scholarly journals Assessing Chlorophyll a Spatiotemporal Patterns Combining In Situ Continuous Fluorometry Measurements and Landsat 8/OLI Data across the Barataria Basin (Louisiana, USA)

Water ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 512 ◽  
Author(s):  
Ivan A. Vargas-Lopez ◽  
Victor H. Rivera-Monroy ◽  
John W. Day ◽  
Julie Whitbeck ◽  
Kanchan Maiti ◽  
...  
Keyword(s):  
Water Quality ◽  
High Frequency ◽  
Environmental Changes ◽  
Spatial Scales ◽  
Landsat 8 ◽  
Coastal System ◽  
Chl A ◽  
Barataria Basin ◽  
The Impact

The acquisition of reliable and accurate data to assess environmental changes over large spatial scales is one of the main limitations to determine the impact of eutrophication, and the effectiveness of management strategies in coastal systems. Here, we used a continuous in situ Chl-a fluorometry sensor and L8/OLI satellite data to develop an algorithm and map Chl-a spatial distribution to assess the impact of freshwater diversions and associated high nutrient loading rates in the Barataria Basin (BB) complex, a coastal system in the northern Gulf of Mexico. We collected water quality samples at 24 sampling stations and high-frequency continuous fluorometry in situ [Chl-a] data along a ~87 km transect from 2019–2020. Field [Chl-a] values were highly correlated (r = 0.86; p < 0.0001) with continuous in situ [Chl-a] fluorometry values. These continuous in situ [Chl-a] values were significantly related to a surface reflectance ratio ([B1 + B4]/B3) estimated using L8/OLI data (exponential model; R2 = 0.46; RMSE = 4.8, p < 0.0001). The statistical model replicated [Chl-a] spatial patterns across the BB complex. This work shows the utility of high-frequency continuous Chl-a fluorometry sampling coupled with L8/OLI image analysis to increase the frequency and number of field data sets to assess water quality conditions at large spatial scales in highly dynamic deltaic regions.

scholarly journals Determinación de calidad de agua en el Lago Vichuquén, con imágenes de satélite Landsat 8, sensor OLI, año 2016, Chile

2018 ◽  
pp. 67 ◽  
Author(s):  
I. Briceño ◽  
W. Pérez ◽  
D. San Miguel ◽  
S. Ramos
Keyword(s):  
Water Quality ◽  
Urban Areas ◽  
Laboratory Data ◽  
Radiata Pine ◽  
Quality Parameters ◽  
Landsat 8 ◽  
Linear Regression Models ◽  
Chl A ◽  
The Mean

<p>Trophic structure deterioration in continental water bodies (lakes and damps) has been a growing problem during the last years. Numerous factors, either natural or man-made contribute in value increments of various water quality indexes ranging toward eutrophication. Our study had objective to use remote sensing as complementary tool to study the spatial distribution and dynamics of Lake Vichuquén water quality parameters in two seasons of 2016 through the use of two satellite images of the Landsat 8 OLI sensor, with in situ and laboratory data. The Chl-a and Z<sub>SD</sub> parameters were estimated from multiple linear regression models. The results indicate that the trophic state of Lake Vichuquén corresponds to a eutrophic level in summer and mesotrophic in autumn. The laboratory analyzes establish for the summer and autumn season that the Chl-a data oscillate between 14.1 and 5.5 μg/l and for the Z<sub>SD</sub> between 3.7 and 2.5 m respectively. The increase in the levels of eutrophication of Lake Vichuquén is influenced in the first place by the seasonality and secondly by the different land uses that accelerate this type of processes; such as the plantations of radiata pine and eucalyptus, the agricultural activities and the urban areas surrounding the lake. The mean square error for each variable and each season varied in Chl-a in summer and another year 0.74 and 0.01 µg/l and Z<sub>SD</sub> 0.16 m respectively.</p>

scholarly journals Assessing an Atmospheric Correction Algorithm for Time Series of Satellite-Based Water-Leaving Reflectance Using Match-Up Sites in Australian Coastal Waters

2021 ◽  
Vol 13 (10) ◽  
pp. 1927
Author(s):  
Fuqin Li ◽  
David Jupp ◽  
Thomas Schroeder ◽  
Stephen Sagar ◽  
Joshua Sixsmith ◽  
...  
Keyword(s):  
Coastal Waters ◽  
Atmospheric Correction ◽  
Correction Method ◽  
In Situ Measurements ◽  
Landsat 8 ◽  
Correction Algorithm ◽  
Simple Method ◽  
North East ◽  
The Impact

An atmospheric correction algorithm for medium-resolution satellite data over general water surfaces (open/coastal, estuarine and inland waters) has been assessed in Australian coastal waters. In situ measurements at four match-up sites were used with 21 Landsat 8 images acquired between 2014 and 2017. Three aerosol sources (AERONET, MODIS ocean aerosol and climatology) were used to test the impact of the selection of aerosol optical depth (AOD) and Ångström coefficient on the retrieved accuracy. The initial results showed that the satellite-derived water-leaving reflectance can have good agreement with the in situ measurements, provided that the sun glint is handled effectively. Although the AERONET aerosol data performed best, the contemporary satellite-derived aerosol information from MODIS or an aerosol climatology could also be as effective, and should be assessed with further in situ measurements. Two sun glint correction strategies were assessed for their ability to remove the glint bias. The most successful one used the average of two shortwave infrared (SWIR) bands to represent sun glint and subtracted it from each band. Using this sun glint correction method, the mean all-band error of the retrieved water-leaving reflectance at the Lucinda Jetty Coastal Observatory (LJCO) in north east Australia was close to 4% and unbiased over 14 acquisitions. A persistent bias in the other strategy was likely due to the sky radiance being non-uniform for the selected images. In regard to future options for an operational sun glint correction, the simple method may be sufficient for clear skies until a physically based method has been established.

The importance of the turbulent ship wake regime for pollutant fate and transport

2021 ◽  
Author(s):  
Amanda T. Nylund ◽  
Rickard Bensow ◽  
Mattias Liefvendahl ◽  
Arash Eslamdoost ◽  
Anders Tengberg ◽  
...  
Keyword(s):  
Fate And Transport ◽  
In Situ Measurements ◽  
Ex Situ ◽  
Landsat 8 ◽  
Turbulent Regime ◽  
Near Wake ◽  
Ship Wake ◽  
Spatiotemporal Scales ◽  
The Impact

&lt;p&gt;This interdisciplinary study with implications for fate and transport of pollutants from shipping, investigates the previously overlooked phenomenon of ship induced mixing. When a ship moves through water, the hull and propeller induce a long-lasting turbulent wake. Natural waters are usually stratified, and the stratification influences both the vertical and horizontal extent of the wake. The altered turbulent regime in shipping lanes governs the distribution of discharged pollutants, e.g. PAHs, metals, nutrients and non-indigenous species. The ship related pollutant load follows the trend in volumes of maritime trade, which has almost tripled since the 1980s. In heavily trafficked areas there may be one ship passage every ten minutes; today shipping constitutes a significant source of pollution.&lt;/p&gt;&lt;p&gt;To understand the environmental impact of shipping related pollutants, it is essential to know their fate following regional scale transport. However, previous modelling efforts assuming discharge at the surface will not adequately reflect the input values in the regional models. Therefore, it is urgent to bridge the gaps between the spatiotemporal scales from high-resolution numerical modeling of the flow hydrodynamics around the ship, mixing processes and interaction of the ship and wake with stratification, and parameterization in regional oceanographic modeling. Here this knowledge gap is addressed by combining an array of methods; in situ measurements, remote sensing and numerical flow modeling.&lt;/p&gt;&lt;p&gt;A bottom-mounted Acoustic Doppler Current Profiler was placed under a ship lane, for &lt;em&gt;in-situ&lt;/em&gt; measurements of the vertical and temporal expansion of turbulent wakes. In addition, &lt;em&gt;ex-situ&lt;/em&gt; measurements with Landsat 8 Thermal Infrared Sensor were used to estimate the longevity and spatial extent of the thermal signal from ship wakes. The computational modelling was conducted using well resolved 3D RANS modelling for the hull and the near wake (up to five ship lengths aft), a method typically used for the near wake behaviour in analysing the propulsion system. As this is not feasible to use for a far wake analysis, the predicted wake is then used as input for a 2D+time modelling for the sustained wake up to 30min after the ship passage. These results, both from measurements and numerical models, are then combined to analyse how ship-induced turbulence influence at what depth discharged pollutants will be found.&lt;/p&gt;&lt;p&gt;This first step to cover the mesoscales of the turbulent ship wake is necessary to assess the impact of ship related pollution. In-situ measurements show median wake depth 13.5m (max 31.5m) and median longevity 10min (max 29min). Satellite data show median thermal wake signal 13.7km (max 62.5km). A detailed simulation model will only be possible to use for the first few 100m of the ship wake, but the coupling to a simplified 2D+time modelling shows a promising potential to bridge our understanding of the impact of the ship wake on the larger scales. Our model results indicate that the natural stratification affects the distribution and retention of pollutants in the wake region. The depth of discharge and the wake turbulence characteristics will in turn affect the fate and transport of pollutants on larger spatiotemporal scales.&lt;/p&gt;

scholarly journals Temporal Variation of Chlorophyll-a Concentrations in Highly Dynamic Waters from Unattended Sensors and Remote Sensing Observations

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2699 ◽  
Author(s):  
Jian Li ◽  
Liqiao Tian ◽  
Qingjun Song ◽  
Zhaohua Sun ◽  
Hongjing Yu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Chlorophyll Fluorescence ◽  
Chlorophyll A ◽  
Temporal Variations ◽  
Lake Area ◽  
Short Term ◽  
Chl A ◽  
Spatio Temporal

Monitoring of water quality changes in highly dynamic inland lakes is frequently impeded by insufficient spatial and temporal coverage, for both field surveys and remote sensing methods. To track short-term variations of chlorophyll fluorescence and chlorophyll-a concentrations in Poyang Lake, the largest freshwater lake in China, high-frequency, in-situ, measurements were collected from two fixed stations. The K-mean clustering method was also applied to identify clusters with similar spatio-temporal variations, using remote sensing Chl-a data products from the MERIS satellite, taken from 2003 to 2012. Four lake area classes were obtained with distinct spatio-temporal patterns, two of which were selected for in situ measurement. Distinct daily periodic variations were observed, with peaks at approximately 3:00 PM and troughs at night or early morning. Short-term variations of chlorophyll fluorescence and Chl-a levels were revealed, with a maximum intra-diurnal ratio of 5.1 and inter-diurnal ratio of 7.4, respectively. Using geostatistical analysis, the temporal range of chlorophyll fluorescence and corresponding Chl-a variations was determined to be 9.6 h, which indicates that there is a temporal discrepancy between Chl-a variations and the sampling frequency of current satellite missions. An analysis of the optimal sampling strategies demonstrated that the influence of the sampling time on the mean Chl-a concentrations observed was higher than 25%, and the uncertainty of any single Terra/MODIS or Aqua/MODIS observation was approximately 15%. Therefore, sampling twice a day is essential to resolve Chl-a variations with a bias level of 10% or less. The results highlight short-term variations of critical water quality parameters in freshwater, and they help identify specific design requirements for geostationary earth observation missions, so that they can better address the challenges of monitoring complex coastal and inland environments around the world.

scholarly journals Assessment of the Impacts of Image Signal-to-Noise Ratios in Impervious Surface Mapping

2019 ◽  
Vol 11 (22) ◽  
pp. 2603
Author(s):  
George Xian ◽  
Hua Shi ◽  
Cody Anderson ◽  
Zhuoting Wu
Keyword(s):  
Spatial Resolution ◽  
Signal To Noise Ratio ◽  
Spatial Scales ◽  
Regression Tree ◽  
Remote Sensing Data ◽  
Impervious Surface ◽  
Landsat 8 ◽  
Signal To Noise ◽  
Study Results ◽  
The Impact

Medium spatial resolution satellite images are frequently used to characterize thematic land cover and a continuous field at both regional and global scales. However, high spatial resolution remote sensing data can provide details in landscape structures, especially in the urban environment. With upgrades to spatial resolution and spectral coverage for many satellite sensors, the impact of the signal-to-noise ratio (SNR) in characterizing a landscape with highly heterogeneous features at the sub-pixel level is still uncertain. This study used WorldView-3 (WV3) images as a basis to evaluate the impacts of SNR on mapping a fractional developed impervious surface area (ISA). The point spread function (PSF) from the Landsat 8 Operational Land Imager (OLI) was used to resample the WV3 images to three different resolutions: 10 m, 20 m, and 30 m. Noise was then added to the resampled WV3 images to simulate different fractional levels of OLI SNRs. Furthermore, regression tree algorithms were incorporated into these images to estimate the ISA at different spatial scales. The study results showed that the total areal estimate could be improved by about 1% and 0.4% at 10-m spatial resolutions in our two study areas when the SNR changes from half to twice that of the Landsat OLI SNR level. Such improvement is more obvious in the high imperviousness ranges. The root-mean-square-error of ISA estimates using images that have twice and two-thirds the SNRs of OLI varied consistently from high to low when spatial resolutions changed from 10 m to 20 m. The increase of SNR, however, did not improve the overall performance of ISA estimates at 30 m.

scholarly journals OPTMIZATION OF BIO-OPTICAL MODEL PARAMETERS FOR TURBID LAKE WATER QUALITY ESTIMATION USING LANDSAT 8 AND WASI-2D

2020 ◽  
Vol XLII-3/W11 ◽  
pp. 67-72
Author(s):  
A. Manuel ◽  
A. C. Blanco ◽  
A. M. Tamondong ◽  
R. Jalbuena ◽  
O. Cabrera ◽  
...  
Keyword(s):  
Water Quality ◽  
Chlorophyll A ◽  
The Philippines ◽  
Quality Data ◽  
Model Parameters ◽  
Landsat 8 ◽  
Water Quality Data ◽  
Chlorophyll A Concentration ◽  
In Situ Data

Abstract. Laguna Lake, the Philippines’ largest freshwater lake, has always been historically, economically, and ecologically significant to the people living near it. However, as it lies at the center of urban development in Metro Manila, it suffers from water quality degradation. Water quality sampling by current field methods is not enough to assess the spatial and temporal variations of water quality in the lake. Regular water quality monitoring is advised, and remote sensing addresses the need for a synchronized and frequent observation and provides an efficient way to obtain bio-optical water quality parameters. Optimization of bio-optical models is done as local parameters change regionally and seasonally, thus requiring calibration. Field spectral measurements and in-situ water quality data taken during simultaneous satellite overpass were used to calibrate the bio-optical modelling tool WASI-2D to get estimates of chlorophyll-a concentration from the corresponding Landsat-8 images. The initial output values for chlorophyll-a concentration, which ranges from 10–40 μg/L, has an RMSE of up to 10 μg/L when compared with in situ data. Further refinements in the initial and constant parameters of the model resulted in an improved chlorophyll-a concentration retrieval from the Landsat-8 images. The outputs provided a chlorophyll-a concentration range from 5–12 μg/L, well within the usual range of measured values in the lake, with an RMSE of 2.28 μg/L compared to in situ data.

scholarly journals Developing of Total Suspended Sediment Model Using Landsat-8 Satellite Image and In-Situ Data at The Surabaya Coast, East Java, Indonesia

2017 ◽  
Vol 49 (1) ◽  
pp. 73 ◽  
Author(s):  
Teguh Hariyanto ◽  
Trismono C. Krisna ◽  
Khomsin Khomsin ◽  
Cherie Bhekti Pribadi ◽  
Nadjadji Anwar
Keyword(s):  
Water Quality ◽  
Suspended Sediment ◽  
Coastal Water ◽  
Satellite Image ◽  
Coastal Region ◽  
Sediment Concentration ◽  
Landsat 8 ◽  
Total Suspended Sediment ◽  
Coastal Water Quality

The decrease of coastal-water quality in the Surabaya coastal region can be recognized from the conceentration of Total Suspended Sediment(TSS ) . As a result we need a system for monitoring sediment concentration in the coastal region of Surabaya which regularly measures TSS. The principle to model and monitor TSSconcentration using remote sensing methods is by the integration of Landsat-8OLI satellites image processing using some ofTSS-models then those are analyzed for looking its suitability with TSS value direcly measured in the field ( in-situ measurement). The TSS value modeled from all algorithms validated usingcorrelation analysis and linear regression . The result shows that TSS model with the highest correlation value is TSS algorithm by Budiman (2004)with r value 0.991. Hence this algorithm can be used to investigate TSS-distribution which represent the coastal water quality of Surabaya with TSS value between 75 mg/L to 125 mg/L.

scholarly journals Retrieving Total and Inorganic Suspended Sediments in Amazon Floodplain Lakes: A Multisensor Approach

2019 ◽  
Vol 11 (15) ◽  
pp. 1744 ◽  
Author(s):  
Daniel Maciel ◽  
Evlyn Novo ◽  
Lino Sander de Carvalho ◽  
Cláudio Barbosa ◽  
Rogério Flores Júnior ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Atmospheric Correction ◽  
Suspended Sediments ◽  
Floodplain Lakes ◽  
Empirical Modeling ◽  
Landsat 8 ◽  
Amazon Floodplain ◽  
The Impact ◽  
Sentinel 2

Remote sensing imagery are fundamental to increasing the knowledge about sediment dynamics in the middle-lower Amazon floodplains. Moreover, they can help to understand both how climate change and how land use and land cover changes impact the sediment exchange between the Amazon River and floodplain lakes in this important and complex ecosystem. This study investigates the suitability of Landsat-8 and Sentinel-2 spectral characteristics in retrieving total (TSS) and inorganic (TSI) suspended sediments on a set of Amazon floodplain lakes in the middle-lower Amazon basin using in situ Remote Sensing Reflectance (Rrs) measurements to simulate Landsat 8/OLI (Operational Land Imager) and Sentinel 2/MSI (Multispectral Instrument) bands and to calibrate/validate several TSS and TSI empirical algorithms. The calibration was based on the Monte Carlo Simulation carried out for the following datasets: (1) All-Dataset, consisting of all the data acquired during four field campaigns at five lakes spread over the lower Amazon floodplain (n = 94); (2) Campaign-Dataset including samples acquired in a specific hydrograph phase (season) in all lakes. As sample size varied from one season to the other, n varied from 18 to 31; (3) Lake-Dataset including samples acquired in all seasons at a given lake with n also varying from 17 to 67 for each lake. The calibrated models were, then, applied to OLI and MSI scenes acquired in August 2017. The performance of three atmospheric correction algorithms was also assessed for both OLI (6S, ACOLITE, and L8SR) and MSI (6S, ACOLITE, and Sen2Cor) images. The impact of glint correction on atmosphere-corrected image performance was assessed against in situ glint-corrected Rrs measurements. After glint correction, the L8SR and 6S atmospheric correction performed better with the OLI and MSI sensors, respectively (Mean Absolute Percentage Error (MAPE) = 16.68% and 14.38%) considering the entire set of bands. However, for a given single band, different methods have different performances. The validated TSI and TSS satellite estimates showed that both in situ TSI and TSS algorithms provided reliable estimates, having the best results for the green OLI band (561 nm) and MSI red-edge band (705 nm) (MAPE < 21%). Moreover, the findings indicate that the OLI and MSI models provided similar errors, which support the use of both sensors as a virtual constellation for the TSS and TSI estimate over an Amazon floodplain. These results demonstrate the applicability of the calibration/validation techniques developed for the empirical modeling of suspended sediments in lower Amazon floodplain lakes using medium-resolution sensors.

scholarly journals The impact of sensors for satellite derived bathymetry within the Canadian Arctic

GEOMATICA ◽  
2020 ◽  
Vol 74 (2) ◽  
pp. 46-64
Author(s):  
Ryan Ahola ◽  
René Chénier ◽  
Mesha Sagram ◽  
Bradley Horner
Keyword(s):  
Optical Sensors ◽  
Landsat 8 ◽  
Sea Floor ◽  
Arctic Regions ◽  
Canadian Context ◽  
Multiple Band ◽  
Survey Accuracy ◽  
The Impact ◽  
Sentinel 2

Canada’s coastline presents challenges for charting. Within Arctic regions, in situ surveying presents risks to surveyors, is time consuming and costly. To better meet its mandate, the Canadian Hydrographic Service (CHS) has been investigating the potential of remote sensing to complement traditional charting techniques. Much of this work has focused on evaluating the effectiveness of empirical satellite derived bathymetry (SDB) techniques within the Canadian context. With greater knowledge of applying SDB techniques within Canadian waters, CHS is now interested in understanding how characteristics of optical sensors can impact SDB results. For example, how does the availability of different optical bands improve or hinder SDB estimates? What is the impact of spatial resolution on SDB accuracy? Do commercial satellites offer advantages over freely available data? Through application of a multiple band modelling technique to WorldView-2, Pléiades, PlanetScope, SPOT, Sentinel-2, and Landsat-8 imagery obtained over Cambridge Bay, Nunavut, this paper provides insight into these questions via comparisons with in situ survey data. Result highlights in the context of these questions include the following: Similarities between sensors: Overall linear error at 90% (LE90) results for each sensor ranged from 0.88 to 1.91 m relative to in situ depths, indicating consistency in the accuracy of SDB estimates from the examined satellites. Most estimates achieved Category of Zone of Confidence level C accuracy, the suggested minimum survey accuracy level for incorporating SDB information into navigational charts. SDB coverage: Between sensors, differences in the area of the sea floor that could be measured by SDB were apparent, as were differences in the ability of each sensor to properly represent spatial bathymetry characteristics. Sensor importance: Though relationships between SDB accuracy and sensor resolution were found, significant advantages or disadvantages for particular sensors were not identified, suggesting that other factors may play a more important role for SDB image selection (e.g., sea floor visibility, sediments, waves). Findings from this work will help inform SBD planning activities for hydrographic offices and SDB researchers alike.

scholarly journals Assessing the potential of integrating Landsat sensors for estimating chlorophyll-a concentration in a reservoir

2017 ◽  
Vol 49 (5) ◽  
pp. 1608-1617 ◽  
Author(s):  
Matias Bonansea ◽  
Claudia Rodriguez ◽  
Lucio Pinotti
Keyword(s):  
Water Quality ◽  
Chlorophyll A ◽  
Landsat Imagery ◽  
Quality Parameters ◽  
Landsat 8 ◽  
Chlorophyll A Concentration ◽  
Chl A ◽  
Sensing Applications ◽  
Wide Range ◽  
Quality Assessments

Abstract Landsat satellites, 5 and 7, have significant potential for estimating several water quality parameters, but to our knowledge, there are few investigations which integrate these earlier sensors with the newest and improved mission of Landsat 8 satellite. Thus, the comparability of water quality assessing across different Landsat sensors needs to be evaluated. The main objective of this study was to assess the feasibility of integrating Landsat sensors to estimate chlorophyll-a concentration (Chl-a) in Río Tercero reservoir (Argentina). A general model to retrieve Chl-a was developed (R2 = 0.88). Using observed versus predicted Chl-a values the model was validated (R2 = 0.89) and applied to Landsat imagery obtaining spatial representations of Chl-a in the reservoir. Results showed that Landsat 8 can be combined with Landsat 5 and 7 to construct an empirical model to estimate water quality characteristics, such as Chl-a in a reservoir. As the number of available and upcoming sensors with open access will increase with time, we expect that this trend will certainly further promote remote sensing applications and serve as a valuable basis for a wide range of water quality assessments.

Sign in / Sign up

Export Citation Format

Share Document