Use of Hyperspectral Remote Sensing to Estimate Water Quality

Approximating and forecasting water variables like phosphorus, nitrogen, chlorophyll, dissolved organic matter, and turbidity are of supreme importance due to their strong influence on water resource quality. This chapter is aimed at showing the practicability of merging water quality observations from remote sensing with water quality modeling for efficient and effective monitoring of water quality. We examine the spatial dynamics of water quality with hyperspectral remote sensing and present approaches that can be used to estimate water quality using hyperspectral images. The methods presented here have been embraced because the blue-green and green algae peak wavelengths reflectance are close together and make their distinction more challenging. It has also been established that hyperspectral imagers permit an improved recognition of chlorophyll and hereafter algae, due to acquired narrow spectral bands between 450 nm and 600 nm. We start by describing the practical application of hyperspectral remote sensing data in water quality modeling. The surface inherent optical properties of absorption and backscattering of chlorophyll a, colored dissolved organic matter (CDOM), and turbidity are estimated, and a detailed approach on analyzing ARCHER data for water quality estimation is presented

Oil spill detection using optical sensors: a multi-temporal approach

Oil pollution is one of the most destructive consequences due to human activities in the marine environment. Oil wastes come from many sources and take decades to be disposed of. Satellite based remote sensing systems can be implemented into a surveillance and monitoring network. In this study, a multi-temporal approach to the oil spill detection problem is investigated. Change Detection (CD) analysis was applied to MODIS/Terra and Aqua and OLI/Landsat 8 images of several reported oil spill events, characterized by different geographic location, sea conditions, source and extension of the spill. Toward the development of an automatic detection algorithm, a Change Vector Analysis (CVA) technique was implemented to carry out the comparison between the current image of the area of interest and a dataset of reference image, statistically analyzed to reduce the sea spectral variability between different dates. The proposed approach highlights the optical sensors’ capabilities in detecting oil spills at sea. The effectiveness of different sensors’ resolution towards the detection of spills of different size, and the relevance of the sensors’ revisiting time to track and monitor the evolution of the event is also investigated

Polarized light - host location and selection cue in phytophagous insects?

Insect herbivores exploit plant cues to discern host and non-host plants. Studies of visual plant cues have focused on color despite the inherent polarization sensitivity of insect photoreceptors and the information carried by polarization of foliar reflectance, most notably the degree of linear polarization (DoLP; 0-100%). The DoLP of foliar reflection was hypothesized to be a host plant cue for insects but was never experimentally tested. I investigated the use of these polarization cues by the cabbage white butterfly, Pieris rapae (Pieridae). This butterfly has a complex visual system with several different polarization-sensitive photoreceptors, as characterized with electrophysiology and histology. I applied photo polarimetry revealing large differences in the DoLP of leaf-reflected light among plant species generally and between host and non-host plants of P. rapae specifically. As polarized light cues are directionally dependent, I also tested, and modelled, the effect of approach trajectory on the polarization of plant-reflected light and the resulting attractiveness to P. rapae, showing that certain approach trajectories are optimal for discriminating among plants based on these cues. I then demonstrated that P. rapae exploit the DoLP of foliar reflections to discriminate among plants. In experiments with paired digital plant images that allowed for independent control of polarization, color and intensity, P. rapae females preferred images of the host plant cabbage with a low DoLP (31%) to images of the non-host plant potato with a high DoLP (50%). These results indicated that the DoLP had a greater effect on foraging decisions than the differential color, intensity or shape of the two plant images. To investigate potential neurological mechanisms, I designed behavioral bioassays presenting choices between images that differed in color, intensity and/or DoLP. The combined results of these bioassays suggest that several photoreceptor classes are involved and that P. rapae females process and interpret polarization reflections in a way different from that described for other polarization-sensitive taxa. My work has focused on P. rapae and its host plants but there is every reason to believe that the DoLP of foliar reflection is an essential plant cue that may commonly be exploited by foraging insect herbivore

Polarisation vision: overcoming challenges of working with a property of light we barely see.

In recent years, the study of polarisation vision in animals has seen numerous breakthroughs, not just in terms of what is known about the function of this sensory ability, but also in the experimental methods by which polarisation can be controlled, presented and measured. Once thought to be limited to only a few animal species, polarisation sensitivity is now known to be widespread across many taxonomic groups, and advances in experimental techniques are, in part, responsible for these discoveries. Nevertheless, its study remains challenging, perhaps because of our own poor sensitivity to the polarisation of light, but equally as a result of the slow spread of new practices and methodological innovations within the field. In this review, we introduce the most important steps in designing and calibrating polarised stimuli, within the broader context of areas of current research and the applications of new techniques to key questions. Our aim is to provide a constructive guide to help researchers, particularly those with no background in the physics of polarisation, to design robust experiments that are free from confounding factors

A Multispectral Look at Oil Pollution Detection, Monitoring, and Law Enforcement

The problems of detecting oil films on water, mapping the areal extent of slicks, measuring the slick thickness, and identifying oil types are discussed. The signature properties of oil in the ultraviolet, visible, infrared, microwave, and radar regions are analyzed

Illumination Variance In Video Processing

Doctor of Philosophy and was awarded by Brunel University LondonInthisthesiswefocusontheimpactofilluminationchangesinvideoand we discuss how we can minimize the impact of illumination variance in video processing systems. Identifyingandremovingshadowsautomaticallyisaverywellestablished and an important topic in image and video processing. Having shadowless image data would benefit many other systems such as video surveillance, tracking and object recognition algorithms. Anovelapproachtoautomaticallydetectandremoveshadowsispresented in this paper. This new method is based on the observation that, owing to the relative movement of the sun, the length and position of a shadow changes linearly over a relatively long period of time in outdoor environments,wecanconvenientlydistinguishashadowfromotherdark regions in an input video. Then we can identify the Reference Shadow as the one with the highest confidence of the mentioned linear changes. Once one shadow is detected, the rest of the shadow can also be identifiedandremoved. Wehaveprovidedmanyexperimentsandourmethod is fully capable of detecting and removing the shadows of stationary and moving objects. Additionally we have explained how reference shadows can be used to detect textures that reflect the light and shiny materials such as metal, glass and water. ..

From Fauna to Flames : Remote Sensing with Scheimpflug Lidar

This thesis presents applications of the Scheimpflug Lidar (S-Lidar) method. The technique has been applied to combustion diagnostics on a scale of several meters as well as fauna detection and monitoring over distances of kilometers. Lidar or laser radar is a remote sensing technique where backscattering of laser light is detected with range resolution along the direction of the laser beam. It is an established method in e.g. atmospheric sensing where it is used to map and monitor gases and aerosols. In contrast to conventional Lidar, which uses a time-of-flight approach, Scheimpflug Lidar uses imaging to achieve range resolution. The laser beam transmitted from the Lidar system is sharply imaged onto a detector, resulting in range resolution along the sensor. This is done by placing the laser beam, the collection optics and the detector according to two trigonometrical conditions called the Scheimpflug and hinge rules. This kind of Lidar technique enables the use of small, continuous-wave diode lasers and line-array detectors with kHz sampling rates. A general description of the equations governing the achievable measurement range and resolution of S-Lidar are presented. The way the equations relate to the conventional Lidar equation is also discussed as well as the impact of the beam width. The instrumentation and experimental considerations for far range S-Lidar for aerial fauna monitoring are described and some temporally and spatially resolved data from field campaigns in Africa, China and Sweden are presented. A method used to reduce and analyze the large amount of collected data is also described. For the short-range applications, down-scaled versions of the system were developed. These systems are described as well as their applications. The short range system has mostly been used to investigate the potential of the technique to be applied for combustion diagnostics, and results from measurements in flames using both elastic and inelastic optical techniques, such as Rayleigh scattering and two-line atomic fluorescence are presented. A hyperspectral Lidar system aimed at aquatic applications is also presented

Sentinel-1 Satellite Data as a Tool for Monitoring Inundation Areas near Urban Areas in the Mexican Tropical Wet

This work shows advances in the field of water body monitoring with radar images. Particularly, a monitoring procedure is developed to define the extension and frequency of inundation for continental waters of the Grijalva-Usumacinta basin, in the state of Tabasco, Mexico. This is a region located in the Mexican tropical wet and under its meteorological conditions, radar technology can be used to characterize monthly inundation frequency. The identification of water bodies were obtained by processing images at a monthly intervals captured by Sentinel-1A during 2015 having kappa indices and overall accuracy higher than 0.9. The chapter describes the seasonal variability of these water bodies, and at the same time, the relationship with human settlements located in their neighborhood. To do this, a proximity analysis was carried out to emphasize the importance of spatial-temporal studies of superficial water bodies, linked to an urban and a rural area. This information is useful to investigate changes in the ecosystem, as well as risks to human settlements, and as a contribution for a comprehensive management of hydric resources