112 research outputs found

    Remote Sensing Spatiotemporal Assessment of Nitrogen Concentrations in Tampa Bay, Florida due to a Drought

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    A long-term low nitrogen to phosphorus (N:P) ratio in the Tampa Bay, Florida, estuary system suggests that nitrogen is more limiting than phosphorus. However, south Florida suffered from a drought around 2007, and the reduction in runoff flowing into the bay affected local ecosystem dynamics. This study presents a remote sensing study to retrieve spatiotemporal patterns of total nitrogen (TN) concentrations in Tampa Bay under drought impacts through the integration of Moderate Resolution Imaging Spectroradiometer (MODIS) images and a genetic programming (GP) model. Research findings show that the drought impact on TN in Tampa Bay is both a seasonal and yearly phenomenon. Without the presence of ocean water intrusion, the whole bay would show a relatively uniform TN distribution during the drought period until the flow input from rivers returned to normal. Based on yearly comparisons, temperature could be the limiting factor on the plankton growth in Tampa Bay. To further substantiate the credibility of a nutrient estimation algorithm, a k-means clustering analysis was conducted to demonstrate sea-bay-land interactions among ebbs, tides, and river discharges. The seasonal cluster distribution in 2007 is generally consistent with the conventional segments division of Tampa Bay

    Remote Sensing Spatiotemporal Assessment of Nitrogen Concentrations in Tampa Bay, Florida due to a Drought

    Get PDF
    A long-term low nitrogen to phosphorus (N:P) ratio in the Tampa Bay, Florida, estuary system suggests that nitrogen is more limiting than phosphorus. However, south Florida suffered from a drought around 2007, and the reduction in runoff flowing into the bay affected local ecosystem dynamics. This study presents a remote sensing study to retrieve spatiotemporal patterns of total nitrogen (TN) concentrations in Tampa Bay under drought impacts through the integration of Moderate Resolution Imaging Spectroradiometer (MODIS) images and a genetic programming (GP) model. Research findings show that the drought impact on TN in Tampa Bay is both a seasonal and yearly phenomenon. Without the presence of ocean water intrusion, the whole bay would show a relatively uniform TN distribution during the drought period until the flow input from rivers returned to normal. Based on yearly comparisons, temperature could be the limiting factor on the plankton growth in Tampa Bay. To further substantiate the credibility of a nutrient estimation algorithm, a k-means clustering analysis was conducted to demonstrate sea-bay-land interactions among ebbs, tides, and river discharges. The seasonal cluster distribution in 2007 is generally consistent with the conventional segments division of Tampa Bay

    Comparative Data Mining Analysis for Information Retrieval of MODIS Images: Monitoring Lake Turbidity Changes at Lake Okeechobee, Florida

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    In the remote sensing field, a frequently recurring question is: Which computational intelligence or data mining algorithms are most suitable for the retrieval of essential information given that most natural systems exhibit very high non-linearity. Among potential candidates might be empirical regression, neural network model, support vector machine, genetic algorithm/genetic programming, analytical equation, etc. This paper compares three types of data mining techniques, including multiple non-linear regression, artificial neural networks, and genetic programming, for estimating multi-temporal turbidity changes following hurricane events at Lake Okeechobee, Florida. This retrospective analysis aims to identify how the major hurricanes impacted the water quality management in 2003-2004. The Moderate Resolution Imaging Spectroradiometer (MODIS) Terra 8-day composite imageries were used to retrieve the spatial patterns of turbidity distributions for comparison against the visual patterns discernible in the in-situ observations. By evaluating four statistical parameters, the genetic programming model was finally selected as the most suitable data mining tool for classification in which the MODIS band 1 image and wind speed were recognized as the major determinants by the model. The multi-temporal turbidity maps generated before and after the major hurricane events in 2003-2004 showed that turbidity levels were substantially higher after hurricane episodes. The spatial patterns of turbidity confirm that sediment-laden water travels to the shore where it reduces the intensity of the light necessary to submerged plants for photosynthesis. This reduction results in substantial loss of biomass during the post-hurricane period

    Application of Machine Learning Techniques to Forecast Harmful Algal Blooms in Gulf of Mexico

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    The Harmful Algal Blooms (HABs) forecast is crucial for the mitigation of health hazards and to inform actions for the protection of ecosystems and fisheries in the Gulf of Mexico (GoM). For the sake of simplicity of our application we assume ocean color satellite imagery from the National Oceanic and Atmospheric Administration as a proxy for HABs. In this study we use a deep neural network trained on the 2-Dimensional time series proxy data to provide a forecast of the HABs’ manifestations in the GoM.Our approach analyzes between both spatial and temporal features simultaneously. In addition, the network also helps to fill in the gaps of the time series data along the way. We use Long Short Term Memory (LSTM) layers to learn the underlying trends in the time series data and Convolutional layers to decode the spatial trends in the 2-Dimensional gridded data. Our unique contribution is an iterative, bidirectional training scheme, where we train two models: for forward and backward prediction. The intention is that if there is a functional dependence within the data in the forward time direction, then such a dependence may also exist in the backward time direction, which may be leveraged for predictions to fill the gaps in the data. We train each model to predict the next data point in their respective time-direction, based on an LSTM recurrence over the “lookback” data points. Since there are missing cells in the grid within each data point, we use a custom loss function that ignores prediction errors on missing cells. Thus the loss function critiques the models based on known cells alone, while the models act with (forward/backward) predictions that are spatiotemporally consistent across both missing and visible cells, thus updating the input training data, and consequently changing the object of critique. This actor-critic training scheme progresses iteratively, leading to the iterative improvement of the models/actors. Several models are developed with varying combinations of convolutional layers and max pooling layers to enable the model to learn the spatial and temporal trends within the month-long training data. The most effective model performs reasonably well with prediction of chlorophyll intensities

    Impact of the spatial resolution of satellite remote sensing sensors in the quantification of total suspended sediment concentration: A case study in turbid waters of Northern Western Australia

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    The impact of anthropogenic activities on coastal waters is a cause of concern because such activities add to the total suspended sediment (TSS) budget of the coastal waters, which have negative impacts on the coastal ecosystem. Satellite remote sensing provides a powerful tool in monitoring TSS concentration at high spatiotemporal resolution, but coastal managers should be mindful that the satellite-derived TSS concentrations are dependent on the satellite sensor's radiometric properties, atmospheric correction approaches, the spatial resolution and the limitations of specific TSS algorithms. In this study, we investigated the impact of different spatial resolutions of satellite sensor on the quantification of TSS concentration in coastal waters of northern Western Australia. We quantified the TSS product derived from MODerate resolution Imaging Spectroradiometer (MODIS)-Aqua, Landsat-8 Operational Land Image (OLI), and WorldView-2 (WV2) at native spatial resolutions of 250 m, 30 m and 2 m respectively and coarser spatial resolution (resampled up to 5 km) to quantify the impact of spatial resolution on the derived TSS product in different turbidity conditions. The results from the study show that in the waters of high turbidity and high spatial variability, the high spatial resolution WV2 sensor reported TSS concentration as high as 160 mg L-1 while the low spatial resolution MODIS-Aqua reported a maximum TSS concentration of 23.6 mg L-1. Degrading the spatial resolution of each satellite sensor for highly spatially variable turbid waters led to variability in the TSS concentrations of 114.46%, 304.68% and 38.2% for WV2, Landsat-8 OLI and MODIS-Aqua respectively. The implications of this work are particularly relevant in the situation of compliance monitoring where operations may be required to restrict TSS concentrations to a pre-defined limit

    Utilizing neural networks for image downscaling and water quality monitoring

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    Remotely sensed images are becoming highly required for various applications, especially those related to natural resource management. The Moderate Resolution Imaging Spectroradiometer (MODIS) data has the advantages of its high spectral and temporal resolutions but remains inadequate in providing the required high spatial resolution. On the other hand, Sentinel-2 is more advantageous in spatial and temporal resolution but lacks a solid historical database. In this study, four MODIS bands in the visible and near-infrared spectral regions of the electromagnetic spectrum and their matching Sentinel-2 bands were used to monitor the turbidity in Lake Nasser, Egypt. The MODIS data were downscaled to Sentinel-2, which enhanced its spatial resolution from 250 and 500m to 10m.Furthermore, it provided a historical database that was used to monitor the changes in lake turbidity. Spatial approach based on neural networks was presented to downscale MODIS bands to the spatial resolution of the Sentinel-2 bands. The correlation coefficient between the predicted and actual images exceeded 0.70 for the four bands. Applying this approach, the downscaled MODIS images were developed and the neural networks were further employed to these images to develop a model for predicting the turbidity in the lake. The correlation coefficient between the predicted and actual measurements reached 0.83. The study suggests neural networks as a comparatively simplified and accurate method for image downscaling compared to other methods. It also demonstrated the possibility of utilizing neural networks to accurately predict lake water quality parameters such as turbidity from remote sensing data compared to statistical methods

    A Comprehensive Review on Water Quality Parameters Estimation Using Remote Sensing Techniques

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    Remotely sensed data can reinforce the abilities of water resources researchers and decision makers to monitor waterbodies more effectively. Remote sensing techniques have been widely used to measure the qualitative parameters of waterbodies (i.e., suspended sediments, colored dissolved organic matter (CDOM), chlorophyll-a, and pollutants). A large number of different sensors on board various satellites and other platforms, such as airplanes, are currently used to measure the amount of radiation at different wavelengths reflected from the water’s surface. In this review paper, various properties (spectral, spatial and temporal, etc.) of the more commonly employed spaceborne and airborne sensors are tabulated to be used as a sensor selection guide. Furthermore, this paper investigates the commonly used approaches and sensors employed in evaluating and quantifying the eleven water quality parameters. The parameters include: chlorophyll-a (chl-a), colored dissolved organic matters (CDOM), Secchi disk depth (SDD), turbidity, total suspended sediments (TSS), water temperature (WT), total phosphorus (TP), sea surface salinity (SSS), dissolved oxygen (DO), biochemical oxygen demand (BOD) and chemical oxygen demand (COD)

    Assessing Interactions between Estuary Water Quality and Terrestrial Land Cover in Hurricane Events with Multi-sensor Remote Sensing

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    Estuaries are environmentally, ecologically and environmentally important places as they act as a meeting place for land, freshwater and marine ecosystems. They are also called nurseries of the sea as they often provide nesting and feeding habitats for many aquatic plants and animals. These estuaries also withstand the worst of some natural disasters, especially hurricanes. The estuaries as well as the harbored ecosystems undergo significant changes in terms of water quality, vegetation cover etc. and these components are interrelated. When hurricane makes landfall it is necessary to assess the damages as quickly as possible as restoration and recovery processes are time-sensitive. However, assessment of physical damages through inspection and survey and assessment of chemical and nutrient component changes by laboratory testing are time-consuming processes. This is where remote sensing comes into play. With the help of remote sensing images and regression analysis, it is possible to reconstruct water quality maps of the estuary affected. The damage sustained by the vegetation cover of the adjacent coastal watershed can be assessed using Normalized Difference Vegetation Index (NDVI) The water quality maps together with NDVI maps help observe a dynamic sea-land interaction due to hurricane landfall. The observation of hurricane impacts on a coastal watershed can be further enhanced by use of tasseled cap transformation (TCT). TCT plots provide information on a host of land cover conditions with respect to soil moisture, canopy and vegetation cover. The before and after TCT plots help assess the damage sustained in a hurricane event and also see the progress of recovery. Finally, the use of synthetic images obtained by use of data fusion will help close the gap of low temporal resolution of Landsat satellite and this will create a more robust monitoring system

    Spatial and temporal dynamics of suspended sediment concentrations in coastal waters of the South China Sea, off Sarawak, Borneo: ocean colour remote sensing observations and analysis

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    High-quality ocean colour observations are increasingly accessible to support various monitoring and research activities for water quality measurements. In this paper, we present a newly developed regional total suspended solids (TSSs) empirical model using MODIS Aqua's Rrs(530) and Rrs(666) reflectance bands to investigate the spatial and temporal variation in TSS dynamics along the southwest coast of Sarawak, Borneo, with the application of the Open Data Cube (ODC) platform. The performance of this TSS retrieval model was evaluated using error metrics (bias = 1.0, MAE = 1.47, and RMSE = 0.22, in milligrams per litre) with a log10 transformation prior to calculation as well as using a k-fold cross-validation technique. The temporally averaged map of the TSS distribution, using daily MODIS Aqua satellite datasets from 2003 until 2019, revealed that large TSS plumes were detected – particularly in the Lupar and Rajang coastal areas – on a yearly basis. The average TSS concentration in these coastal waters was in the range of 15–20 mg L−1. Moreover, the spatial map of the TSS coefficient of variation (CV) indicated strong TSS variability (approximately 90 %) in the Samunsam–Sematan coastal areas, which could potentially impact nearby coral reef habitats in this region. Study of the temporal TSS variation provides further evidence that monsoonal patterns drive the TSS release in these tropical water systems, with distinct and widespread TSS plume variations observed between the northeast and southwest monsoon periods. A map of relative TSS distribution anomalies revealed strong spatial TSS variations in the Samunsam–Sematan coastal areas, while 2010 recorded a major increase (approximately 100 %) and widespread TSS distribution with respect to the long-term mean. Furthermore, study of the contribution of river discharge to the TSS distribution showed a weak correlation across time at both the Lupar and Rajang river mouth points. The variability in the TSS distribution across coastal river points was studied by investigating the variation in the TSS pixels at three transect points, stretching from the river mouth into territorial and open-water zones, for eight main rivers. The results showed a progressively decreasing pattern of nearly 50 % in relation to the distance from shore, with exceptions in the northeast regions of the study area. Essentially, our findings demonstrate that the TSS levels on the southwest coast of Sarawak are within local water quality standards, promoting various marine and socio-economic activities. This study presents the first observation of TSS distributions in Sarawak coastal systems with the application of remote sensing technologies and aims at enhancing coastal sediment management strategies for the sustainable use of coastal waters and their resources.</p

    Application of Satellite Remote Sensing to Water Quality and Pathogenic Bacteria Prediction in the Chesapeake Bay

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    The Chesapeake Bay is home to an extensive in situ sampling campaign that has provided water quality measurements over multiple decades, aiding in the detection and regulation of environmental conditions that affect aquatic life, public health, and local economies. However, the current bi-monthly sampling can lack the temporal and spatial coverage needed for monitoring and modeling dynamic estuarine systems. While the time and cost of obtaining additional in situ samples can exceed available resources, satellite remote sensing has the potential to provide this higher temporal and spatial resolution data. The objective of this dissertation is to investigate the use of satellite remote sensing in the Chesapeake Bay for both water quality monitoring and the prediction of a naturally-occurring pathogenic bacterium, Vibrio parahaemolyticus, that is a leading cause of food-born illness. The dissertation does this by exploring the use of multispectral information to improve satellite-derived total suspended solids concentration and the potential for remotely sensed water quality products to predict V. parahaemolyticus in the Chesapeake Bay. In addition, the dissertation uses the application of remote sensing for V. parahaemolyticus prediction as a case study to present a prospective tool for communicating predictive model uncertainty to environmental management decision-makers and end-users. The work in this dissertation provides insights and recommendations that can aid in future development of operational models for water quality parameters or bacterial pathogens that incorporate remotely sensed data. As the effects of poor water quality are better understood and the incidence of Vibrio illness increases, improved operational models and uncertainty communication will become progressively important for protecting public and ecosystem health
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