ~ 0.74 and 0.72; RMSE ~ 1.45 and 1.64) for validation data, respectively. The spatial distribution maps depicting pollution levels of two WQP classes were generated in GIS framework, substantiating to the actual in situ pollution concentration levels in the river segment. The methodology adopted in the present study and results obtained validate the potential of high-resolution GeoEye-2 imagery for monitoring and mapping pollution levels in the water bodies.
The present article utilizes high resolution Geoeye 2 imagery for mapping and monitoring pollution concentrations of 22 km stretch of river Yamuna passing through Delhi state, by developing regression models between water quality parameters (WQP's) and the corresponding spectral reflectance values. Water samples collected from the sampling locations were analysed for 20 WQP's and grouped into four classes namely; (WQP)organic, (WQP)inorganic, (WQP)anion and (WQP)cation. Several spectral band combinations as well as single bands were probed for performing multiple linear regression (MLR) analysis with the four WQP classes. Results reveal relatively strong positive correlations for band combination viz. [mean RGB × √B/R] with all four WQP classes yielding high R2 value (∼0.85) and RMSE (∼1.03) amongst other selected band combinations. Spatial distribution maps were generated that substantiates to the actual in-situ pollution concentration levels thereby evidences the potential of high resolution Geoeye-2 imagery for monitoring and mapping pollution concentrations in the water bodies.
The present article utilizes high resolution Geoeye 2 imagery for mapping and monitoring pollution concentrations of 22 km stretch of river Yamuna passing through Delhi state, by developing regression models between water quality parameters (WQP's) and the corresponding spectral reflectance values. Water samples collected from the sampling locations were analysed for 20 WQP's and grouped into four classes namely; (WQP)organic, (WQP)inorganic, (WQP)anion and (WQP)cation. Several spectral band combinations as well as single bands were probed for performing multiple linear regression (MLR) analysis with the four WQP classes. Results reveal relatively strong positive correlations for band combination viz. [mean RGB × √B/R] with all four WQP classes yielding high R2 value (~0.85) and RMSE (~1.03) amongst other selected band combinations. Spatial distribution maps were generated that substantiates to the actual in-situ pollution concentration levels thereby evidences the potential of high resolution Geoeye-2 imagery for monitoring and mapping pollution concentrations in the water bodies.
The Middle Mississippi River (MMR) and lower Missouri River (MOR) provide critical navigation waterways, ecological habitat, and flood conveyance. They are also directly linked to processes affecting geomorphic and ecological conditions in the lower MR and Delta. For this study, a method was developed to measure suspended-sediment concentration (SSC) and turbidity along the MMR and the lower MOR using Landsat imagery. Data from nine United States Geological Survey water-quality monitoring stations were used to create a model-development dataset and a model-validation dataset. Concurrent gaging data were identified for available Landsat images to generate the datasets. Surface-reflectance filters were developed to eliminate images with cirrus cloud coverage or vessel traffic. Using the filtered model-development dataset, unique reflectance-SSC and reflectance-turbidity models were developed for three Landsat sensors: Landsat 8 Operational Land Imager, Landsat 7 Enhanced Thematic Mapper Plus, and Landsat 4–5 Thematic Mapper. Coefficient of determination values for the models ranged from 0.72 to 0.88 for the model-development dataset. The model-validation dataset was used to evaluate the performance of the models and had coefficient of determination values ranging from 0.62 to 0.79.
In order to monitor the response of thermokarst lakes on the Qinghai–Tibet Plateau (QTP) to rapid climatic changes and human activities, an automated method for extracting shorelines from Chinese GaoFen-2 (GF-2) imagery is proposed. First, the water index (WI) images and the potential lake areas are calculated from the preprocessed multispectral imagery and digital elevation model data, respectively. Second, the initial segmentation obtained by global thresholding of the WI images and masking in the potential lake areas are used to implement the contour initialization of active contours models efficiently. Finally, the nonlocal active contours (NLAC) approach is applied to refine the initial segmentation of the WI images, and the final shoreline vector files are produced by some simple and automatic postprocessing steps. Experiments on the GF-2 imagery demonstrate that 1) by exploiting the capability of WI to locate the approximate shoreline effectively around the evolving contour, the processing time of the proposed method can be saved significantly; 2) the NLAC approach can efficiently identify the shoreline by integrating the nonlocal interactions between pairs of patches inside and outside the lake; and 3) the proposed method can conveniently adapt to the multitemporal and multifeature image analysis. Using the manual digitized shorelines as the reference data, an average error of less than one pixel with standard deviation of 0.1320 can be obtained. These results prove that the proposed method is feasible for the identification and monitoring of thermokarst lakes on the QTP.
The objective of this chapter was to explore the potential of Landsat TM data, calibrated by in situ measurements, to map the spatial distribution of water quality in the rivers and lakes of Dhaka. The relationship of satellite brightness values and ground measurement was established through correlation and regression analyses. The results showed that the ratio of TM1 and TM3 was highly correlated with Secchi disk transparency (SDT), a measure of water clarity, while total suspended sediment (TSS) was strongly correlated with brightness values in the near-infrared portion of the electromagnetic spectrum. Regression analysis indicated that TM1 and the ratio of TM1/TM3 was the best predictor for SDT, and TM3 and the ratio of TM1 and TM3 was suitable for the estimation of TSS in waters. Maps of SDT and TSS are presented that illustrate the spatial variation of water quality in the inland water systems of Dhaka.
In this paper, a satellite based remote sensing technique of acquiring water quality data is proposed. A review has been presented on retrieval of five major independent water quality variables (chlorophyll-a, Secchi disk depth, total phosphorus, turbidity and temperature) from the satellite data. A frame work has been developed for mapping water quality variables at landscape level using satellite and ground measured data with an intent to help classify source waters according to pollution levels in Alberta.
The coastal water quality of Mumbai is deteriorating by receiving partially treated effluent from wastewater treatment facilities, sewage discharges from ocean outfalls and discharges from point and non-point sources in the creek and coast. A novel approach of object-based image analysis has been used in this research study to assess the extent of sewage pollution in the coastal environment of Mumbai. For this, Indian Remote Sensing P6 Linear Imaging Self Scanning IV image was used for multiresolution segmentation and rule-based image classification as per normalised difference water index and normalised difference turbidity index. Water quality regions as per classification were strongly correlated with observed water quality parameters. Based on classified regions and water quality parameters, extent of sewage pollution in the coast was ranked from high to least polluted. The approach developed in this methodology should be tested in similarly polluted waters to ascertain its adaptability for assessing the spatial extent of sewage pollution.
Maintaining the ecological diversity and hydrologic connectivity of freshwater delta systems depends on regular recharge of floodplains with river water, which can be difficult to observe on the ground. Rivers that form deltas often carry large amounts of suspended sediment, but floodplain lakes and wetlands usually have little sediment in suspension. Remote observation of high sediment water in lakes and wetlands therefore often indicates connectivity with the river network. In this study, we use daily 250-m MODIS imagery in band 1 (620–670 nm) and band 2 (841–876 nm) to monitor suspended sediment transport and, by proxy, hydrologic recharge in the Peace–Athabasca Delta, Canada. To identify an appropriate suspended sediment concentration (SSC)-reflectance model, we compare 31 published empirical equations using a field dataset containing 147 observations of SSC and in situ spectral reflectance. Results suggest potential for spatial transferability of such models, but success is contingent on the equation meeting certain criteria: 1) use of a near infrared band in combination with at least one visible band, 2) development based on SSCs similar to those in the observed region, and 3) a nonlinear form. Using a highly predictive SSC-reflectance model (Spearman's ρ = 0.95), we develop a twelve-year time series of SSC in the westernmost end of Lake Athabasca, observe the timing and sources of major sediment flux events, and identify a threshold river discharge of ~ 1700 m3/s above which SSC in Lake Athabasca is clearly associated with flow in the Athabasca River. We also track the influx of Athabasca River water to floodplain lakes, and in three of the lakes identify distinct discharge thresholds (1040 m3/s, 1150 m3/s, and 1850 m3/s) which result in lake recharge. For each of these lakes, we find a statistically significant decline in the threshold exceedence frequency since 1970, suggesting less frequent recharge during the summer.
Inland lakes are important resources to humans, while the eutrophication effect caused by an overload of nutrients is a significant problem. This study focuses on utilizing the satellite remote sensing to monitor the water quality of Lake Simcoe, Ontario, Canada, which has been suffering from the overload of Total Phosphorus (TP) and therefore eutrophication for decades. The data employed in this study includes 22 cloud-free Landsat 5 TM images, as well as the nearly simultaneous in-situ data from 15 observation stations on the lake. Compared to the generally used model, an improved model is developed in this study to estimate the Secchi Disk Transparency (SDT), a parameter for water clarity measurements, using the TM images. Models based on different band combinations are compared to estimate the chlorophyll- a (chl-a) concentration. The results of these estimations are validated using the in-situ data by the linear regression analysis, and the accuracies are measured by the correlation coefficients R 2. The results reveal that the improved SDT model provides higher prediction accuracies than the general model when applied to 68.2% (15 out of 22) of the images. The majority of the SDT predictions show high R 2, whereas some of the estimated chl-a concentrations have weak relationships with the in-situ data. The possible reasons for this are the geo-location of stations, as well as the influences of chl- a and Dissolved Organic Carbon (DOC). The resultant concentration maps indicate that the eutrophic water is normally distributed at the near-shore areas and the northeastern part of Lake Simcoe. In addition, the southern Cook's Bay has always been suffering from an extremely serious water quality problem even until now. Meanwhile, the water quality of the southwestern part of Lake Simcoe is much better than the other parts of this lake. The results also show that the water quality of Lake Simcoe was at its worst in August and September for the past 22 years while it was much better in the other sampling seasons. According to the trend of the monthly averaged SDT, on an overall scale, the SDT dropped from 1980 to 1982 and then kept relatively stable until the fall of 1992, followed by a gradual increase until 2000, and then stayed constant until the summer of 2008. The chl-a concentration reveals an inverse trend, i.e., the higher the chl-a concentration, the more turbid the water.
Two Landsat Thematic Mapper (TM) scenes of the same area of the midlands of Ireland were required to be radiometrically normalized. Standard techniques were reviewed and considered unsuitable. A hybrid approach was adopted that used linear transformation functions. As part of this approach, a novel thresholding-based method for extracting reference data from a land cover class with sub-pixel scale elements was developed. This method was shown to be robust with a consistency of ca - 1 digital number (DN) being achievable in the extracted reference data. A procedure for removing pixels influenced by thin cloud and associated shadow was included. The radiometric normalization approach produced acceptable Root Mean Square Errors in the range 0.8-2.5 DN. Whereas the strategies and methodologies developed were appropriate to the particular environment, the developmental process may serve as a template for analysts working in similar environments elsewhere.
The Shandong southwest area is located on the fracture zone connecting Shandong hills and north china plain. Since Holocene, affected by development of Huanghe alluvial fan, Zhanghe alluvial fan and Hutuhe alluvial fan, a coastal lagoon was formed in the low-lying area of Huanghe alluvial fan. Under the influence of the development of Huanghe alluvial fan, climate change in north china and human activity, paleolakes in Shandong southeast area appeared in different levels of development and shapes in times of early Holocene, Holocene and late Holocene. Referring to the record information of lake terraces on 1 Ka.B.P and measuring spots of JiediI, Huyan, Zhakou, Doumen, and Shuiba which could reflect geographic boundaries of the lakes, we are able to recover the geographic paleoenvironment of the lakes 1Ka.B.P.
The transport of fine sediment, carried in suspension by water, is central to the hydrology, geomorphology, and ecological functioning of river floodplains and deltas. An extensive new field data set for the Peace-Athabasca Delta (PAD), Canada quantifies robust positive relationships between in situ suspended sediment concentration (SSC) and remotely sensed visible/near-infrared reflectance. These relationships are exploited using SPOT and ASTER satellite images to map suspended sediment concentrations across the PAD for four days in 2006 and 2007, revealing strong variations in water sources and flow patterns, including flow reversals in major distributaries. Near-daily monitoring with 276 MODIS satellite images tracks hydrologic recharge of floodplain lakes, as revealed by episodic infusions of sediment-rich water from the Athabasca River. The timing and magnitude of lake recharge are linked to springtime water level on the Athabasca River, suggesting a system sensitive to changes in river flow regime. Moreover, recharge timing differentiates lakes that are frequently and extensively recharged from those recharged more rarely. Finally, we present a first estimation of river flow velocity based on remotely sensed SSC, though saturation may occur at velocities >0.6 m/s. Viewed collectively, the different remote sensing methodologies presented here suggest strong value for visible/near-infrared remote sensing of suspended sediment to assess hydrologic and sediment transport processes in complex flow environments. Field observations including nephelometric turbidity, specific conductivity, water temperature, Secchi disk depth, suspended sediment concentration, and water level are archived at the Oak Ridge National Laboratory Distributed Active Archive Center for Biogeochemical Dynamics (available at http://daac.ornl.gov//HYDROCLIMATOLOGY/guides/PAD.html).
Water quality in Reelfoot Lake, Tennessee, was investigated in the field over 15 years ago. However, the spatial variations of water quality were not studied. The remote sensing technique has been proved a powerful tool in mapping spatial distributions of some water quality parameters such as chlorophyll‐a concentration. Additionally, different regression methods and various independent variables have been used to establish relationships between water quality parameters and spectral reflectance. The results from this study indicate that Landsat TM2 and TM3, as a set of independent variables in multivariate regression analysis, are good predictors of water quality in Reelfoot Lake. TM2 is positively correlated to water quality, and TM3 is negatively correlated to water quality. Poor water quality, or a high algae load, results in a high reflectance measured by TM2 and a low reflectance measured by TM3. Maps of spatial distribution of Secchi disk depth, turbidity, chlorophyll‐a, and total suspended solids present apparent spatial variations of water quality in the lake.
Because of the complicated shorelines, inaccessibility and vast spread of some lakes, information on changing shorelines is difficult to acquire. A new water index (WI) has been applied to quantify changes in five saline and non‐saline Rift Valley lakes in Kenya using Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper (ETM+) data. The WI is based on a logical combination of the Tasseled Cap Wetness (TCW) index and the Normalized Difference Water Index (NDWI). Using regression analysis with estimated shoreline coordinates, the WI detected the shorelines with an accuracy of 98.4%, which was 22.3% higher than the TCW, and 43.2% more accurate than the NDWI. Change detection was derived using image differencing followed by density slicing and unsupervised classification. The saline lakes (Bogoria, Nakuru and Elementaita) changed more with respect to the ratio of the change in the original surface areas than the non‐saline lakes (Baringo and Naivasha).
The primary objective of this study was to determine relationships between water quality parameters (WQPs) and digital data from the Landsat satellite to estimate and map the WQP in the Porsuk Dam reservoir. Suspended sediments (SS), chlorophyll a (chl-a), NO3-N and transmitted light intensity depth (TLID) were the parameters for water quality determination used in this study. Collection of these data, obtained from the General Directorate of State Hydraulic Works (GDSHW) was synchronized with the Landsat satellite overpass of the September 1987. The relationships between the brightness values (BV) of the TM data and WQP were determined. Using the TM data, we developed multiple regression equations to estimate the WQPs, and the validation of these equations was checked by using ANOVA. The effects of SS, NO3-N and chl-a on TLID were tested not only for ground data, but also for TM datasets. Regression equations were developed for two different datasets and the homogeneity of those equations was tested. Finally, these regression equations evaluated from digital TM data and ground data were applied to map TLID values.
The objective of this research is to explore a precise and fast way of water quality monitor. Water quality change in 1988 and 1996 are detected by remote sensing. One scene TM image in winter of 1996 was acquired and simultaneous in situ measurement, sampling and analysis were performed. Main methods include radiometric calibration of TM remote sensor, atmospheric correction to image data and model construction. The results indicate that satellite-based estimates and in situ measured water reflectance have very high correlation, and the root mean square difference between two kinds of indexes are close to 0.02-0.03 for each TM band in VI-NIR range. Quantity relationship between calibrated image data (average of 5x5 pixels) of TM bands and laboratory analyzed data of water samples indicated reflectance of TM band 1 to band 4 and organic pollution measurements such as TOC, BOD and COD had higher correlation. The same scene TM data in the same season of 1988 was processed in the same procedure. Results indicate water quality of most reservoirs has become worse. Water of eastern reservoirs near Dongjiang River is characterized with higher TOC and TSS, and water of western reservoirs is characterized with higher BOD and COD.
We investigated changes in spectral reflectance associated with manipulations of algal densities and both bright and dark “bottoms”; at various depths in a 9500 L pool. Spectroradiometer data were collected at close range under natural sunlight. We found orderly patterns of either increasing or decreasing volume reflectance dependent on algal density, bottom color, and bottom depth. The color of the submerged bottom affected reflectance signals, especially the magnitude of the difference between the points of peak near‐infrared reflectance and chlorophyll‐dependent red absorption. Wavelength shifts in reflectance parks were detected as algal chlorophyll was reduced from 336 to 32 mg/m3. The wavelength of peak green reflectance increased from 552 to 567 nm and the near‐infrared peak from 695 to 717 nm.
Remote sensing of highly turbid finfish aquaculture impoundments using the Calibrated Airborne Multispectral Scanner (CAMS) mounted on a Lear jet flown at 900 m was conducted in central Mississippi on 16 May 1990. Concurrent in situ data consisted of phytoplankton pigment concentrations and standing crop, water color, turbidity, and surface-water temperature. Surface and near-surface assemblages of cyanophytes and chlorophytes varied dramatically among impoundments; total chlorophyll concentrations and standing crop values ranged from 8 to 483 mg·m−3 and 8.0 × 102 to 2.2 × 106 cells-mL−1, respectively. Regression models fit to CAMS data provided reliable estimates for and produced accurate digital cartographs of total chlorophyll and carotenoid concentrations, phytoplankton standing crop, and turbidity. Although a model to effectively estimate in situ c-phycocyanin concentrations was not identified, the lack of a suitable model may have resulted from variability of pigment extraction during quantification rather than failure of remotely sensed imagery to detect c-phycocyanin. Models derived from imagery of impoundments directly beneath the aircraft sufficiently described in situ parameters in imagery of adjacent series of impoundments not directly below the aircraft. High-resolution airborne remote sensing provides a means for monitoring local phytoplankton dynamics in temporal and spatial scales analogous to biotic and abiotic processes affecting such dynamics and necessary for applications to ecological research and fisheries or aquacultural management.
This dissertation applied remote sensing to several questions regarding lake water quality in the Nebraska Sand Hills. In Chapter 2, baselines of reflectivities were defined, from May to October, 1972 through 1986, as measured by the Landsat Multispectral Scanner, for 130 lakes in Garden and Sheridan Counties, NE. This yielded multispectral signatures and records of variation for the individual lakes. Chapters 3 and 4 addressed the basic problem of associating lake reflectivities with water quality parameters specific to Sand Hills lakes, using: (a) 252 bands of hyperspectral reflectance data collected with a spectroradiometer from a helicopter, (b) 7 bands of multispectral reflectance data collected by the Landsat Thematic Mapper, and (c) water quality conditions of turbidity, algal chlorophyll-a and alkalinity. Results from both chapters yielded significant correlations of turbidity and chlorophyll-a with the spectral information. A record of hyperspectral reflectance from 32 lakes was established. Chapter 5 was a cursory study on the spatial variability expressed by waterscape (landscape ecology analog) metrics within and among lakes, using reflectance classes as surrogates to aquatic habitats. Results showed that spatial metrics vary among lake spectral classes. Chapter 6 provided estimates of albedo, from hyperspectral reflectance data among lakes, and then used the results to examine the variability in lake radiation budgets. There were shifts in relative dominance between reflected shortwave and longwave components, which may be important in assessing lake impacts on local and regional climate or biological production. Each of these studies presented either new approaches to the study of water resources with remote sensing, or new and useful information about the many lakes in Nebraska's Sand Hills. Both aspects should prove useful in regional monitoring or comparisons with other regions of the world. Overall, the results provided evidence that remote sensing is a useful tool for studying water quality, and a developing science with new basic questions to be addressed.
Information on the concentration of suspended sediments in coastal waters in necessary for the understanding and management of the coastal environment. Traditienally, suspended sedimdent concentration (SSC)has been measured by time-consuming and costly boat surveya which allaw the accurate measurement of SSC for single points in space and time. Remote sensing from airborne and spaceborne sensors has proved to be a useful adjunct to such surveys as it provides an instantaneous and synoptics view of sediments that would otherwise be unavaible. The key to the sucess of remotely sensing in such a role is the strong positive relationship that exists between SSC and remotely sensed spectral radiance (L(A)). This review provides an introduction to this SSC/L(A)relationship, exploring physical basis, itis robustness under a range of envirnmental conditions and its utility as a tool for estimation. It is concluded that future research on this relationship should concentrate upon the suppression of environmental influences and the optium sampling of SSC. Pages: 351-363
Discrimination of the chlorophyll signal from those of suspended sediments and the water itself has proven to be a difficult problem in optical remote sensing of algal biomass. Our study uses numerical differentiation of high resolution spectral data collected at close range over experimental tanks to address this problem. Results indicate that pure water effects can be reduced by a first-order derivative curve and suspended sediment effects can be removed by a second-order transformation. Chlorophyll content is correlated with the difference between the second derivatives at 660 and 895 nanometers. This relationship holds even in the presence of background turbidity. Thus, it is an effective means of compensating for interference from suspended solids. Our findings are discussed as they relate to the use of remote sensing for lake and reservoir management.
The reflectance properties of lakes in the Adirondack mountain region are described. Five distinct types of reflectance spectra are identified and related to differences in water quality. No relationship was found between reflectance and acidification status.
A reflectance model based on literature absorbance and scattering cross‐sections of water, plant pigments, dissolved organic carbon (DOC), and suspended inorganic matter is used to generate reflectance spectra of each reflectance type. Use of a variable Q factor during the conversion of remotely sensed reflectance to irradiance reflectance improves the correspondence between measured and modeled irradiance reflectance spectra. Comparison of measured reflectance with model reflectance also demonstrates the importance of small concentration changes as well as the interactive influence of plant pigments, DOC, and suspended material on lake color.
Correlograms depict the wavelength‐dependent relationships between reflectance and water quality. Plant pigments, DOC, and suspended material are negatively correlated with reflectance from 400 to 600 nm and positively correlated from 600 to 750 nm; parameters associated with lake acidification, pH, alkalinity, and aluminum concentrations are poorly correlated with reflectance. Adirondack lakes exhibit a high degree of covariance between pigment, DOC, and suspended matter concentrations.
Regression models predicting total pigments and DOC are developed which suggest that these parameters can be estimated remotely in these lakes, but remote estimation of pH, alkalinity, and aluminum is not possible with reflectance measures. A regression model estimating total pigments from the reflectance ratio of 525 : 554 nm is similar in slope and accuracy to one derived for the Coastal Zone Color Scanner (CZCS) for coastal marine waters. However, neither of the “recommended” CZCS pigment algorithms perform best with the Adirondack lake reflectance and pigment data.
This study was conducted to establish correlations between reflectance spectra, turbidity and chlorophyll a among 22 fresh to alkaline lakes in Nebraska, U.S.A., sampled in June and July of 1994. Peak reflectance ranged from 2-22 per cent between 500 and 600nm. Turbidity and chlorophyll a ranged from 1-82 NTU and 1-17 1 mu gl-1, respectively, with significant correlation (P Document Type: Research Article DOI: http://dx.doi.org/10.1080/014311698215360 Publication date: May 20, 1998 More about this publication? Editorial Board Information for Authors Subscribe to this Title ingentaconnect is not responsible for the content or availability of external websites $(document).ready(function() { var shortdescription = $(".originaldescription").text().replace(/\\&/g, '&').replace(/\\, '<').replace(/\\>/g, '>').replace(/\\t/g, ' ').replace(/\\n/g, ''); if (shortdescription.length > 350){ shortdescription = "" + shortdescription.substring(0,250) + "... more"; } $(".descriptionitem").prepend(shortdescription); $(".shortdescription a").click(function() { $(".shortdescription").hide(); $(".originaldescription").slideDown(); return false; }); }); Related content In this: publication By this: publisher In this Subject: Geography , Optics & Light By this author: Fraser, R. N. GA_googleFillSlot("Horizontal_banner_bottom");
The Normalized Difference Water Index (NDWI) is a new method that has been developed to delineate open water features and enhance their presence in remotely-sensed digital imagery. The NDWI makes use of reflected near-infrared radiation and visible green light to enhance the presence of such features while eliminating the presence of soil and terrestrial vegetation features. It is suggested that the NDWI may also provide researchers with turbidity estimations of water bodies using remotely-sensed digital data.
The purpose of this paper is to investigate the spectral responses of algal chlorophyll and water, under natural sunlight with varying suspended sediment concentrations (SSC). Twenty levels of SSC with each of two sediment types were generated, ranging from 50 to 1000 mgl, in 75101 of water containing chlorophyll-a concentrations of 718 μgl and 295 μgl. Results indicate that suspended sediments do not eliminate the prominent spectral patterns of algal chlorophyll, even as SSC reached 1000 mgl. Between 400 and 900 nm, the relation between reflectance and SSC satisfies the expression: dR(λ)/dS
The objective of the study is to select the best possible array size of Indian Remote Sensing Satellite (IRS-IB) linear imaging self scanning (LISS-IIA) digital data for the estimation of the suspended solids concentration on a surface water body. For this purpose a lake namely Hussain Sagar in Hyderabad (India) has been considered. The lake water samples were collected on 21 February 1992 in concurrence with the date of IRS-IB overpass. These water samples have been analysed to determine the suspended solids concentration at predetermined sample locations. Different pixel array sizes of IRS-IB LISS-IIA digital data has been analysed for the selection of the size of the pixel array for the estimation of water quality variables. This selection has been conducted by using various statistical methods such as analysis of variance, paired t-test and linear regression techniques. Analysis of variance and paired t-test are basically used for the selection of minimum pixel array size and linear regression techniques have been used for the selection of the best favourable band and pixel array for the estimation of suspended solids concentration. The relations between digital data and measured values of suspended solids concentrations have been quantified using simple linear and multiple regression. The possible combinations of bands, i.e., model 1, model 2 are developed. From possible combinations model 1 has been chosen for the estimation of suspended solids concentration based on the highest coefficient of determination (R ) lowest standard error of estimate and F-ratio (four times greater than critical F-ratio (Fcr). Based on the results of this study it is observed that the statistical approach has a strong potential for the application of remote sensing data for quantification of suspended solids concentration.
The Alkali Lakes region of the western Sandhills, Nebraska, has lakes that range in composition from freshwater to brine with TDS exceeding 250,000 mg/1. An unusual geochemical feature of these lakes is the conservative behavior of K with concentrations exceeding 1,900 mmoles/kg (86,000 mg/1). The lakes are dominantly Na-K-CO3-(SO4)-(Cl) and Na-K-SO4-CO3-(Cl) waters. Lakes occupy interdunal areas where there is little or no surface runoff. Groundwater primarily from locally derived precipitation is the principle source of water and solutes. This origin for the source water contrasts with closed-basin saline lake complexes, where surface water from the adjacent areas flows directly into the lakes or recharges the groundwater system. The principle geochemical process controlling lake chemistry is evaporative concentration. Other processes are operating, but to a lesser extent; these include mineral precipitation and dissolution and organically mediated sulfate reduction. Geochemical mass-balance modeling indicates distinct differences in the amount of water that is required to be evaporated to produce the observed lake compositions. These differences are related to the groundwater inflow to outflow ratio for individual lakes. This emphasizes that, although evaporation and related processes control the geochemical evolution, the local hydrology of individual lakes regulates the extent to which these processes will proceed.
Analysis of water-level fluctuations in about 30 observation wells and 5 lakes in the Crescent Lake National Wildlife Refuge in the sandhills of Nebraska indicates water-table configuration beneath sand dunes in this area varies considerably, depending on the configuration of the topography of the dunes. If the topography of an interlake dunal area is hummocky, ground-water recharge is focused at topographic lows causing formation of water-table mounds. These mounds prevent ground-water movement from topographically high lakes to adjacent lower lakes. If a dune ridge is sharp, the opportunity for focused recharge does not exist, resulting in water-table troughs between lakes. Lakes aligned in descending altitudes, parallel to the principal direction of regional ground-water movement, generally have seepage from higher lakes toward lower lakes.
Chlorophyll distribution in Lake Kinneret was estimated in a period of low chlorophyll-a concentrations (3–7 mg m−3) using remotely sensed data. The data set included high-spectral-resolution radiometric measurements in the range 400–750
nm, chlorophyll and suspended matter concentrations, Secchi disk transparency and vertical attenuation coefficients at 20
stations. The spectroradiometric data were used to create the algorithms suitable for quantitative determination of chlorophyll
content. The present paper presents experimental field evidence showing that fluorescence can be successfully used for remote
monitoring of chlorophyll-a content (with an estimation error <0.5 mg m−3) in productive inland waters with a background of variable and relatively high suspended matter concentration.
Detection of Optical Water Quality Parameters for Eutrophic Waters by High Resolution Remote Sensing, Doctorate Thesis, Vrije Universiteit, Amsterdam. Estep, L., 1994, E ects of bottom re¯ ected light on the computation of chlorophyll from remotely sensed data
- A G Dekker
Dekker, A. G., 1993, Detection of Optical Water Quality Parameters for Eutrophic Waters by High Resolution Remote Sensing, Doctorate Thesis, Vrije Universiteit, Amsterdam. Estep, L., 1994, E ects of bottom re¯ ected light on the computation of chlorophyll from remotely sensed data. Proceedings of the Second T hematic Conference on Remote Sensing for Marine and Coastal Environments, 31 January ± 2 February, 1994, New Orleans, Louisiana, pp. 182± 192.