Applications of remote sensing to estuarine management

There are no author-identified significant results in this report

Applications of remote sensing to estuarine management

Remote sensing was used in the resolution of estuarine problems facing federal and Virginia governmental agencies. A prototype Elizabeth River Surface Circulation Atlas was produced from photogrammetry to aid in oil spill cleanup and source identification. Aerial photo analysis twice led to selection of alternative plans for dredging and spoil disposal which minimized marsh damage. Marsh loss due to a mud wave from a highway dyke was measured on sequential aerial photographs. An historical aerial photographic sequence gave basis to a potential Commonwealth of Virginia legal claim to accreting and migrating coastal islands

Detection of Seagrass Scars Using Sparse Coding and Morphological Filter

We present a two-step algorithm for the detection of seafloor propeller seagrass scars in shallow water using panchromatic images. The first step is to classify image pixels into scar and non-scar categories based on a sparse coding algorithm. The first step produces an initial scar map in which false positive scar pixels may be present. In the second step, local orientation of each detected scar pixel is computed using the morphological directional profile, which is defined as outputs of a directional filter with a varying orientation parameter. The profile is then utilized to eliminate false positives and generate the final scar detection map. We applied the algorithm to a panchromatic image captured at the Deckle Beach, Florida using the WorldView2 orbiting satellite. Our results show that the proposed method can achieve \u3e90% accuracy on the detection of seagrass scars

Remote sensing in shallow lake ecology

Shallow lakes are an important ecological and socio-economic resource. However, the impact of human pressures, both at the lake and catchment scale, has precipitated a decline in the ecological status of many shallow lakes, both in the UK, and throughout Europe. There is now, as direct consequence, unprecedented interest in the assessment and monitoring of ecological status and trajectory in shallow lakes, not least in response to the European Union Water Framework Directive (2000/60/EC). In this context, the spatially-resolving and panoramic data provided by remote sensing platforms may be of immense value in the construction of effective and efficient strategies for the assessment and monitoring of ecological status in shallow lakes and, moreover, in providing new, spatially-explicit, insights into the function of these ecosystems and how they respond to change. This thesis examined the use of remote sensing data for the assessment of (i) phytoplankton abundance and species composition and (ii) aquatic vegetation distribution and ecophysiological status in shallow lakes with a view to establishing the credence of such an approach and its value in limnological research and monitoring activities. High resolution in-situ and airborne remote sensing data was collected during a 2-year sampling campaign in the shallow lakes of the Norfolk Broads. It was demonstrated that semi-empirical algorithms could be formulated and used to provide accurate and robust estimations of the concentration of chlorophyll-a, even in these optically-complex waters. It was further shown that it was possible to differentiate and quantify the abundance of cyanobacteria using the biomarker pigment C-phycocyanin. The subsequent calibration of the imagery obtained from the airborne reconnaissance missions permitted the construction of diurnal and seasonal regional-scale time-series of phytoplankton dynamics in the Norfolk Broads. This approach was able to deliver unique spatial insights into the migratory behaviour of a potentially-toxic cyanobacterial bloom. It was further shown that remote sensing can be used to map the distribution of aquatic plants in shallow lakes, importantly including the extent of submerged vegetation, which is central to the assessment of ecological status. This research theme was subsequently extended in an exploration of the use of remote sensing for assessing the ecophysiological response of wetland plants to nutrient enrichment. It was shown that remote sensing metrics could be constructed for the quantification of plant vigour. The extrapolation of these techniques enabled spatial heterogeneity in the ecophysiological response of Phragmites australis to lake nutrient enrichment to be characterised and assisted the formulation of a mechanistic explanation for the variation in reedswamp performance in these shallow lakes. It is therefore argued that the spatially synoptic data provided by remote sensing has much to offer the assessment, monitoring and policing of ecological status in shallow lakes and, in particular, for facilitating the development of pan-European scale lake surveillance capabilities for the Water Framework Directive (2000/60/EC). It is also suggested that remote sensing can make a valuable contribution to furthering ecological understanding and, most significantly, in enabling ecosystem processes and functions to be examined at the lake-scale

Estuarine Sediment Deposition during Wetland Restoration: A GIS and Remote Sensing Modeling Approach

Restoration of the industrial salt flats in the San Francisco Bay, California is an ongoing wetland rehabilitation project. Remote sensing maps of suspended sediment concentration, and other GIS predictor variables were used to model sediment deposition within these recently restored ponds. Suspended sediment concentrations were calibrated to reflectance values from Landsat TM 5 and ASTER using three statistical techniques -- linear regression, multivariate regression, and an Artificial Neural Network (ANN), to map suspended sediment concentrations. Multivariate and ANN regressions using ASTER proved to be the most accurate methods, yielding r2 values of 0.88 and 0.87, respectively. Predictor variables such as sediment grain size and tidal frequency were used in the Marsh Sedimentation (MARSED) model for predicting deposition rates for three years. MARSED results for a fully restored pond show a root mean square deviation (RMSD) of 66.8 mm (<1) between modeled and field observations. This model was further applied to a pond breached in November 2010 and indicated that the recently breached pond will reach equilibrium levels after 60 months of tidal inundation

ERTS-1 investigation of wetlands ecology

The author has identified the following significant results. Data from aircraft can be used for large scale mapping where detailed information is necessary, whereas Landsat-1 data are useful for rapid mapping of gross wetland boundaries and vegetative composition and assessment of seasonal change plant community composition such as high and low growth forms of Spartina alterniflora, Juncus roemarianus, and Spartina cynosuroides. Spoil disposal and wetland ditching activities may also be defined. Wetland interpretation is affected by tidal stage; drainage patterns are more easily detected at periods of low water. Species discrimination is easier at periods of high water during the growing season; upper wetland boundaries in fresh water tidal marshes are more easily delineated during the winter months when marsh vegetation is largely dead or dormant. Fresh water discharges from coastal streams may be inferred from the species composition of contiguous wetlands

Modeling Habitat and Environmental Factors Affecting Mosquito Abundance in Chesapeake, Virginia

The models I present in this dissertation were designed to enable mosquito control agencies in the mid-Atlantic region that oversee large jurisdictions to rapidly track the spatial and temporal distributions of mosquito species, especially those species known to be vectors of eastern equine encephalitis and West Nile virus. I was able to keep these models streamlined, user-friendly, and not cost-prohibitive using empirically based digital data to analyze mosquito-abundance patterns in real landscapes. This research is presented in three major chapters: (II) a series of semi-static habitat suitability indices (HSI) grounded on well-documented associations between mosquito abundance and environmental variables, (III) a dynamic model for predicting both spatial and temporal mosquito abundance based on a topographic soil moisture index and recent weather patterns, and (IV) a set of protocols laid out to aid mosquito control agencies for the use of these models. The HSIs (Chapter II) were based on relationships of mosquitoes to digital surrogates of soil moisture and vegetation characteristics. These models grouped mosquitoes species derived from similarities in habitat requirements, life-cycle type, and vector competence. Quantification of relationships was determined using multiple linear regression models. As in Chapter II, relationships between mosquito abundance and environmental factors in Chapter III were quantified using regression models. However, because this model was, in part, a function of changes in weather patterns, it enables the prediction of both \u27where\u27 and \u27when\u27 mosquito outbreaks are likely to occur. This model is distinctive among similar studies in the literature because of my use of NOAA\u27s NEXRAD Doppler radar (3-hr precipitation accumulation data) to quantify the spatial and temporal distributions in precipitation accumulation. \ Chapter IV is unique among the chapters in this dissertation because in lieu of presenting new research, it summarizes the preprocessing steps and analyses used in the HSIs and the dynamic, weather-based, model generated in Chapters II and III. The purpose of this chapter is to provide the reader and potential users with the necessary protocols for modeling the spatial and temporal abundances and distributions of mosquitoes, with emphasis on Culiseta melanura, in a real-world landscape of the mid-Atlantic region. This chapter also provides enhancements that could easily be incorporated into an environmentally sensitive integrated pest management program

CARETS: A prototype regional environmental information system. Volume 2, parts A and B: Norfolk and environs; a land use perspective

The author has identified the following significant results. The Norfolk-Portsmouth metropolitan statistical area in southeastern Virginia was the site of intensive testing of a number of land resources assessment methods. Land use and land cover data at three levels of detail were derived by manual image interpretation from both aircraft and satellite sources and used to characterize the 1,766 sq km (682 sq mi) area from the perspective of its various resource-related activities and problems. Measurements at level 1 from 1:100, 000 scale maps revealed 42 percent of the test area (excluding bays and estuaries) to be forest, 28 percent agriculture, 23 percent urban and built-up, 4 percent nonforested wetlands, and 2 percent water. At the same scale and level of detail, 10 percent of the area underwent change from one land use category to another in the period 1959-70, 62 percent of which involved the relatively irreversible change from forest or agriculture to urban uses

Applied Deep Learning: Case Studies in Computer Vision and Natural Language Processing

Deep learning has proved to be successful for many computer vision and natural language processing applications. In this dissertation, three studies have been conducted to show the efficacy of deep learning models for computer vision and natural language processing. In the first study, an efficient deep learning model was proposed for seagrass scar detection in multispectral images which produced robust, accurate scars mappings. In the second study, an arithmetic deep learning model was developed to fuse multi-spectral images collected at different times with different resolutions to generate high-resolution images for downstream tasks including change detection, object detection, and land cover classification. In addition, a super-resolution deep model was implemented to further enhance remote sensing images. In the third study, a deep learning-based framework was proposed for fact-checking on social media to spot fake scientific news. The framework leveraged deep learning, information retrieval, and natural language processing techniques to retrieve pertinent scholarly papers for given scientific news and evaluate the credibility of the news