MODIS derived sea surface salinity, temperature, and chlorophyll-a data for potential fish zone mapping: West red sea coastal areas, Saudi Arabia

© 2019 by the authors. In this study, a multi-linear regression model for potential fishing zone (PFZ) mapping along the Saudi Arabian Red Sea coasts of Yanbu’ al Bahr and Jeddah was developed, using Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data derived parameters, such as sea surface salinity (SSS), sea surface temperature (SST), and chlorophyll-a (Chl-a). MODIS data was also used to validate the model. The model expanded on previous models by taking seasonal variances in PFZs into account, examining the impact of the summer, winter, monsoon, and inter-monsoon season on the selected oceanographic parameters in order to gain a deeper understanding of fish aggregation patterns. MODIS images were used to effectively extract SSS, SST, and Chl-a data for PFZ mapping. MODIS data were then used to perform multiple linear regression analysis in order to generate SSS, SST, and Chl-a estimates, with the estimates validated against in-situ data obtained from field visits completed at the time of the satellite passes. The proposed model demonstrates high potential for use in the Red Sea region, with a high level of congruence found between mapped PFZ areas and fish catch data (R2 = 0.91). Based on the results of this research, it is suggested that the proposed PFZ model is used to support fisheries in determining high potential fishing zones, allowing large areas of the Red Sea to be utilized over a short period. The proposed PFZ model can contribute significantly to the understanding of seasonal fishing activity and support the efficient, effective, and responsible use of resources within the fishing industry

Remote Sensing of Harmful Algal Blooms in the Mississippi Sound and Mobile Bay: Modelling and Algorithm Formation

The incidence and severity of harmful algal blooms have increased in recent decades, as have the economic effects of their occurrence./The diatom Pseudo-nitzschia spp. caused fisheries closures in Mobile Bay during 2005 due to elevated levels of domoic acid. In the previous 4 years Karenia brevis counts of \u3e5,000 cells L 1 have occurred in Mobile Bay and the Mississippi Sound. Population levels of this magnitude had previously been recorded only in 1996. Increases in human populations, urban sprawl, development of shoreline properties, sewage effluent and resultant changes in NP ratios of discharge waters, and decline in forest and marsh lands, will potentially increase future harmful algal bloom occurrences in the northern Gulf of Mexico. Due to this trend in occurrence of harmful algal populations, there has been an increasing awareness of the need for development of monitoring systems in this region. Traditional methods of sampling have proven costly in terms of time and resources, and increasing attention has been turned toward use of satellite data in phytoplankton monitoring and prediction. This study shows that remote sensing does have utility in monitoring and predicting locations of phytoplankton blooms in this region. It has described the composition and spatial and temporal relationships of these populations, inferring salinity, total nitrogen and total phosphorous as the primary variables driving phytoplankton populations in Mobile Bay and the Mississippi Sound. Diatoms, chlorophytes, cryptophytes, and dinoflagellates were most abundant in collections. Correlations between SeaWiFS, MODIS and in situ data have shown relationships between Rrs reflectance and phytoplankton populations. These data were used in formation of a decision tree model predicting environmental conditions conducive to the formation of phytoplankton blooms that is driven completely by satellite data. Empirical algorithms were developed for prediction of salinity, based on Rrs ratios of 510 nm/ 555 nm, creating a new data product for use in harmful algal bloom prediction. The capacity of satellite data for rapid, synoptic coverage shows great promise in supplementing future efforts to monitor and predict harmful algal bloom events in the increasingly eutrophic waters of Mobile Bay and the Mississippi Sound

Monitoring chlorophyll-a with remote sensing techniques in the Tagus Estuary

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para obtenção do grau de Mestre em Gestão e Sistemas AmbientaisEstuaries are transitional ecosystems with high temporal and spatial variability and suffer high anthropogenic pressures. At the present there is a major challenge to monitor these systems in a robust, frequent, systematic and accurate fashion. With the implementation of the Water Framework Directive (WFD), the EU Member States must monitor regularly the most relevant physical and biological parameters. Estuarine information is attained using in-situ samples, model analysis and/or remote sensing data. This work assessed the applicability and accuracy of chlorophyll-a products from the MODIS sensor in the Tagus estuary, comparing them (2000-2002) with simulations of an ecological model, the EcoWin2000. The latter was previously calibrated (1998 & 1999) and validated(2000). It is proposed a conceptual and methodological framework for future monitoring of the estuary using remote sensing data. In a first stage, in the year 2000, typical Case 1 algorithms were pre-assessed and Case 2 algorithms were regionally calibrated. The GSM and Clark algorithms had the best performances, with errors of approximately of 1.1 μg chl-a l-1 (or 20%) and correlations ranging 0.4-0.5. During calibration, the ratio R678/R551 had a good correlation (r = 0.83) and low errors (~1μg chl-a l-1). Its evaluation in 2002, showed low and sometimes negative correlations, with errors of about 2 μg chl-a l-1. In agreement with the preliminary assessment,in 2002, the GSM algorithm had the best correlation (r~0.50) and errors of approximately 0.8μg chl-a l-1. The reliability of remote sensing is higher in the Spring and Summer, and spatially, in the wider mid estuary sections. Although remote sensing needs extensive further development, it was proven to be a reliable tool with several advantages for systematic chl-a monitoring in the Tagus estuary. Specifically, it is a tool with high to assist the EU Member States to accomplish the WFD objectives

Ocean colour remote sensing of the Great Barrier Reef waters

The research undertaken has developed relationships between the concentrations of optically-significant substances (phytoplankton, Colour Dissolved Organic Matter (CDOM), and particulates) found in Great Barrier Reef waters and their respective inherent optical properties. Based on this knowledge, a physics-based spectral deconvolution routine was developed that successfully retrieved the concentrations of these substances from passive ocean colour observations such as those from the MODIS imaging satellite

Development of Satellite-Assisted Forecasting System for Oyster Norovirus Outbreaks

Norovirus outbreaks can cause the closure of oyster harvesting waters and acute gastroenteritis in humans associated with consumption of contaminated raw oysters. The overall goal of this study was to develop a satellite-assisted forecasting system for oyster norovirus outbreaks. The forecasting system is comprised of three components: (1) satellite algorithms for retrieval of environmental variables, including salinity, temperature, and gage height, (2) an Artificial Neural Network (ANN) based model, called NORF model, for predicting relative risk levels of oyster norovirus outbreaks, and (3) a mapping method for visualizing spatial distributions of norovirus outbreak risks in oyster harvest areas along Louisiana coast. The new satellite algorithms, characterized with linear correlation coefficient ranging from 0.7898 to 0.9076, make it possible to produce spatially distributed daily data with a high resolution (1 kilometer) for salinity, temperature, and gage height in coastal waters. Findings from this study suggest that oyster norovirus outbreaks are predictable, and in Louisiana oyster harvest areas, the NORF model predicted historical outbreaks from 1994 - 2014 without any confirmed false positive or false negative predictions when the estimated relative risk level was \u3e 0.6, while no outbreak occurred when the risk level was \u3c 0.5. However, more outbreak data are needed to confirm the threshold for norovirus outbreaks. Gage height and temperature were the most important environmental predictors of oyster norovirus outbreaks while wind, rainfall, and salinity also predicted norovirus outbreaks. The ability to predict oyster norovirus outbreaks at their onset makes it possible to prevent or at least reduce the risk of norovirus outbreaks by closing potentially affected oyster beds. By combining the NORF model with the remote sensing algorithms created in this dissertation, it is possible to map oyster norovirus outbreak risks in all oyster growing waters and particularly in the areas without direct measurements of relevant environmental variables, greatly expanding the coverage and enhancing the effectiveness of oyster monitoring programs. The hot spot (risk) maps, constructed using the methods developed in this dissertation, make it possible for oyster monitoring programs to manage oyster harvest waters more efficiently by focusing on hot spot areas with limited resources

Advanced Geoscience Remote Sensing

Nowadays, advanced remote sensing technology plays tremendous roles to build a quantitative and comprehensive understanding of how the Earth system operates. The advanced remote sensing technology is also used widely to monitor and survey the natural disasters and man-made pollution. Besides, telecommunication is considered as precise advanced remote sensing technology tool. Indeed precise usages of remote sensing and telecommunication without a comprehensive understanding of mathematics and physics. This book has three parts (i) microwave remote sensing applications, (ii) nuclear, geophysics and telecommunication; and (iii) environment remote sensing investigations

Monitoring the Sustainable Intensification of Arable Agriculture:the Potential Role of Earth Observation

Sustainable intensification (SI) has been proposed as a possible solution to the conflicting problems of meeting projected increases in food demand and preserving environmental quality. SI would provide necessary production increases while simultaneously reducing or eliminating environmental degradation, without taking land from competing demands. An important component of achieving these aims is the development of suitable methods for assessing the temporal variability of both the intensification and sustainability of agriculture. Current assessments rely on traditional data collection methods that produce data of limited spatial and temporal resolution. Earth Observation (EO) provides a readily accessible, long-term dataset with global coverage at various spatial and temporal resolutions. In this paper we demonstrate how EO could significantly contribute to SI assessments, providing opportunities to quantify agricultural intensity and environmental sustainability. We review an extensive body of research on EO-based methods to assess multiple indicators of both agricultural intensity and environmental sustainability. To date these techniques have not been combined to assess SI; here we identify the opportunities and initial steps required to achieve this. In this context, we propose the development of a set of essential sustainable intensification variables (ESIVs) that could be derived from EO data