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Water Quality Retrievals from High Resolution Ikonos Multispectral Imagery: A Case Study in Istanbul, Turkey
Abstract
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.
... Successful correlations have also been found in past studies that utilized airborne remote sensing [30,39]. Limited success has been found in estuarine environments using imagery from Landsat 5 satellite and may need fine resolution imagery, as shown in [19,40] where IKONOS imagery was used. ...
... SSC has optically active properties that can be easily correlated against in situ measurements using regression analysis techniques. SSC has been successfully estimated in lacustrine, fluvial, and estuarine environments by several modern spaceborne sensors such as IKONOS, Landsat 8, Sentinel-2, RapidEye, and MODIS [34,[40][41][42][43][44]. ...
... SDD has been successfully measured for decades via satellite and airborne remote sensing methods in a variety of environments including estuaries, rivers, reservoirs, and lakes. The most common method is to use varying techniques of regression analysis for predicting SDD as tested against in situ measurements [20,30,32,39,40]. ...
The Tennessee River in the United States is one of the most ecologically distinct rivers in the world and serves as a great resource for local residents. However, it is also one of the most polluted rivers in the world, and a leading cause of this pollution is storm water runoff. Satellite remote sensing technology, which has been used successfully to study surface water quality parameters for many years, could be very useful to study and monitor the quality of water in the Tennessee River. This study developed a numerical turbidity estimation model for the Tennessee River and its tributaries in Southeast Tennessee using Landsat 8 satellite imagery coupled with near real-time in situ measurements. The obtained results suggest that a nonlinear regression-based numerical model can be developed using Band 4 (red) surface reflectance values of the Landsat 8 OLI sensor to estimate turbidity in these water bodies with the potential of high accuracy. The accuracy assessment of the estimated turbidity achieved a coefficient of determination (R2) value and root mean square error (RMSE) as high as 0.97 and 1.41 NTU, respectively. The model was also tested on imagery acquired on a different date to assess its potential for routine remote estimation of turbidity and produced encouraging results with R2 value of 0.94 and relatively high RMSE.
... Advanced very-high-resolution radiometers (AVHRR) can detect phytoplankton blooms and equally possess a greater ability in providing estimates of Ch-a above 10 mg/m 3 in turbid estuaries. Ekercin (2007) employed IKONOS Band 1 (445-530 nm), Band 2 (520-610 nm), Band 3 (640-720 nm), and Band 4 (770-880 nm) data to assess the Chl-a concentrations in Goldoni Horn, Istanbul, Turkey. Another important factor that affects water quality is the concentration of particulate matter such as turbidity and total dissolved solids (TDS). ...
... Owing to the reduced spectral resolution of Sentinel-2, only limited algorithmic models are obtained for various quality indices when compared with the PRISMA-generated models. Here, the chlorophyll concentration showed a significant correlation with green and near-infrared bands with an R 2 value equal to 0.72 and a p-value of 0.004 which typically agrees with the previous studies (Ekercin, 2007). The pH values fit well with the NIR bands having an R 2 value of 0.62 and a p-value of 0.012 which shows many similarities with the case studies by Japitana et al. (2019). ...
The continuous availability of spatial and temporal distributed data from satellite sensors provides more accurate and timely information regarding surface water quality parameters. Remote sensing data has the potential to serve as an alternative to traditional on-site measurements, which can be resource-intensive due to the time and labor involved. This present study aims in exploring the possibility and comparison of hyperspectral and multispectral imageries (PRISMA) for accurate prediction of surface water quality parameters. Muthupet estuary, situated on the south side of the Cauvery River delta on the Bay of Bengal, is selected as the study area. The remote sensing data is acquired from the PRISMA hyperspectral satellite and the Sentinel-2 multispectral instrument (MSI) satellite. The in situ sampling from the study area is performed, and the testing procedures are carried out for analyzing different water quality parameters. The correlations between the water sample results and the reflectance values of satellites are analyzed to generate appropriate algorithmic models. The study utilized data from both the PRISMA and Sentinel satellites to develop models for assessing water quality parameters such as total dissolved solids, chlorophyll, pH, and chlorides. The developed models demonstrated strong correlations with R² values above 0.80 in the validation phase. PRISMA-based models for pH and chlorophyll displayed higher accuracy levels than Sentinel-based models with R² > 0.90.
... Pesquisas científicas relacionadas com a qualidade das águas utilizam, dentre os métodos estatísticos disponíveis para análises ambientais, as análises de correlação entre as concentrações dos componentes opticamente ativos e informações espectrais de corpos d'água obtidas em laboratório e em levantamentos de campo (Rundquist et al., 1996;Louchard, 2002;Barbosa, 2005;Nobrega, 2002;Rudorff, 2006), enquanto outros autores enfatizam a estimativa de concentrações desses componentes a partir de dados orbitais (Dekker, 1993;Ritchie & Cooper, 1998;Giardino et al., 2001;Ekercin, 2007;Galvão et al., 2003;Novo et al., 2006). Em ambos os casos, a construção de modelos empíricos para estimar alguns dos componentes, a partir de outros observados em campo, permitem uma maior representatividade espacial da variável e reduzem os custos do trabalho de campo, muitas vezes com a redução das análises de laboratório. ...
... Já Ekercin (2007) correlacionou os parâmetros de qualidade da água: TSS, clorofila a e transparência (através da profundidade do disco de Secchi), com imagens multiespectrais de alta resolução do satélite Ikonos, no mapeamento da qualidade da água de um lago na Turquia, a partir da análise em nove pontos de observação. O autor encontrou uma correlação alta entre os parâmetros medidos e os estimados (R 2 = 0,97), e realizou a distribuição espacial dos parâmetros de qualidade da água usando algoritmos de regressão múltipla, fornecendo informações sobre as variações na qualidade da água no lago para os especialistas ambientais. ...
Pesquisas científicas de qualidade das águas utilizam modelos de inferência de componentes da água, a partir de outros observados em campo, permitindo uma maior representatividade espacial da variável, além de redução de custos. Pesquisadores da área ambiental utilizam a Profundidade de Secchi no cálculo indireto do coeficiente vertical de atenuação de luz na água e na avaliação da extensão da zona eufótica, para a classificação de tipos de água para os ecossistemas aquáticos brasileiros. O propósito desta pesquisa foi, então, realizar a inferência da profundidade da zona eufótica e do coeficiente vertical de atenuação de luz na água do Reservatório de Itupararanga/SP, utilizando uma imagem multiespectral IKONOS, nas bandas 1 (450 - 520 nm); 2 (520 - 600 nm); 3 (630 - 690 nm); e 4 (760 - 900 nm) e dados espectrais obtidos "in situ" com o espectrorradiômetro FieldSpec UV/VNIR (400 - 900 nm), assim como gerar um modelo de inferência da transparência da água, a partir dos dados disponíveis. Após ajustes e processamentos iniciais, os dados foram submetidos a uma análise de correlação, gerando-se o modelo de inferência, o qual, juntamente com as variáveis medidas "in situ" em pontos amostrais específicos, possibilitaram estimar o coeficiente de atenuação vertical de luz na água do reservatório; classificar a água como clara; e estimar a profundidade da zona eufótica, apresentando-se como estreita (entre 5,94 m e 7,96 m). Com isso concluiu-se que o reservatório em questão apresenta pouca concentração de sólidos em suspensão.
... List of Figures 1-1 The ocean color remote sensing paradigm leverages knowledge from a wide range of optical disciplines (1-10) to retrieve the optical constituent products from a measured spectrum. In this paradigm, light from the sun (1) propagates through the atmosphere (2) and ocean surface (3) 2-1 Center wavelengths used in common algorithms for various WQPs [Lee et al., 2010;Mobley, 1999;Mueller and Austin, 1995;Shahraiyni et al., 2007;Keith et al., 2014;Ekercin, 2007;Woźniak et al., 2018;Gurlin et al., 2011;O'Reilly et al., 1998;Gholizadeh et al., 2016]. [Wei et al., 2016] quality assurance (QA) of the R rs generally demonstrates an inverse relationship with the MAPE of the WQP estimates. ...
... Second, glint correction is a particular aquatic challenge which requires simultaneous measurement of additional radiances in the NIR, beyond those needed to compute a particular WQP of interest ( Fig. 2-1). These measured NIR radiances may exhibit a lower SNR than the visible radiances, due to a combination of the spectral efficiency of the imager (i.e., the spectral efficiency of the optics and the quantum efficiency of the detector) and a lower ra- Figure 2-1: Center wavelengths used in common algorithms for various WQPs [Lee et al., 2010;Mobley, 1999;Mueller and Austin, 1995;Shahraiyni et al., 2007;Keith et al., 2014;Ekercin, 2007;Woźniak et al., 2018;Gurlin et al., 2011;O'Reilly et al., 1998;Gholizadeh et al., 2016]. Highest accuracy algorithms often utilize a larger number of wavelengths. ...
... La concentración del total de contaminantes y sedimentos suspendidos provenientes del lavado continental y materia orgánica (detritos), constituye uno de los parámetros de calidad del agua más importantes (Rodríguez y Gilbes 2009), principalmente porque se relaciona con la producción y flujo de metales pesados y microcontaminantes (Ekercin 2007). ...
... Desde finales la década de 1970, se han realizado estudios de transporte de sedimentos suspendidos (TSS) utilizando datos de plataformas satelitales (Ritchie y Cooper 2001) cuyos sensores miden la cantidad de radiación solar reflejada por la superficie del agua a diferentes longitudes de onda. Actualmente el TSS es uno de los parámetros que se mide con más éxito por medio de la percepción remota (Ekercin 2007). ...
Remote sensing is a very efficient alternative with regard to the monitoring of detritus plumes and organic pollutants. Colorimetry and unsupervised classification methods outline these plumes, which can be vectorized and their evolution traced with respect to time and space. In practice, this lowers the cost of research and allows for greater spatial and temporal coverage. This work presents the case study of Bahía de Banderas, Jalisco-Nayarit, Mexico, where the detritus discharge by the Ameca river in the period 2006-2014 was studied through a combined product of Landsat and Modis images.
... The availability of satellite remote sensing platforms has provided coastal managers with tools and capabilities to effectively monitor the coastal environment at spatial and temporal scales previously unconceivable from the perspective of traditional in situ based observation methods[10]. Coastal water quality in the form of water turbidity or Total Suspended Sediment (TSS) concentration has been widely studied across diverse geographical locations[11][12][13][14][15][16][17][18][19][20]by using a suite of remote sensing sensors such as, Landsat[21][22][23][24][25][26][27][28][29][30], MEdium Resolution Imaging Spectrometer (MERIS)[7,[31][32][33], MODerate resolution Imaging Spectroradiometer (MODIS)[16,17,20,29,[34][35][36][37][38][39][40][41][42][43][44], and Sea-viewing Wide Field-of-view Sensor (SeaWiFS)[13,[45][46][47][48][49]. In addition to these most commonly used and " free to ground " sensors, commercial high spatial resolution sensors such as Syst?m Pour l'Observation de la Terra (SPOT)[22,50,51], IKONOS[14]and WorldView-2 (WV2)[52]are also employed to map the TSS. ...
... Coastal water quality in the form of water turbidity or Total Suspended Sediment (TSS) concentration has been widely studied across diverse geographical locations[11][12][13][14][15][16][17][18][19][20]by using a suite of remote sensing sensors such as, Landsat[21][22][23][24][25][26][27][28][29][30], MEdium Resolution Imaging Spectrometer (MERIS)[7,[31][32][33], MODerate resolution Imaging Spectroradiometer (MODIS)[16,17,20,29,[34][35][36][37][38][39][40][41][42][43][44], and Sea-viewing Wide Field-of-view Sensor (SeaWiFS)[13,[45][46][47][48][49]. In addition to these most commonly used and " free to ground " sensors, commercial high spatial resolution sensors such as Syst?m Pour l'Observation de la Terra (SPOT)[22,50,51], IKONOS[14]and WorldView-2 (WV2)[52]are also employed to map the TSS. The high spatial resolution commercial satellite sensors such as IKONOS, WV2, and GeoEye-1 can provide data at spatial resolutions of approximately 0.5 m?4.0 m with temporal resolutions of ~1?8 days[53]. ...
The impact of anthropogenic activities on coastal waters is a cause of concern because such activities add to the total suspended sediment (TSS) budget of the coastal waters, which have negative impacts on the coastal ecosystem. Satellite remote sensing provides a powerful tool in monitoring TSS concentration at high spatiotemporal resolution, but coastal managers should be mindful that the satellite-derived TSS concentrations are dependent on the satellite sensor’s radiometric properties, atmospheric correction approaches, the spatial resolution and the limitations of specific TSS algorithms. In this study, we investigated the impact of different spatial resolutions of satellite sensor on the quantification of TSS concentration in coastal waters of northern Western Australia. We quantified the TSS product derived from MODerate resolution Imaging Spectroradiometer (MODIS)-Aqua, Landsat-8 Operational Land Image (OLI), and WorldView-2 (WV2) at native spatial resolutions of 250 m, 30 m and 2 m respectively and coarser spatial resolution (resampled up to 5 km) to quantify the impact of spatial resolution on the derived TSS product in different turbidity conditions. The results from the study show that in the waters of high turbidity and high spatial variability, the high spatial resolution WV2 sensor reported TSS concentration as high as 160 mg L⁻¹ while the low spatial resolution MODIS-Aqua reported a maximum TSS concentration of 23.6 mg L⁻¹. Degrading the spatial resolution of each satellite sensor for highly spatially variable turbid waters led to variability in the TSS concentrations of 114.46%, 304.68% and 38.2% for WV2, Landsat-8 OLI and MODIS-Aqua respectively. The implications of this work are particularly relevant in the situation of compliance monitoring where operations may be required to restrict TSS concentrations to a pre-defined limit.
... There is a strong correlation (R 2 = 0.95) between SDD and PROBA-CHRIS, with in situ values ranging from 0.1 to 6 m, according to a 1-month study of ten lakes in Poland [99]. A strong correlation (R 2 = 0.989) was observed between SDD and Ikonos OSA imagery and in situ values ranging from 0.8 to 6.5 m in a Turkish estuary during a 1-month study [100]. Two Finnish studies [36,101] demonstrate a strong correlation (R 2 > 0.86) between SDD and AISA imagery within the in situ range of 0.3 to 7 m. ...
Remote sensing methods have the potential to improve lake water quality monitoring and decision-making in water management. This review discusses the use of remote sensing methods for monitoring and assessing water quality in lakes. It explains the principles of remote sensing and the different methods used for retrieving water quality parameters in complex waterbodies. The review highlights the importance of considering the variability of optically active parameters and the need for comprehensive studies that encompass different seasons and time frames. The paper addresses the specific physical and biological parameters that can be effectively estimated using remote sensing, such as chlorophyll-α, turbidity, water transparency (Secchi disk depth), electrical conductivity, surface salinity, and water temperature. It further provides a comprehensive summary of the bands, band combinations, and band equations commonly used for remote sensing of these parameters per satellite sensor. It also discusses the limitations of remote sensing methods and the challenges associated with satellite systems. The review recommends integrating remote sensing methods using in situ measurements and computer modelling to improve the understanding of water quality. It suggests future research directions, including the importance of optimizing grid selection and time frame for in situ measurements by combining hydrodynamic models with remote sensing retrieval methods, considering variability in water quality parameters when analysing satellite imagery, the development of advanced technologies, and the integration of machine learning algorithms for effective water quality problem-solving. The review concludes with a proposed workflow for monitoring and assessing water quality parameters in lakes using remote sensing methods.
... Remote Sens. 2024,16,68 ...
Water scarcity and quality deterioration, driven by rapid population growth, urbanization, and intensive industrial and agricultural activities, emphasize the urgency for effective water management. This study aims to develop a model to comprehensively monitor various water quality parameters (WQP) and evaluate the feasibility of implementing this model in real-world scenarios, addressing the limitations of conventional in-situ sampling. Thus, a comprehensive model for monitoring WQP was developed using a 38-year dataset of Landsat imagery and in-situ data from the Water Information System of Europe (WISE), employing Back-Propagated Artificial Neural Networks (ANN). Correlation analyses revealed strong associations between remote sensing data and various WQPs, including Total Suspended Solids (TSS), chlorophyll-a (chl-a), Dissolved Oxygen (DO), Total Nitrogen (TN), and Total Phosphorus (TP). Optimal band combinations for each parameter were identified, enhancing the accuracy of the WQP estimation. The ANN-based model exhibited very high accuracy, particularly for chl-a and TSS (R2 > 0.90, NRMSE < 0.79%), surpassing previous studies. The independent validation showcased accurate classification for TSS and TN, while DO estimation faced challenges during high variation periods, highlighting the complexity of DO dynamics. The usability of the developed model was successfully tested in a real-case scenario, proving to be an operational tool for water management. Future research avenues include exploring additional data sources for improved model accuracy, potentially enhancing predictions and expanding the model’s utility in diverse environmental contexts
... To attain these explanatory capacities, different studies' authors used different sampling universes. For example, other studies, such as [38], used a density of approximately 0.32 samples/km 2 , whereas [39] used a density of 0.000216 samples/km 2 . The study with the highest sampling density corresponded to 0.64 samples/km 2 for TSS and COD [17]. ...
Remote sensing plays a crucial role in modeling surface water quality parameters (WQPs), which aids spatial and temporal variation assessment. However, existing models are often developed independently, leading to uncertainty regarding their applicability. This study focused on two primary objectives. First, it aimed to evaluate different models for chemical oxygen demand (COD), total phosphorus (TP), total nitrogen (TN), and total suspended solids (TSS) in a surface water body, the J. A. Alzate dam, in the Mexican highland region (R2 ≥ 0.78 and RMSE ≤ 16.1 mg/L). The models were estimated using multivariate regressions, with a focus on identifying dilution and dragging effects in inter-annual flow rate estimations, including runoff from precipitation and municipal discharges. Second, the study sought to analyze the potential scope of application for these models in other water bodies by comparing mean WQP values. Several models exhibited similarities, with minimal differences in mean values (ranging from −9.5 to 0.57 mg/L) for TSS, TN, and TP. These findings suggest that certain water bodies may be compatible enough to warrant the exploration of joint modeling in future research endeavors. By addressing these objectives, this research contributes to a better understanding of the suitability of remote sensing-based models for characterizing surface water quality, both within specific locations and across different water bodies.
... Indicators of this nature are frequently evaluated through statistical correlations with other indicators [56]. In this review, the focus of research is on eight optically active water parameters ( 82,83,90,92,102,115,, EC [148][149][150][151][152][153][154], TUR [32,33,35,58,60,67,75,98,102,104,109,110,150,152,153,155,156], SS [150,, TSM [30,58,59,67,75,82,90,92,97,[99][100][101]113,119,[182][183][184][185][186][187][188][189][190][191][192], and WT [87,103,150,. ...
Remote sensing methods have the potential to improve lake water quality monitoring and deci-sion-making in water management. This reviews introduces novel findings in the field of opti-cally active water quality parameters using remote sensing. It summarizes existing retrieval methods (analytical, semi-analytical, empirical, semi-empirical, and artificial intelli-gence/machine learning (AI/ML)), examines measurement methods used to determine concen-tration of specific water quality parameters, summarizes satellite systems that enable temporal data for understanding the state of the lake with focus on water quality parameters, and pro-poses enhancements for future research of lake water quality using remote sensing. As part of this review, eight optically active biological and physical water quality parameters were ana-lyzed, including chlorophyll-α (chl-α), transparency (Secchi disk depth (SDD)), colored dis-solved organic matters (CDOM), turbidity (TUR), electrical conductivity (EC), surface salinity (SS), total suspended matter (TSM), and water temperature (WT). The research proposes a shift from point-based data representation to a more reliable raster representation and encourages optimizing grid selection for in situ measurements by combining hydrodynamic model with re-mote sensing methods. This review presents a comprehensive summary of the bands, band combinations, and band equations per sensor for eight optically active water quality parameters as listed in Tables A1-A8. The review’s findings indicate that use of remotely sensed data is an effective method for estimating water quality parameters in lakes, with a significant increase in global utilization. The review highlights potential solutions and limitations to the challenges of remote sensing water quality determination in lakes.
... Coastal waterbodies can also monitor through multispectral image analysis (Thiemann and Kaufmann 2000;Vijay et al., 2015). Spectral reflectance of wastewater effluent (Gitelson et al., 1997;Ekercin, 2007) can be detected by a specific portion of the electromagnetic spectrum. In the present decade, satellites with high-resolution sensors with advanced techniques are available for assessment of the water quality (Ritchie et al., 2003). ...
Evaluation of water pollution is a priority work nowadays. The signature of the waterbody reveals its excellence or mediocrity and reflectance that can measure by a sensor used to analyse the health status of the waterbody. The remote sensing analysis has become the latest state of art technologies for monitoring large-scale waterbodies. High-resolution satellite data are now available to estimate water pollution through various water quality parameters like clarity, chlorophyll, suspended solids, turbidity, temperature, salinity, organic matter, etc. In this review study, a special emphasise has been given to the various satellites like Landsat, sentinel, satellite pourl’Observation de la terre (SPOT), moderate resolution imaging spectroradiometer (MODIS), medium resolution imaging spectrometer (MERIS), Indian remote sensing satellites (IRS) and its application on water pollution. Availability of satellite data, algorithms, and models to assess water quality has also been reviewed in detailed. The review suggests development and innovation in satellites, sensors and techniques to assess the non-optically active constituents of water quality for better understanding and management of water pollution.
... A retrieval model is constructed to predict the distribution of Chl-a concentration in water based on Chl-a's optical properties of high absorption and reflectivity in visible and near-infrared bands (Vadakke-Chanat et al., 2017). However, there are great differences in water quality parameters, the spatial resolution and radiation resolution of the sensors used in different regions, resulting in different retrieval algorithms (Ekercin, 2007). ...
Estimation of large-scale and high-precision water quality parameters is critical in explaining the spatiotemporal dynamics and the driving factors of water quality variability, especially in areas with environmental complexity (e.g., crisscrossing waterways, high flood risk in rainy season and seawater invasion). Thus, in this study, a retrieval algorithm was developed to predict chlorophyll-a (Chl-a), total nitrogen (TN) and total phosphorus (TP) concentrations in the Pearl River Estuary (PRE) based on a large amount of in situ measurements and Landsat 8 remote sensing images. Random Forest (RF) machine learning was conducted to identify the relationship between environmental indicators (pH, turbidity, conductivity, total dissolved solids and water temperature), Chl-a, TN and TP. The results showed that the NIR/R Binomial algorithm for Chl-a estimation presented appreciable reliability with R² of 0.7429, root mean square error (RMSE) of 1.2089 and mean absolute percent error (MAPE) of 15.33%. The water quality variation in the PRE showed a characteristic of overall improvement and regional deterioration with average concentrations of 7.28 μg/L, 1.15 mg/L and 0.12 mg/L for Chl-a, TN, and TP respectively. Turbidity and pH were identified as the most important indicators to explain Chl-a (52.86%, 39.91%), TN (52.38%, 40.57%) and TP (55.23%, 40.03%) variation. Agricultural pollution was the main pollution source due to the intensive application of fertilizer and increased field size. Besides, land use patterns (e.g., increasing farmland but decreasing forest) greatly influenced water quality from 2010 to 2020. Moreover, light limitation caused by high turbidity reduced the algae productivity and further lowered the Chl-a concentration. The driving factors for regional water quality variations were anthropologically dominated and supplemented by climate change. This study improved the monitoring accuracy of regional water environment and provided quantitative early warning of water pollution events for environmental practitioners, so as to achieve long-term monitoring, precise pollution management and efficient water resources management.
... From the data perspective, satellite remote sensing provides many multi-source data with long-term series and high-spatial-resolution. The data sources commonly used in the research mainly include MODIS [11,12], Landsat [13][14][15], SPOT [16,17], ALOSE [18], ASTER [19,20], HJ-1A/1B [21,22], WorldView [23,24], QuickBird [25], IKONOS [26], GaoFen [27,28], ZY-3 [29,30], SAR [31], and hyperspectral data [32,33]. To better monitor global climate change, studies usually require an image covering the entire QTP at least once a year, with an image resolution preferably no greater than 32 m (capable of identifying lakes with an area of 1 km 2 ). ...
Monitoring the lake waterbody area in the Qinghai–Tibet Plateau (QTP) is significant in dealing with global climate change. The latest released Landsat-9 data, which has higher radiation resolution and can be complemented with other Landsat data to improve imaging temporal resolution, have great potential for applications in lake area extraction. However, no study is published on identifying waterbodies and lakes in large-scale plateau scenes based on Landsat-9 data. Therefore, we relied on the Google Earth Engine (GEE) platform and selected ten waterbody extraction algorithms to evaluate the quantitative evaluation of waterbody and lake area extraction results on the QTP and explore the usability of Landsat-9 images in the relationship between the extraction accuracy and the algorithm. The results show that the random forest (RF) algorithm performs best in all models. The overall accuracy of waterbody extraction is 95.84%, and the average lake waterbody area extraction error is 1.505%. Among the traditional threshold segmentation waterbody extraction algorithms, the overall accuracy of the NDWI waterbody extraction method is 89.89%, and the average error of lake waterbody area extraction is 3.501%, which is the highest performance model in this kind of algorithm. The linear regression coefficients of NDVI and reflectance of Landsat-8 and Landsat-9 data are close to 1, and R2 is more significant than 0.91. At the same time, the overall accuracy difference of water extraction between the two data is not better than 1.1%. This study proves that Landsat-9 and Landsat-8 data have great consistency, which can be used for collaborative analysis to identify plateau waterbodies more efficiently. With the development of cloud computing technologies, such as Gee, more complex models, such as RF, can be selected to improve the extraction accuracy of the waterbody and lake area in large-scale research.
... Many studies have shown that SDD can be estimated using visual spectral bands and various band ratios. For instance, Ekercin (2007) presented an algorithm using bands 1, 2, and 3 of the high-resolution IKONOS data for monitoring SDD, Chl-a, and TSS concentration, with a correlation coefficient of more than 0.97 for SSD estimation. Besides the empirical algorithms, semi-analytical algorithms have been used to retrieve Z SD in different water bodies (Doron et al., 2011;Liu et al., 2020). ...
Ocean Water Quality (OWQ) monitoring provides insights into the quality of water in marine and near-shore environments. OWQ measurements can contain the physical, chemical, and biological characteristics of oceanic waters, where low OWQ values indicate an unhealthy ecosystem. Many parameters of water can be estimated from Remote Sensing (RS) data. Thus, RS offers significant opportunities for monitoring water quality in estuaries, coastal waterways, and the ocean. This paper reviews various RS systems and techniques for OWQ monitoring. It first introduces the common OWQ parameters, followed by the definition of the parameters and techniques of OWQ monitoring with RS techniques. In this study, the following OWQ parameters were reviewed: chlorophyll-a, colored dissolved organic matter, turbidity or total suspended matter/solid, dissolved organic carbon, Secchi disk depth, suspended sediment concentration, and sea surface temperature. This study presents a systematic analysis of the capabilities and types of spaceborne systems (e.g., optical and thermal sensors, passive microwave radiometers, active microwave scatterometers, and altimeters) which are commonly applied to OWQ assessment. The paper also provides a summary of the opportunities and limitations of RS data for spatial and temporal estimation of OWQ. Overall, it was observed that chlorophyll-a and colored dissolved organic matter are the dominant parameters applied to OWQ monitoring. It was also concluded that the data from optical and passive microwave sensors could effectively be applied to estimate OWQ parameters. From a methodological perspective, semi-empirical algorithms generally outperform the other empirical, analytical, and semi-analytical methods for OWQ monitoring.
... In similar studies, TSS has well correlated with the spectral wavelength of 450 nm to 880 nm from Landsat 8 OLI sensor in the water quality assessment in Nakdong River, Korea (Lim and Choi 2015). The spectral wavelengths of 445-530 nm, 520-610 nm, and 640-720 nm in IKONOS data were used for TSS estimation in Golden Horn, Turkey (Ekercin 2007). The spectral wavelengths of 705 nm and 842 nm were good sensitive to the assessment of turbidity in the water in the present study and whereas 550-850 nm combinations give good results in France's Gironde Estuary (Doxaran et al.2009). ...
Inland lake of Vembanad has benefited from continuous monitoring to evaluate water quality which has declined due to increased anthropogenic activities and climate change. Remote sensing techniques can be used to estimate and monitor inland water quality both spatially and temporally. An empirical model is presented in Vemaband lake that retrieves the specific water quality parameters through correlations between various spectral wavelengths of Sentinel-2MSI (S2MSI) with field-measured water quality parameters. This approach includes the combinations of various bands, band ratios, and band arithmetic computation of satellite sensors of spectral datasets. The specific inland water quality parameters such as chlorophyll-a (chl-a), total suspended solids (TSS), turbidity, and secchi disc depth (SDD) were retrieved from the developed water quality model through Sentinel-2A remote sensing reflectance. The result illustrates that Specific Inland Water Quality Parameters (SIWQP) strongly correlated with S2MSI reflection spectral wavelengths. The SIWQP models are constructed for TSS (R² = 0.8008), Chl-a (R² = 0.8055), Turbidity (R² = 0.6329) and SDD (R² = 0.7174).The spatial distribution of SIWQPs in Vembanad lake for March 2018 is mapped and shows the lake's water quality distribution. The research from Sentinel-2, MSI has potential and is appropriate in high spectral and spatial characteristics for retrieving and continuous monitoring of water quality parameters in the regional scale of inland water bodies.
... Reflectance in the Band 2 and Band 4 of Landsat ETM+ have also been used in the past to retrieve SSC values (Ouillon et al. 2004). Ekercin (2007) conducted a study on a river in Istanbul, Turkey using High Resolution Ikonos Multispectral Imagery to retrieve Total Suspended Solids (TSS). Few studies also used algorithms to define the relationship between in situ SSC and corresponding spectral radiance or reflectance to indicate SSC directly using satellite images (Pavelsky and Smith 2009;Reddy and Srinivasulu 1994;Xia 1993). ...
The studies selected for this contribution form part of the inter- and transdisciplinary exchange conducted in the scope of the International Conference Series: Water Security and Climate Change (WSCC). In this chapter, we summarize the main messages emerging from the contributions published in this volume and discussed in the scope of the WSCC conference series.
... Reflectance in the Band 2 and Band 4 of Landsat ETM+ have also been used in the past to retrieve SSC values (Ouillon et al. 2004). Ekercin (2007) conducted a study on a river in Istanbul, Turkey using High Resolution Ikonos Multispectral Imagery to retrieve Total Suspended Solids (TSS). Few studies also used algorithms to define the relationship between in situ SSC and corresponding spectral radiance or reflectance to indicate SSC directly using satellite images (Pavelsky and Smith 2009;Reddy and Srinivasulu 1994;Xia 1993). ...
Agriculture is a major economic activity of rural people in Bangladesh and a fundamental pillar for ensuring food security in the country. Agricultural productivity in coastal zone of Bangladesh has been decreasing in the last decade due to water scarcity caused by groundwater contamination coming from soil salinity. This paper aims at assessing the progress of soil salinity and at identifying the effects of salinity intrusion on agricultural production using the perception of the farmers in the Upazilas (sub-district) of Satkhira district located in the southwest coastal region of Bangladesh. Accordingly, landsat images of the years 2006 and 2016 were analyzed to assess and map the progress of soil salinity. In 2006, soils of none of the study areas had electrical conductivity (EC) beyond 16 dS/m. But in 2016, a total of 5644 ha in the two study areas combined had EC values higher than 16 dS/m. Only a very few varieties of crops can sustain in soils with EC values higher than 16 dS/m. Furthermore, interviews with 300 farmers of 15 randomly selected locations of the two Upazilas were conducted and the results validated by statistical tests and compared with the Landsat Images. Farmers identified water scarcity for irrigation purposes as the most prominent effect of salinity intrusion followed by a decline of prices in the livestock market.
... In recent years, ANN modeling has been widely utilized to quantify the severity of water quality issues due to its fast training process and ability to solve linear and nonlinear complex problems (Bonansea et al., 2015;Nasri, 2010;Nathan et al., 2017). Many studies utilized the BPNN and radial basis function (RBF) neural network for evaluating water quality and provided favorable outcomes through modeling complex nonlinear response functions, such as spectral reflectance values and WQP estimates (Ekercin, 2007;Gürsoy & Atun, 2019;Marquez et al., 2018;Zhang et al., 2003;Zhao et al., 2014). In river management programs, ANNs have effectively been used to evaluate the WQI levels to simulate wetland processes (Reynolds & Maberly, 2002;Kuo et al., 2007;Li et al., 2009;Song et al., 2012;Wang et al., 2012). ...
The present study evaluates the water quality status of 6-km-long Kali River stretch that passes through the Aligarh district in Uttar Pradesh, India, by utilizing high-resolution IRS P6 LISS IV imagery. In situ river water samples collected at 40 random locations were analyzed for seven physicochemical and four heavy metal concentrations, and the water quality index (WQI) was computed for each sampling location. A set of 11 spectral reflectance band combinations were formulated to identify the most significant band combination that is related to the observed WQI at each sampling location. Three approaches, namely multiple linear regression (MLR), backpropagation neural network (BPNN) and gene expression programming (GEP), were employed to relate WQI as a function of most significant band combination. Comparative assessment among the three utilized approaches was performed via quantitative indicators such as R2, RMSE and MAE. Results revealed that WQI estimates ranged between 203.7 and 262.33 and rated as “very poor” status. Results further indicated that GEP performed better than BPNN and MLR approaches and predicted WQI estimates with high R2 values (i.e., 0.94 for calibration and 0.91 for validation data), low RMSE and MAE values (i.e., 2.49 and 2.16 for calibration and 4.45 and 3.53 for validation data). Moreover, both GEP and BPNN depicted superiority over MLR approach that yielded WQI with R2 ~ 0.81 and 0.67 for calibration and validation data, respectively. WQI maps generated from the three approaches corroborate the existing pollution levels along the river stretch. In order to examine the significant differences among WQI estimates from the three approaches, one-way ANOVA test was performed, and the results in terms of F-statistic (F = 0.01) and p-value (p = 0.994 > 0.05) revealed WQI estimates as “not significant,” reasoned to the small water sample size (i.e., N = 40). The study therefore recommends GEP as more rational and a better alternative for precise water quality monitoring of surface water bodies by producing simplified mathematical expressions.
... Par la suite, d'autres programmes spatiaux ont suivi la lignée de Landsat tels que le programme français SPOT (Satellites Pour l'Observation de la Terre (Smith, 1997). De plus, grâce à l'expérience Take peuvent ainsi fournir une information sur la surface avec un niveau de détails important, ce qui s'est avéré utile dans des applications telles que la gestion des catastrophes naturelles (Stumpf et al., 2014), l'analyse de la qualité de l'eau (Ekercin, 2007), le suivi des glaciers (Holzer et al., 2015) ou encore l'urbanisme (Lee et al., 2003). ...
Compte tenu de la forte hétérogénéité spatio-temporelle des surfaces continentales, la télédétection spatiale s’est avérée être un moyen indispensable pour réaliser un suivi à la fois régulier, local et global des processus qui régissent ces surfaces. Les facteurs dont ils dépendent, tels que l’humidité du sol ou la végétation sont variables sur de larges gammes d’échelles auxquelles seuls les satellites peuvent accéder. En raison du nombre grandissant d’observations satellitaires présentes à plusieurs échelles spatiales et fondées sur de multiples technologies, diverses méthodes ont alors été développées pour permettre d’analyser et d’extraire au mieux l’information riche et conséquente acquise par satellite. Les méthodes basées sur l’analyse multi-échelle fournissent un moyen efficace pour décrire l’hétérogénéité de ces observations et ainsi mieux comprendre la complexité des processus de surface. En particulier, une possibilité consiste à s’intéresser à l’existence de lois d’échelles statistiques qui offrent un outil conceptuel générique applicable à la caractérisation de tout type de géométrie. Cela peut contribuer à caractériser les processus de surface selon une approche multi-échelle rarement prise en compte dans les modèles actuels de surface.Dans ce contexte, l’objectif de cette thèse est de montrer le potentiel d’une méthode permettant de caractériser sur plusieurs échelles spatiales les comportements de variables géophysiques de surface. Pour cela, différentes observations satellitaires complémentaires ont été analysées au moyen du modèle des Multifractales Universelles (Schertzer and Lovejoy, 1987). Deux cas d’étude ont permis de répondre à cet objectif. La première application porte sur l’analyse multifractale des produits intervenant dans l’algorithme de désagrégation spatiale d’humidité du sol DisPATCh (Disaggregation based on Physical And Theoretical scale Change; Merlin et al., 2008; Molero et al., 2016), sur la partie Sud-Est de l’Australie. Dans le deuxième cas d’étude, nous avons étudié le comportement multi-échelle de réflectances de surface et indices optiques acquis par le satellite Sentinel-2 sur la région Sud-Ouest de la France, et corrigés des effets atmosphériques par la chaine MAJA (MACCS-ATCOR Joint Algorithm; Hagolle et al., 2010, 2015; Rouquié et al., 2017). Dans ces deux cas d’étude, l’analyse de séries temporelles d’images nous a permis de mettre en relation l’évolution temporelle des propriétés d’échelle avec les variations saisonnières de la région d’étude (conditions météorologiques, cycles de cultures).Ce travail a révélé dans les produits de surface la présence de lois d’échelles qui diffèrent en fonction de la gamme d’échelles considérée. Ces comportements différents mettent en évidence des régimes d’échelles spécifiques qui, selon le produit étudié, peuvent s’expliquer de deux manières. D’une part, les régimes observés peuvent traduire la présence de processus de surface non-linéaires tels que les précipitations, le ruissellement ou l’évapotranspiration, agissant à différentes échelles spatiales et modulés par divers facteurs tels que la composition et la structure du sol (distribution de la végétation, présence de parcelles agricoles, etc.). D’autre part, ces comportements d’échelle peuvent également refléter l’impact sur les variables de surface des méthodes d’acquisition (fonction de transfert des capteurs) ou de traitement (combinaison de produits au sein des modèles) qui sont couramment utilisées en télédétection. De cette manière, cette étude a montré le potentiel de l’analyse multifractale pour décrire l’hétérogénéité des surfaces continentales, mais également pour évaluer la fiabilité de produits ou modèles de surface. Cette méthode pourrait être utile à la préparation de futures missions spatiales afin de déterminer les limites des capteurs en termes de propriétés multi-échelles, et ainsi mieux estimer la résolution effective de différents produits satellitaires.
... Chl-a absorbs light between blue (450-475 nm) and red (670 nm), but reflects at green (550 nm) and NIR (near infra-red, 700 nm). Thus, several studies have been performed to develop Chl-a estimation algorithms by using the ratios of different spectral bands of these sensors, varying from blue to NIR (Gitelson et al., 2008;Han & Jordan, 2005;Hoogenboom, Dekker, & Althuis, 1998), in addition to remote sensing measurements of Chl-a (Alparslan, Coskun, & Alganci, 2009;Colella, Falcini, Rinaldi, Sammartino, & Santoleri, 2016;Ekercin, 2007;Oguz & Gilbert, 2007). Therefore, Chl-a is accepted as one of the main indicators of water quality. ...
Detection of biological, physical and chemical parameters is needed for the determination of water quality. Some of these water quality parameters such as turbidity, chlorophyll-a, harmful algae, suspended sedi-ment, submerged habitat and temperature, can be derived directly via the satellite remote sensing facilities, particu-larly through the ocean color sensors. The competitiveness of satellite remote sensing comes from its capability of extensive geographical range and temporal coverage. Thus, changes and trends in water quality can be monitored and assessed to a greater degree, especially under the dynamic conditions of coastal zones. This study focuses on the water quality parameters in the vicinity of Green Ports of Turkey located in the Marmara Sea. There are 12 certified Green Ports in Turkey, located mostly in the Marmara Sea. Marmara Sea is a semi-enclosed inland sea and a passageway, which connects the Black Sea to the Mediterranean. There are 7 cities surrounding the Marmara Sea, representing the different anthropogenic aspects of civilization: Population, industry and agriculture. These aspects affect the water quality of the coastal zones in the Marmara Sea in different scales. Briefly, the aim of this study is to monitor and assess the impact of the Green Ports in the Marmara Sea region, in terms of water quality parameters detected via the Earth Observation System. Consequently, it is concluded that remote sensing capabilities of the contemporary Earth Observation Systems provide reliable results of water quality parameters when coupled with the field measurements in order to use in further decision-making mechanisms.
... Increasing the spatial resolution from~300 m (MODIS, MEdium Resolution Imaging Spectrometer (MERIS), Ocean and Land Color Instrument (OLCI)) to~30 m (OLI, SPOT) or~10 m (MSI) allows for remote sensing of SPM in small estuaries and nearshore zones, and allows for the monitoring of ports and dredging operations. Very high-spatial-resolution satellite sensors such as IKONOS or WorldView-2 and airborne portable remote imaging spectrometers have been already used to map water quality parameters [27][28][29][30][31]. However, there was no operational atmospheric correction processing for such data over coastal or inland waters. ...
This study investigated the use of frequent metre-scale resolution Pléiades satellite imagery to monitor water quality parameters in the highly turbid Gironde Estuary (GE, SW France). Pléiades satellite data were processed and analyzed in two representative test sites of the GE: 1) the maximum turbidity zone and 2) the mouth of the estuary. The main objectives of this study were to: (i) validate the Dark Spectrum Fitting (DSF) atmospheric correction developed by Vanhellemont and Ruddick (2018) applied to Pléiades satellite data recorded over the GE; (ii) highlight the benefits of frequent metre-scale Pléiades observations in highly turbid estuaries by comparing them to previously validated satellite observations made at medium (250/300 m for MODIS, MERIS, OLCI data) and high (20/30 m for SPOT, OLI and MSI data) spatial resolutions. The results show that the DSF allows for an accurate retrieval of water turbidity by inversion of the water reflectance in the near-infrared (NIR) and red wavebands. The difference between Pléiades-derived turbidity and field measurements
was proven to be in the order of 10%. To evaluate the spatial variability of water turbidity at metre scale, Pléiades data at 2 m resolution were resampled to 20 m and 250 m to simulate typical coarser resolution sensors. On average, the derived spatial variability in the GE is lower than or equal to 10% and 26%, respectively, in 20-m and 250-m aggregated pixels. Pléiades products not only show, in great detail, the turbidity features in the estuary and river plume, they also allow to map the turbidity inside ports and capture the complex spatial variations of turbidity along the shores of the estuary. Furthermore, the daily acquisition capabilities may provide additional advantages over other satellite constellations when monitoring highly dynamic estuarine systems.
... Hellweger et al. (2004) used the red band, whereas Lathrop and Lillesand (1986) used the green band from the Landsat TM to determine SDD. Ekercin (2007) found blue, green, and red bands of the IKONOS to explain 98.93% of the variance in SDD. According to Allee and Johnson (1999), the Landsat TM red band is most useful in determining the SDD. ...
Water resources are critical to the sustainability of life on Earth. With a growing population and climate change, it is imperative to assess the security of these resources. Over the past five decades, satellite remote sensing has become indispensable in understanding the Earth and atmospheric processes. Satellite sensors have the capability of providing data at global scales, which is economical compared to the ground or airborne sensor acquisitions. The science community made significant advances over recent years with the help of satellite remote sensing. In view of these efforts, the current review aims to present a comprehensive review of the role of remote sensing in assessing water security. This review highlights the role of remote sensing applications to assess water quality, quantity, and hydroclimatic extreme events that play an important role in improving water security. Four water quality parameters, namely, chlorophyll-a, turbidity and Total Suspended Solids (TSS), Secchi Disk Depth (SDD), and Colored Dissolved Organic Matter (CDOM), are considered. Under water quantity assessment, we review three aspects, streamflow estimation, terrestrial water storage, and reservoir operations. Remote sensing applications in quantifying floods and droughts extremes are reviewed in this work. We present how satellite sensor information acquired from different spectral bands, including optical, thermal, and microwave ranges, along with gravity field measurements, have contributed towards the applications in the above areas. We also assess the role of physical models, empirical models, and data assimilation strategies, among others, in the above areas. Finally, possible future research pathways needed to address the issues faced by the science community are discussed. This work is the second of the two-part review series, wherein the first part deals with the applications of satellite remote sensing for agriculture management.
... Second, glint correction is a particular aquatic challenge that requires simultaneous measurement of additional radiances in the NIR, beyond those needed to compute a particular WQP of interest (Fig. 1). These measured NIR radiances may exhibit a lower SNR than the visible radiances, due to a combination of the [12][13][14][15][16][17][18][19][20][21]. Highest accuracy algorithms often utilize a larger number of wavelengths. ...
Low-power, lightweight, off-the-shelf imaging spectrometers, deployed on above-water fixed platforms or on low-altitude aerial drones, have significant potential for enabling fine-scale assessment of radiometrically derived water quality properties (WQPs) in oceans, lakes, and reservoirs. In such applications, it is essential that the measured water-leaving spectral radiances be corrected for surface-reflected light, i.e., glint. However, noise and spectral characteristics of these imagers, and environmental sources of fine-scale radiometric variability such as capillary waves, complicate the glint correction problem. Despite having a low signal-to-noise ratio, a representative lightweight imaging spectrometer provided accurate radiometric estimates of chlorophyll concentration—an informative WQP—from glint-corrected hyperspectral radiances in a fixed-platform application in a coastal ocean region. Optimal glint correction was provided by a spectral optimization algorithm, which outperformed both a hardware solution utilizing a polarizer and a subtractive algorithm incorporating the reflectance measured in the near infrared. In the same coastal region, this spectral optimization approach also provided the best glint correction for radiometric estimates of backscatter at 650 nm, a WQP indicative of suspended particle load.
... In many parts of the world, remote sensing and GIS have been used in different water body, coastal https://doi.org/10.1016/j.jafrearsci.2019.103569 Received 10 October 2018; Received in revised form 18 June 2019; Accepted 24 July 2019 T zone and climate change studies by different researchers (Alesheikh et al., 2007;Brivio and Zilioli, 1996;Durduran, 2009;Rao et al., 1999;Sesli et al., 2009;Ekercin, 2007;Donoghue and Mironnet, 2002;Şener et al., 2010;Şener, 2016). In addition, GIS techniques are useful in the hydrological studies such as evaluation of flash flood hazard with limitation or the absence of measured hydrological information and evaluation of morphometric parameters which are controlled flash floods (Elfeki et al., 2017;Masoud, 2016). ...
... Toxic black blooms, unlike BOW, are an ecological disaster that involves eutrophication and algal blooms [30]- [35]. Several studies currently exist on water quality assessment in rivers using high-resolution satellite imagery [36]- [40]. In China, some algorithms have been developed for BOW. ...
Urban black and odorous water (BOW) is a serious global environmental problem. Since these waters are often narrow rivers or small ponds, the detection of BOW waters using traditional satellite data and algorithms is limited both by a lack of spatial resolution and by imperfect retrieval algorithms. In this paper, we used the Chinese high-resolution remote sensing satellite Gaofen-2 (GF-2, 0.8 m). The atmospheric correction showed that the mean absolute percentage error of the derived remote sensing reflectance (Rrs) in visible bands is 25.19%. We first measured Rrs spectra of two classes of BOW [BOW with high concentrations of iron (II) sulfide, i.e., BOW1 and BOW with high concentrations of total suspended matter, i.e., BOW2] and ordinary water in Shenyang. Then, in situ Rrs data were converted into Rrs corresponding to the wide GF-2 bands using the spectral response functions. We used the converted Rrs data to calculate several band combinations, including the baseline height, [Rrs(green) - Rrs(red))/(Rrs(green) + Rrs(red)], and the color purity on a Commission Internationale de L'Eclairage (CIE) chromaticity diagram. The color purity was found to be the best index to extract BOW from ordinary water. Then, Rrs (645) was applied to categorize BOW into BOW1 and BOW2. We applied the algorithm to two synchronous GF-2 images. The recognition accuracy of BOW2 and ordinary water are both 100%. The extracted river water type near Weishanhu Road was BOW1, which agreed well with ground truth. The algorithm was further applied to other GF-2 data for Shenyang and Beijing.
... However, since most coastal zones stretch for thousands of kilometers, it is not an easy task to identify the coastal reaches that require early and increased attention. In this context, remote sensing emerges as a potentially important source of information for the detection of marine pollution [42][43][44][45]. ...
Bathing water quality has been monitored in the west coast of Tangier, Morocco due to increased urban and industrial discharge through the Boukhalef river, using in-situ bacteriological measurements which demand high economical and temporal costs. In this study, Landsat 8 Thermal Infrared Sensor (TIRS) images were used as an alternative to the classical method, for determining bathing water quality to help decision makers obtain up-to-date and cost-effective information for coastal environment protection. For this purpose, during spring and summer 2017, seven sampling points were examined in terms of bacteriological parameters: Total Coliforms (TC), Faecal Coliforms (FC), Intestinal Enterococci (IE) and Escherichia coli (E. coli). Also, a spatial-temporal analysis was performed in this temporal window to detect temperature anomalies and their spatial distribution along the coastal bathing area. In addition, a relationship between in-situ bacteriological parameter measurements and temperature from satellite images was analyzed. The results of the water temperature distribution showed the highest values next to the Boukhalef river mouth, as well as the poorest water quality according to in-situ measurements, while lower values and better water quality status were observed moving away from the Boukhalef river mouth. The relationship between water temperature and bacterial concentration showed a high correlation coefficient (R 2 = 0.85). Consequently, the model development approaches used may be useful in estimating bacterial concentration in coastal bathing areas and can serve to create a monitoring system to support decision makers in the protection actions of the coast.
... In addition, some of the parameters 1 3 46 Page 2 of 8 that define the criteria for healthy river water and being widely analyzed in the water quality-based studies include pH, turbidity, dissolved oxygen (DO), total suspended solids (TSS), total dissolved solids (TDS), turbidity, total hardness as CaCO 3 , biochemical oxygen demand (BOD), chemical oxygen demand (COD), calcium (Ca), magnesium (Mg), alkalinity, phosphate (PO 4 ), sodium (Na), potassium (K), sulfate (SO 4 ) and nitrate (NO 3 ) to name a few. Monitoring and assessment of these water quality parameters require sampling from widely distributed locations, which is time-consuming and requires a lot of field and laboratory efforts to present statistical results (Shi et al. 2018;Nazeer and Nichol 2015;Singh et al. 2013;Duong 2012;Amandeep 2011;Kazi et al. 2009;Ekercin 2007;Icaga 2007;Wang et al. 2004;Silvert 1998;Pattiaratchi et al. 1994). ...
River Yamuna is the largest tributary of river Ganges and has been acclaimed as a heavenly waterway in Indian mythology. However, 22-km segment of river Yamuna passing through Delhi from downstream of Wazirabad barrage up to Okhla barrage is considered as the filthiest stretch having been rendered into a sewer drain. The present study employs high-resolution GeoEye-2 imagery for mapping and monitoring pollution levels within the river segment by testing correlation between water quality parameters (WQPs) and the corresponding spectral reflectance values of the image. A total of 100 water samples collected from random sampling locations along the river segment were analyzed for 12 WQPs in the laboratory and grouped into two classes, namely (WQP)organic and (WQP)inorganic. Several spectral band combinations as well as single bands were tested for any significant correlation with the two formulated WQP classes by performing multiple linear regression analysis. Results reveal that spectral band combination, i.e., \(\left\{ {\overline{{\left( {RGB} \right)}} \times \sqrt {B/R} } \right\},\) and the two formulated WQP classes exhibit strong positive correlation with R = 0.92 and 0.91 (R² ~ 0.85 and 0.82; RMSE ~ 1.03 and 1.12) for calibration data and 0.85 and 0.84 (i.e., R² ~ 0.74 and 0.72; RMSE ~ 1.45 and 1.64) for validation data, respectively. The spatial distribution maps depicting pollution levels of two WQP classes were generated in GIS framework, substantiating to the actual in situ pollution concentration levels in the river segment. The methodology adopted in the present study and results obtained validate the potential of high-resolution GeoEye-2 imagery for monitoring and mapping pollution levels in the water bodies.
... In this study, it was aimed to determine the fishing areas with the high fishing yield by associating the spectral characteristics and water parameters of the habitats of C. gibelio living in Lake Egirdir with remote sensing. In our country and in the wetlands in other countries, there are various studies using remote sensing techniques [4][5][6][7][8][9][10][11][12]. In this way, it is thought that a contribution can be provided to the planning of the most efficient and economical stock management in the inland waters. ...
In this research, the water quality parameters and spectral characteristics of C. gibelio (Bloch, 1782) living as a dominant species in Lake Egirdir were seasonally examined. 975 C. gibelio individuals were caught from 4 different stations between November 2009 and October 2010. C. gibelio was fished with trammel nets with 30, 40, 45, 50, 60 mm sized meshes. The catch compositions and produc-tivities of trammel nets and stations were determined individually and in terms of proportion and weight. During the study, the highest number of individuals was fished by nets with 40, 45, 50 mm sized meshes in Egirdir (38.14%) region. The water samples taken from the stations seasonally were analyzed. As a result of these analyses, water temperature at the stations was measured to vary between 12-16°C, pH between 8-8.95, turbidity between 1-2.31 NTU, chlorophyll-a concentration between 0.267-1.622 mg/l, dissolved oxygen between 6.86-11.37 mg/l, and suspended matter between 25-32.1 mg/l. It was determined according to the chlorophyll-a values that Lake Eğirdir has an oligotrophic structure. Spectral measurements were carried out with a spectrora-diometer (ASD Fieldspec (UV/ VNIR)). It was identified that the chlorophyll-a and reflection values were at the highest values at all stations (except for Hoyran) during summer and fishing yield was parallel with this.
... Regression analysis between observed TSS/Turbidity as dependent variable and reflectance were performed. Preliminary, results agree to a certain level with previous applications stating that suspended sediment concentration is highly sensitive around remote sensing reflectance at a wavelength of 0.65 µm (Li et al., 2003;Binding et al., 2005;Bowers & Binding, 2006;Ekercin, 2007;Chen et al., 2007). However, modifications and finer band combination and coefficients were required to fit site specifications. ...
Coastal systems of the Nile Delta experience active interactions and continuous alteration. Beside anthropogenic activities, the delta coast is the end point of the Egyptian widely distributed irrigation-drainage network, and directly connected with coastal lakes. Concerns about water quality conditions and compromised environmental health, and consequently beneficial uses have triggered regular monitoring campaigns. Yet, field sampling and in-situ measurements of water quality indicators for the coastline of hundreds of kilometers are laborious, costly, time-consuming, and frequently faced with inaccessibility. This research investigates the usefulness of using satellite-based techniques in deriving water clarity trends, with wider spatial coverage and more frequent data acquisition. The research study uses the Geographical Information System (GIS) ArcView, ERDAS Imagine image 2010, and Landsat 7 satellite-Enhanced Thematic Mapper Plus (ETM+) imageries in the nearest corresponding overpass dates of ground truth reference data. Distributed ground control points (GCPs) of turbidity and suspended solids for years 2008 to 2011 were used for calibration (R 2 were 0.92 and 0.70, respectively). Validation of developed algorithm, using data from years 2012 and 2013, proved successful estimations (R 2 were 0.78 and 0.65 for turbidity and suspended solids, respectively). The study establishes a predictive relationship with acceptable accuracy results to follow changes in clarity indications along the delta coastline, allowing development of wide spatial and temporal database.
... Regression analysis between observed TSS/Turbidity as dependent variable and reflectance were performed. Preliminary, results agree to a certain level with previous applications stating that suspended sediment concentration is highly sensitive around remote sensing reflectance at a wavelength of 0.65 µm (Li et al., 2003;Binding et al., 2005;Bowers & Binding, 2006;Ekercin, 2007;Chen et al., 2007). However, modifications and finer band combination and coefficients were required to fit site specifications. ...
Coastal systems of the Nile Delta experience active interactions and continuous alteration. Beside anthropogenic activities, the delta coast is the end point of the Egyptian widely distributed irrigation-drainage network, and directly connected with coastal lakes. Concerns about water quality conditions and compromised environmental health, and consequently beneficial uses have triggered regular monitoring campaigns. Yet, field sampling and in-situ measurements of water quality indicators for the coastline of hundreds of kilometers are laborious, costly, time-consuming, and frequently faced with inaccessibility. This research investigates the usefulness of using satellite-based techniques in deriving water clarity trends, with wider spatial coverage and more frequent data acquisition. The research study uses the Geographical Information System (GIS) ArcView, ERDAS Imagine image 2010, and Landsat 7 satellite-Enhanced Thematic Mapper Plus (ETM+) imageries in the nearest corresponding overpass dates of ground truth reference data. Distributed ground control points (GCPs) of turbidity and suspended solids for years 2008 to 2011 were used for calibration (R 2 were 0.92 and 0.70, respectively). Validation of developed algorithm, using data from years 2012 and 2013, proved successful estimations (R 2 were 0.78 and 0.65 for turbidity and suspended solids, respectively). The study establishes a predictive relationship with acceptable accuracy results to follow changes in clarity indications along the delta coastline, allowing development of wide spatial and temporal database.
... In this study, it was aimed to determine the fishing areas with the high fishing yield by associating the spectral characteristics and water parameters of the habitats of C. gibelio living in Lake Egirdir with remote sensing. In our country and in the wetlands in other countries, there are various studies using remote sensing techniques [4][5][6][7][8][9][10][11][12]. In this way, it is thought that a contribution can be provided to the planning of the most efficient and economical stock management in the inland waters. ...
In this research, the water quality parameters and spectral characteristics of C. gibelio (Bloch, 1782) living as a dominant species in Lake Egirdir were seasonally examined. 975 C. gibelio individuals were caught from 4 different stations between November 2009 and October 2010. C. gibelio was fished with trammel nets with 30, 40, 45, 50, 60 mm sized meshes. The catch compositions and productivities of trammel nets and stations were determined individually and in terms of proportion and weight. During the study, the highest number of individuals was fished by nets with 40, 45, 50 mm sized meshes in Egirdir (38.14%) region. The water samples taken from the stations seasonally were analyzed. As a result of these analyses, water temperature at the stations was measured to vary between 12-16°C, pH between 8-8.95, turbidity between 1-2.31 NTU, chlorophyll-a concentration between 0.267-1.622 mg/l, dissolved oxygen between 6.86-11.37 mg/l, and suspended matter between 25-32.1 mg/l. It was determined according to the chlorophyll-a values that LakeEgirdir has an oligotrophia structure. Spectral measurements were carried out with a spectroradiometer (ASD Fieldspec (UV/VNIR)). It was identified that the chlorophyll-a and reflection values were at the highest values at all stations (except for Hoyran) during summer and fishing yield was parallel with this.
... Since the 20 th century, a lot of commercial satellite companies provide high resolution remote sensing images (i.e., IKONOS, SPOT series, WorldView series, GeoEye-1) that make it more suitable for monitoring inland waters, especially for those quite narrow rivers. [22] adopted the approach based on analytical optical model to estimate C TSM taking TM and SPOT satellite data, while [23] examined and validated the retrievals of C TSM and chlorophyll-a with high resolution IKONOS multispectral data. Although these high resolution satellite data could not be analyzed using specific absorption characteristics due to its broad bandwidths and relative low signalto-noise ratio compared with medium and low spatial resolution satellite data, the advantages of high resolution make it capable of monitoring inland rivers and reservoirs due to that these water body usually has a relative small area. ...
Satellite imagery has played an important role in monitoring water quality of lakes or coastal waters presently, but scarcely been applied in inland rivers. This paper presents an attempt of feasibility to apply regression model to quantify and map the concentrations of total suspended matter (CTSM) in inland rivers which have a large scale of spatial and a high CTSM dynamic range by using high resolution satellite remote sensing data, WorldView-2. An empirical approach to quantify CTSM by integrated use of high resolution WorldView-2 multispectral data and 21 in situ CTSM measurements. Radiometric correction, geometric and atmospheric correction involved in image processing procedure is carried out for deriving the surface reflectance to correlate the CTSM and satellite data by using single-variable and multivariable regression technique. Results of regression model show that the single near-infrared (NIR) band 8 of WorldView-2 have a relative strong relationship (R2=0.93) with CTSM. Different prediction models were developed on various combinations of WorldView-2 bands, the Akaike Information Criteria approach was used to choose the best model. The model involving band 1, 3, 5, and 8 of WorldView-2 had a best performance, whose R2 reach to 0.92, with SEE of 53.30 g/m3. The spatial distribution maps were produced by using the best multiple regression model. The results of this paper indicated that it is feasible to apply the empirical model by using high resolution satellite imagery to retrieve CTSM of inland rivers in routine monitoring of water quality.
... Since the launch of the early remote sensing satellites in the early 1970s many studies have been conducted in remotely mapping TSS, driven in part by the capability of satellite remote sensing to cover large spatial domains in near real time [7]. Considering the past decade, remote sensing studies of the spatial and temporal mapping of TSS have utilized moderate resolution sensors including Landsat [9][10][11][12][13][14][15], MERIS [6,[16][17][18][19][20], MODIS [17,[21][22][23][24][25][26][27], and high resolution sensors including SPOT [28], IKONOS [29], and THEOS [30]. Further, the TSS mapping studies encompassed waters with diverse optical and physical properties, from inland lakes and river systems [21,25,[31][32][33] to coastal waters [34][35][36][37][38] and from different geographical locations including America [3,32,39], Africa [26,40], Asia [16,34,41], Australia [42,43], and Europe [17,23,27,37]. ...
A quantitative comparative study was performed to assess the relative applicability of Total Suspended Solids (TSS) models published in the last decade for the Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat-based sensors. The quantitative comparison was performed using a suite of statistical tests and HydroLight simulated data for waters ranging from clear open ocean case-1 to turbid coastal case-2 waters. The quantitative comparison shows that there are clearly some high performing TSS models that can potentially be applied in mapping TSS concentration for regions of uncertain water type. The highest performing TSS models tested were robust enough to retrieve TSS from different water types with Mean Absolute Relative Errors (MARE) of 69.96%–481.82% for HydroLight simulated data. The models were also compared in regional waters of northern Western Australia where the highest performing TSS models yielded a MARE in the range of 43.11%–102.59%. The range of Smallest Relative Error (SRE) and Largest Relative Error (LRE) between the highest and the lowest performing TSS models spanned three orders of magnitude, suggesting users must be cautious in selecting appropriate models for unknown water types.
A population estimation of Wilson’s Phalaropes was conducted in the Confinada Lagoon of the inner bay of Lake Titicaca in the city of Puno between August and May of 2016/2017, 2017/2018 and 2018/2019. We employed the quadrat method of population estimation with three repetitions using 10×50 mm binoculars between 06:00 and 08:00 h. We recorded population fluctuations in August and September (immigration season) and April and May (emigration season). We counted a maximum population of 112,000 Wilson’s Phalaropes during the immigration season, and then in the emigration season. During their stay of approximately 8 months, the birds feed on water fleas, insects and detritus amounted to 146,496 t. The inner bay of Lake Titicaca is an important aquatic habitat for these migratory birds, which has a positive impact on the contribution to the decontamination of the lake.
The high spatial resolution of satellite data and the capability of physics-based approaches are considered highly suitable for testing the integration of remote sensing technologies into the water quality monitoring of small and medium-sized inland lakes. This research thus aimed to investigate an operational algorithm for chlorophyll-a (Chla) estimation based on China’s recently launched high-spatial-resolution GF-1 satellite data for Lake Dianshan, a eutrophic lake in Shanghai city, eastern China. For the calibration of the empirical model, an enhanced three-band model and an improved four-band model (IFB) developed by model derivation and statistical analysis based on in situ water sampling and satellite reflectance data were proposed. The IFB model could account for more than 90% of the Chla variation in the GF-1 satellite data. For the calibration of the semi-empirical model, the performance of ΔΦ and an improved NCI model (NCI’) were analyzed and validated with field spectral measurements and GF-1 satellite data. The corresponding GF-1 satellite ΔΦ model and NCI’ model reached high estimation accuracies of R2 = 0.80 and 0.76, respectively. The good estimated results indicate that the established GF-1 satellite models are promising and applicable to estimating Chla in small and medium-sized eutrophic inland lakes.
The monitoring of lake waterbody area in the Qinghai Tibet Plateau (QTP) is of great significance to deal with global climate change. As the latest generation of Landsat series satellites, Landsat-9 data not only have higher radiometric resolution, but also cooperate with other Landsat satellites to greatly improve the temporal resolution. It has great application potential in lake waterbody area monitoring. In order to explore the performance of different algorithms for extracting waterbody and lake waterbody area in Landsat-9 data under large-scale QTP regions, this study relies on Google Earth Engine (GEE) platform and selects 10 waterbody extraction algorithms as the basis to realize the quantitative evaluation of QTP lake waterbody area extraction results. The results show that the Random Forest (RF) algorithm performs best in all models. The overall accuracy of waterbody extraction is 95.84%, and the average error of lake waterbody area extraction is 1.505%. Among the traditional threshold segmentation waterbody extraction algorithms, the overall accuracy of the NDWI waterbody extraction method is 89.89%, and the average error of lake waterbody area extraction is 3.501%, which is the highest performance model in this kind of algorithms. This study proves that Landsat-9 data can effectively classify QTP waterbodies. With the development of cloud computing technologies such as Gee, more complex models such as RF can be selected to improve the extraction accuracy of water body and Lake area in large-scale research.
Recently, remote sensing considered as important tool in studies of water quality issues. The Aras River flows across a transboundary basin in northern Iran. In this study, the aim is to model the water quality parameters (WQPs) using remote sensing and an artificial neural network (ANN), which is a new method proposed to find WQPs based on multivariate regression approaches. The relationship between WQPs and digital data from the Sentinel-2 satellite was determined to estimate and map the WQPs in this river. Using the field data and digital image data, the obtained results show that multivariate regression approaches and high-resolution remote sensing could monitor and predict the distribution of WQPs in this basin. There was a meaningful correlation between bands calculated from image data and the river’s WQPs, including electrical conductivity (EC) (R2 = 0.91), Na+ (R2 = 0.81), Mg2+ (R2 = 0.90), SO42− (R2 = 0.89), Cl− (R2 = 0.81), and Ca2+ concentrations (R2 = 0.60). Likewise, the WQPs including EC, SO42−, Mg2+, and Cl− in the river are predicted using ANN. This powerful model is built for the rapid assessment and forecasting of experimentally mentioned WQPs at any location in the domain of interest. The results represent the good ability of ANN to simulate the mentioned WQPs. Simulation accuracy, measured by the determination coefficient (DC), varies between 0.982 and 0. 991 for training and overfitting EC test data. It is concluded that the management of transboundary rivers can be achieved by using remote sensing techniques and machine learning methods, which help the beneficiary countries in the sustainable operation.
The combination of remote sensing technology and traditional field sampling provides a convenient way to monitor inland water. However, limited by the resolution of remote sensing images and cloud contamination, the current water quality inversion products do not provide both high temporal resolution and high spatial resolution. By using the spatio-temporal fusion (STF) method, high spatial resolution and temporal fusion images were generated with Landsat, Sentinel-2, and GaoFen-2 data. Then, a Chl-a inversion model was designed based on a convolutional neural network (CNN) with the structure of 4-(136-236-340)-1-1. Finally, the results of the Chl-a concentrations were corrected using a pixel correction algorithm. The images generated from STF can maintain the spectral characteristics of the low-resolution images with the R2 between 0.7 and 0.9. The Chl-a inversion results based on the spatio-temporal fused images and CNN were verified with measured data (R2 = 0.803), and then the results were improved (R2 = 0.879) after further combining them with the pixel correction algorithm. The correlation R2 between the Chl-a results of GF2-like and Sentinel-2 were both greater than 0.8. The differences in the spatial distribution of Chl-a concentrations in the BYD lake gradually increased from July to August. Remote sensing water quality inversion based on STF and CNN can effectively achieve high frequency in time and fine resolution in space, which provide a stronger scientific basis for rapid diagnosis of eutrophication in inland lakes.
Suspended Sediment Concentration (SSC) is a significant parameter in the hydrologic, morphologic, and ecosystem studies of large alluvial rivers of the Ganges-Brahmaputra-Meghna (GBM) delta. Traditional in situ measurement of SSC in the Lower Ganges River is challenging in terms of time, cost, and spatial coverage. This study investigated the applicability of open-access Landsat Enhanced Thematic Mapper (ETM+) images in estimating the SSC of the Lower Ganges. Multiple-temporal Landsat 7 ETM+ images were processed to extract Digital Numbers (DN) of pixels corresponding to Bangladesh Water Development Board (BWDB)’s river measurement station, Mawa (SW93.5L). The DNs were converted to radiance and ultimately to top-of-atmosphere (ToA) reflectance. The ToA values for Landsat-7 bands 1–4, which sense electromagnetic radiation of 0.45–0.52, 0.52–0.60, 0.63–0.69, and 0.76–0.90 µm, respectively, were combined with corresponding measured values of SSC, between the years 2000 and 2012 for determination of statistical relationship between them. R² for bands 1, 2, 3, and 4 were 0.64, 0.51, 0.44, and 0.67, respectively; showing that Coefficient of Determination (R²) value of band 4 presented the best relationship—therefore chosen as the best SSC indicator. Scatter plot of predicted SSC values from a polynomial equation based on band 4 against in situ values of SSC with 1:1 fit line generated strong positive coefficient of determination of 0.89 and Root Mean Square Error of 88.3 ppm. Using a polynomial model based on the band 4 data, spatial distribution maps and temporal variation of SSC, between the years 2000 and 2010, for monsoon and post-monsoon seasons were demonstrated and river cross-sectional analysis was performed.
Water is an essential natural source for human being and environment. To conserve water sources, monitoring them by using remote sensing data and technologies is an efficient way. In this study, water quality of the Sea of Marmara (Turkey), which has lots of currents, was examined. The main aim of the study was developing a common model to monitor chlorophyll-a concentration in time by using satellite data. After, the coefficients of the OC2 (ocean chlorophyll 2) model were detected by curve fitting, it was applied to Landsat images. The bias and RMSE (Root Mean Square Error) were found as 0.73 µg/l and 5.80 µg/l, respectively. The high RMSE was stemmed from dynamic structure of the sea. Thus, the temporal resolution has a profound impact on the accuracy of estimations. The developed model was applied to the HLS (Harmonized Landsat Sentinel-2) data, which has high temporal resolution. The results of the HLS and Landsat images were compared, and HLS is found as proper to monitor the water quality. The combined data (SST (Sea Surface Temperature) daily data from 1981 to present derived from satellite observations Level-4 product) was used for the secondary aim of the study which was monitoring SST. The bias and RMSE of the data, which was acquired on 19.07.2017, were found as 0.33 °C and 1.12 °C, respectively. The bias and RMSE of the data, which was acquired on 18.07.2018, were found as −0.02 °C and 1.03 °C, respectively. The combined data is found appropriate to monitor the SST.
Les travaux de dragage peuvent occasionner la remise en suspension de sédiments contaminés dans la colonne d'eau et leur transport vers les sites aquacoles. Effigis, en collaboration avec Travaux publics et Services gouvernementaux Canada et l'Agence spatiale canadienne, a développé une méthodologie de suivi des concentrations de sédiments en suspension (CSS) en mer à partir d'imagerie satellitaire. Un examen de l'état de l'art a permis de passer en revue les méthodes existantes pour l'estimation des CSS, les capteurs qui répondent aux besoins et les protocoles d'échantillonnage terrain permettant de valider l'approche. La méthodologie retenue repose sur l'utilisation d'un modèle empirique pour l'estimation de la CSS à partir des réflectances de l'eau. La constellation Pléiades a été retenue en raison de son potentiel à estimer la CSS, sa capacité à acquérir des images à des dates spécifiques et à mettre en place une application opérationnelle. Les résultats montrent que les corrélations entre les mesures et les estimations de CSS s'approchent des 80%, avec des erreurs RMSE de l'ordre de 25%.
Gracias a técnicas de colorimetría y métodos de clasificación no supervisada, pueden ser vectorizada y trazada la evolución de contaminantes vertidos en el mar con res- pecto al tiempo y al espacio, lo que, en la práctica, abarata el costo de investigación y permite tener una mayor cobertura espacio-temporal. Este trabajo presenta el caso de estudio de Bahía de Banderas, Jalisco-Nayarit, México, donde se estudió el vertido de detritos por el río Ameca en el periodo 2006-2014 a través de un producto combinado de imágenes LandSat y Modis y con el análisis de las firmas espectrales de las diferentes bandas que presentan estos satélites se infiere los porcentajes de la materia orgánica disuelta en las plumas.
Economic development, population growth, industrialization, and urbanization dramatically increase urban water quality deterioration, and thereby endanger human life and health. However, there are not many efficient methods and techniques to monitor urban black and odorous water (BOW) pollution. Our research aims at identifying primary indicators of urban BOW through their spectral characteristics and differentiation. This research combined ground in-situ water quality data with ground hyperspectral data collected from main urban BOWs in Guangzhou, China, and integrated factorial data mining and machine learning techniques to investigate how to monitor urban BOW. Eight key water quality parameters at 52 sample sites were used to retrieve three latent dimensions of urban BOW quality by factorial data mining. The synchronically measured hyperspectral bands along with the band combinations were examined by the machine learning technique, Lasso regression, to identify the most correlated bands and band combinations, over which three multiple regression models were fitted against three latent water quality indicators to determine which spectral bands were highly sensitive to three dimensions of urban BOW pollution. The findings revealed that the many sensitive bands were concentrated in higher hyperspectral band ranges, which supported the unique contribution of hyperspectral data for monitoring water quality. In addition, this integrated data mining and machine learning approach overcame the limitations of conventional band selection, which focus on a limited number of band ratios, band differences, and reflectance bands in the lower range of infrared region. The outcome also indicated that the integration of dimensionality reduction with feature selection shows good potential for monitoring urban BOW. This new analysis framework can be used in urban BOW monitoring and provides scientific data for policymakers to monitor it.
Environmental monitoring is necessary to ensure the overall health and conservation of an ecosystem. However, ecosys-tems (e.g. air, water, soil), are complex, involving numerous processes (both native and external), inputs, contaminants, and living organisms. As such, monitoring an environmental system is not a trivial task. The data obtained from natural systems is often multifaceted and convoluted, as a multitude of inputs can be intertwined within the matrix of the infor-mation obtained as part of a study. This means that trends and important results can be easily overlooked by convention-al and single dimensional data analysis protocols. Recently, chemometric methods have emerged as a powerful method for maximizing the details contained within a chemical data set. Specifically, chemometrics refers to the use of mathemat-ical and statistical analysis methods to evaluate chemical data, beyond univariant analysis. This type of analysis can pro-vide a quantitative description of environmental measurements, while also having the capacity to reveal previously over-looked trends in data sets. Applying chemometrics to environmental data allows us to identify and describe the inter-relationship of environmental drivers, sources of contamination, and their potential impact upon the environment. This review aims to provide a detailed understanding of chemometric techniques, how they are currently used in environmen-tal monitoring, and how these techniques can be used to improve current practices. An enhanced ability to monitor envi-ronmental conditions and to predict trends would be greatly beneficial to government and research agencies in their abil-ity to develop environmental policies and analytical procedures.
Satellite remote sensing of inland water body requires a high spatial resolution and a multiband narrow spectral resolution, which makes the fusion between panchromatic (PAN) and multi-spectral (MS) images particularly important. Taking the Daquekou section of the Qiantang River as an observation target, four conventional fusion methods widely accepted in satellite image processing, including pan sharpening (PS), principal component analysis (PCA), Gram-Schmidt (GS), and wavelet fusion (WF), are utilized to fuse MS and PAN images of GF-1. The results of subjective and objective evaluation methods application indicate that GS performs the best, followed by the PCA, the WF and the PS in the order of descending. The existence of a large area of the water body is a dominant factor impacting the fusion performance. Meanwhile, the ability of retaining spatial and spectral informations is an important factor affecting the fusion performance of different fusion methods. The fundamental difference of reflectivity information acquisition between water and land is the reason for the failure of conventional fusion methods for land observation such as the PS to be used in the presence of the large water body. It is suggested that the adoption of the conventional fusion methods in the observing water body as the main target should be taken with caution. The performances of the fusion methods need re-assessment when the large-scale water body is present in the remote sensing image or when the research aims for the water body observation.
The present article utilizes high resolution Geoeye 2 imagery for mapping and monitoring pollution concentrations of 22 km stretch of river Yamuna passing through Delhi state, by developing regression models between water quality parameters (WQP's) and the corresponding spectral reflectance values. Water samples collected from the sampling locations were analysed for 20 WQP's and grouped into four classes namely; (WQP)organic, (WQP)inorganic, (WQP)anion and (WQP)cation. Several spectral band combinations as well as single bands were probed for performing multiple linear regression (MLR) analysis with the four WQP classes. Results reveal relatively strong positive correlations for band combination viz. [mean RGB × √B/R] with all four WQP classes yielding high R2 value (∼0.85) and RMSE (∼1.03) amongst other selected band combinations. Spatial distribution maps were generated that substantiates to the actual in-situ pollution concentration levels thereby evidences the potential of high resolution Geoeye-2 imagery for monitoring and mapping pollution concentrations in the water bodies.
The present article utilizes high resolution Geoeye 2 imagery for mapping and monitoring pollution concentrations of 22 km stretch of river Yamuna passing through Delhi state, by developing regression models between water quality parameters (WQP's) and the corresponding spectral reflectance values. Water samples collected from the sampling locations were analysed for 20 WQP's and grouped into four classes namely; (WQP)organic, (WQP)inorganic, (WQP)anion and (WQP)cation. Several spectral band combinations as well as single bands were probed for performing multiple linear regression (MLR) analysis with the four WQP classes. Results reveal relatively strong positive correlations for band combination viz. [mean RGB × √B/R] with all four WQP classes yielding high R2 value (~0.85) and RMSE (~1.03) amongst other selected band combinations. Spatial distribution maps were generated that substantiates to the actual in-situ pollution concentration levels thereby evidences the potential of high resolution Geoeye-2 imagery for monitoring and mapping pollution concentrations in the water bodies.
Water (including drinking water, wastewater and wastewater management) is one of the 10 key sectors of the Canada's Critical Infrastructure at the national level. Water clarity influences many functions of the Great Lakes, such as fishing and recreation. Researches have shown that water clarity is correlated well with a number of other water quality variables, such as trophic state, hypolimnetic oxygen concentrations, along with suspended sediment concentrations. Several studies have demonstrated the Secchi disk transparency (SDT), a crude but useful measure of water clarity, is strongly related to satellite spectral-radiometric observation of the lakes. However it is impossible to monitor water clarity in a large area for a long continual period because of the expense and excess time required. SDT consists of a true colour and turbidity, which reflects dissolved and suspended materials respectively. By analyzing the relationship between the SDT and the multi-and hyper-spectral images, satellite remote sensing can be a timely and economical method of monitoring water clarity. This article introduces the principles of satellite remote sensing for monitoring lake water clarity and attempts to provide an overview of existing research efforts in this area. Limitations of this method are discussed and suggestions for future research are made in order to promote Canadian researchers to develop the innovative satellite remote sensing technology suitable for monitoring Canadian lakes.
The empirical line method is an atmospheric correction technique that provides an alternative to radiative transfer modelling approaches. It offers a relatively simple means of surface reflectance calibration, providing that a series of invariant-in-time calibration target measurements are available. This technique has been applied with variable success to both airborne data and coarser spatial resolution satellite sensor data. However, with the advent of higher spatial resolution space-borne sensors there is a need to re-evaluate its potential. The empirical line method was tested for correcting multispectral IKONOS imagery acquired over the tropical island of San Andres, Colombia. The high spatial resolution (4 m) of the data made it possible to identify a number of homogeneous targets with a range of reflectances that were used for the calibration. Coefficients of determination of the prediction equations observed were large, ranging from 0.96-0.99 for each of the four wavebands. An accuracy assessment was performed using a set of independent targets. It demonstrated that the empirical line method can be applied to correct such imagery with accurate results.
A major benefit of multitemporal, remotely sensed images is their applicability to change detection over time.(...) However, to maximize the usefulness of data from multitemporal point of view, an easy-to-use, cost-efective, and accurate radiometric calibration and correction procedure is needed.
The utility of satellite imagery for water quality studies in New York Harbor is investigated. Ground data from a routine sampling program (New York Harbor Water Quality Survey) are compared to imagery from the Landsat Thematic Mapper (TM) and Terra Moderate Resolution Imaging Spectroradiometer (MODIS) sensors. New York Harbor is a challenging environment for remote sensing because of the complex hydrography and strong tidal influence. Using a time-averaged spatial analysis it is shown that turbidity as determined from Secchi depth correlates with Landsat TM red reflectance in regions affected by the Hudson River sediments (N = 21, R2 = 0.85). Based on this correlation the estuarine turbidity maximum of the Hudson River is mapped. Landsat TM red reflectance is also used to identify and map plumes of increased turbidity caused by rainfall runoff and/or spring tide resuspension in Newark Bay. Chlorophyll a concentration correlates with the ratio of Landsat TM green to red reflectance in the eutrophic East River and Long Island Sound (N = 16, R2 = 0.78). Terra MODIS estimates of chlorophyll a show no correlation with ground observations, are biased towards low values and are therefore not directly useable for New York Harbor.
Remote sensing has the potential to provide truly synoptic views of water quality, the assessment of which is known to be affected by suspended sediment, phytoplankton biomass (chlorophyll alpha (Chl alpha) concentration) and dissolved organic carbon. These three components also control the spectral reflectance characteristics of waterbodies. This Letter uses in situ reflectance measurements of water in Lake Balaton on three different occasions, supplemented by controlled tank experiments, to characterize the influences of suspended sediment concentrations (SSC) and Chl alpha on spectral reflectance and simulated Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) response. The results confirm that Chl alpha cannot be estimated directly from Landsat reflectance data in waters characterized by heterogeneous SSC. However, principal component analysis (PCA) demonstrated that spectrally unique end-members can be identified, indicating that a spectral linear mixture modelling approach combined with a multivariate regression analysis may be used to provide estimates of Chl alpha concentrations, which would be independent of SSC.
This paper presents the applicability of combined Landsat Thematic Mapper and European Remote Sensing 2 synthetic aperture radar (SAR) data to turbidity, Secchi disk depth, and suspended sediment concentration retrievals in the Gulf of Finland. The results show that the estimated accuracy of these water quality variables using a neural network is much higher than the accuracy using simple and multivariate regression approaches. The results also demonstrate that SAR is only a marginally helpful to improve the estimation of these three variables for the practical use in the study area. However, the method still needs to be refined in the area under study.
Water quality variables were examined in Florida Bay, an ecologically sensitive ecosystem, in a pilot monitoring effort using remotely sensed sea surface salinity and in situ bio-optical observations. An airborne scanning low frequency microwave radiometer provided the first fine spatial resolution description of the surface salinity field in Florida Bay, USA. Low salinity levels to the north and central region of the Bay indicated freshwater inflows from the Everglades, while marine conditions influenced by the Gulf of Mexico waters prevailed in the western (outer) region of the Bay. Bio-optical variables such as chlorophyll a, suspended solids, coloured dissolved organic matter (yellow substance) and remote sensing reflectance exhibited different distribution patterns in the low and high salinity regions of the Bay. The results demonstrate the importance of salinity measurements in delineating diverse bio-optical regimes to aid the development of regional ocean colour remote sensing algorithms for coastal waters.
Downwelling irradiance and upwelling radiance from the water surface were measured in the Gulf of Eilat (Red Sea) on 55 separate occasions in a pelagic station in the period from January 1994 to December 1996. The reflectance was calculated in seven of the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) spectral channels throughout the visible spectrum. Concurrent with the optical measurement, water samples were taken to the laboratory and the concentrations of chlorophyll-a (Chl-a) were determined. The ratio of the reflectance in the blue (R443) to the reflectance in the green (R550) was regressed against the Chl-a concentration, and showed that the best-fit of the three datasets (1994, 1995 and 1996) assumed an exponential form, as found in previous work in oligotrophic waters. The coefficients determined in the current study were, however, different from those published in the literature; the reason for this probably originates in the composition of water constituents in the studied area. Small cells dominate the phytoplankton in the pelagic waters in the Gulf of Eilat, and the concentration of suspended and soluble materials of terrestrial origin is very small. The suggested algorithm enabled the prediction of Chl-a concentration within 0.08 mg m23. It was shown that calibration of the coefficients, based on empirical data, is important for increasing its prediction accuracy.
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.
We present a new operational algorithm for the retrieval of water quality from optical remote sensing data for both clear and turbid waters. It contains an array of neural networks providing input for the Levenberg–Marquardt multivariate optimization procedure as the final retrieval tool. With a given accuracy threshold, the developed algorithm is sufficiently robust to data with noise up to 15% for certain hydro‐optical conditions. To avoid inadequate retrieval results, the algorithm identifies and eventually discards the pixels with inadequate atmospheric correction and/or water optical properties incompatible with the applied hydro‐optical model. This procedure also identifies coccolith expressions. Examples of practical applications of the developed algorithm are given.
Remote sensing techniques can be use to assess several water quality parameters (i.e., suspended sediments (turbidity), chlorophyll, temperature) that are key factors in defining TMDLs. These optical and thermal sensors on boats, aircraft, and satellites provide both spatial and temporal information needed to understand changes in water quality parameters necessary for developing better management practices to improve water quality. With recent and planned launches of satellites with improved spectral and spatial resolution sensors, greater application of remote sensing techniques to assess and monitor water quality parameters will be possible. These remote sensing techniques should improve our abilities to assess the landscape and thus better define TMDLs and then provide monitoring data to follow clean-up efforts.
The author's introduction to remote sensing provides coverage of the subject irrespective of disciplines of study or the academic department in which remote sensing is taught. All the ''classical'' elements of aerial photographic interpretation and photogrammetry are described, but equal emphasis is placed on non-photographic sensing systems and the analysis of data from these systems using digital image processing procedures. This text includes coverage of image restoration, enhancement, classification, and data merging, and new sensor systems such as the Large Format Camera, solid-state linear arrays, the Shuttle Imaging radar systems, the Landsat Thematic Mapper, the SPOT satellite system, and the NOAA Advanced Very High Resolution Radiometer. Also covers imaging spectrometry and lidar systems. It contains extensive illustrations.
The radiance reflected at the sea surface (RW()) of the Ariake Sea, Japan, was first estimated by subtracting Lowtran 7 estimated Rayleigh and aerosol scattered radiances from Landsat Thematic Mapper measured radiance. Then RW() was averaged from 4×4 pixel windows centred at 33 sampling sites of the Ariake Sea and the data calibrated against the observed Secchi disk depth (SDD) using linear (LR) and nonlinear (NLR) regressions, and an artificial neural network (ANN) algorithm called the Modified Counter Propagation Network (MCPN). We found that at the validation stage, multi-date RW() data that are mainly based on the visible channels of Landsat Thematic Mapper (TM) predict more accurate and dependable SDDs than single-date RW() data. Furthermore, the NLR describes the SDD/RW() relationship more closely than the LR. As an ANN, MCPN possesses non-linearity, inter-connectivity, and an ability to learn and generalize information from complex or poorly understood systems, which enables it to even better represent the SDD/RW() relationship than the NLR. Our study confirms the feasibility of retrieving SDD (or turbidity) from Landsat TM data, and it seems that the calibrated MCPN and possibly NLR are portable temporally within the Ariake Sea. Lastly, the coefficient of efficiency Ef is a more stringent and probably a more accurate statistical measure than the popular coefficient of determination R2.
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.
The goal of this research was to compare narrowband hyperspectral Hyperion data with broadband hyperspatial IKONOS data and advanced multispectral Advanced Land Imager (ALI) and Landsat-7 Enhanced Thematic Mapper Plus (ETM+) data through modeling and classifying complex rainforest vegetation. For this purpose, Hyperion, ALI, IKONOS, and ETM+ data were acquired for southern Cameroon, a region considered to be a representative area for tropical moist evergreen and semi-deciduous forests. Field data, collected in near-real time to coincide with satellite sensor overpass, were used to (1) quantify and model the biomass of tree, shrub, and weed species; and (2) characterize forest land use/land cover (LULC) classes.The study established that even the most advanced broadband sensors (i.e., ETM+, IKONOS, and ALI) had serious limitations in modeling biomass and in classifying forest LULC classes. The broadband models explained only 13–60% of the variability in biomass across primary forests, secondary forests, and fallows. The overall accuracies were between 42% and 51% for classifying nine complex rainforest LULC classes using the broadband data of these sensors. Within individual vegetation types (e.g., primary or secondary forest), the overall accuracies increased slightly, but followed a similar trend. Among the broadband sensors, ALI sensor performed better than the IKONOS and ETM+ sensors.When compared to the three broadband sensors, Hyperion narrowband data produced (1) models that explained 36–83% more of the variability in rainforest biomass, and (2) LULC classifications with 45–52% higher overall accuracies. Twenty-three Hyperion narrowbands that were most sensitive in modeling forest biomass and in classifying forest LULC classes were identified and discussed.
Preface to the third edition Part I. The Underwater Light Field: 1. Concepts of hydrologic optics 2. Incident solar radiation 3. Absorption of light within the aquatic medium 4. Scattering of light within the aquatic medium 5. Characterizing the underwater light field 6. The nature of the underwater light field 7. Remote sensing of the aquatic environment Part II. Photosynthesis in the Aquatic Environment: 8. The photosynthetic apparatus of aquatic plants 9. Light capture by aquatic plants 10. Photosynthesis as a function of the incident light 11. Photosynthesis in the aquatic environment 12. Ecological strategies References and author index Index to symbols Index to organisms Index to water bodies Subject index.
A textbook prepared primarily for use in introductory courses in remote sensing is presented. Topics covered include concepts and foundations of remote sensing; elements of photographic systems; introduction to airphoto interpretation; airphoto interpretation for terrain evaluation; photogrammetry; radiometric characteristics of aerial photographs; aerial thermography; multispectral scanning and spectral pattern recognition; microwave sensing; and remote sensing from space.
ETM+ sensors in the study of African rainforests
- Hyperion
- Ikonos
- Ali
Hyperion, IKONOS, ALI, and ETM+ sensors in the study of African rainforests. Remote Sensing of Environment, 90, 23–43.
Impacts of urban growth on surface water and groundwater quality
- J B Ellis
- J. B. Ellis
Chapter 3, Modern passive and active optical and microwave remote sensing: Advanced feasibilities for application in contemporary limnological stidies
- K Y Kondratyev
- N N Filatov
- O M Johannessen
- V V Melentyev
- D V Pozdnyakov
- Ryanzhin
- K. Y. Kondratyev