Article

The Relationship of MSS and TM Digital Data With Suspended Sediments, Chlorophyll, and Temperature in Moon Lake, Mississippi

August 1990
Remote Sensing of Environment 33(2):137-148

August 1990
33(2):137-148

Source
OAI

Authors:

A comparison of six concurrent Landsat MSS and TM scenes was made to determine the relationship of Landsat digital data with suspended sediments, chlorophyll, and temperature in the surface water of an agricultural lake. There were no significant differences in best correlations between MSS or TM data with surface suspended sediments. Thus, the advantage of using MSS is the ability to monitor large areas with significantly less data. TM data can be efficiently used to monitor smaller lakes and reservoirs. TM Band 1 reflectance was the only Landsat data that accounted for at least 50% of the variability in the chlorophyll data. This would not be adequate for a monitoring program for chlorophyll in sediment dominated lakes, such as Moon Lake. TM thermal data were highly correlated with surface water temperature. TM measured surface water temperatures could be useful in determining water balance in small agricultural reservoirs. A monitoring program based on Landsat MSS and TM scanners can provide data on suspended sediments that would allow the location of reservoirs with significant suspended sediment and allow better conservation assessment and planning.

Remote sensing of phytoplankton biomass in oligotrophic and mesotrophic lakes: addressing estimation uncertainty through machine learning

Thesis

Full-text available

May 2022

Mortimer Werther

Phytoplankton constitute the bottom of the aquatic food web, produce half of Earth’s oxygen and are part of the global carbon cycle. A measure of aquatic phytoplankton biomass therefore functions as a biological indicator of water status and quality. The abundance of phytoplankton in most lakes on Earth is low because they are weakly nourished (i.e., oligotrophic). It is practically infeasible to measure the millions of oligotrophic lakes on Earth through field sampling. Fortunately, phytoplankton universally contain the optically active pigment chlorophyll-a, which can be detected by optical sensors. Earth-orbiting satellite missions carry optical sensors that provide unparalleled high spatial coverage and temporal revisit frequency of lakes. However, when compared to waters with high nutrient loading (i.e., eutrophic), the remote sensing estimation of phytoplankton biomass in oligotrophic lakes is prone to high estimation uncertainties. Accurate retrieval of phytoplankton biomass is severely constrained by imperfect atmospheric correction, complicated inherent optical property (IOP) compositions, and limited model applicability. In order to address and reduce the current estimation uncertainties in phytoplankton remote sensing of low - moderate biomass lakes, machine learning is used in this thesis.

Current methodologies and algorithms used to identify and quantify pollutants in sub-basins: A review

Article

Full-text available

Jun 2021

Over the past few years, water quality has been threatened and is vulnerable to various pollutants and climate variables. The deteriorating state of water resources/bodies has been further exacerbated by the impacts of climate change patterns in Southern Africa. Therefore, modelling and predicting the quality of water in sub-basins has become important in controlling water pollution. Remote sensing techniques gained popularity over the past few years as these techniques have been used to monitor water quality parameters such as suspended sediments, chlorophyll, temperature and other parameters in surface water bodies. Furthermore, optical and thermal sensors on aircrafts and satellites provide both spatial and temporal information needed to monitor changes in water quality parameters, for the development of management practices which seek to improve the quality of water, at sub-basin level. Thus, the integration of remotely sensed data, geographical information system (GIS), machine learning technologies and in-situ measurements provide valuable tools to monitor the impacts of climate change on water quality. According to literature cited in this paper, measurements and collection of water samples for subsequent laboratory analyses are currently used to evaluate water quality, not only in the South African context but in other developing countries as well. While such measurements are accurate for a point in time and space, they do not give either the spatial or temporal view of water quality needed for accurate assessments and management of water bodies. Hence, the need for and purpose of this study, to explore and review current methodologies and algorithms used to identify microbial and other pollutants that have increased above standard thresholds in sub-basins.

Analyzing the shoreline dynamicity and associated socio-ecological risk along southern Odisha coast, India, using remote sensing and statistics

Article

Full-text available

Feb 2021

The coastal zone is an extremely volatile environment and is constantly changing. We assessed short- and long-term shoreline changes in the Ganjam district of Odisha on the eastern coast of India from 1990 − 2019 using Landsat satellite imagery and the Digital Shoreline Analysis System (DSAS) tool in the Geographic Information System. In addition, we have also projected the likely future coastline position for the 2030 − 2040 period and the possible impact on the socioecology of the shoreline. In this study, we used the endpoint rate (EPR) analysis, weighted linear regression (WLR) analysis, and trigonometric functions to analyze the shoreline from 1990 − 2019 and 2030 − 2040. The shoreline of the Ganjam coastal zone is one of the most biologically productive ecosystems in the world, and it is well-known due to the breeding and mass nesting grounds of olive ridley turtles and the economically connected ports, famous beaches, and cyclone-prone areas. During the study period (1990-2019), the average erosion and accretion rates in the Ganjam shoreline were −2.58 m a⁻¹ and 11.63 m a⁻¹, respectively. The rate of shoreline erosion increased during years of cyclone landfall, which was revealed during the short-term shoreline analysis of the periods from 1995 − 2000 (1999 super cyclone) and 2015 − 2019 (2019 category – IV cyclone Fani). The short- to long-term analysis of the shoreline assisted in identifying erosion (Ramyapatna, Podampetta) and accretion (Gopalpur port, spits along the Bahuda and Rushikulya Rivers) hotspots within the Ganjam coastal zone. The identified erosion hotspots could cause a significant number of coastal villages that serve as breeding and mass nesting grounds for olive ridley turtles to become submerged. The dominant erosion patterns along the Ganjam coastline are likely to enhance socioecological risk and further threaten coastal communities in the future. The output of the undertaken research will benefit coastal planners, policymakers, and conservationists by helping them to formulate the most suitable action plan for coastal zone management with consideration of all stakeholders.

A Review of Remote Sensing Applications for Water Security: Quantity, Quality, and Extremes

Article

Mar 2020
J HYDROL

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.

Estimation of suspended sediment concentration using VNREDSat – 1A multispectral data, a case study in Red River, Hanoi, Vietnam

Article

Full-text available

Sep 2018

The traditional methods for measuring water quality variables are timeconsuming and do not give a synoptic view of a water body or, more significantly, a synoptic view of different water bodies across the landscape. However, remote sensing technology with advantages such as wide area coverage and short revisit interval have been effectively used for environmental pollution applications, such as for monitoring water quality parameters. Many studies around the world show that optical satellite imagery can be used effectively in evaluating suspended sediment concentration. This article presents results of monitoring suspended sediment concentration in Red River, Hanoi, Vietnam through ground truth measurements and VNREDSat-1A multispectral data. The results obtained in the study can be used to serve the management, monitoring and evaluation of surface water quality.

Remote Sensing and Geographic Information Systems Driven Data Analysis

Chapter

Apr 2024

Remote sensing (RS) and Geographic Information Systems (GIS) are routinely used in hydrologic data monitoring, mapping and modelling. This chapter introduces the basic concepts of the RS and GIS. The earth observation satellites and missions, image processing techniques, and spectral indices are discussed. Similarly, the popular GIS spatial and attribute data models are presented. Data sources for hydrology and water resources modelling are highlighted. Besides, a few prevalent commercial and open-source GIS and remote sensing software are enlisted. The chapter includes the RS and GIS applications in flood management, drought monitoring, water quality monitoring and water body mapping.

Study of the Response of Environmental Factors of the Coastal Area in Zhoushan Fishery to Typhoon In-fa Based on Remote Sensing

Article

Full-text available

Jun 2023

The response of typical environmental factors in Zhoushan Fishery, including sea surface temperature (SST), sea surface salinity (SSS), and chlorophyll a (Chl-a), before and after Typhoon In-fa was analyzed using satellite data and reanalysis data in this study. Additionally, this study simultaneously elucidated the mechanism by which the typhoon affected these factors. The results showed that: (1) the strong vertical mixing caused by In-fa provoked a decrease in SST, while the asymmetric typhoon wind stress and vertical difference in temperature structure before the typhoon caused a more robust cooling of SST on the right side of the In-fa track; (2) despite the strong mixing and inflow of hypersaline seawater increasing SSS, the combined effect of intense rainfall and diluted water inflow caused an overall decrease in SSS after In-fa’s landing; (3) In-fa caused the Chl-a concentration to decrease first and then increase. The high cloudiness and low Chl-a seawater inflow inhibited the phytoplankton growth during the typhoon, while the abundant light, rich surface nutrients under the upwelling effect, and transport of rich land-based substances induced rapid phytoplankton reproduction after the typhoon; and (4) the change in Chl-a concentration, current, temperature, and salinity induced by a typhoon are essential factors that affect fish behavior and community composition in fisheries. This study provides a point of reference to reveal the response of environmental factors to typhoons and their effects on fishery resources in fisheries located on nearshore estuarine shallow waters with intensive islands.

A Review of Remote Sensing Applications for Determining Lake Water Quality

Preprint

Full-text available

Sep 2023

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.

Remote Sensing of Suspended Sediment Dynamics in the Arctic Nearshore Zone

Thesis

Jan 2023

Konstantin Paul Klein

The Arctic nearshore zone plays a key role in the carbon cycle. Organic-rich sediments get eroded off permafrost affected coastlines and can be directly transferred to the nearshore zone. Permafrost in the Arctic stores a high amount of organic matter and is vulnerable to thermo-erosion, which is expected to increase due to climate change. This will likely result in higher sediment loads in nearshore waters and has the potential to alter local ecosystems by limiting light transmission into the water column, thus limiting primary production to the top-most part of it, and increasing nutrient export from coastal erosion. Greater organic matter input could result in the release of greenhouse gases to the atmosphere. Climate change also acts upon the fluvial system, leading to greater discharge to the nearshore zone. It leads to decreasing sea-ice cover as well, which will both increase wave energy and lengthen the open-water season. Yet, knowledge on these processes and the resulting impact on the nearshore zone is scarce, because access to and instrument deployment in the nearshore zone is challenging. Remote sensing can alleviate these issues in providing rapid data delivery in otherwise non-accessible areas. However, the waters in the Arctic nearshore zone are optically complex, with multiple influencing factors, such as organic rich suspended sediments, colored dissolved organic matter (cDOM), and phytoplankton. The goal of this dissertation was to use remotely sensed imagery to monitor processes related to turbidity caused by suspended sediments in the Arctic nearshore zone. In-situ measurements of water-leaving reflectance and surface water turbidity were used to calibrate a semi-empirical algorithm which relates turbidity from satellite imagery. Based on this algorithm and ancillary ocean and climate variables, the mechanisms underpinning nearshore turbidity in the Arctic were identified at a resolution not achieved before. The calibration of the Arctic Nearshore Turbidity Algorithm (ANTA) was based on in-situ measurements from the coastal and inner-shelf waters around Herschel Island Qikiqtaruk (HIQ) in the western Canadian Arctic from the summer seasons 2018 and 2019. It performed better than existing algorithms, developed for global applications, in relating turbidity from remotely sensed imagery. These existing algorithms were lacking validation data from permafrost affected waters, and were thus not able to reflect the complexity of Arctic nearshore waters. The ANTA has a higher sensitivity towards the lowest turbidity values, which is an asset for identifying sediment pathways in the nearshore zone. Its transferability to areas beyond HIQ was successfully demonstrated using turbidity measurements matching satellite image recordings from Adventfjorden, Svalbard. The ANTA is a powerful tool that provides robust turbidity estimations in a variety of Arctic nearshore environments. Drivers of nearshore turbidity in the Arctic were analyzed by combining ANTA results from the summer season 2019 from HIQ with ocean and climate variables obtained from the weather station at HIQ, the ERA5 reanalysis database, and the Mackenzie River discharge. ERA5 reanalysis data were obtained as domain averages over the Canadian Beaufort Shelf. Nearshore turbidity was linearly correlated to wind speed, significant wave height and wave period. Interestingly, nearshore turbidity was only correlated to wind speed at the shelf, but not to the in-situ measurements from the weather station at HIQ. This shows that nearshore turbidity, albeit being of limited spatial extent, gets influenced by the weather conditions multiple kilometers away, rather than in its direct vicinity. The large influence of wave energy on nearshore turbidity indicates that freshly eroded material off the coast is a major contributor to the nearshore sediment load. This contrasts results from the temperate and tropical oceans, where tides and currents are the major drivers of nearshore turbidity. The Mackenzie River discharge was not identified as a driver of nearshore turbidity in 2019, however, the analysis of 30 years of Landsat archive imagery from 1986 to 2016 suggests a direct link between the prevailing wind direction, which heavily influences the Mackenzie River plume extent, and nearshore turbidity around HIQ. This discrepancy could be caused by the abnormal discharge behavior of the Mackenzie River in 2019. This dissertation has substantially advanced the understanding of suspended sediment processes in the Arctic nearshore zone and provided new monitoring tools for future studies. The presented results will help to understand the role of the Arctic nearshore zone in the carbon cycle under a changing climate.

Consistency Measurement for Different-Scale Satellite Data Sets Applied on Vegetation Assessment: Case Study Al-Ahsa Province, Saudi Arabia

Article

Full-text available

May 2022

The variability in satellite's spectral and spatial resolutions has become a critical issue in the application of remotely sensed data to vegetation monitoring and assessment. This study aimed to examine the consistency between three spatial data sets having a synchronous-imaging time with different spatial resolutions, working on vegetation indices (VIs) applied over a date palms region in Saudi Arabia. A point-based correlation was applied at sub-pixel levels, where Landsat (30 m and pan-sharpened 15 m) and Sentinel-2A (10 m) were applied. The extracted VIs (the normalized difference vegetation index (NDVI) and soil-adjusted vegetation index (SAVI)) pixels were vectorized into points, considering their identical locations in the other sensor's maps, then were correlated. The result statistics showed noticeable differences in the VIs, exhibiting the least variability between the sensors for SAVI. However, NDVI was slightly varying. The extracted vegetation-area showed noticeable differences of 4.32% and 4.54% between Sentinel-2A and the original pixels of Landsat for SAVI and the NDVI, respectively, exhibiting the impact of spatial resolution in land use/cover mapping accuracy. The applied correlation revealed moderate agreements of SAVIs for Sentinel-2A against both Landsat sets, producing R 2 of 0.76, while Landsat's (original pixels) NDVI poorly correlated Sentinel's (R 2 = 0.50). The results were validated and were found to be weak, producing R 2 of 0.60 for both VIs, which was attributed to the differences in the spectral regions and crop status. The study findings confirm the suitability of using SAVI in areas dominated by palm trees.

Consistency Measurement for Different-Scale Satellite Data Sets Applied on Vegetation Assessment: Case Study Al-Ahsa Province, Saudi Arabia

Article

Full-text available

May 2022

2022 Modelling of Greek Lakes Water Quality USING eo

Article

Full-text available

Nov 2022

George P. Petropoulos

Ocean water quality monitoring using remote sensing techniques: A review

Article

Aug 2022
MAR ENVIRON RES

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.

Geospatial analysis of short term shoreline change behavior between Subarnarekha and Rasulpur estuary, east coast of India using intelligent techniques (DSAS)

Article

Full-text available

Jun 2022
GeoJournal

A geospatial analysis of shoreline change pattern is most significant parameter to understand the behavioral interaction between land and sea water. Geospatial analyses using various statistical and quantitative methods which are more applicable, accurate and dependable to measures the spatio-temporal trend of erosion accretion and estimate the change rate of shoreline. Remote sensing and GIS techniques have been used for the identification of shoreline change over the various time scales. To identify the rate Digital Shoreline Analysis System (DSAS) was applied in the current research. The present study aimed to identify the trend of coastal erosion accretion during 43 years (1975–2018) which is divided into four short term period (1975–1988, 1988–2000, 2000–2010 and 2010–2018) between the coastal stretch of Subarnarekha and Rasulpur estuary along Bay of Bengal using multi temporal satellite images. The accurate shoreline position has been delineated by the histogram threshold method using the images of Landsat Multi Spectral Scanner, Thematic Mapper and Enhanced Thematic Mapper. The shoreline change rate has been calculated based on cast transect method through some statistical techniques such as End Point Rate (EPR) and Net Shoreline Movement (NSM) in GIS application. 70.42 km long coastal stretch along Bay of Bengal has been divided into three littoral zones (LZ) to analyze the shoreline shifting on a zone basis. From the analysis it has been observed that maximum erosion occurred between 1988 and 2000 time period in all zones. The result shows that highest rate of net shoreline movement has been found in LZ I (− 1715.71 m) in 1975–1988 and LZ III (− 1719.65 m) in 2000–2010 at Subarnarekha estuary and Junput respectively. The present study reveals that most of the accretive formation is observed in 2000–2010 and 2010–2018. Major accretion is identified in the southern part of Subarnarekha estuary, 23.93 m/year in EPR method. Maximum shades of changes was experienced in LZ I, especially in Subarnarekha estuary area. In the time span of 1975–1988 this area faced the highest erosion and highest accretion with the rate of − 78.54 m/year and 23.93 m/year respectively through EPR method. During 1988–2000, − 37.35 m/year erosional rate was found in the Subarnarekha estuary. The highest erosional rate was − 8.48 m/year in Beguran Jalpai during 1988–2000 by EPR. The maximum rate of accretion has been noticed as 7.7 m/year in LZ II in the time period of 2010–2018.

Monitoring multi-temporal and spatial variations of water transparency in the Jiaozhou Bay using GOCI data

Article

Full-text available

Jul 2022
MAR POLLUT BULL

Water transparency, commonly measured as Secchi disk depth (SDD), is essential for describing the optical properties of coastal waters. We proposed a regional linear corrected SDD estimation model based on the North Sea Mathematical Models for GOCI and the mechanical model developed by Lee et al. (2015) in the Jiaozhou Bay. Combined with the multiple variable linear regression analysis, the diurnal SDD variations of the bay inside and the bay mouth are controlled by the solar zenith angle (SZA) and tides. The bay outside mainly varies with SZA. From GOCI observations between 2011 and 2021, wind force influenced the entire area on the inner-annual SDD variations. It exhibits an increasing trend in the inter-annual dynamics, which was more stable inside the bay with an annual increase of 0.035 m, and air temperature was the most significant contribution. However, human activities cannot be ignored in causing water environment changes.

LS – An International Journal of Life Sciences Vol. 11, Issue 1, January-April 2022 Ashwani Kumar Editor in Chief

Book

Full-text available

May 2022

Ashwani Kumar

From Chief Editor’s Desk After Robert Emerson’s tragic death in a plane crash on February 4, 1959, Carl Cederstrand joined the PhD program in Biophysics, working under the joint mentorship of Eugene Rabinowitch and Govindjee. His very first experiments, were done on projects initiated by Govindjee, which led to the discovery of the two-light effect in chlorophyll a fluorescence (Govindjee et al., 1960), and to the existence of new absorption bands in the far-red region, particularly prominent at 750 nm in the cyanobacterium Anacystis nidulans (Govindjee et al., 1961) (See: Laura Cederstrand and Govindjee this issue). Paul C. Lauterbur (1929 —2007), was the father of 13C NMR (Carbon-13 Nuclear Magnetic Resonance) and inventor of MRI (Magnetic Resonance Imaging). An elegant article on the 2003 Nobel Laureate Paul C. Lauterbur is also included here (see Elise Lauterbur and Govindjee, this issue). Global warming, climate change and human health are interconnected and this issue of our journal focuses on some of these current topics. Omicron is still a problem with new strains emerging; an excellent paper by Yau and Khandelwal, eminent scientists in this area, deals with the ‘striking immune evasion and less disease severity’, in this volume. I quote: “Vaccine waning plus immure evasion have led to the significant increase of ‘breakthrough’ infections during Omicron wave in many countries. For now, the best protection is to take boosters”. Different aspects and possibilities have been discussed in detail in this paper, included in this issue. Pollinators are key components of global biodiversity, providing vital ecosystem services to crops and wild plants. There is clear evidence of recent decline in both wild and domesticated pollinators, and there is a parallel decline in plants that rely upon them. Exposure to multiple interacting stressors is responsible for the loss of honeybee colonies and the consequent decline of wild pollinators. Taking immediate steps to reduce the stress on bees is necessary for sustainable farming methods by enforcing effective quarantine measures on bee movements (See Kumar et al., this volume, for a thorough discussion of this topic). In aquatic ecosystems, an understanding of biomass accumulation dynamics, carbon sequestration and primary productivity (using chlorophyll estimation) at a regional to global scale is undoubtedly crucial in dealing with changing climatic conditions. Future remote sensing missions for aquatic science will bring new offerings and capabilities to monitor biomass and productivity dynamics. Potential of ‘remote sensing’ for improved understanding of aquatic chlorophyll, biomass and primary productivity estimation is presented by Gupta et al. (in this volume). Forests, the most valuable ecological resources, are greatly being impoverished economically, aesthetically and environmentally in India and elsewhere. To evaluate the present status of forests in the light of species richness and vegetation, a study was conducted in Dakshin Dinajpur district of West Bengal (see Das and Chakraborty, this issue). For future volumes of our journal, we welcome articles on current issues, in life sciences, and hope that it will have a great impact our readership. We thank all the reviewers, who have done excellent work for the journal. My special thanks go to Frank Yau for his crucial help for this issue of our journal. We also thank Govindjee (of the University of Illinois at Urbana- Champaign) for his support to our journal. We sincerely hope that you will find this issue very useful for research and teaching. Prof. Ashwani Kumar Chief Editor

Modelling of Greek Lakes Water Quality Using Earth Observation in the Framework of the Water Framework Directive (WFD)

Article

Full-text available

Feb 2022

Given the great importance of lakes in Earth’s environment and human life, continuous water quality (WQ) monitoring within the frame of the Water Framework Directive (WFD) is the most crucial aspect for lake management. In this study, Earth Observation (EO) data from Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and Landsat 8 Operational Land Imager (OLI) sensors have been combined with co-orbital in situ measurements from 50 lakes located in Greece with the main objective of delivering robust WQ assessment models. Correlation analysis among in situ co-orbital WQ data (Chlorophylla, Secchi depths, Total phosphorus-TP-) contributed to distinguishing their inter-relationships and improving the WQ models’ accuracy. Subsequently, stepwise multiple regression analysis (MLR) of the available TP and Secchi depth datasets was implemented to explore the potential to establish optimal quantitative models regardless of lake characteristics. Then, further MLR analysis concerning whether the lakes are natural or artificial was conducted with the basic aim of generating different remote sensing derived models for different types of lakes, while their combination was further utilized to assess their trophic status. Correlation matrix results showed a high and positive relationship between TP and Chlorophyll-a (0.85), whereas high negative relationships were found between Secchi depth with TP (−0.84) and Chlorophyll-a (−0.83). MLRs among Landsat data and Secchi depths resulted in 3 optimal models concerning the assessment of Secchi depth of all lakes (Secchigeneral; R = 0.78; RMSE = 0.24 m), natural (Secchinatural; R = 0.95; RMSE = 0.14 m) and artificial (Secchiartificial; R = 0.62; RMSE = 0.1 m), with reliable accuracy. Study findings showed that TP-related MLR analyses failed to deliver a statistically acceptable model for the reservoirs; nevertheless, they delivered a robust TPgeneral (R = 0.71; RMSE = 1.41 mg/L) and TPnatural model (R = 0.93; RMSE = 1.43 mg/L). Subsequently, trophic status classification was conducted herein, calculating Carlson’s Trophic State Index (TSI) initially throughout all lakes and then oriented toward natural-only and artificial-only lakes. Those three types of TSI (general, natural, artificial) were calculated based on previously published satellite-derived Chlorophyll-a (Chl-a) assessment models and the hereby specially designed WQ models (Secchi depth, TP). The higher deviation of satellite-derived TSI values in relation to in situ ones was detected in reservoirs and shallower lakes (mean depth < 5 m), indicating noticeable divergences among natural and artificial lakes. All in all, the study findings provide important support toward the perpetual WQ monitoring and trophic status prediction of Greek lakes and, by extension, their sustainable management, particularly in cases when ground truth data is limited.

Assessment of some water quality parameters in the Red River downstream, Vietnam by combining field monitoring and remote sensing method

Article

Full-text available

Jun 2022
ENVIRON SCI POLLUT R

The Red River is the largest river in northern Vietnam, and it serves as the main water source for production and human activities in the Red River Delta region. Cities and provinces located in the Red River Delta, for example, Hanoi, Nam Dinh, and Ha Nam, have experienced rapid economic growth with various large urban, industrial zones, and agricultural areas. As a result of urbanization and industrialization, surface water in this region has been contaminated by multiple anthropogenic sources. In this study, in addition to water quality assessment using WQI, we used the reflectance values of visible and near-infrared bands and in situ data to build a regression model for several water quality parameters. Among ten parameters examined, two parameters, including total suspended solids (TSS) and turbidity, were used to construct regression correlation models using the Sentinel-2 multispectral images. Our results can contribute useful information for comprehensive monitoring, evaluation, and management scheme of water quality in the Red River Delta. The application of this method can overcome the limitation of actual observation results that only reflect local contamination status and require a lot of sampling and laboratory efforts.

Analysis of long-term (2002–2020) trends and peak events in total suspended solids concentrations in the Chesapeake Bay using MODIS imagery

Article

Dec 2021
J ENVIRON MANAGE

Water quality monitoring programs have been widely implemented worldwide to monitor and assess water quality and to understand its trends. However, water quality analysis based on point-source field observations is difficult to perform at large spatial and temporal scales. In this paper, a fully automated Google Earth Engine (GEE) application algorithm was developed to estimate the total suspended solids (TSS) concentration in the Chesapeake Bay based on the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra imagery. Combining long-term archived satellite data (2002–2020) with field observations, the concentrations and spatiotemporal patterns of TSS in the bay water were evaluated. Time series analysis showed a statistically significant decreasing trend in TSS concentration between 2002 and 2020, suggesting that the sediment concentration in the bay has gradually been decreasing over the last two decades. The decreasing trend was observed in 49 out of 60 segments of the bay, implying that substantial progress has been made toward attaining the Chesapeake Bay water quality standards. Based on the monthly TSS analysis, 12 major peak events of TSS were identified in the Chesapeake Bay, which coincided with extreme winter blizzards and summer hurricane events. The GEE application and the results presented herein complement the existing monitoring program in attaining the water quality standards of the bay.

Assessment of water quality in a tropical ramsar wetland of southern India in the wake of COVID-19

Article

Aug 2021

The novel SARS-CoV-2 virus influenced the world severely in the first half of 2020 caused shut down of all kind of human activities. It is reported that a word-wide ecological improvement in terms of air quality and water quality during this lock down period. In the present study, an attempt has been made to study the progression in water quality through examining suspended particulate matter using remote sensing data in a tropical Ramsar site viz, Asthamudi Lake in Southern India. The change in spectral reflectance of water along the study area were analyzed and suspended particulate matter (SPM) is estimated from Landsat 8 OLI images. A comparison analysis of pre and co lockdown periods reveal that the concentration of SPM values during lockdown (mean SPM 8.01 mg/l) is lower than that of pre-lockdown (10.03 mg/l). The time series analysis of last five-year data from 2015 to 2020 also shows an average decrease of 43% in SPM concentration during lockdown period compared to the last five-year average value of 9.1 mg/l. The reasons for improvement of SPM in water quality during the lockdown period in April–May 2020 was discussed, in terms of the role of anthropogenic activities and strategies for the sustainable management of coastal ecosystems and water resources in the Asthamudi Lake were also presented.

Application of hyperspectral analysis of chlorophyll a concentration inversion in Nansi Lake

Article

Jul 2021
ECOL INFORM

Chlorophyll-a (Chl-a) is an important water quality safety evaluation index, and accurate Chl-a concentration monitoring is important for the development of aquaculture, aquatic ecosystem balance, and drinking water safety. Rapid and accurate Chl-a concentration determination in water using hyperspectral remote sensing is an important subject in water ecological environment monitoring. In this study, the spectral reflectance and Chl-a concentration of Nansi Lake were measured, and the time-frequency method of empirical mode decomposition (EMD) analysis was used for the noise reduction and reconstruction of the first-order differential of the spectrum to extract sensitive spectral features. The eXtreme Gradient Boosting (XGBoost) machine learning algorithm was used to establish a Chl-a concentration estimation model, and the best parameters and model combinations for the inversion of the Chl-a concentration in the water column of Nansi Lake were determined. The results show that the combined three-band algorithm combination parameters obtained from the EMD noise-reduced reconstruction of spectral first-order differential (OFODSR-D) data fit the measured Chl-a concentrations better than the original spectral (OSR) and OFODSR data, with a maximum correlation coefficient of 0.8588. Second, the models based on OFODSR-D achieved more satisfactory prediction results, with XGBoost having the highest estimation accuracy (R² of 0.9024 and root-mean-square error (RMSE) of 1.1312 μg·L⁻¹ for the inverse model), followed by the partial least squares regression (PLSR) model and the linear model (R² of 0.8474 and 0.8326, and RMSE of 13.3031 and 7.6987 μg·L⁻¹, respectively). This study innovatively introduces the EMD method to the spectral processing of water bodies, obtains optimal parameters for the inversion of the Chl-a concentration, and achieves better results. This study provides a new approach to obtaining optimal inversion parameters for Chl-a monitoring in inland lake water bodies using remote-sensing methods.

Modeling Water Quality Parameters Using Landsat Multispectral Images: A Case Study of Erlong Lake, Northeast China

Article

Full-text available

Apr 2021

Erlong Lake is considered one of the largest lakes in midwest Jilin, China, and one of the drinking water resources in neighboring cities. The present study aims to explore the usage of Landsat TM5, ETM7, and OLI8 images to assess water quality (V-phenol, dissolved oxygen (DO), NH4-N, NO3-N) in Erlong Lake, Jilin province, northeast China. Thirteen multispectral images were used in this study for May, July, August, and September in 2000, 2001, 2002, and October 2020. Radiometric and atmospheric corrections were applied to all images. All in situ water quality parameters were strongly correlated to each other, except DO. The in situ measurements (V-phenol, dissolved oxygen, NH4-N, NO3-N) were statistically correlated with various spectral band combinations (blue, green, red, and NIR) derived from Landsat imagery. Regression analysis reported that there are strong relationships between the estimated and retrieved water quality from the Landsat images. Moreover, in calibrations, the highest value of the coefficient of determination (R2) was ≥0.85 with (RMSE) = 0.038; the lowest value of R2 was >0.30 with RMSE= 0.752. All generated models were validated in different statistical indices; R2 was up to 0.95 for most cases, with RMSE ranging from 1.390 to 0.050. Finally, the empirical algorithms were successfully assessed (V-phenol, dissolved oxygen, NH4-N, NO3-N) in Erlong Lake, using Landsat images with very good accuracy. Both in situ and model retrieved results showed the same trends with non-significant differences. September of 2000, 2001, and 2002 and October of 2020 were selected to assess the spatial distributions of V-phenol, DO, NH4-N, and NO3-N in the lake. V-phenol, NH4-N, and NO3-N were reported low in shallow water but high in deep water, while DO was high in shallow water but low in deep water of the lake. Domestic sewage, agricultural, and urban industrial pollution are the most common sources of pollution in the Erlong Lake.

Modeling Water Quality Parameters Using Landsat Multispectral Images: A Case Study of Erlong Lake, Northeast China

Article

Full-text available

Apr 2021

Erlong Lake is considered one of the largest lakes in midwest Jilin, China, and one of the drinking water resources in neighboring cities. The present study aims to explore the usage of Landsat TM5, ETM7, and OLI8 images to assess water quality (V-phenol, dissolved oxygen (DO), NH4 -N, NO3 -N) in Erlong Lake, Jilin province, northeast China. Thirteen multispectral images were used in this study for May, July, August, and September in 2000, 2001, 2002, and October 2020. Radiometric and atmospheric corrections were applied to all images. All in situ water quality parameters were strongly correlated to each other, except DO. The in situ measurements (V-phenol, dissolved oxygen, NH4 -N, NO3 -N) were statistically correlated with various spectral band combinations (blue, green, red, and NIR) derived from Landsat imagery. Regression analysis reported that there are strong relationships between the estimated and retrieved water quality from the Landsat images. Moreover, in calibrations, the highest value of the coefficient of determination (R2 ) was ≥0.85 with (RMSE) = 0.038; the lowest value of R2 was >0.30 with RMSE= 0.752. All generated models were validated in different statistical indices; R2 was up to 0.95 for most cases, with RMSE ranging from 1.390 to 0.050. Finally, the empirical algorithms were successfully assessed (V-phenol, dissolved oxygen, NH4 -N, NO3 -N) in Erlong Lake, using Landsat images with very good accuracy. Both in situ and model retrieved results showed the same trends with non-significant differences. September of 2000, 2001, and 2002 and October of 2020 were selected to assess the spatial distributions of V-phenol, DO, NH4 -N, and NO3 -N in the lake. V-phenol, NH4 -N, and NO3 -N were reported low in shallow water but high in deep water, while DO was high in shallow water but low in deep water of the lake. Domestic sewage, agricultural, and urban industrial pollution are the most common sources of pollution in the Erlong Lake.

Remote Sensing of Lake Water Clarity: Performance and Transferability of Both Historical Algorithms and Machine Learning

Article

Full-text available

Apr 2021

There has been little rigorous investigation of the transferability of existing empirical water clarity models developed at one location or time to other lakes and dates of imagery with differing conditions. Machine learning methods have not been widely adopted for analysis of lake optical properties such as water clarity, despite their successful use in many other applications of environmental remote sensing. This study compares model performance for a random forest (RF) machine learning algorithm and a simple 4-band linear model with 13 previously published empirical non-machine learning algorithms. We use Landsat surface reflectance product data aligned with spatially and temporally co-located in situ Secchi depth observations from northeastern USA lakes over a 34-year period in this analysis. To evaluate the transferability of models across space and time, we compare model fit using the complete dataset (all images and samples) to a single-date approach, in which separate models are developed for each date of Landsat imagery with more than 75 field samples. On average, the single-date models for all algorithms had lower mean absolute errors (MAE) and root mean squared errors (RMSE) than the models fit to the complete dataset. The RF model had the highest pseudo-R2 for the single-date approach as well as the complete dataset, suggesting that an RF approach outperforms traditional linear regression-based algorithms when modeling lake water clarity using satellite imagery.

Analysis of long-and short-term shoreline change dynamics: A study case of João Pessoa city in Brazil

Article

Full-text available

Jan 2021
SCI TOTAL ENVIRON

The coastal area of João Pessoa city, Paraíba, Brazil, is densely populated and has a large flow of trade and services. More recently, this region has been suffering from the advance of the sea, which has caused changes in the shoreline and caused a decrease in the beach area and damage to various urban facilities. Thus, the spatiotempo-ral changes of the short-and long-term characteristics of the shoreline of João Pessoa city over the past 34 years (1985-2019) were calculated and the forcing mechanisms responsible for the shoreline changes were analyzed. Remote sensing data (Landsat 5-TM and 8-OLI) and statistical techniques, such as endpoint rate (EPR), linear regression rate (LRR) and weighted linear regression (WLR), using Digital Shoreline Analysis System (DSAS), were used. In this study, 351 transects ranging from~1.1 km to~6 km were analyzed within four zones (Zones I to IV), and the main controlling factors that influence the shoreline changes in these zones, such as sea level, tidal range, wave height, beach morphology and ocean currents, were discussed. The long-term change from 1985 to 2019 showed primarily accretion on the shoreline of João Pessoa city, with the rate of 0.55 m/year (WLR method); 282 transects showed accretion. The results showed that Zone-I, which was located in the south of the study area, was the only zone that primarily recorded erosion from 1985 to 2019, with a mean rate of −0.23 m/ year according to the WLR method. According to the short-term shoreline change analysis, a cyclical pattern of erosion was observed in the 1985

TOTAL SUSPENDED SOLID DISTRIBUTION IN HAU RIVER USING SENTINEL 2A SATELLITE IMAGERY

Article

Full-text available

Nov 2020

This research was applied high-resolution Sentinel-2A imagery which aims to monitor a suitability of MultiSpectral Imager (MSI) at higher resolution (10 m) for mapping of Total Suspended Solids (TSS) ) in the upper reaches of the Mekong Delta – An Giang province. The field survey is carried out to collect TSS at random distribution sites outside full-dyke protection. A remote sensing algorithm with a regression function method is developed to estimate TSS concentration automatically to select between the most sensitive TSS and water reflectance relationship. The regression analytical algorithm is predicted the output values based on normalized suspended material index (NSMI) (r2 = 0.92), showing the MSI sensor’s great potential to estimate TSS. The results confirm that suspended materials in the surface water reached the minimum of 10.36 mg/l and the maximum of 328.56 mg/l in An Giang province, the suspended materials distributional tendency with high content was mainly in flooded fields near the upstream of the basin of Hau river, especially in areas without the dike enclosure and the content was low in the areas within the dike enclosure. These findings promote further research in water quality studies relying on both operational Sentinel-2A and Sentinel-2B imageries with great implications to improve the understanding of turbid coastal and inland water environments.

Using a semi-analytical model to retrieve Secchi depth in coastal and estuarine waters

Article

Full-text available

Aug 2020

Secchi depth (SD, m) is a direct and intuitive measure of water’s transparency, which is also an indicator of water quality. In 2015, a semi-analytical model was developed to derive SD from remote sensing reflectance, thus able to provide maps of water’s transparency in satellite images. Here an in-situ dataset (338 stations) is used to evaluate its potential ability to monitor water quality in the coastal and estuarine waters, with measurements covering the Zhujiang (Pearl) River Estuary, the Yellow Sea and the East China Sea where measured SD values span a range of 0.2–21.0 m. As a preliminary validation result, according to the whole dataset, the unbiased percent difference (UPD) between estimated and measured SD is 23.3% (N=338, R2=0.89), with about 60% of stations in the dataset having relative difference (RD) ⩽ 20%, over 80% of stations having RD ⩽ 40%. Furthermore, by excluding the field data which with relatively larger uncertainties, the semi-analytical model yielded the UPD of 17.7% (N=132, R2=0.92) with SD range of 0.2–11.0 m. In addition, the semi-analytical model was applied to Landsat-8 images in the Zhujiang River Estuary, and retrieved high-quality mapping and reliable spatial-temporal patterns of water clarity. Taking into account the uncertainties associated with both field measurements and satellite data processing, and that there were no tuning of the semi-analytical model for these regions, these findings indicate highly robust retrieval of SD from spectral techniques for such turbid coastal and estuarine waters. The results suggest it is now possible to routinely monitor coastal water transparency or visibility at high-spatial resolutions from measurements, like Landsat-8 and Sentinel-2 and newly launched Gaofen-5.

Estimating Chlorophyll-a of Inland Water Bodies in Greece Based on Landsat Data

Article

Full-text available

Jun 2020

Assessing chlorophyll-a (Chl-a) pigments in complex inland water systems is of key importance as this parameter constitutes a major ecosystem integrity indicator. In this study, a methodological framework is proposed for quantifying Chl-a pigments using Earth observation (EO) data from Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and 8 Operational Land Imager (OLI) sensors. The first step of the methodology involves the implementation of stepwise multiple regression (MLR) analysis of the available Chl-a dataset. Then, principal component analysis (PCA) is performed to explore Greek lakes’ potential interrelationships based on their Chl-a values in conjunction with certain criteria: their characteristics (artificial/natural), typology, and climatic type. Additionally, parameters such as seasonal water sampling and the date difference between sampling and satellite overpass are taken into consideration. Next, is implemented a stepwise multiple regression analysis among different groups of cases, formed by the criteria indicated from the PCA itself. This effort aimed at exploring different remote sensing-derived Chl-a algorithms for various types of lakes. The practical use of the proposed approach was evaluated in a total of 50 lake water bodies (natural and artificial) from 2013–2018, constituting the National Lake Network Monitoring of Greece in the context of the Water Framework Directive (WFD). All in all, the results evidenced the suitability of Landsat data when used with the proposed technique to estimate log-transformed Chl-a. The proposed scheme resulted in the development of models separately for natural (R = 0.78; RMSE = 1.3 μg/L) and artificial lakes (R = 0.76; RMSE = 1.29 μg/L), while the model developed without criteria proved weaker (R = 0.65; RMSE = 1.85 μg/L) in comparison to the other ones examined. The methodological framework proposed herein can be used as a useful resource toward a continuous monitoring and assessment of lake water quality, supporting sustainable water resources management.

COVID-19 and surface water quality: Improved lake water quality during the lockdown

Article

Full-text available

Apr 2020
SCI TOTAL ENVIRON

Human life comes to a standstill as many countries shut themselves off from the work due to the novel coronavirus disease pandemic (COVID-19) that hit the world severely in the first quarter of 2020. All types of industries, vehicle movement, and people's activity suddenly halted, perhaps for the first time in modern history. For a long time, it has been stated in various literature that the increased industrialization and anthropogenic activities in the last two decades polluted the atmosphere, hydrosphere, and biosphere. Since the industries and people's activities have been shut off for a month or more in many parts of the world, it is expected to show some improvement in the prevailing conditions in the aforementioned spheres of environment. Here, with the help of remote sensing images, this work quantitatively demonstrated the improvement in surface water quality in terms of suspended particulate matter (SPM) in the Vembanad Lake, the longest freshwater lake in India. The SPM estimated based on established turbidity algorithm from Landsat-8 OLI images showed that the SPM concentration during the lockdown period decreased by 15.9% on average (range: −10.3% to 36.4%, up to 8 mg/l decrease) compared with the pre-lockdown period. Time series analysis of satellite image collections (April 2013 – April 2020) showed that the SPM quantified for April 2020 is the lowest for 11 out of 20 zones of the Vembanad lake. When compared with preceding years, the percentage decrease in SPM for April 2020 is up to 34% from the previous minima.

Long-Term High-Resolution Sediment and Sea Surface Temperature Spatial Patterns in Arctic Nearshore Waters Retrieved Using 30-Year Landsat Archive Imagery

Article

Full-text available

Nov 2019

The Arctic is directly impacted by climate change. The increase in air temperature drives the thawing of permafrost and an increase in coastal erosion and river discharge. This leads to a greater input of sediment and organic matter into coastal waters, which substantially impacts the ecosystems, the subsistence economy of the local population, and the climate because of the transformation of organic matter into greenhouse gases. Yet, the patterns of sediment dispersal in the nearshore zone are not well known, because ships do not often reach shallow waters and satellite remote sensing is traditionally focused on less dynamic environments. The goal of this study is to use the extensive Landsat archive to investigate sediment dispersal patterns specifically on an exemplary Arctic nearshore environment, where field measurements are often scarce. Multiple Landsat scenes were combined to calculate means of sediment dispersal and sea surface temperature under changing seasonal wind conditions in the nearshore zone of Herschel Island Qikiqtaruk in the western Canadian Arctic since 1982. We use observations in the Landsat red and thermal wavebands, as well as a recently published water turbidity algorithm to relate archive wind data to turbidity and sea surface temperature. We map the spatial patterns of turbidity and water temperature at high spatial resolution in order to resolve transport pathways of water and sediment at the water surface. Our results show that these pathways are clearly related to the prevailing wind conditions, being ESE and NW. During easterly wind conditions, both turbidity and water temperature are significantly higher in the nearshore area. The extent of the Mackenzie River plume and coastal erosion are the main explanatory variables for sediment dispersal and sea surface temperature distributions in the study area. During northwesterly wind conditions, the influence of the Mackenzie River plume is negligible. Our results highlight the potential of high spatial resolution Landsat imagery to detect small-scale hydrodynamic processes, but also show the need to specifically tune optical models for Arctic nearshore environments.

Estimating the suspended sediment concentration from TM/Landsat-5 images for the Araguaia River – Brazil

Article

Full-text available

Oct 2019

In this study, 68 images from TM/Landsat-5 sensor were used to estimate Suspended Sediment Concentration (SSC) along of the Araguaia River, Brazil. These were combined with in-situ SSC, hydrosedimentometric station (categorical variable), and remote sensing reflectance. Top-of-Atmosphere (ToA) and surface reflectance data were evaluated. Multiple regression models with ToA reflectance using VNIR bands, band ratios, SWIR band 5 as input and station as categorical variable were more accurate with adjusted coefficient of determination (adjusted R²) = 0.87 and normalized root mean square error (NRMSE) = 10.09% compared to the models with surface reflectance with adjusted R² = 0.60 and NRMSE = 15.43%. Results confirm the potential for estimation of SSC from TM/Landsat-5 historical series data between 1984 and 2012, for which in-situ database is rare. Based on this empirical model, future studies may provide better analysis of spatiotemporal variations of sediment transport along the Araguaia River with the SSC temporal series reconstitution.

Response of long‑ to short‑term changes of the Puri coastline of Odisha (India) to natural and anthropogenic factors: a remote sensing and statistical assessment

Article

Full-text available

May 2019
ENVIRON EARTH SCI

The coastal regions of India are densely populated and most biological productive ecosystems which are threatened by erosion, natural disaster, and anthropogenic interferences. These threats have made priority in appraisal of shoreline dynamicity as part of sustainable management of coastal zones. The present study assessed the long- to short-term dynamicity of shoreline positions along the coast of Puri district, Odisha, India, during the past 25 years (1990–2015) using open-source multi-temporal satellite images (Landsat TM, ETM + , and OLI) and statistical-based methods (endpoint rate, linear regression rate and weighted linear regression). The long-term assessment during 1990–2015 shows that shoreline accredited at the rate of 0.3 m a−1 with estimated mean accretion and erosional rate of 1.18 m a−1 and 0.64 m a−1, respectively. A significant trend of coastal erosion is primarily observed on the northern side of Puri district coast. A cyclic pattern of accretion (during 1990–1995 and 2000–2004) and erosion (during 1995–2000 and 2009–2015) was observed during the assessment of short-term shoreline change. It exhibited significant correlation with the landfall of severe cyclones and identified cyclic phases after severe cyclonic storms, i.e., phase of erosion, phase of accretion and phase of stabilization. Overall, the natural processes specifically the landfall of tropical cyclones and anthropogenic activities such as the construction of coastal structures, encroachment and recent construction in the coastal regulatory zone, and construction of dams in upper catchment areas are the major factors accountable for shoreline changes. The output of the research undertaken is not only crucial for monitoring the dynamism of coastal ecosystem boundaries but to enable long- to short-term coastal zone management planning in response to recently reported high erosion along the Puri coast. Moreover, the usage of open-source satellite imageries and statistical-based method provides an opportunity in developing cost-effective spatial data infrastructure for shoreline monitoring and vulnerability mapping along the coastal region.

UAV-Based Measurements of Spatio-Temporal Concentration Distributions of Fluorescent Tracers in Open Channel Flows

Article

Mar 2019
ADV WATER RESOUR

Toward Long-Term Aquatic Science Products from Heritage Landsat Missions

Article

Full-text available

Aug 2018

This paper aims at generating a long-term consistent record of Landsat-derived remote sensing reflectance (R rs) products, which are central for producing downstream aquatic science products (e.g., concentrations of total suspended solids). The products are derived from Landsat-5 and Landsat-7 observations leading to Landsat-8 era to enable retrospective analyses of inland and nearshore coastal waters. In doing so, the data processing was built into the SeaWiFS Data Analysis System (SeaDAS) followed by vicariously calibrating Landsat-7 and-5 data using reference in situ measurements and near-concurrent ocean color products, respectively. The derived R rs products are then validated using (a) matchups using the Aerosol Robotic Network (AERONET) data measured by in situ radiometers, i.e., AERONET-OC, and (b) ocean color products at select sites in North America. Following the vicarious calibration adjustments, it is found that the overall biases in R rs products are significantly reduced. The root-mean-square errors (RMSE), however, indicate noticeable uncertainties due to random and systematic noise. Long-term (since 1984) seasonal R rs composites over 12 coastal and inland systems are further evaluated to explore the utility of Landsat archive processed via SeaDAS. With all the qualitative and quantitative assessments, it is concluded that with careful algorithm developments, it is possible to discern natural variability in historic water quality conditions using heritage Landsat missions. This requires the changes in R rs exceed maximum expected uncertainties, i.e., 0.0015 [1/sr], estimated from mean RMSEs associated with the matchups and intercomparison analyses. It is also anticipated that Landsat-5 products will be less susceptible to uncertainties in turbid waters with R rs (660) > 0.004 [1/sr], which is equivalent of ~1.2% reflectance. Overall, end-users may utilize heritage R rs products with "fitness-for-purpose" concept in mind, i.e., products could be valuable for one application but may not be viable for another. Further research should be dedicated to enhancing atmospheric correction to account for non-negligible near-infrared reflectance in CDOM-rich and extremely turbid waters.

ARTIFICIAL NEURAL NETWORK IN MONITORING WATER QUALITY OF DANKIA LAKE DALAT USING LANDSAT IMAGE DATA

Article

Full-text available

Jul 2015

Monitoring water quality using sampling techniques and traditional analysis is very costly and time consuming. Influence of nutrient status on the basis of remote sensing monitoring and identification of surface water quality is important. In this work Landsat ETM data were used to build the multiple linear regression models to quantify the quality of the lake water. The best linear regression model of parameters COD, BOD 5 and suspended solids SS are in R 2 values ranging from 0.989 of parameter SS to 0.999 of parameter BOD 5. Six spectral Landsat ETM bands were included in the regression models as well as predictibility was also tested. This paper pointed out the applicability of artificial neural network to predict the water quality Dankia, Dalat using Landsat ETM data that helped to make the process of determining water quality quickly and effectively. The six Landsat ETM spectral bands were used as input parameters of neural network, and the output parameters consist of the parameter COD, BOD 5 and suspended solids SS. This gave the indirectly monitoring method of water quality based on remote sensing techniques. This shows very promising in the sustainable management of water resources.

Landsat TM/ETM+ and machine-learning algorithms for limnological studies and algal bloom management of inland lakes

Article

Apr 2018
J APPL REMOTE SENS

Accumulating remotely sensed and ground-measured data and improvements in data mining such as machine-learning techniques open new opportunities for monitoring and managing algal blooms over large spatial scales. The goal of this study was to test the accuracy of remotely sensed algal biomass determined with machine-learning algorithms and Landsat TM/ETM+ imagery. We used chlorophyll-A concentration data from the 2007 National Lake Assessment (NLA) (lake N=1157) by the US Environmental Protection Agency to train and test Landsat TM/ETM+ algorithms. Results showed significant improvements in chlorophyll-A retrieval accuracy using machine-learning algorithms compared with traditional empirical models using linear regression. Specifically, the results from boosted regression trees and random forest explained, respectively, 45.8% and 44.5% of chlorophyll-A variation. Multiple linear regression could only explain 39.8% of chlorophyll-A variation. The chlorophyll-A concentration derived from Landsat TM/ETM+ and a simple to use Google Earth Engine application, accurately characterized a 2009 algal bloom in western Lake Erie to show the model worked well for the analysis of temporal changes in algal conditions. Compared with chlorophyll-A data from the NLA, chlorophyll-A measurements with our Landsat TM/ETM+ model had almost the same correlation with lake's total phosphorus concentrations, especially when using multiple Landsat images. Therefore, Landsat measurements of chlorophyll-A have value for ecological assessments and managing algal problems in lakes. © 2018 Society of Photo-Optical Instrumentation Engineers (SPIE).

Quantifying suspended sediment dynamics in mega deltas using remote sensing data: A case study of the Mekong floodplains

Article

Jun 2018

Temporal and spatial concentrations of suspended sediment in floodplains are difficult to quantify because in situ measurements can be logistically complex, time consuming and costly. In this research, satellite imagery with long temporal and large spatial coverage (Landsat TM/ETM+) was used to complement in situ suspended sediment measurements to reflect sediment dynamics in a large (70,000 km 2) floodplain. Instead of using a single spectral band from Landsat, a Principal Component Analysis was applied to obtain uncorrelated reflectance values for five bands of Landsat TM/ETM+. Significant correlations between the scores of the 1st principal component and the values of continuously gauged suspended sediment concentration, shown via high coefficients of determination of sediment rating curves (R 2 ranging from 0.66 to 0.92), permit the application of satellite images to quantify spatial and temporal sediment variation in the Mekong floodplains. Estimated suspended sediment maps show that hydraulic regimes at Chaktomuk (Cambodia), where the Mekong, Bassac, and Tonle Sap rivers diverge, determine the amount of seasonal sediment supplies to the Mekong Delta. The development of flood prevention systems to allow for three rice crops a year in the Vietnam Mekong Delta significantly reduces localized flooding, but also prevents sediment (source of nutrients) from entering fields. A direct consequence of this is the need to apply more artificial fertilizers to boost agricultural productivity, which may trigger environmental problems. Overall, remote sensing is shown to be an effective tool to understand temporal and spatial sediment dynamics in large floodplains.

A dataset of trophic state index for nation-scale lakes in China from 40-year Landsat observations

Article

Full-text available

Jun 2024

Trophic state index (TSI) serves as a key indicator for quantifying and understanding the lake eutrophication, which has not been fully explored for long-term water quality monitoring, especially for small and medium inland waters. Landsat satellites offer an effective complement to facilitate the temporal and spatial monitoring of multi-scale lakes. Landsat surface reflectance products were utilized to retrieve the annual average TSI for 2693 lakes over 1 km² in China from 1984 to 2023. Our method first distinguishes lake types by pixels with a decision tree and then derives relationships between trophic state and algal biomass index. Validation with public reports and existing datasets confirmed the good consistency and reliability. The dataset provides reliable annual TSI results and credible trends for lakes under different area scales, which can serve as a reference for further research and provide convenience for lake sustainable management.

Recent advances in geostationary satellites for inland and coastal aquatic systems: scientific research and applications

Article

Full-text available

Feb 2024

Applications of remote sensing in water quality assessment

Chapter

Jan 2024

Earth observation techniques for the assessment of water surface quality. A review of current methodologies.

Conference Paper

Full-text available

Jun 2022

At this work, an initial literature review has been carried out, relating remote sensing with the condition of aquatic environment and how satellite data is expected to dominant at the near future. The growing demand for continuous information and data on water quality is impossible to achieve using only traditional in-situ techniques, as they present a number of limitations in their implementation thus creating a rather costly and time-consuming water monitoring process. This is largely an obstacle to achieve the objectives of European directives such the Water Framework Directive (WFD), bringing the EU Member States to the brink of collapse with several difficulties in complying with its requirements. On the other hand, Earth Observation (EO) has an immense potential as an enabling tool for the effective implementation of EU directives and national priorities. Undoubtably, the synergy between remote sensing and in-situ techniques can provide a strong monitoring system and near real time information of various water quality indicators, mainly due to their geospatial stability and repeatability. In this regard, at the current review, a thorough analysis of innovative EO and remote sensing technologies based on the results of research and scientific analysis is presented. Subsequently, it is attempted to determine an appropriate way of contributing modern surveillance techniques for surface water in the design and decision-making related to WFD by the competent bodies. The main objective of this effort is to investigate the appropriate ways and methods for disseminating data for the information produced to reach and exploit the actual actors involved.

Optimization of Landsat Chl-a Retrieval Algorithms in Freshwater Lakes through Classification of Optical Water Types

Article

Full-text available

Nov 2021

The application of remote sensing data to empirical models of inland surface water chlorophyll-a concentrations (chl-a) has been in development since the launch of the Landsat 4 satellite series in 1982. However, establishing an empirical model using a chl-a retrieval algorithm is difficult due to the spatial heterogeneity of inland lake water properties. Classification of optical water types (OWTs; i.e., differentially observed water spectra due to differences in water properties) has grown in favour in recent years over traditional non-turbid vs. turbid classifications. This study examined whether top-of-atmosphere reflectance observations in visible to near-infrared bands from Landsat 4, 5, 7, and 8 sensors can be used to identify unique OWTs using a guided unsupervised classification approach in which OWTs are defined through both remotely sensed reflectance and surface water chemistry data taken from samples in North American and Swedish lakes. Linear regressions of algorithms (Landsat reflectance bands, band ratios, products, or combinations) to lake surface water chl-a were built for each OWT. The performances of chl-a retrieval algorithms within each OWT were compared to those of global chl-a algorithms to test the effectiveness of OWT classification. Seven unique OWTs were identified and then fit into four categories with varying degrees of brightness as follows: turbid lakes with a low chl-a:turbidity ratio; turbid lakes with a mixture of high chl-a and turbidity measurements; oligotrophic or mesotrophic lakes with a mixture of low chl-a and turbidity measurements; and eutrophic lakes with a high chl-a:turbidity ratio. With one exception (r2 = 0.26, p = 0.08), the best performing algorithm in each OWT showed improvement (r2 = 0.69–0.91, p < 0.05), compared with the best performing algorithm for all lakes combined (r2 = 0.52, p < 0.05). Landsat reflectance can be used to extract OWTs in inland lakes to provide improved prediction of chl-a over large extents and long time series, giving researchers an opportunity to study the trophic states of unmonitored lakes.

Assessing climate and human activity effects on lake characteristics using spatio-temporal satellite data and an emotional neural network

Preprint

Full-text available

Feb 2021

Different sensing methods provide valuable information for comprehensive monitoring strategies, which are crucial for the ecological management of lakes and watersheds. Subsequently, the resulting spatio-temporal information can be considered the fundamental knowledge for the water resources management of watersheds. Lake Urmia is deemed one of the most important aquatic habitats in Iran. It has been experiencing significant changes during recent years due to climate change, anthropogenic activities, and a lack of coherent management approaches. Hence, awareness of the hydro-ecological factors during the last few decades is critical for identifying the problems. In this research, the impacts of changes in key parameters such as precipitation, evapotranspiration, water surface temperatures, suspended sediment concentration, saline features, and vegetation are explored using satellite imagery. The primary purpose of this study is to evaluate the Lake Urmia crisis concerning human-involved and climate factors such as the agriculture sector and construction of the causeway. In this regard, a limbic based Emotional Artificial Neural Network (EANN) is developed as a non-linear universal mapping and implemented for the first time to demonstrate the interactions between the considered hydro-ecological factors and the sensitivity of the two indicators the lake health. Providing a comprehensive spatio-temporal analysis is another objective of this study in order to detect the onset of deterioration in the parameters. The values of the efficiency criteria were measured to evaluate the sensitivity of the EANN models to the related inputs. The quantitative results confirm that the combination of both climate and anthropogenic factors, including the agricultural sector's overdraft, leads to the most efficient EANN model and, consequently, is considered the leading cause of the crisis.

Determining the Concentration of Suspended Sediment in the Lower Đáy River (Northern Vietnam) Using MSI Sentinel 2 High Spatial Resolution Data

Article

Dec 2020

Geographic Information Systems and Remote Sensing Applications in Environmental and Water Resources

Chapter

Feb 2021

The fundamentals of both remote sensing (RS) and geographic information systems (GIS) are introduced in detail. For RS, the characteristics of various sensor data and the satellite data processing technology are explained. For GIS, the authors describe various GIS subsystems and data models. There are many applications to water resources, such as mapping, monitoring, and modeling. The US Environmental Protection Agency (USEPA) has established many useful programs to support the activities of American water resources engineers and planners. These important USEPA support programs are presented.

Remote sensing of aquatic systems and quality of surface waters in the semiarid region of Brazil

Thesis

Full-text available

Jan 2016

Luiz Carlos Guerreiro Chaves

Continental waters are an important water system that must be preserved for the maintenance of life on Earth, where the Brazilian semi-arid region stands out for the considerable amount of surface reservoirs that are used to store water used for multiple purposes. The contamination of these surface waters by anthropic action, makes it urgent to adopt monitoring techniques that can assist water quality managers. Thus, remote sensing techniques have been shown to be a promising tool for the study of these systems, allowing detailed monitoring through the use of field and/or orbital data radiometric. Given the above, the hypothesis arises that the spectral features of the surface waters of the Brazilian semiarid allow to characterize, through a set of reflectance spectra, different types of water quality in terms of the concentration ranges of their Optically Active Constituents. As a way to analyze this hypothesis, this study was developed, trying to characterize, through limnological attributes and optical properties of water, the spectral behavior of the surface waters of reservoirs located in the Brazilian semiarid region.. The study was carried out in Orós dam, Trussu dam and Muquém dam in Alto Jaguaribe Basin, in State of Ceará, Brazil. Limnologiy data and spectral data were obtained at each three months, from 2012 to 2014, in these reservoirs. To correlate the limnological data and the spectral responses of water were applied the Cluster Analysis and the the Multivariate Analysis to limnological and spectral data. Results showed that according to spectral responses the water of the reservoirs were grouped in three groups. These groups showed that in rain season the water from Trussu and Muquém dams seems clear water. The water from Orós dam showed spectral response of aquatic systems water with high turbidity and chlorophyll-a. There was a good correlation between the reflectance response and the cyanobacteria density in water from Orós dam. However, the models that were developed for the estimation of cyanobacteria density using the derived by the spectral responses showed satisfactory results only for the middle third of the reservoir. The peak of reflectance representative of the presence of cyanobacteria in Orós dam presented offset to longer wavelengths in the visible range. The size of water body, the land use and the seasonality of rainfall influenced the optically active components of the water.

Prediction of Photochemically Produced Reactive Intermediates in Surface Waters via Satellite Remote Sensing

Article

May 2020

Absorption of solar radiation by colored dissolved organic matter (CDOM) in surface waters results in the formation of photochemically-produced reactive intermediates (PPRIs) that react with pollutants in water. Knowing the steady-state concentrations of PPRIs ([PPRI]ss) is critical to predicting the persistence of pollutants in sunlit surface waters. CDOM levels (a440) can be measured remotely for lakes over large areas using satellite imagery. Laboratory measurements of [PPRI]ss and apparent quantum yields (Φ) of three PPRIs (³DOM∗, ¹O2), and, •OH) were made for 24 lake samples under simulated sunlight. The total rate of light absorption by the water samples (Ra), the rates of formation (Rf), and [PPRI]ss of ³DOM∗ and ¹O2, linearly increased with increasing a440. The production rate of •OH was linearly correlated with a440, but the steady state concentration was best fit by a logarithmic function. The relationship between measured a440 and Landsat 8 reflectance was used to map a440 for more than 10,000 lakes across Minnesota. Relationships of a440 with Rf, [PPRIs]ss, and Ra were coupled with satellite-based a440 assessments to map reactive species production rates and concentrations, as well as contaminant transformation rates. This study demonstrates the potential for using satellite imagery for estimating contaminant loss via indirect photolysis in lakes.

Research Trends in the Use of Remote Sensing for Inland Water Quality Science: Moving Towards Multidisciplinary Applications

Article

Full-text available

Jan 2020

Remote sensing approaches to measuring inland water quality date back nearly 50 years to the beginning of the satellite era. Over this time span, hundreds of peer-reviewed publications have demonstrated promising remote sensing models to estimate biological, chemical, and physical properties of inland waterbodies. Until recently, most of these publications focused largely on algorithm development as opposed to implementation of those algorithms to address specific science questions. This slow evolution contrasts with terrestrial and oceanic remote sensing, where methods development in the 1970s led to publications focused on understanding spatially expansive, complex processes as early as the mid-1980s. This review explores the progression of inland water quality remote sensing from methodological development to scientific applications. We use bibliometric analysis to assess overall patterns in the field and subsequently examine 236 key papers to identify trends in research focus and scale. The results highlight an initial 30 year period where the majority of publications focused on model development and validation followed by a spike in publications, beginning in the early-2000s, applying remote sensing models to analyze spatiotemporal trends, drivers, and impacts of changing water quality on ecosystems and human populations. Recent and emerging resources, including improved data availability and enhanced processing platforms, are enabling researchers to address challenging science questions and model spatiotemporally explicit patterns in water quality. Examination of the literature shows that the past 10–15 years has brought about a focal shift within the field, where researchers are using improved computing resources, datasets, and operational remote sensing algorithms to better understand complex inland water systems. Future satellite missions promise to continue these improvements by providing observational continuity with spatial/spectral resolutions ideal for inland waters.

Research trends in the use of remote sensing for inland water quality science: Moving towards multidisciplinary applications

Preprint

Oct 2019

Remote sensing approaches to measuring inland water quality date back nearly 50 years to the beginning of the satellite era. Over this time span, hundreds of peer reviewed publications have demonstrated promising remote sensing models to estimate biological, chemical, and physical properties of inland waterbodies. Until recently, most of these publications focused largely on algorithm development as opposed to implementation of those algorithms to address specific science questions. This slow evolution contrasts with terrestrial and oceanic remote sensing, where methods development in the 1970s led to publications focused on understanding spatially expansive, complex processes as early as the mid-1980s. This review explores the progression of inland water quality remote sensing from methodological development to scientific applications. We use bibliometric analysis to assess overall patterns in the field and subsequently examine 236 key papers to identify trends in research focus and scale. The results highlight an initial 30-year period where the majority of publications focused on model development and validation followed by a spike in publications, beginning in the early-2000s, applying remote sensing models to analyze spatiotemporal trends, drivers, and impacts of changing water quality on ecosystems and human populations. Recent and emerging resources, including improved data availability and enhanced processing platforms, are enabling researchers to address challenging science questions and model spatiotemporally explicit patterns in water quality. Examination of the literature shows that the past 10-15 years has brought about a focal shift within the field, where researchers are using improved computing resources, data sets, and operational remote sensing algorithms to better understand complex inland water systems. Future satellite missions promise to continue these improvements by providing observational continuity with spatial/spectral resolutions ideal for inland waters.

DEVELOPMENT OF ALGORITHM FOR RETRIEVAL OF CHLOROPHYLL-a AND SUSPENDED SEDIMENT CONCENTRATIONS FOR COASTAL ENVIRONMENT USING IRS-P4 OCM DATA 1 2 3 4 5

Article

Full-text available

Jan 2019

Nagalakshmi Radhakrishnan

Chlorophyll-a and suspended sediment are two important parameters of case II water quality. In case I water, these two parameters can be effectively quantified by empirical algorithm developed using the radiance values obtained from remote sensing data through the surface concentration samples collected from the field. In case II waters, however, the task becomes difficult due to the presence of anthropogenic activities. This paper explores the usefulness of remote sensing technology for analysis of chlorophyll-a (chl-a) and suspended sediment (SS) by developing an algorithm for two seasons using IRS-P4 OCM sensor. The algorithm has been validated and variation between the field and computed value is round 5%. This algorithm can be used to estimate the chlorophyll-a and suspended sediment concentration in the coastal environment using the IRS-P4 OCM sensor without any field sample values for further analysis.

Monitoring Lake Recovery Using Landsat MSS

Article

Full-text available

Jan 1989

Suspended sediment loads in Lake Chicot, an oxbow lake in southeastern Arkansas, have declined since the initiation of control structures in April, 1985. Inflows with high suspended sediment loads derived from agricultural runoff are now diverted into the River Mississippi. Water quality data collection at four sites within the lake was timed to correspond with Landsat satellite fly over. Over ten years of Landsat MSS digital data were obtained for statistical analysis with the suspended sediment, secchi depth, turbidity, and chlorophyll a data. Comparison of satellite and water quality data, from before and after the initiation of the lake recovery operation, document the ability of satellite-based remote sensing to monitor changes in water quality. It is concluded that the relatively frequent and synoptic coverage of remote sensing satellites provides an ability to assess geographic variations in lake and reservoir water quality and to monitor changes in water quality through time.

Télédétection par satellite dans l'évaluation de la qualité de l'eau: Avec 6 figures dans le texte

Article

Jan 1974

L'échantillonnage des eaux lacustres, soit pour suivre leur évolution dans le temps, soit pour déterminer les processus de mélange, est une approche laborieuse dont on peut difficilement tirer des données synoptiques. Les images multispectrales transmises par le satellite ERTS constituent une source nouvelle et importante de données dont le potentiel n'a pas encore été pleinement délimité. A partir d'une analyse préliminaire de quelques centaines de lacs du Québec, nous montrons comment ces données peuvent étre utilisées pour étudier l'évolution temporelle des lacs ou la distribution spatiale de certaines de leurs caractéristiques. La prochaine génération de ce type de satellites possédera une plus grande résolution spectrale, spatiale et temporelle. Pour tirer pleinement profit de ces techniques, il nous semble urgent de développer la méthodologie d'analyse de ces données et de recueillir dès maintenant les mesures de contrôie au sol nécessaires à l'interprétation de ces mêmes données.

Landsat MSS and TM Post Calibration Dynamic Ranges, Exoatmospheric Reflectance and at-satellite Temperatures

Article

Jan 1986

Landsat MSS and TM post-calibration dynamic ranges, exoatmospheric reflectances and at-satellite temperatures. Earth Observation Satellite Co., Lanham, MD, Landsat Tech

Article

Jan 1986

Monitoring water quality conditions in a large Western reservoir with Landsat imagery ( USA).

Article

Jan 1985
PHOTOGRAMM ENG REM S

J.P. Verdin

Seven Landsat multispectral scanner scenes were processed to portray water quality conditions in Flaming Gorge Reservoir, a large Bureau of Reclamation impoundment in Utah and Wyoming. Concurrent surface sampling data were available for four of the seven scenes. A deterministic approach employing an atmospheric radiative transfer model was used to account for effects of sun angle and atmosphere in the Landsat imagery. This permitted the development of water quality predictive regression equations using surface sampling data from all four dates at once. It also permitted the estimation of reservoir conditions for the three scenes for which no concurrent surface sampling was carried out. The two equations, providing estimates of Secchi transparency and chlorophyll a concentration, were used to monitor the year-to-year spatial variation of trophic zones in the reservoir. -Author

A further development of the chromaticity technique for satellite mapping of suspended sediment load.

Article

Jan 1986
PHOTOGRAMM ENG REM S

A further development of the technique for mapping suspended sediment load using the chromaticity method is presented. The calibration is based on several Landsat scenes from Sweden and Canada covering differnt atmospheric conditions and different solar angles. The method is continuously used for water quality surveillance of Swedish lakes.-Authors

Use of Thematic Mapper Data to Assess Water Quality in Green Bay and Central Lake Michigan

Article

Jan 1986
PHOTOGRAMM ENG REM S

The major objective of this study was to assess the technical feasibility of using TM data to evaluate, both qualitatively and quantitatively, the general water quality of southern Green Bay and central Lake Michigan. An empirical approach of relating TM data with simultaneously acquired 'sea truth' data through multiple linear regression analysis was employed. Subsequently, the regression models were used to prepare digital cartographic products depicting the water quality and thermal distributions over the entire study area.-from Author

Remote Sensing of Suspended Sediments in Surface Water

Article

Dec 1976
PHOTOGRAMM ENG REM S

Reflected and incident solar radiation 20 to 50 cm above the water surface were measured on six northern Mississippi reservoirs between August 1973 and December 1974. Linear regression analyses showed the best fit for the relationship between reflected solar radiation, or reflectance, and suspended sediment concentration of surface water was between 700 and 800 nm. Further analyses, using sun angle grouping, showed that sun angle had a definite effect on these relationships. These studies showed that quantitative estimates of suspended sediment concentration of surface water could be made using reflected solar radiation.

Comparison of Landsat MSS Pixel Array Sizes for Estimating Water Quality

Article

Jan 1987
PHOTOGRAMM ENG REM S

A problem in the analyses of Landsat Multispectral Scanner (MSS) data is sampling pixels representative of the area being used for calibration. This study reports the analyses of different size pixel arrays for estimating water quality variables. Nested arrays of pixels with sizes of 5 by 5, 3 by 3, and 2 by 2, and the single center pixel of the 5 by 5 array were sampled at 5 different locations in the lake where water quality variables had been measured. Fourteen Landsat scenes for the period between January 1983 and June 1985 were analyzed. -from Authors

Computations with Physical Values from Landsat Digital Data

Article

Jan 1982
PHOTOGRAMM ENG REM S

C.J. Robinove

Landsat digital images are commonly analyzed by using the digital numbers for each pixel recorded on a computer-compatible magnetic tape. Although this procedure may be satisfactory when only a single, internally consistent image is used, the procedure may produce incorrect results if more than one image is used for analysis as in mosaics or temporal overlays. The digital numbers for each pixel should be converted to their dimensioned equivalents such as radiance, as measured at the satellite, in milliwatts per square centimetre per steradian, or reflectance.-from Author

Landsat Digital Data for Estimating Suspended Sediment in Inland Water

Article

Jan 1989

Suspended sediment is a visible indicator of water quality in lakes and reservoirs and a potential indicator of soil erosion on the drainage basin. An economical method is needed for surveying the landscape to locate water bodies with significant suspended sediment concentrations. This paper discusses remote sensing studies using multispectral scanner (MSS) digital data from the Landsat satellite to estimate the concentration of surface suspended sediment in two oxbow lakes in the lower Mississippi River valley. These studies show that MSS data can be used to estimate the concentration of suspended sediment. Using this remote sensing technology, it would be possible to survey large segments of the landscape economically and efficiently to locate water bodies with significant suspended sediment concentrations.

The Economic Efficiency of Voluntary Soil Conservation Programs

Article

Jan 1989

Application of Landsat Thematic Mapper data for coastal thermal plume analysis at Diablo Canyon

Article

Jun 1989
PHOTOGRAMM ENG REM S

The possibility of using Landsat Thematic Mapper (TM) thermal data to derive absolute temperature distributions in coastal waters that receive cooling effluent from a power plant is demonstrated. Landsat TM band 6 (thermal) data acquired on June 18, 1986, for the Diablo Canyon power plant in California were compared to ground truth temperatures measured at the same time. Higher-resolution band 5 (reflectance) data were used to locate power plant discharge and intake positions and identify locations of thermal pixels containing only water, no land. Local radiosonde measurements, used in LOWTRAN 6 adjustments for atmospheric effects, produced corrected ocean surface radiances that, when converted to temperatures, gave values within approximately 0.6 C of ground truth. A contour plot was produced that compared power plant plume temperatures with those of the ocean and coastal environment. It is concluded that Landsat can provide good estimates of absolute temperatures of the coastal power plant thermal plume. Moreover, quantitative information on ambient ocean surface temperature conditions (e.g., upwelling) may enhance interpretation of numerical model prediction. 12 refs.

Opportunities and difficulties associated with using Landsat Thematic Mapper data for determining surface water temperatures

Conference Paper

Jun 1985

This paper describes results of research efforts to estimate surface-water temperatures using Landsat 4 and 5 TM thermal band data. Recent research involved the analysis of day- and night-time TM Band 6 data in both corrected (P tape) and uncorrected (A tape) formats. Results reported are for (1) a reservoir reactor cooling system (PAR Pond) at the Department of Energy's (DOE) Savannah River Plant in Aiken, South Carolina, and (2) the Columbia River adjacent to the DOE's Hanford site in southeastern Washington State. Differences between Landsat-derived surface water temperatures and ground truth values before and after correcting for atmospheric effects (using LOWTRAN) are described. The results substantiate the consistent performance of the Landsat TM thermal sensor for providing potentially useful estimates of relative and absolute temperatures for large water bodies within and between TM scenes. In addition, technical difficulties encountered that currently limit routine use of such data for environmental monitoring, such as calibration, mixed pixel phenomena, and atmospheric effects are addressed. 5 refs., 2 figs., 3 tabs.

Comparison of Measured Suspended Sediment Concentrations with Suspended Sediment Concentrations Estimated from Landsat MSS Data

Article

Mar 1988

Landsat multispectral scanner (MSS) data for 27 dates between January 1983 and June 1985 for Moon Lake in Coahoma County, Mississippi, were analysed to determine if Landsat MSS digital data could be used to estimate suspended sediment concentrations in the surface waters of a small agricultural lake. Field measurements of suspended sediment concentration in the surface water and other water quality variables were available for five locations in Moon Lake within 0 to 13 days of each of the 27 Landsat overpass dates. Pixel values, radiance and reflectance measurements from Landsat MSS data from 14 scenes were compared with field data and used to develop simple and multiple regression equations to estimate suspended sediment concentration in the surface water of the lake. The best equations were used to estimate the suspended sediment concentrations for the 13 other Landsat scenes. The coefficient of determination for the relationship between measured and estimated suspended sediment concentrations was greater than 0·80 and the root mean square error was less than 40 mg l−1 when equations using either the radiance and reflectance data were used to estimate suspended sediment concentrations. This study shows that good estimates of suspended sediment concentrations can be made using Landsat MSS data especially in the range of concentrations between 50 and 250mg l−1 which are the critical concentrations for assessing conservation needs. Suspended sediment concentrations greater than 250mg l−1 were underestimated by most of the equations indicating a saturation of reflected solar radiation at higher suspended sediment concentrations. Thus a technique using Landsat MSS digital data can be developed to monitor the landscape to locate those reservoirs with critical suspended sediment concentrations quickly. Soil and water conservation efforts can then be concentrated on the watershed of those reservoirs where suspended sediment is greatest.

Surface Water Quality Measurements of Lake Chicot, Arkansas Using Data from Landsat Satellites

Article

Jun 1986

Thirty-three Landsat satellite multispectral scanner (MSS) scenes of Lake Chicot, Arkansas, collected between July 1976 and November 1979 were analyzed and compared with measurements of total solids, suspended solids and chlorophyll-a in the surface water. Total solids ranged from 117 to 908 mg l−1 with a mean of 234 mg l−1. Suspended solids ranged from 1 to 828 mg l−1 with a mean of 93 mg l−1. Chlorophyll-a ranged from 2 to 113 mg m−3 with a mean of 27 mg m−3. Radiance in milliwatts per square centimeter per steradian (mW cm−2 Sr−1) and reflectance in MSS band 6 (700–800 nm) had the highest correlation with total (r = 0.64 for radiance and 0.73 for reflectance) and suspended (r = 0.69 for radiance and 0.78 for reflectance) solids. Landsat MSS band 5 (600–700 nm) had the highest correlation coefficient (r = −0.55 for radiance and r = −0.57 for reflectance) with chlorophyll-a. Multiple linear regressions with the 4 MSS bands did not improve the correlations for either total or suspended sediment or chlorophyll-a.

Determination of Suspended Sediments from Multiple Day Landsat and AVHRR Data

Article

Jun 1988
REMOTE SENS ENVIRON

A combination of satellite data, on-site sampling, and hydrodynamic and water quality model simulations was used to evaluate surface sediment concentrations in Sandusky Bay, Lake Erie. Both satellite brightness values and categorizations of total suspended sediment concentrations from Landsat and AVHRR data were evaluated for the period of 10–28 June 1981. The satellite data products displayed many of the trends in concentration recorded by the on-site data, and were similar to the results of hydrodynamic and water quality (HWQ) model simulations reported elsewhere.

Using Landsat Multispectral Scanner Data to Estimate Suspended Sediments in Moon Lake, Mississippi

Article

Oct 1987
REMOTE SENS ENVIRON

Suspended sediments are a major factor affecting water quality in many aquatic ecosystems. Research was undertaken to determine the application of digital spectral data collected by the multispectral scanner (MSS) on the Landsat satellite for estimating suspended sediments in aquatic ecosystems where mean annual concentrations of suspended sediments are greater than 50 mg L−1. Digital spectral data from 14 Landsat MSS scenes of Moon Lake in Coahoma County, Mississippi were analyzed and compared with ground measurements of total solids and suspended sediments in the lake surface water for the period between January 1983 and May 1985. Coefficients of determination (R2) greater than 0.81 were calculated between MSS Band 2 (0.6–0.7 μm) or Band 3 (0.7–0.8 μm) and suspended sediments or total solids. Coefficients of determination for multiple regression using three or four MSS bands were greater than 0.90. This study showed that digital spectral data from the Landsat satellites can be used to locate and monitor surface-suspended sediments in aquatic ecosystems. With such a digital computer technique, entire regions can be surveyed quickly to locate aquatic ecosystems with suspended sediment problems. Conservation efforts can then be concentrated on those aquatic ecosystems with the most serious suspended sediment problems and soil conservation strategies developed in their watersheds to control erosion and sediment yield and thus to improve water quality.

Calibration of Satellite Radiometers and the Comparison of Vegetation Indices

Article

Feb 1987
REMOTE SENS ENVIRON

John C. Price

Satellite technology provides a steadily improving capability to monitor surface land use and vegetation. However, the increasing number of satellite sensors has led to a variety of spectral indices which may be used to characterize vegetation. A basis is developed for comparing results from different sensors using instrument calibration coefficients, and the derived radiances are related to reflectances, principal component variables such as greenness, and spectral vegetation indices.

Toward Universal Multispectral Suspended Sediment Algorithms

Article

Oct 1978
REMOTE SENS ENVIRON

Ronald J Holyer

A data acquisition and analysis program has been undertaken to demonstrate the feasibility of remote multispectral techniques for monitoring suspended sediment concentrations in natural water bodies. Two hundred surface radiance measurements (400–1000 nm) were made at Lake Mead with coincident water sampling for laboratory analysis. Water volume spectral reflectance is calculated from the recorded surface radiance and volume reflectance-suspended sediment relationships investigated. Statistical analysis indicates that quantitative estimates of nonfilterable residue and nephelometric turbidity can be obtained from volume spectral reflectance data with sufficient accuracy (based on U.S. Environmental Protection Agency standards) to make the multispectral technique feasible for sediment monitoring. Algorithms exhibit sufficient universality to indicate they can be implemented in many cases with little or no ground truth for calibration.

Monitoring Inland Water Quality Using Landsat Data

Article

Sep 1983
REMOTE SENS ENVIRON

The water quality parameters turbidity and algal pigment concentration of freshwater lakes have been modeled and predicted using Landsat multispectral scanner data as multiple linear predictors. Satellite data for an area in South East Australia from seven occasions during 1978 and 1979 were used along with concurrent ground-based measurements from sampling sites on three lakes covering a wide range of water quality regimes. Date-independent models for turbidity and algal pigment were obtained using the satellite multispectral data and the water quality data from up to 21 sampling sites on one lake on six occasions. The sun elevation at the time of satellite overpass was included in the models to account for differences between dates, and the time of sample collection was included to compensate for diurnal variations in pigment fluorescence. These models were used to successfully predict these water quality parameters for this lake on a new occasion and for the two other lakes on three occasions.

APHA Standard Methods for the Examination of Water and Wastewater

Article

Jun 1961

William. G. Walter

Research for reliable quantification of water sediment concentrations from multispectral scanner remote sensing data

Article

Jun 1981

The assessment of conservation practice through remote monitoring of sediment concentration in drainage basin waters is considered. The problem of large sediment concentrations is discussed, laboratory spectral signature results are presented, remote sensing penetration depth knowledge is assessed, and research aimed at quantifying atmospheric transmission, specular reflection and research aimed at quantifying atmospheric transmission, specular reflection, and path radiance is described. The results are expected to be improved experimental techniques and more reliable data analysis procedures for quantification of water sediment concentrations in future years. Such data should be useful in validating rain-full-runoff models of large drainage basins.

Comments, Controlling soil erosion, Soil Water Conserv Remote Sensing of suspended sediments of Lake Chicot, Arkansas

Jan 1987
77-85

W Scaling
F R Schiebe
J A Harrington
Jr
J C Ritchie

Scaling, W. (1987), Comments, Controlling soil erosion, Soil Water Conserv. News 8:2. Schiebe, F. R., Harrington, J. A., Jr., and Ritchie, J. C. (1988), Remote Sensing of suspended sediments of Lake Chicot, Arkansas, in Proceedings" ~f the 6th U.S. Army Corps of Engineering Remote Sensing Symposium, Galveston, TX, pp. 77-85.

Landsat digital data fur estimating suspended sediment in inland water, Intenmtional Association of Hydrological Sciences Publ

Jan 1989
151-158

J C Ritchie
F R Schiehe
C M Cooper

Ritchie, J. C., Schiehe, F. R., and Cooper, C. M. (1989), Landsat digital data fur estimating suspended sediment in inland water, Intenmtional Association of Hydrological Sciences Publ. No. 182, pp. 151-158.

Teledetection par satellite dans l'evahmtion de la qualite de l'eau

Jan 1974

Ruchon
E J Langham

Ruchon, (;., and Langham, E. J. (1974), Teledetection par satellite dans l'evahmtion de la qualite de l'eau, Vehr. Int.

Thermal band characterization of TM

Jan 1984
233

Lansing

Lansing, J., and Barker, J. L. (1984), Thermal band characteri-zation of TM, in Landsat-4 Science Characterization Early Results Symposium Proceedings (J. L. Barker, Ed.), God-dard Space Flight Center, Greenbelt, MD, Vol. 3, pp. 233-256.

Corrected vs. uncorrected Landsat-4 MSS data, Landsat Data Users

Jan 1983
4-8

J P Verdin

Verdin, J. P. (1983), Corrected vs. uncorrected Landsat-4 MSS data, Landsat Data Users' Notes 27:4-8.

Sediment deposition in United States reservoirs, in Man-Made Lakes: Their Problems and Environmental Effects

Jan 1978
349-357

F E Dendy
W A Champion
R B Wilson

Dendy, F. E., Champion, W. A., and Wilson, R. B. (1978), Sediment deposition in United States reservoirs, in Man-Made Lakes: Their Problems and Environmental Effects (W. C. Ackermann, G. F. White, and E. B. Worthington, Eds.), Geophysical Monograph Series Vol. 17, American Geophysical Union, Washington, DC, pp. 349-357.

Comments, Controlling soil erosion, Soil Water Conserv

Jan 1987

W Scaling

Scaling, W. (1987), Comments, Controlling soil erosion, Soil Water Conserv. News 8:2.

Sediment deposition in United States reservoirs

Jan 1978
349

Dendy

Comments, Controlling soil erosion

Jan 1987
2

Scaling

Corrected vs. uncorrected Landsat-4 MSS data

Jan 1983
4

Verdin

The Relationship of MSS and TM Digital Data With Suspended Sediments, Chlorophyll, and Temperature in Moon Lake, Mississippi

Abstract

No full-text available

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