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Statistical evaluation of remotely sensed data for water quality monitoring
International Journal of Remote Sensing
Abstract
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.
... Based on a literature review of relationships between Landsat TM bands with spectral characteristics that have a high probability of predicting variations in SS, turbidity, chl-a, and SDD, we selected the following 28 bands or band combinations for statistical analysis: TM1, TM2, TM3, TM4, (TM4/TM3), (TM2/ TM1), (TM1/TM3), (TM3/TM1), (TM4/TM1), (TM3/TM2), (TM4/TM2), (TM4/TM3), (TM1 and TM2), (TM1 and TM3), (TM1 and TM4), (TM2 and TM3), (TM2 and TM4), (TM3 and TM4), (TM1, TM2, and TM3), (TM1, TM2, and TM4), (TM2, TM3, and TM4), (TM1, TM2, TM3, and TM4),[(TM1/TM4) + TM2],[(TM1/TM4) + TM1],[(TM1/ TM3) + TM3], [(TM1/TM3) + TM2], [(TM1/TM3) + TM1], and[(TM4/TM1) + TM4]. In our study, only the first four TM bands were used for analysis because the long-wave bands provide little information for water quality assessment (Bilge et al. 2003; Dekker et al. 2002; Wang et al. 2006). Suspended sediment, turbidity, Secchi disk depth, and chlorophyll-a concentration were selected as water quality parameters because these parameters are optically active in terms of estimation from satellite remote sensing image data (Ekercin 2007). ...
... TM3 values had the strongest relationshipTable 5 Determination coefficients (R 2 ) and multiple regression equations for water quality parameters WQP Regression equations R 2 Adjusted R 2 Chl-a Chl À a ¼ À1:663 À 0:037 Â TM1 þ 0:958 Â TM4 0.50 0.47 Chl À a ¼ À3:645 À 0:023 Â TM3 þ 0:961 Â TM4 0.48 0.45 Chl À a ¼ À2:195 À 0:170 Â TM2 þ 0:192 Â TM3 þ 0:835 Â TM4 0.50 0.46 Chl À a ¼ 7:394 À 0:377 Â TM1 þ 0:536 Â TM 2 þ 0:732 Â TM 4 0.60 0.56 Chl À a ¼ 7:428 À 0:379 Â TM1 þ 0:544 Â TM 2 À 0:006 Â TM 3 þ 0:735 Â TM 4 0.60 0.55 Chl À a ¼ À3:857 þ 14:81 Â TM4=TM1 ð Þþ 0:697 Â TM4 0. with measured SS concentrations among all Landsat bands tested (seeTable 3). This result is consistent with several previous investigations (Dekker et al. 2002; Bilge et al. 2003; Tyler et al. 2006). In our research, there is a significant relationship between TM3 and SS (seeTable 4). ...
... Tyler et al. (2006) mapped the distribution of SS with Landsat TM band 3 in Lake Balaton. Our results confirm the findings of Bilge et al. (2003), Tassan (1997), Dekker et al. (2002), Tyler et al. (2006), and Zhou et al. (2006) in which TM3 had the strongest relationship with SS concentrations. TM3 is a significant (R 2 =0.67) predictor of SS concentrations for Lake Beysehir. ...
The main goal of this study was to investigate spatial patterns in water quality in Lake Beysehir, which is the largest freshwater reservoir in Turkey, by using Landsat-5TM (Thematic Mapper) data and ground surveys. Suspended sediment (SS), turbidity, Secchi disk depth (SDD), and chlorophyll-a (chl-a) data were collected from 40 sampling stations in August, 2006. Spatial patterns in these parameters were estimated using bivariate and multiple regression (MR) techniques based on Landsat-5TM multispectral data and water quality sampling data. Single TM bands, band ratios, and combinations of TM bands were estimated and correlated with the measured water quality parameters. The best regression models showed that the measured and estimated values of water quality parameters were in good agreement (0.60 < R
2 < 0.71). TM3 provided a significant relationship (R
2 = 0.67, p < 0.0001) with SS concentration. MR between chl-a and various combinations of TM bands showed that TM1, TM2, and TM4 are strongly correlated with measured chl-a concentrations (R
2 = 0.60, p < 0.0001). MR of turbidity showed that TM1, TM2, and TM3 explain 60% (p < 0.0001) of the variance in turbidity. MR of SDD showed a strong relationship with measured SDD, with R
2 = 0.71 (p < 0.0001) for the ratio TM1/TM3 and TM1 band combinations. The spatial distribution maps present apparent spatial variations of selected parameters for the study area covering the largest freshwater lake and drinking water reservoir in Turkey. Interpretation of thematic water quality maps indicated similar spatial distributions for SS, turbidity, and SDD. A large area in the middle portion of the lake showed very low chl-a concentrations as it is far from point and nonpoint sources of incoming nutrients. The trophic state index values were calculated from chl-a and SDD measurements. Lake Beysehir was classified as a mesotrophic or eutrophic lake according to chl-a or SDD parameters, respectively.
... To handle this problem, it is necessary to carry out water quality assessment, planning, and management, in which water quality monitoring plays an important role [2]. The current in situ techniques for measuring water quality variables are time-consuming and do not give a synoptic view of a water body or, more significantly, a synoptic view of different water bodies across the land- scape [3]. It requires excessive traveling, sampling, and expensive laboratory analysis, especially for a large area; thus it is very difficult to report and predict the water quality situation in time [4]. ...
... But these studies have not been able to address all the needs of water quality management. Most of them focused on only a few water quality variables which are usually considered optically active variables, such as chlorophyll-a (chl-a) [8], total suspended solids (TSS) [3], and turbidity [17]. In addition, previous studies were mostly carried out on seriously polluted inland water bodies. ...
... The results of our study also indicate that it is essential to select feasible combinations of bands in correlation analysis. Although algorithms were quite different in the selected bands when compared with those used in other studies [3,4,9] , correlation coefficients of relevant water quality variable models in our study were higher or at least comparable to other studies. Wang et al. has pointed out that TM 4 has no correlation with other bands and water quality variables in their study [4]. ...
This study focused on the water quality of the Guanting Reservoir, a possible auxiliary drinking water source for Beijing.
Through a remote sensing (RS) approach and using Landsat 5 Thematic Mapper (TM) data, water quality retrieval models were
established and analyzed for eight common water quality variables, including algae content, turbidity, and concentrations
of chemical oxygen demand, total nitrogen, ammonia nitrogen, nitrate nitrogen, total phosphorus, and dissolved phosphorus.
The results show that there exists a statistically significant correlation between each water quality variable and remote
sensing data in a slightly-polluted inland water body with fairly weak spectral radiation. With an appropriate method of sampling
pixel digital numbers and multiple regression algorithms, retrieval of the algae content, turbidity, and nitrate nitrogen
concentration was achieved within 10% mean relative error, concentrations of total nitrogen and dissolved phosphorus within
20%, and concentrations of ammonia nitrogen and total phosphorus within 30%. On the other hand, no effective retrieval method
for chemical oxygen demand was found. These accuracies were acceptable for the practical application of routine monitoring
and early warning on water quality safety with the support of precise traditional monitoring. The results show that performing
the most traditional routine monitoring of water quality by RS in relatively clean inland water bodies is possible and effective.
... Remote sensing techniques has been widely used to estimate and map the turbidity and concentrations of suspended particles [3]. The advantage of remote sensing is, it can provide a synoptical view of the complete water body due to it's continuous, spatial and temporal coverage of large areas coverage. ...
... where ( 3) is reflectance in Red band, ( 2) is reflectance in Green band. ...
Turbidity is an optical determination of water clarity. It is one of the most important optically active water parameter to assess the water quality through the remote sensing observations. Turbidity measurements come from suspension of sediment such as silt or clay, inorganic materials, or organic matter such as algae, plankton and decaying material. Turbidity and total suspended matter often overlap each other. However, it is not a direct measurement of the total suspended materials in water. Instead, as a measure of relative clarity, turbidity is often used to indicate changes in the total suspended solids concentration in water without providing an exact measurement of solids. Through remote sensing we can monitor the turbidity in large water bodies, rives, coastal areas etc. An algorithm has been developed to estimate the turbidity (in NTU: Nephelometric Turbidity Unit) over inland waters (Ukai reservoir) using empirical relationship between normalized Green and Red bands (NDTI : Normalized Difference Turbidity Index) of Resourcesat-2 and Resourcsat-2A Linear Imaging Self Scanning-III (RS2 and R2A LISS-III) dataset. Derived algorithm shows a strong coefficient of determination (R2 = 0.97) with the in-situ turbidity measurements. The field measurements were carried out over Ukai reservoir on 27-28th March 2018, where synchronous in situ water leaving reflectance and turbidity were measured. Model was derived between in situ measured turbidity and NDTI derived from spectral reflectance of band 2 (Green) and band 3 (Red) of RS2 and R2A LISS-III. The model was applied to derive the turbidity maps of Ukai reservoir for pre-monsoon (March, April and May months) season during the period 2012 to 2018. Overall turbidity ranges from 1.47-20 NTU during the field data collection of pre-monsoon season and overall scene derived turbidity ranges are between 2-33 NTU. The highest observed turbidity value was more than fourteen times International Journal of Scientific Research in Science and Technology (www.ijsrst.com) | Volume 9 | Issue 3 V Pompapathi et al Int J Sci Res Sci & Technol. May-June-2022, 9 (3) : 377-386 378 greater than the lowest value that shows the natural variability within the reservoir for the same season. Remotely sensed data sets can increase the abilities of water resources researchers and decision making persons to monitor waterbodies more effectively and frequently.
... In contrast, remote-sensing techniques have the advantage of reconnaissance and synoptic view of water body for its qualitative and quantitative assessment at low cost (Sheela et al. 2011;Somvanshi et al. 2012). Several investigators have studied the applicability of earth observation techniques with sufficient high-resolution satellite data in determining and monitoring water quality (Bilge et al. 2003;Chen et al. 2004;Stefouli and Charou 2012). Landsat 5 TM data have been used to study river water (Lavery et al. 1993) and LISS III data have been used to develop model for water-quality parameters (Somvanshi et al. 2012). ...
... However, only four ETM + bands (Blue, Green, Red, and NIR) were used for analysis, because these bands contain important information for water-quality monitoring (Wang et al. 2006). The long-wave bands (5, 6, 7, and 8) provide limited information for water-quality assessment (Bilge et al. 2003;Nas et al. 2010) and hence not considered in the analysis. ...
Abstract
The present study aims to provide a better understanding for appraisal and monitoring of surface water quality of the river
Ganga at Allahabad (India) using open-access earth observation data set. The Landsat 7 (Enhanced Thematic Mapper plus,
ETM+) data has been used for this study. The band rationing technique has been employed for this study. Water samples were
collected according to the satellite passing. The ratio of the radiances at the sampling sites was obtained and validated with
in situ measurements of water-quality parameters. The water-quality parameters were assessed viz. turbidity, pH, chemical
oxygen demand (COD), biological oxygen demand (BOD), dissolved oxygen (DO), temperature, alkalinity, and total hardness.
Multiple linear regression models were developed based on satellite bands. The result shows that water-quality parameters
were significantly correlated with the radiance values of the ETM + image except turbidity. Multiple linear regression equations
models were applied on ETM + bands for estimation of water-quality parameters and preparation of water-quality maps
for different water-quality parameters of the study area. Moreover, the current study suggests that the Landsat 7 ETM + image
can be effectively used for the assessment of water-quality parameters of a river system.
Keywords Ganga river · Water-quality parameters · Open access · Earth observation · Regression models
... In contrast, remote-sensing techniques have the advantage of reconnaissance and synoptic view of water body for its qualitative and quantitative assessment at low cost (Sheela et al. 2011;Somvanshi et al. 2012). Several investigators have studied the applicability of earth observation techniques with sufficient high-resolution satellite data in determining and monitoring water quality (Bilge et al. 2003;Chen et al. 2004;Stefouli and Charou 2012). Landsat 5 TM data have been used to study river water (Lavery et al. 1993) and LISS III data have been used to develop model for water-quality parameters (Somvanshi et al. 2012). ...
... However, only four ETM + bands (Blue, Green, Red, and NIR) were used for analysis, because these bands contain important information for water-quality monitoring (Wang et al. 2006). The long-wave bands (5, 6, 7, and 8) provide limited information for water-quality assessment (Bilge et al. 2003;Nas et al. 2010) and hence not considered in the analysis. ...
The present study aims to provide a better understanding for appraisal and monitoring of surface water quality of the river Ganga at Allahabad (India) using open-access earth observation data set. The Landsat 7 (Enhanced Thematic Mapper plus, ETM+) data has been used for this study. The band rationing technique has been employed for this study. Water samples were collected according to the satellite passing. The ratio of the radiances at the sampling sites was obtained and validated with in situ measurements of water-quality parameters. The water-quality parameters were assessed viz. turbidity, pH, chemical oxygen demand (COD), biological oxygen demand (BOD), dissolved oxygen (DO), temperature, alkalinity, and total hardness. Multiple linear regression models were developed based on satellite bands. The result shows that water-quality parameters were significantly correlated with the radiance values of the ETM + image except turbidity. Multiple linear regression equations models were applied on ETM + bands for estimation of water-quality parameters and preparation of water-quality maps for different water-quality parameters of the study area. Moreover, the current study suggests that the Landsat 7 ETM + image can be effectively used for the assessment of water-quality parameters of a river system.
... In contrast, reflectance in visible and near infrared bands usually presents high variability (e.g. Jerlov, 1968; Bilge et al., 2003; Han and Rundquist, 2003; Beget and Di Bella, 2007). This variability depends on the reflectance of the submerged soil, water depth and the amount of the suspended particles and their optical properties (e.g. ...
... Baret, 1990 ). The abundance of optically active components, such as phytoplankton, suspended minerals and dissolved organic carbon affects water turbidity directly (Bilge et al., 2003). The water that is most turbid of all has higher reflectance levels in the longest wavelengths of the visible spectrum. ...
In this manuscript we present a radiative transfer model for submerged vegetation called SAILHFlood. It
simulates reflectance for a partial submerged canopy from vegetation variables, water level, measurement
geometry and soil reflectance. It is a version of the proven SAILH model in which, two vegetation
layers are included instead of one: the emerged vegetation layer and the submerged vegetation layer,
for which the water attenuation is considered. The model validation was performed with a experiment
in laboratory conditions varying leaf area index, water level and illumination and observation angles.
A least square linear fit of simulated data used to reproduce measured data shows a satisfactory root
mean square error (RMSE) of 0.0355, and a spectral angle of 0.2591 radians. The model could be applied
to the diversity of vegetation found in flooded situations, both to understand spectral behavior of these
environments under different scenarios and to estimate vegetation variables from model inversion.
... A relatively large number of papers that utilize remote sensing data to detect mucilage were published after the outbreak. This was to be expected as remote sensing provides an important alternative to address this problem with respect to field studies and chemical and/or biological analyses, which require expert knowledge, are costly, and may have limited generalizability due to under-sampling of a large region (Bilge et al. 2003). Additionally, although the mucilage outbreak in 2021 lasted a considerable time period and affected a very large region, field studies (and in situ measurements) normally have to be conducted in the small-time window that the environmental issue, e.g. ...
... Moreover, it can reveal the pollution source, diffusion direction, and influence range of pollutants. Therefore, researchers have done a lot of work in surface water quality analysis based on remote sensing [4,5]. These studies mainly focus on oil pollution, suspended solids (e.g., sediment and microorganisms), and water eutrophication [6][7][8]. ...
To estimate the key water quality parameters on a large scale, based on Pearson’s correlation analysis and band ratio, this study first obtains multiple sensitive band combinations (R ≥ 0.30, p < 0.01) for three key water quality parameters: dissolved oxygen (DO), total nitrogen (TN), and total phosphorus (TP). Then, principal component analysis is used to reduce the dimensions and analyze multiple optimal combinations, and the first three principal components (PCs) of the optimal combinations are selected to analyze the water quality parameters. Finally, the water quality parameter models of DO, TN, and TP are proposed and compared based on spectral analysis and field measured water quality data respectively using Gaussian process regression and PCs for each parameter. Through model verification and by comparing the performance of the three models, it is found that the TP model performed well (R = 0.9824, p < 0.01), and TP grade accuracy rate is up to 94.97%. Through the error analysis of TN and DO, it is found that 93.0% of error samples occurs when TP < 0.1 mg/L in the water quality. These results would provide a scientific basis for water quality monitoring and water environment management in the study area and could also be used as a reference for water quality monitoring in other basins.
... Alternatively, Bilge et al. (2013) determined the relationships between water quality parameters and digital data from the Landsat satellite to estimate and map the WQPs in the Porusk Dam reservoir. The suspended solids (SS), chlorophyll-a, NO3-N, and transmitted light intensity depth (TLID) were the parameters for brightness values (BV) of the TM data, and thus the WQPs were determined. ...
... To retrieve water quality parameters from a multispectral satellite image, four different methods were applied (Campbell et al. 2011;Gholizadeh et al. 2016): (i) the look up table approach that compares the measured spectra response of optical water constituents with stored spectra; (ii) the neural network method that compares a large number of training data spectra to the measured spectra (Pozdnyakov et al. 2005a;El Din et al. 2017); (iii) the emPiricalregression that relates a linear combination of image bands with in-situ measurements (Bilge et al. 2003;Ficek et al. 2011;Lessels and Bishop 2013;Doña et al. 2014;Bonansea et al. 2015); (iv) the inversion/optimization method to simulate the spectra from a set of parameters that minimizes a cost function (Campbell et al. 2011). All four methods require a multispectral satellite image with adequate spatial and temporal resolution and in-situ measurements gathered close in time to the date of image acquisition. ...
Geofísica internacional (2020) 59-1: 13-25 13 oriGinal paper Resumen Se desarrolló un modelo para cuantificar la calidad de los cuerpos de agua abiertos sobre la base de la lógica probabilística multivariada. El modelo se basa en parámetros de calidad del agua que ya habían sido reportados en la literatura, tales como: turbidez, clorofila-a, índice de vegetación y usos de la temperatura superficial derivadas de la distribución de valores de píxeles de los parámetros. Dichas funciones se combinaron mediante la lógica probabilística multivariada que produjo un mapa de niveles de calidad del agua. Posteriormente, el modelo se aplicó a los humedales Centla, ubicados en el sureste de México. En ellos pueden observarse numerosos cuerpos de agua en niveles de eutrofización variables. Para probar el modelo propuesto, se desarrollaron ejemplos usando una imagen Terra/Aster. Además, se propuso una escala cualitativa de grados de calidad del agua. Palabras clave: calidad del agua, lógica probabilística, parámetros del agua, humedales Centla Abstract
A model to quantify the quality of open water bodies was developed on the grounds of multivariate probabilistic logic. The model is based on water quality parameters reported in the scientific literature such as: turbidity, chlorophyll-a, vegetation index and superficial temperature and uses probabilistic functions derived from the distribution of Pixel values of such parameters. Such functions were combined by means of the multivariate probabilistic logic that produced a map of water quality levels. The model was then applied to the Centla Wetlands in South East Mexico. In these wetlands, numerous water bodies can be observed in varying levels of eutrophication. To test the proposed model, examples were developed using a Terra/Aster image. A qualitative scale of water quality degrees was proposed.
... Validation results proved acceptable predictive capacity of the developed algorithm model, with R2 of 0.66 (Fig. 9). Descriptive statistics for observed versus modeled yearly average DO levels within Lake Edku are presented in table (4). The developed model shows highly agreeable predictions with field measurements. ...
Progressive anthropogenic intrusion and increasing water demand necessitate frequent water quality monitoring for sustainability management. Unlike laborious, time consuming field-based measurements, remote sensing-based water quality retrieval proved promising to overcome difficulties with temporal and spatial coverage. However, remotely estimated water quality parameters are mostly related to visibility characteristic and optically active property of water. This study presents results of an investigated approach to derive oxygen –related water quality parameter, namely Dissolved Oxygen (DO), in a shallow inland water body from satellite imagery. The approach deduces DO levels based on interrelated optical properties that dictate oxygen consumption and release in waters. Comparative analysis of multiple regression algorithms was carried out, using various combinations of parameters; namely, Turbidity, Total Suspended Solids (TSS), Chlorophyll-a, and Temperature. To cover the wide range of conditions that is experienced by Edku coastal lake, ground truth measurements covering the four seasons were used with corresponding satellite imageries. While results show successful statistically significant correlation in certain combinations considered, yet optimal results were concluded with Turbidity and natural logarithm of temperature. The algorithm model was developed with summer and fall data (R2 0.79), then validated with winter and spring data (R2 0.67). Retrieved DO concentrations highlighted the variability in pollution degree and zonation nature within that coastal lake, as related to boundary interactions and irregularity in flow dynamics within. The approach presented in this study encourages expanded applications with space-based earth observation products for exploring non-detectable water quality parameters that are interlinked with optically active properties in water.
... Chlorophyll-a = Band2 ref/ Band4 ref (5) Thermal spectral data have been converted to Temperature "T", using the conversion formula presented in Equation (6) ...
... In this study, only the VNIR ETM+ bands were used for analysis and detection of the optically active water quality parameters. This is because the long-wave bands provide little or no information for water quality assessment [55][56][57]. The VNIR are in the spectral range where light passing through the water body provides some information about the optically active water constituents [58]. ...
This study presents a comparative evaluation of three real-time imaging-based approaches for the prediction of optically active water constituents as chlorophyll- a (Chl- a ), turbidity, suspended particulate matter (SPM), and reservoir water colour. The imaging models comprise of Landsat ETM+-visible and NIR (VNIR) data and EyeOnWater and HydroColor Smartphone sensor apps. To estimate the selected water quality parameters (WQP) from Landsat ETM+-VNIR, predictive models based on empirical relationships were developed. From the in situ measurements and the Landsat regression models, the results from the remote reflectances of ETM+ green, blue, and NIR independently yielded the best fits for the respective predictions of Chl- a , turbidity, and SPM. The concentration of Chl- a was derived from the Landsat ETM+ and HydroColor with respective Pearson correlation coefficients r of 0.8977 and 0.8310. The degree of turbidity was determined from Landsat, EyeOnWater, and HydroColor with respective r values of 0.9628, 0.819, and 0.8405. From the same models, the retrieved SPM was regressed with the laboratory measurements with r value results of 0.6808, 0.7315, and 0.8637, respectively, from Landsat ETM+, EyeOnWater, and HydroColor. The empirical study results showed that the imaging models can be effectively applied in the estimation of the physical WQP.
... Kutser et al, 2005;Sudheer et al., 2006;Nouri et al., 2008). Other research comparing the measurement values in situ (field) with the values of reflectance spectral bands of various satellite data (Pulliainen et al., 2001;Bilge et al, 2003). ...
Water is a key component to the process of earth’s life. However, with increasing industrial development and anthropogenic activities, water quality has been decreased dramatically. Therefore, monitoring is necessary to anticipate the threat of contamination and to take effective action at all levels in local or central government. Methods or algorithms were proposed for detecting or mapping or extraction the concentrations of dissolved oxygen (DO) derived from Landsat remote sensing imagery using empirical formulation. The aim of this study to monitor the quality of coastal waters over large areas. The method begins with the calculation of water surface temperature derived from Landsat data, using the correlation function obtained by correlating the temperature measurement by the infrared band reflectance values. Then the image is used to calculate the concentration of DO using the correlation function. the correlation function is obtained by correlating the results of field measurements of DO with temperature. The study conducted in the Ringgung coastal waters located in Padang Cermin District, Pesawaran municipal conducted on August 7 to 11, 2012. Based on the analysis, dissolved oxygen concentration of Ringgung coastal waters is inversely proportional to the amount of fresh water entering the coastal waters and directly proportional to the aeration process. As a result, in June the concentration of dissolved oxygen near the beach (on shore water) greater than in the offshore water. While in August, the concentration of dissolved oxygen near the coast (on shore water) is lower than in the offshore water.
... Reflectance in the visible and IR regions depends on the reflectance of the submerged soil, the water depth, the amount of suspended particles, and their optical properties [50,51]. The abundance of optically active components, such as phytoplankton, suspended minerals, and dissolved organic carbon directly affect water turbidity and colour [52]. The more turbid water bodies have higher reflectance values in the green and red visible bands [22]. ...
This paper presents a semi-automatic procedure to discriminate seasonally flooded areas in the shallow temporary marshes of Doñana National Park (SW Spain) by using a radiommetrically normalized long time series of Landsat MSS, TM, and ETM+ images (1974-2014). Extensive field campaigns for ground truth data retrieval were carried out simultaneous to Landsat overpasses. Ground truth was used as training and testing areas to check the performance of the method. Simple thresholds on TM and ETM band 5 (1.55-1.75 μm) worked significantly better than other empirical modeling techniques and supervised classification methods to delineate flooded areas at Doñana marshes. A classification tree was applied to band 5 reflectance values to classify flooded versus non-flooded pixels for every scene. Inter-scene cross-validation identified the most accurate threshold on band 5 reflectance (ρ{variant} < 0.186) to classify flooded areas (Kappa = 0.65). A joint TM-MSS acquisition was used to find the MSS band 4 (0.8 a 1.1 μm) threshold. The TM flooded area was identical to the results from MSS 4 band threshold ρ{variant} < 0.10 despite spectral and spatial resolution differences. Band slicing was retrospectively applied to the complete time series of MSS and TM images. About 391 flood masks were used to reconstruct historical spatial and temporal patterns of Doñana marshes flooding, including hydroperiod. Hydroperiod historical trends were used as a baseline to understand Doñana's flooding regime, test hydrodynamic models, and give an assessment of relevant management and restoration decisions. The historical trends in the hydroperiod of Doñana marshes show two opposite spatial patterns. While the north-western part of the marsh is increasing its hydroperiod, the southwestern part shows a steady decline. Anomalies in each flooding cycle allowed us to assess recent management decisions and monitor their hydrological effects.
... In these studies several sensors have been used. One of the most common satellite used to retrieve data is Landsat satellites Keinel, 1998;Zhang, 2002;Bilge et al., 2003;Liu et al., 2003;Sudheer et al., 2006;Yang and Du, 2008). These satellites have a spatial resolution of 60 m (Keinel et al., 1998). ...
... The various in situ techniques currently used for measuring and monitoring DIN concentrations in water are time consuming and do not provide a synoptic view of the water body within the landscape (Bilge et al. 2003;He et al. 2008). Fortunately, remote sensing can provide a tool for DIN monitoring because it has been used successfully to monitor other water quality variables, such as temperature (e.g. ...
Remote sensing has been widely used for water quality monitoring, but most monitoring studies have only focused on a few water quality variables, such as chlorophyll-a, turbidity, and total suspended solids, which have typically been considered optically active variables. Remote sensing presents a challenge in estimating dissolved inorganic nitrogen (DIN) concentration in water. DIN in inland waters and estuaries had been estimated from remotely sensed observations. However, remote-sensing estimation of DIN in seawater over a large area had not yet been performed. Moreover, the bands used to estimate DIN in water were limited to 4 or 7 anterior bands of Moderate Resolution Imaging Spectroradiometer (MODIS) data at high spatial resolution rather than high spectral resolution. In this study, we attempted to establish a model to estimate DIN concentration in the Bohai Sea using band combinations derived from all the visible/near-infrared (Vis-NIR) bands of MODIS data. The results showed that regional multiple stepwise regression analysis (MLSR) yields a highly significant positive relationship between DIN concentration and certain remotely sensed combination variables. The modelling yielded higher accuracy for DIN concentration estimation in the Bohai Sea compared with previous studies. DIN concentration values showed a clear spatial variability, being high in coastal waters and relatively low further out. These results strongly suggest that the modelling demonstrates advantages for estimating DIN concentration in the Bohai Sea and has major potential for universal application in DIN concentration estimation in other waters.
... Univariate, bivariate and multivariate regression methods were used to explore the relationships between water quality parameters and spectral reflectance. Although algorithms were quite different in the selected bands when compared with those used in other studies (Lavery & Pattiaratchi 1993;Wang & Ma 2001;Bilge et al. 2003;Somvanshi et al. 2012;Mohammed et al. 2013). It was found that the parameters used in this study produced good correlation for linear and exponential fit with univariate regression model. ...
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.
... 이러한 결과로 동·식물 및 그 수역의 생태계를 파괴하며 사람의 건강에도 치명적인 영향을 미쳐 경제, 사회, 환경적인 측면에서 많은 문제를 야기 시키게 된다 (Matthews et al., 2010 (Giardino et al., 2001;Sudheer et al., 2006). 이러한 한계를 보완하거나 혹은 효과적인 수질 인자들의 농도 측정을 위해 선행연구에서는 인공위성 (satellite)센서나 레이더를 이용하여 수질 측정과 모니터 링에 관한 연구를 수행되었다 (Giardino et al., 2001;Bilge et al., 2003;Bolgrien et al., 1995). 지구상의 모든 물질들 은 각각의 구성입자에 따라 태양에너지를 흡수하거나 반 사 혹은 산란과 같은 고유의 특성을 가지고 있다. ...
... The linear least squares fitting technique is the simplest and most commonly applied form of linear regression and provides a solution to the problem of finding the best fitting straight line through a set of points (Bilge, 2003). A mathematical procedure was utilized for finding the best-fitting curve to a given set of points by minimizing the sum of the squares of the offsets (the ISRS Proceeding Papers of Sort Interactive Session ISPRS TC VIII International Symposium on "Operational Remote Sensing Applications: Opportunities, Progress and Challenges", Hyderabad, India, December 9 -12, 2014 residuals) of the points from the curve. ...
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.
... However, the related literature has a relatively limited number of studies about remote sensing-based monitoring of optically inactive lake water quality variables such as total phosphorus (PO 4 -P), Dissolved oxygen (DO), Biological oxygen demand (BOD 5 ), Total organic carbon (TOC), nitrite (NO 2 -N), and nitrate (NO 3 -N) (Lavery et al., 1993;Dewidar and Khedr, 2001;Wang et al., 2004;Sass et al., 2007;Nouri et al., 2009). In the related literature, in situ measurement values of lake water quality variables were compared to reflectance values of spectral bands of various satellite data without the inclusion of spatio-temporal components in regression models (Pulliainen et al., 2001;Bilge et al., 2003;Sawaya et al., 2003;Giardino et al., 2007;Gitelson et al., 2008). The objectives of this study were: 1) To monitor dynamics of multiple water quality variables (DO, Chl-a, S depth , T w , and turbidity) for Lakes Abant and Yenicaga along oligotrophic-to-eutrophic gradient, respectively, using Landsat ETM+ time series data in 2009, and 2) To explore effects of trophic gradient on detection by landsat-based Multiple linear regression (MLR) models of dynamics of water quality variables. ...
Effect of differential trophic states on remote sensing-based monitoring and quantification of surface water quality is an important but understudied context. Landsat ETM+ data-based multiple linear regression models were conducted to quantify dynamics of lake surface water quality along oligotrophic-to-eutrophic gradient and to explore the influence of trophic state on the detection of water quality dynamics by the best multiple linear regression models. The best multiple linear regression models of dissolved oxygen, chlorophyll-a, Secchi depth, water temperature, and turbidity had R 2 adj
... The current in situ techniques for measuring and monitoring the phosphorus concentrations in lakes and reservoirs are timeconsuming and do not provide a synoptic view of a water body across the landscape (Wu et al., 2010;Bilge et al., 2003;He et al., 2008). Fortunately, remote sensing may provide a tool for phosphorus monitoring because it has been used successfully to monitor other water quality variables, such as temperature (e.g., Alcântara et al., 2010), chlorophyll-a (e.g., Yacobi et al., 2011;Le et al., 2009;Bresciani et al., 2011), turbidity (e.g., Nellis et al., 1998), and total suspended solids (e.g., Bistani, 2009), with a higher precision for lakes and reservoirs (Lillesand et al., 1983;Lathrop and Lillesand, 1989;Wu et al., 2010) around the world (Bistani, 2009). ...
Remote sensing has been widely used for ater quality monitoring, but most of these monitoring studies have only focused on a few water quality variables, such as chlorophyll-a, turbidity, and total suspended solids, which have typically been considered optically active variables. Remote sensing presents a challenge in estimating the phosphorus concentration in water. The total phosphorus (TP) in lakes has been estimated from remotely sensed observations, primarily using the simple individual band ratio or their natural logarithm and the statistical regression method based on the field TP data and the spectral reflectance. In this study, we investigated the possibility of establishing a spatial modeling scheme to estimate the TP concentration of a large lake from multi-spectral satellite imagery using band combinations and regional multivariate statistical modeling techniques, and we tested the applicability of the spatial modeling scheme. The results showed that HJ-1A CCD multi-spectral satellite imagery can be used to estimate the TP concentration in a lake. The correlation and regression analysis showed a highly significant positive relationship between the TP concentration and certain remotely sensed combination variables. The proposed modeling scheme had a higher accuracy for the TP concentration estimation in the large lake compared with the traditional individual band ratio method and the whole-lake scale regression-modeling scheme. The TP concentration values showed a clear spatial variability and were high in western Lake Chaohu and relatively low in eastern Lake Chaohu. The northernmost portion, the northeastern coastal zone and the southeastern portion of western Lake Chaohu had the highest TP concentrations, and the other regions had the lowest TP concentration values, except for the coastal zone of eastern Lake Chaohu. These results strongly suggested that the proposed modeling scheme, i.e., the band combinations and the regional multivariate statistical modeling techniques, demonstrated advantages for estimating the TP concentration in a large lake and had a strong potential for universal application for the TP concentration estimation in large lake waters worldwide.
... A solution could be to optimise our efforts and more frequently base our surveillance on remote sensing techniques to improve the information content and limit the costs (Östlund et al. 2001). Today, there are many satellites which have high enough resolution for use in water quality monitoring studies (Bilge at al. 2003). Many research projects have examined for estimating water quality parameters in inland, estuarine and near-shore ocean waters using various satellite imagery (Baban 1993;Nellis et al. 1998;Thiemann and Kaufmann 2000;Wang and Ma 2001;Koponen et al. 2002;Östlund et al. 2001;Hellweger et al. 2004;Hedger et al. 2001;Lillesand et al. 1983;Kloiber et al. 2002a and2002b). ...
The Beysehir lake is the most important drinking and irrigation water source for the Central Anatolia. The lake has an area of approximately 656 km2 with an average depth of 5 meters. The purposes of this investigation were to (1) provide an overview of present water quality in the Beysehir Lake, Turkey and (2) to determine spatial distribution of water quality parameters in the lake surface area using GIS, Geostatistics and Remote Sensing techniques. The water samples were collected from 40 stations. Physical, chemical parameters (pH, Dissolved Oxygen, Secchi disk depth (SDD), Turbidity, Conductivity, TSS, Alkalinity, COD, BOD, TN, TP, NO3, NH4) and chlorophyll-a (chl-a) values were determined in the Beysehir Lake in August 19, 2005. According to water quality values (TP, SDD, chl-a) the trophic level of the lake was determined. Based on chl-a concentrations, the lake is classified as mesotrophic and based on TP and SDD, the lake seem to be a eutrophic lake. In order to analyze the data determining water quality, a GIS software package ArcGIS 9.0 and ArcGIS Geostatistical Analyst extension were used. An interpolation technique called "ordinary kriging" was used to produce the spatial distribution of water quality parameters over the lake. Spatial distribution maps of TN, TP, Turbidity, Secchi disk depth and chlorophyll-a were produced for the lake surface area. Terra ASTER satellite image is used as remote sensing data source for water quality mapping in addition to simultaneously performed in-situ measurements. Ground data is collected simultaneously with the ASTER overpass on June 09, 2005 over the Beysehir Lake. The results indicate that simultaneous ground and satellite remote sensing data are highly correlated (R2>0.86). Image processing procedure and the evaluation of results were carried out using Erdas Imagine© software package.
... Space and time (spatiotemporal) SSC variation can be driven by both natural and human factors within a lake, including aquatic vegetation cover, water level, wind speed, rainfall, and shipping (Bailey and Hamilton 1997, Wu et al. 2007. Variation may also be affected by soil erosion within a lake's contributing watersheds through surface water inputs (Bilge et al. 2003). Lake managers can use spatiotemporal measurements of SSC to understand driving factors and determine artificially controllable ones (such as water level or shipping) to improve the health of a lake. ...
This study applied Moderate-Resolution Imaging Spectroradiometer (MODIS) images from 2000 to 2010 to obtain and analyze the spatiotemporal variation of suspended sediment concentration (SSC) and discussed factors affecting it in Poyang Lake, China. Results showed that (1) the mean SSC was lower in the south, higher in the north, and moderate in the central lake region; (2) the mean SSC in the south was lower than or close to 20 mg/L, with no clear annual trend; (3) the mean SSC in the north was slightly higher than 20 mg/L in 2000 and increased from 2001, with the highest value >60 mg/L in 2006; (4) the mean SSC in the central lake region, except for 2009, ranged from 20 to 40 mg/L and had an annual pattern similar to that in the southern lake region; (5) for the entire lake, the mean SSC declined from January to March, increased from September to December, and fluctuated from April to August; and (6) several higher SSC values were found in the central or southern lake regions. The spatiotemporal variation of SSC was controlled by natural and human factors, in which dredging was dominant. Limiting the area of dredging and reducing dredging intensity would decrease SSC and maintain sustainable development of Poyang Lake. Remote sensing can obtain the spatiotemporal information of some water quality parameters, which will help managers understand the lake dynamics and mechanisms to make better decisions for lake management.
... Remote sensing studies on coastal waters have generally used regression analysis of satellite data and simultaneous measurements of ground observation data. Several investigations have uncovered that reliable empirical relationships can be developed between satellite and ground observations of suspended sediments (Abdullah et al. 2002;Bilge et al. 2003;Lim et al. 2011;Hariyanto et al. 2011). Hakvoort et al. (2002 have demonstrated the use of regression models that do not require complementary ground data. ...
Remote sensing has been extensively used for water delineation and has played an important role in water quality evaluation and environmental management strategies. Suspended sediments are important determinants of water quality in coastal zones. Remote sensing enables the effective monitoring of total suspended sediments (TSS) and the detection of areas with critical water quality issues. This study aims to develop and implement regression models for estimating and mapping TSS concentrations from Advanced Land Observation Satellite (ALOS) images over the coastal waters of Langkawi Island, Malaysia. The algorithm was developed based on the water reflectance model, which is a function of the inherent optical properties of water. Such properties can then be linked to TSS concentration. In this study, an ALOS Advanced Visible and Near Infrared Radiometer type 2 device was used as the imaging sensor system. Concurrent complementary in-situ water samples were collected within the area coverage of the sensor, and digital numbers (DN) for each band corresponding to the sea-truth locations were determined. The extracted DN values were converted into reflectance values and then regressed with their respective sea-truth data. An algorithm was proposed to obtain the regression coefficient. This algorithm can estimate TSS concentrations with a high correlation coefficient (R2 = 0.96) and low root-mean-square error (RMSE = 1.98 mg/l). Finally, a map of the TSS concentration was generated by using the proposed algorithm. This study found that TSS mapping can be conducted by using ALOS data over the coastal waters of Langkawi Island, Malaysia.
... The task of analysing coastal (case II) waters is much more complicated. Bilge et al. (2003) performed a linear regression analysis between Landsat TM bands 1–4 and suspended sediments, chlorophyll a, and transmitted light intensity depth, which resulted in regression sum of squares ranging between 0.8 and 0.95. However, this study was performed only once, and did not test the predictive capabilities of the models. ...
Decline of seagrasses has been documented in many parts of the world. Reduction in water clarity, through increased turbidity
and increased nutrient concentrations, is considered to be the primary cause of seagrass loss. Recent studies have indicated
the need for new methods that will enable early detection of decline in seagrass extent and productivity, over large areas.
In this review of current literature on coastal remote sensing, we examine the ability of remote sensing to serve as an information
provider for seagrass monitoring. Remote sensing offers the potential to map the extent of seagrass cover and monitor changes
in these with high accuracy for shallow waters. The accuracy of mapping seagrasses in deeper waters is unclear. Recent advances
in sensor technology and radiometric transfer modelling have resulted in the ability to map suspended sediment, sea surface
temperature and below-surface irradiance. It is therefore potentially possible to monitor the factors that cause the decline
in seagrass status. When the latest products in remote sensing are linked to seagrass production models, it may serve as an
early-warning system for seagrass decline and ultimately allow a better management of these susceptible ecosystems.
... The linear least squares fitting technique is the simplest and most commonly applied form of linear regression and provides a solution to the problem of finding the best fitting straight line through a set of points. Regression equations were developed for two different datasets (Bilge et al. 2003). A mathematical procedure was utilized for finding the best-fitting curve to a given set of points by minimizing the sum of the squares of the offsets (the residuals) of the points from the curve. ...
Geospatial approaches to monitoring and mapping water quality over a wide range
of temporal and spatial scales have the potential to save field and laboratory efforts.
The present study depicts the estimation of water quality parameters, namely turbidity
and phosphate, through regression analysis using the reflectance derived from
remote sensing data on the west coast of Mumbai, India. The predetermined coastal
water samples were collected using the global positioning system (GPS) and were
measured concurrently with satellite imagery acquisition. To study the influence of
wastewater, the linear correlations were established between water quality parameters
and reflectance of visible bands for either set of imagery for the study area,
which was divided into three zones: creek water, shore-line water and coastal water.
Turbidity and phosphate have the correlation coefficients in the range 0.75–0.94 and
0.78–0.98, respectively, for the study area. Negative correlation was observed for
creek water owing to high organic content caused by the discharges of domestic
wastewater from treatment facilities and non-point sources. Based on the least
square method, equations are formulated to estimate turbidity and phosphate, to map the spatial variation on the GIS platform from simulated points. The applicability
of satellite imagery for water quality pattern on the coast is verified for efficient
planning and management.
... La abundancia de componentes ópticamente activos, como el fitoplancton, los minerales suspendidos y el carbono orgánico disuelto afectanFuente: Mari et al. 2007). directamente la turbidez del agua (Bilge et al. 2003). De esta manera, los cuerpos de agua más turbia presentan mayor reflectancia en las bandas del visible. ...
Los incendios y las inundaciones son dos de los disturbios que más frecuentemente afectan a la población humana y a los recursos naturales. La teledetección, a través de sensores remotos activos y pasivos, constituye una herramienta muy útil para el desarrollo de sistemas de prevención, seguimiento y evaluación a diferentes escalas espaciales y temporales. En este trabajo se reseñan algunos de los principales avances logrados en el campo de la teledetección de áreas quemadas e inundadas, y en el análisis de sus condiciones predisponentes y de su dinámica posterior a la perturbación. Se ha dado especial énfasis en describir los alcances y las limitaciones de algunos productos derivados de la teledetección que ya están disponibles para los usuarios en general. Fires and floods are among the most frequent perturbations that negatively affect human societies and natural resources. The availability of prevention, monitoring and evaluation systems is therefore crucial to diminish their consequences. Active and passive remote sensing instruments are a valuable tool to achieve these goals because they provide information on different spatial and temporal scales. In this article we review the progress experienced in the field of remote sensing of burnt or flooded areas, its predisposing conditions and its post perturbation dynamics. Special emphasis is given to the description of the strengths and weaknesses of some of currently available remote sensing products. Inter-American Institute for Global Change Research (CRN-2031 - US NSF GEO-0452325), el INTA (AERN4 y AERN4642) y el MINCyT (PICT 08-13931 y PICT No 32415).
... On the other hand, at the river basin scale, highresolution remote sensing imagery analyses may be feasible, both from a financial and workload perspective , owing to the smaller region of analysis. Remote sensing of freshwater systems is becoming increasingly sophisticated, with applications such as assessments of water quality (Glasgow et al. 2004; Bilge et al. 2003; BirdLife International 2004; Sawaya et al. 2003), aquatic and floodplain vegetation mapping (Costa 2004; Vis et al. 2003; Williams et al. 2003), invasive species mapping (Verma et al. 2003), wetland flooding assessments (Hess et al. 2003; Kasischke et al. 2003), land cover mapping (Ballester et al. 2003), wetland restoration success (Shuman & Ambrose 2003), instream habitats (Whited et al. 2002) and lake change area (Yu & Jiang 2003). Some problems with low precision that permeate global and continental analyses are easier to resolve for individual river basins, where specific data manipulations and manual corrections also become feasible. ...
Human activities have severely affected the condition of freshwater ecosystems worldwide. Physical alteration, habitat loss, water withdrawal, pollution, overexploitation and the introduction of non-native species all contribute to the decline in freshwater species. Today, freshwater species are, in general, at higher risk of extinction than those in forests, grasslands and coastal ecosystems. For North America alone, the projected extinction rate for freshwater fauna is five times greater than that for terrestrial fauna—a rate comparable to the species loss in tropical rainforest. Because many of these extinctions go unseen, the level of assessment and knowledge of the status and trends of freshwater species are still very poor, with species going extinct before they are even taxonomically classified.
Increasing human population growth and achieving the sustainable development targets set forth in 2002 will place even higher demands on the already stressed freshwater ecosystems, unless an integrated approach to managing water for people and ecosystems is implemented by a broad constituency. To inform and implement policies that support an integrated approach to water management, as well as to measure progress in halting the rapid decline in freshwater species, basin-level indicators describing the condition and threats to freshwater ecosystems and species are required. This paper discusses the extent and quality of data available on the number and size of populations of freshwater species, as well as the change in the extent and condition of natural freshwater habitats. The paper presents indicators that can be applied at multiple scales, highlighting the usefulness of using remote sensing and geographical information systems technologies to fill some of the existing information gaps. Finally, the paper includes an analysis of major data gaps and information needs with respect to freshwater species to measure progress towards the 2010 biodiversity targets.
It’s great significance for protection of water ecological and water resources to identify water quality rapidly and conveniently. In the past time, water quality was test and monitored with traditional laboratory methods, which was hard to meet the requirements of urgent demand. A rapid and convenient method for the identification of water quality based on machine learning was used in this study. By sampling and photographing, the image of water was acquired. Then nine dimensional digital information features of the color information were obtained by the moment method. Based on the historical data and expert experience, a support vector machine (SVM) model was successfully built and well trained. Then the model was verified with the test data, and the accuracy reaches 95%, which proves this method has good effect and high precision. This work will generate fresh insight into water quality identification and contribute to water resources protection.
The effluent from a vertically integrated alumina and aluminum production and (since the
late 2000s) thermal electric power production plant located on Antikyra bay in the Gulf of
Corinth is being examined. This study processes satellite sea surface temperature and
chlorophyll-a data and examines the possibility of thermal pollution from the
aluminum processing plant considering the local geomorphology. The sea surface
temperatures were derived from 58 Landsat 5 and 7 images from July 2009 until December
2016. Respectively, the Chl-a concentrations were retrieved using reflectance data of 6
Landsat OLI images from April 2014 until October 2015 in order to statistically correlate the
various combinations of Landsat bands with in-situ measurements and to quantify
algorithms that best describe this relationship and calculate accurately the concentration of
chlorophyll-a. A polynomial model employing the band ratio B4/B1 was found to be the
most efficient algorithm for the chlorophyll-a estimation of the Antikyra Bay with a
maximum correlation coefficient of R2
= 0.99. Based on the correlation coefficients, the most
sufficient local algorithm was found to be chl-a= 59.423x(b4/b1)2
-22.687x(b4/b1)+2.174. The
results confirmed the suitability of the method for assessing the concentration of
chlorophyll-a in the Gulf of Corinth with statistically accuracy. Furthermore, the atmosphere
influences on the sea surface temperature variation were indicated using air-sea heat flux
data.
Satellite based prediction of inland water quality
هدف اصلی این پژوهش دست یافتن به مدلی جهت بررسی کیفیت آب رودخانه دز با استفاده از سنجش
از دور می باشد. در این پژوهش، شاخص کیفیت آب به عنوان پارامتر مناسب جهت نشان دادن
وضع کیفی آب رودخانه دز درنظر گرفته شد. این شاخص از ترکیب ۹ پارامتر کیفیت آب مختلف بدست می
آید؛ لذا بررسی مستمر شرایط کیفی آب های سطحی با این شاخص و به روش های متداول میدانی
بسیار زمان بر و هزینه بر می باشد. در این تحقیق مدلی به منظور تخمین شاخص کیفیت اب از روی
تصاویر ماهواره لندست ۸ و با کمک مدل های رگرسیونی خطی بدست آمد. به منظور ایجاد مدل ها، از
باندهای محدوده مریی و مادون قرمز نزدیک تصاویر سنجنده OLI و در حالت های باند منفرد و نسبت باندی
استفاده شد. اندازه گیری های میدانی از پارامترهای کیفیت آب در سه دوره زمانی متفاوت، در شش
ایستگاه مختلف در مسیر رودخانه دز و همزمان با گذر ماهواره لندست ۸ از منطقه انجام شد. رابطه
نهایی بدست آمده به منظور تخمین شاخص کیفیت آب NSF ، دارای مقادیر ۲)R ,۳.۵=RMSE )= ۰.۸۵ و
سطح معنی داری ۹۹ درصد می باشد. نتایج این پژوهش نشان داد که سنجش از دور در تخمین و ارزیابی
کیفیت آب رودخانه دز از کارایی مناسبی برخوردار است.
کلمات کلیدی:
سنجنده OLI ، شاخص کیفیت اب NSF ، رودخانه دز، لندست
Reliable and consistent carbon fractions estimates are crucial in studying the role of coasts in the global carbon cycle. Remote sensing offers the potential to estimate carbon fractions with its advantages of large spatial coverage and real-time surveys. Colored dissolved organic matter (CDOM) absorption was generally used as a proxy to estimate dissolved organic carbon (DOC). However, the CDOM–DOC relationship varies by region and remains inconstant. Thus, the correlation between the reflectivity of visible band and DOC concentration was directly adopted in DOC estimation and performed well in former studies. Atomic groups of the various components of carbon fractions produce electronic transition by absorbing photons, and this process occurs both in the visible bands and in the near-infrared bands. Thus, the wide range of absorption band provides an approach to estimate carbon fractions by using the correlation between the reflectivity of the whole visible/near infrared (Vis-NIR) bands of optical satellite sensors and carbon fractions. A new ratio band combination was developed and performed well in carbon fractions concentration retrievals, and the yielded estimation accuracies (R2 > 0.77, RPD > 2.02) were sufficient to map the spatial distributions of carbon fractions with the MODIS image.
In recent years, U.S. Department of Agriculture, Forest Service, and other land managers in Utah and other states have raised concerns over accelerated eutrophication (nutrient enrichment) of lakes and reservoirs. Traditional field-based methods of assessing water quality are costly and time consuming. A more efficient and cost-effective way to assess and monitor trends in trophic status and water quality is needed. To address this need, the Forest Service Remote Sensing Applications Center (RSAC) conducted a study to investigate the feasibility of using Landsat satellite imagery to assess water clarity in Utah water bodies. Water clarity is one indicator of eutrophication that has the potential to be assessed economically and efficiently with satellite imagery. In 2010, water clarity was measured in 13 Utah water bodies using a Secchi disk. A natural log transformation of the Secchi depth data and the red band of Landsat 5 imagery produced a linear relationship that was modeled using linear regression. The coefficient of determination (R2) for this relationship was 0.94 with a root mean-squared error of 0.73 meters. The model was applied to 24 other lakes and reservoirs in the Landsat path to predict their water clarity. The report also discusses ways to develop a regional water-clarity assessment program based on Landsat imagery.
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.
Ocean Color products have been used to understand marine ecosystem. In high latitude region, ice melting optically influences the ocean color products. In this study, we assessed optical properties in fjord around Svalbard Arctic sea, and estimated distribution of chlorophyll-a and suspended sediment by using high resolution satellite data, Landsat-8 Operational Land Imager (OLI). To estimate chlorophyll-a and suspended sediment concentrations, various regression models were tested with different band ratio. The regression models were not shown high correlation because of temporal difference between satellite data and in-situ data. However, model-derived distribution of ocean color products from OLI showed a possibility that fjord and coastal areas around Arctic Sea can be monitored with high resolution satellite data. To understand climate change pattern around Arctic Sea, we need to understand ice meting influences on marine ecosystem change. Results of this study will be used to high resolution monitoring of ice melting and its influences on the marine ecosystem change at high latitude. KOPRI (Korea Polar Research Institute) has been operated the Dasan station on Svalbard since 2002, and study was conducted using Arctic station.
The goal of this study was to demonstrate the application of aerial imagery as a tool in detecting water quality indicators in a three mile segment of Tibbee Creek in, Clay County, Mississippi. Water samples from 10 transects were collected per sampling date over two periods in 2010 and 2011. Temperature and dissolved oxygen (DO) were measured at each point, and water samples were tested for turbidity and total suspended solids (TSS). Relative reflectance was extracted from high resolution (0.5 meter) multispectral aerial images. A regression model was developed for turbidity and TSS as a function of values for specific sampling dates. The best model was used to predict turbidity and TSS using datasets outside the original model date. The development of an appropriate predictive model for water quality assessment based on the relative reflectance of aerial imagery is affected by the quality of imagery and time of sampling.
Traditional estimate of suspended sediments relied on the discrete sample data collected in river, which takes a lot of time or manpower and works out approximately. Because of these characters, it is difficult to measure suspended sediments of the whole river at the same time, and analyze the distributing status of suspended sediments. Estimating suspended sediments using satellite data, however, is a quick and effective way for measuring and analyzing in the whole river. In this research, an estimation model of suspended sediments in Yangtze River was built using spectral analysis based on correlative researches. Firstly, an attempt to fit the relation curve between concentration of suspended sediments and reflectance of river was based on three classical models used widely. Land spectral experiment, which simulated the situation of Yangtze River, was carried out to estimate the parameters of model. And then, we analyzed the Landsat TM data using the models and compared the results with the hydrological data for error quantitative analysis. And the errors are calculated to be almost 20%, for that we did the experiment indoor, and the satellite image data is obtained outdoors. The traditional estimate of suspended sediments relied on the discrete sample data collected in Yichang area, while results of models listed here are average values of river in Yichang area. For the reasons stated before, we think the models are effective to estimate suspended sediments concentration. During the experiment, we didn't consider the depth and the color of the river, which may also, influenced the precision of the results. In order to calculate the parameters more accurately, field experiment, in which other aspects such as the depth and the color would be taken into account, is going to put in practice.
The primary focus of this module will be applications of remote sensing for biodiversity conservation as a subset of earth observation techniques. This will involve linking remote sensing capabilities to practical and strategic environmental and conservation applications. The module will cover biodiversity conservation applications in terrestrial, aquatic, and atmospheric contexts, often illustrated with focused examples that portray land managers seeking new tools and methods to better address the state of their managed natural systems.
Remote sensing techniques can be used to estimate and map the concentrations of suspended matter in inland water, providing both spatial and temporal information. Although an empirical approach to remote sensing of inland waters has been carried out frequently, satellite imagery has not been incorporated into routine lake monitoring programmes due in part to the lack of a standard prediction equation with multi-temporal capacity for suspended matter. Empirical and physical models must be developed for each lake and its corresponding turbidity composition if they are to be compared over time, or with other bodies of water. This study aimed to develop and apply multi-temporal models to estimate and map the concentrations of total suspended matter (TSM) in Lake Taihu, China. Two Landsat-5 Thematic Mapper (TM) images and nearly contemporaneous in situ measurements of TSM were used. A modified Dark-Object Subtraction (DOS) method was used, and appeared to be adequate for atmospheric correction. The relationships were examined between TSM concentrations and atmospherically corrected TM band and band ratios. Results of this study show that the ratio TM4/TM1 has a strong relationship with TSM concentrations for lake waters with relatively low concentrations of phytoplankton algae. However, TM3 provided a strong predictive relationship with TSM concentrations despite varied water quality conditions. Different prediction models were developed and compared using multiple regression analysis. The Akaike Information Criteria (AIC) approach was used to choose the best models. The validation of the multi- temporal capability of the best models indicated that it is feasible to apply the linear regression model using TM3 to estimate TSM concentrations across time in Lake Taihu, even if no in situ data were available.
Remote sensing techniques have been widely applied to capture spatial/temporal information for water resource studies and they provide great useful information for keeping the managements and sustainable developments of aquatic ecosystems. Poyang Lake, the largest freshwater lake in China, is located at the southern bank of the middle Yangtze River, and the high water quality makes it an important international wetland, allowing its ecosystem to provide significant benefits to the society. This paper aims to review recent applications of remote sensing techniques on capturing spatial/temporal information for water resource studies in Poyang Lake. The Poyang Lake and remote sensing techniques are briefly introduced first. Then the applications of remote sensing techniques on the studies of water level, water area, flooding disaster, water quality (e.g. water clarity and suspended sediment concentration) and eutrophication of Poyang Lake are reviewed. Finally some potential applications of remote sensing techniques on Poyang Lake and conclusion are summarized. It is hoped that this paper might provide necessary and integrated information for the researchers to understand the applications of remote sensing techniques in the water resource studies and to establish foundation for their further studies in Poyang Lake.
This paper deals with development of decision making tool for mapping of water quality
parameters of Gomti River in parts of Lucknow, Sitapur and Barabanki districts of Uttar
Pradesh, India. Mapping was done using IRS LISS III data combined with measurement of
selected sample points. Water quality data was collected for both pre-monsoon and postmonsoon
seasons. Radiance value of each band of IRS LISS III data has been calculated and
observed radiance value on those sample points of each band along with band ratios and
principal components were compared with in situ measurements of water quality parameters.
The water quality parameters included, TS, DS, SS, pH, COD, BOD, DO, Chloride and TH.
Using radiance data of pre-monsoon images and in situ measurement data of water quality
parameters correlation and multiple linear regression models were developed and selected
most appropriate band combinations which were having highest R2 value. With the help of
these multiple linear regression water quality parameters were predicted which were then
compared with the values obtained through laboratory analysis of water quality. These
appropriate band combinations and principal components of pre-monsoon satellite data were
used in estimation of water quality parameters. The all these water quality parameters were
significantly correlated with LISS III radiance data except SS. The same band combinations
of post-monsoon satellite data were also used for estimation of water quality parameters in
post-monsoon. Subsequently, multiple linear regression equations models were used in
estimation of water quality parameters and preparation of digital cartographic maps depicting
the water quality over the entire study area for both the seasons respectively.
This paper focuses mainly on the investigation of water reserve changes in Salt Lake, Turkey, using remote-sensing data. The study is performed in two stages: (1) correlation analysis for real-time ground and satellite data and (2) assessment of water reserve changes using multi-temporal Landsat imagery. First, correlation analysis is conducted to investigate the relationship between digital data from Landsat-5 TM and spectral (in situ) measurements collected using a field spectroradiometer on the same day and time. A radiometric correction procedure, including conversions from digital numbers to radiance and from radiance to at-satellite reflectance, is executed to make satellite data comparable to in situ measurements. This procedure show that simultaneous ground and satellite remote-sensing data are highly correlated (0·84 > R2 > 97) and the near-infrared region (for this study TM4-Landsat-5 TM, band 4) is the best spectral range to distinguish salt and water on the satellite data for the multi-temporal analysis of the water reserve in Salt Lake. It also shows that the use of shortwave infrared band(s) will result in confusion for the determination of the water reserve in this water-covered study area. In a second and last phase, the water reserve change in the lake is examined using multi-temporal Landsat imagery collected in 1990, 2001 and 2005. The remotely sensed, sampled and treated data show that the water reserve in the lake has decreased markedly between 1990 and 2005 due to drought and uncontrolled water usage. It is suggested that the use of water supplies around Salt Lake should be controlled and that the lake should regularly be monitored by up-to-date remote-sensing data (at least annually) for better management of water resources. Copyright © 2007 John Wiley & Sons, Ltd.
This paper presents an application of high resolution satellite remote sensing data for mapping water quality in the Goldon
Horn, Istanbul. It is an applied research emphasizing the present water quality conditions in this region for water quality
parameters; secchi disc depth (SDD), chlorophyl-a (chl-a) and total suspended sediment (TSS) concentration. The study also
examines the retrievals of these parameters through high resolution IKONOS multispectral data supported by in situ measurements.
Image processing procedure involving radiometric correction is carried out for conversion from digital numbers (DNs) to spectral
radiance to correlate water quality parameters and satellite data by using multiple regression technique. The retrieved and
verified results show that the measured and estimated values of water quality parameters in good agreement (R
2 > 0.97). The spatial distribution maps are developed by using multiple regression algorithm belonging to water quality parameters.
These maps present apparent spatial variations of selected parameters and inform the decision makers of water quality variations
in a large water region in the Istanbul metropolitan area.
Chlorophyll a (Chl-a) is an indicator of water quality, especially of eutrophic state. Remote sensing has been used to mapping Chl-a in lake, large river, estuary and ocean, due to its andvantages such as to provide data logistically and economically. The aim of this paper was to use LandSat TM image and suitable algorithm to estimate and map Chl-a concentration in reservoir Dajingshan, an important source for drinking water supply in Zhuhai city, China. After comparing several simple linear regression algorithms, a most suitable algorithm using brightness value of bands 1 to 4, was adopted. The results showed that the modeled chl-a concentration in November 23 th , 2005 had reasonable accuracy, with a mean absolute error of 0.002 mg/L when compared with the ground truth measurements. The chl-a concetration map showed that the reservior Dajingshan was in eutrophic conditions and the water could not be used as drinking water.
This paper presents an application of water quality mapping through real-time satellite and ground data. The Lake Beysehir which is the largest freshwater lake and drinking water reservoir in Turkey was selected as the study area. Terra ASTER satellite image is used as remote sensing data source for water quality mapping in addition to simultaneously performed in-situ measurements. Ground data is collected simultaneously with the ASTER overpass on June 09, 2005 over the Lake Beysehir. The spatial distribution map is developed by using multiple regression (MR) technique for water quality parameter, which is chlorophyll-a (chl-a). The results indicate that simultaneous ground and satellite remote sensing data are highly correlated (R
2 > 0.86). In the image processing step, geometric correction, image filtering and development of water quality map procedures are performed with the ERDAS Imagine and ArcGIS 9.0 software. The trophic status of Lake Beysehir is considered to be oligotrophic with an average 1.55 µg/l chl-a concentration.
Due to the impacts of rural and urban development on southwestern Australian estuaries, and the general isolation of these water bodies, there is a need to develop water quality monitoring systems that are both repetitive and cost-effective. The literature suggests that Landsat Thematic Mapper (TM) has spectral and spatial characteristics that are suited to monitoring small coastal water bodies. This study examined the potential for the satellite-based TM sensor to serve as a regulator monitoring tool. Atmospherically corrected TM digital data acquired on four dates over summer 1990/91 and concurrent field measurements collected at the time of the satellite over-pass over the Peel-Harvey Estuarine System were used to obtain multitemporal, empirical algorithms for predicting pigment concentration, Secchi disk depth (SDD), and salinity. Highly significant, predictive algorithms were developed for these parameters. It is concluded that Landsat TM has the resolution and accuracy to be a potentially very useful monitoring tool. However, cloud cover and delays in data acquisition seriously diminish its usefulness for monitoring on anything less than a seasonal basis. Laboratory-based radiometric studies also indicated that Landsat TM was unlikely to be useful in determining the taxonomic composition of phytoplankton blooms in coastal waters.
The purpose of the research was to investigate the relationships between and among surface spectral reflectance, the underwater light field, and suspended sediment concentrations (SSC). Both spectroradiometer and quantum-sensor data were collected over and in an 8543 litre vinyl pool, under natural sunlight. Twenty levels of SSCs ranging from 50 to 1000 mg/l were put into solution. Both downwelling and up-welling irradiances below the water surface decreased with increasing SSC, even though atmospheric downwelling increased. A relationship for PAR (Photosynthetically Active Radiation) transmittance, reflectance, and absorption with varying levels of SSC was illustrated. -from Authors
The major objective of this study was to assess the technical feasibility of using TM data to evaluate, both qualitatively and quantitatively, the general water quality of southern Green Bay and central Lake Michigan. An empirical approach of relating TM data with simultaneously acquired 'sea truth' data through multiple linear regression analysis was employed. Subsequently, the regression models were used to prepare digital cartographic products depicting the water quality and thermal distributions over the entire study area.-from Author
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.
Two experiments were conducted outdoors to investigate how bottom brightness impacts the spectral response of a water column under varied suspended sediment concentrations. A white aluminum panel placed at the bottom of the tank was used as the bright bottom, and a flat-black tank liner served as the dark bottom. Sixteen levels of suspended sediment from 25 to 400 mg litre were used in each experiment. Spectral data were collected using a Spectron SE-590 spectroradiometer. The major findings include the following: the bright bottom had the greatest impact at visible wavelengths; when suspended sediment concentrations exceeded 100 mg litre , the bright bottom response was found to be negligible; and, substrate brightness has minimal impact between 740 and 900 nm, suggesting that these wavelengths are best for measuring suspended sediment concentrations by means of remote sensing.
Based on suggested earlier optical models of Lakes Ontario and Ladoga, numerical modelling experiments have been carried out to the effect of revealing the responsiveness of chromaticity coordinates (x, y, z), dominant wavelength (lambda dom) and associated spectral purity (p) to the abundance in water of optically active components (OAC), i.e., phytoplankton (chl), suspended minerals (sm) and dissolved organic carbon (doc). It has been shown that highly turbid waters (i.e., waters with high chl and sm concentrations) with low content of doc display colour varying from green to brownish. High turbidity or large doc concentration invariably is characteristic of waters with brown colour. With growing OAC content in water, the chromaticity coordinates and, consequently, the dominant wavelength lambda dom tend asymptotically to respective limit values that seem to be intrinsically characteristic of natural waters. It is also shown that the colour purity p asymptotically tends to values of about 35-45 per cent (with the only exception for waters containing exclusively chl and this in small quantities (0.5mu gl-1)) when concentrations of one or more OAC are over 10 (in respective concentration units). These findings clearly indicate that neither panchromatic nor two-channel ratio images could be meaningful for an unambiguous inference of water quality parameters. Furthermore, the correspondence between water colour physical characteristics (i.e., x, y, z, lambda dom and p) and water colour scales traditionally used in limnology is established.
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");
Four dates of Landsat Thematic Mapper data from 1993, April 9, July 30, August 15, and September 16, were used to assess temporal and spatial patterns of lake area and dimensions of suspended sediment concentration in Tuttle Creek Reservoir, Kansas. In 1993, excessive precipitation in the Big Blue River Basin, and throughout much of the Upper Middle West, led to widespread flooding. Rains produced substantial erosion, sediment movement down the stream network, and a runoff volume that filled Tuttle Creek Reservoir, a U.S. Army Corps of Engineers flood control structure. The April 9 data are from before the flood, the July 30 data are from the time of maximum pool size and use of the emergency spillway, and the August and September data document the declining pool sizes. Three separate analyses were performed on each of the four dates of Thematic Mapper data. One set of analyses involved applying an existing physical model that uses at-satellite reflectance for TM Band 3 to estimate variations in suspended sediment, turbidity, and Secchi depth throughout the reservoir. Maps of estimated parameters of water quality for the four individual dates were compared and analyzed to document spatial and temporal changes. The second research method involved unsupervised classification (ERDAS ISODATA algorithm) of the data from the Tuttle Creek Reservoir. Water areas were grouped into coherent classes for further spatial analysis using a two-step or layered classification procedure for each date. The third analysis used a GIS overlay technique to compare the area of the water surface for each of the four dates with the flood pool as marked on U.S.G.S. 7-1/2 minute quadrangles. Comparisons document the major change in lake area between April and July, the high levels of suspended sediment in mid-summer, and the decline in pool size and concentrations of suspended sediment by mid-September. The study illustrates the advantages of using remote sensing to assist in documenting a relatively short-term environmental hazard. This study also demonstrates the value of Landsat Thematic Mapper data for use in mapping geographic variations in water area and quality in conjunction with a major flood event.
The objective of this study was to use Landsat-MSS digital data converted for the mapping of suspended sediments (SS) in the Krishna Bay Estuary. The relation between Landsat-MSS radiance values for all four bands and measured values of suspended solid concentrations were quantified using simple linear and multiple regression equations. An optimum and best fitted equation was chosen based on the percentage error of estimation, Chi values and simplicity of the model. This calibrated regression model was then applied to map the SS(mgl) concentration for the entire study area. It is shown that Landsat-MSS data can be used successfully to quantify suspended sediment concentrations in this geographical area and possibly in other areas which have similar environmental and climatic conditions, if the regression equation is tested using an independent data set.
Multitemporal data sets of the LISS-III sensor mounted on the Indian Remote Sensing Satellite (IRS-1C) and field reflectance spectra have been evaluated for estimating chlorophyll-a content in lakes. The results were compared to laboratory analyses of in situ water samples. Quantification from field reflectance spectra was carried out using the 678 nm absorption maximum and the 705 nm reflectance peak. For the evaluation of the LISS-III satellite data three approaches were compared: spectral height of the green peak, supervised maximum likelihood classification, and linear spectral unmixing. The latter gave the best results with the highest certainty measure of R2=0.85 and was applied to all five LISS-III data sets. The results are maps of the chlorophyll-a content in 10 μg/l classes for each of the five dates. For comparison of the accuracy of the different methods for water quality analysis, the trophic state index was calculated based on chlorophyll-a determination from laboratory, field spectra, and satellite data. Regarding the five lakes for which all the data were available, each method shows similar results for the estimation of trophic state
Suspended sediments are a major factor affecting water quality in many aquatic ecosystems. Research was undertaken to determine the application of digital spectral data collected by the multispectral scanner (MSS) on the Landsat satellite for estimating suspended sediments in aquatic ecosystems where mean annual concentrations of suspended sediments are greater than 50 mg L−1. Digital spectral data from 14 Landsat MSS scenes of Moon Lake in Coahoma County, Mississippi were analyzed and compared with ground measurements of total solids and suspended sediments in the lake surface water for the period between January 1983 and May 1985. Coefficients of determination (R2) greater than 0.81 were calculated between MSS Band 2 (0.6–0.7 μm) or Band 3 (0.7–0.8 μm) and suspended sediments or total solids. Coefficients of determination for multiple regression using three or four MSS bands were greater than 0.90. This study showed that digital spectral data from the Landsat satellites can be used to locate and monitor surface-suspended sediments in aquatic ecosystems. With such a digital computer technique, entire regions can be surveyed quickly to locate aquatic ecosystems with suspended sediment problems. Conservation efforts can then be concentrated on those aquatic ecosystems with the most serious suspended sediment problems and soil conservation strategies developed in their watersheds to control erosion and sediment yield and thus to improve water quality.
This research used water quality data from Lake Chicot, Arkansas and a corresponding set of Landsat MSS data to compare the ability of satellite-based sensor systems to monitor suspended sediment concentration, Secchi disk depth, and nephelometric turbidity. Lake Chicot was selected, in part, because of the availability of a wide range of water quality conditions. Secchi disk depth and nephelometric turbidity are both optical measures of water quality and differ from suspended sediment concentration, which is a measure of the weight of inorganic particulates suspended in the water column. four different models for these relationships between the satellite data and the water quality data were tested: 1) simple linear regression analysis with the satellite data transformed to exo-atmospheric reflectance, 2) a simple linear regression involving a natural logarithm transformation of the satellite and water quality variables, 3) simple linear regression analysis of the digital chromaticity transformation of the satellite data and the natural logarithm of the water quality data, and 4) optimized curve fitting of a theoretically derived exponential model for the relationship between exoatmospheric reflectance and the water quality data. Two different solar spectral irradiance curves and an orbital eccentricity correction factor are tested using the exponential model. Results suggest: 1) Remote sensing from space-based platforms can provide meaningful information on water quality variability; 2) an exponential model best characterizes the relationship between the satellite data and the water quality measures investigated; 3) slight differences result from using the solar curve proposed by the World Radiation Center (as opposed to the NASA standard); and 4) predictions based on optical measures of water quality, rather than measures of the weight of particles in the water column, are slightly better when using Landsat MSS data.
Using Landsat multi spectral scanner data to estimate suspended sediments in Moon Lake, Mississippi. Remote Sensing of Environment Determination of chlorophyll content and trophic state of lakes using field spectrometer and IRS-1C satellite data in the Mecklenburg lake district
- J C Cooper
- C M Yongquing
- J 1 Thiemann
- S Kaufmann
RITCHIE, J. C., COOPER, C. M., and YONGQUING, J., 1987, Using Landsat multi spectral scanner data to estimate suspended sediments in Moon Lake, Mississippi. Remote Sensing of Environment, 23, 65-8 1. THIEMANN, S., and KAUFMANN, H., 2000, Determination of chlorophyll content and trophic state of lakes using field spectrometer and IRS-1C satellite data in the Mecklenburg lake district, Germany. Remote Sensing of Environment, 73, 227-235.
Comparison of different regression models prediction of transmitted light intensity depth (TLID) values in the Porsuk Dam reservoir
- B Bilge
- F Dogeroglu
YAZICI (BALOGLU), B., BILGE, F., DOGEROGLU, T., and AYDAY, C., 2002, Comparison of
different regression models prediction of transmitted light intensity depth (TLID)
values in the Porsuk Dam reservoir, EPMR-2002 International Conference
Environmental Problems of the Mediterranean Region Proceedings Book, Cyprus (in
press).
Evaluation of the digital spectral data and geographic information system for estimating water quality parameters in the Porsuk Dam Lake
- F Bilge
BILGE, F., DOGEROGLU, T., and AYDAY, C., 1996, Evaluation of the digital spectral data by
Landsat satellite for estimating and mapping water quality parameters in the Porsuk
Dam Lake, Eskisehir-Turkey. Space of Service to Humanity Preserving Earth and
Improving Life International Symposium, 5-7 February 1996, Strasbourg, France,
Poster Presentation Document, Final Book of Abstracts, (Strasbourg, France:
International Space University), p. 4.
Mapping of water quality parameters by using Landsat images in Porsuk Dam Lake
- F Bilge
- T Dogeroglu
BILGE, F., DOGEROGLU, T., and AYDAY, C., 1997, Mapping of water quality parameters by
using Landsat images in Porsuk Dam Lake, Eskisehir-Turkey, Proceedings of the
International Symposium on Geology and Environment, Istanbul, Turkey, 1999, edited
by I. Yilmazer, (Ankara: Geoenv'97, Chamber of Geological Engineers of UCEAT),
pp. 101-107.
Use of Thematic Mapped data to assess Remotely sensed data for water quality monitoring water quality in Green Bay and Central Lake Michigan
LATHROP, R. G., and LILLESAND, T. M., 1986, Use of Thematic Mapped data to assess
Remotely sensed data for water quality monitoring
water quality in Green Bay and Central Lake Michigan. Photogrammetric
Engineering and Remote Sensing, 52, 671-680.