Achieving high-resolution thermal imagery in low-contrast lake surface waters by aerial remote sensing and image registration

A two-platform measurement system for realizing airborne thermography of the Lake Surface Water Temperature (LSWT) with ~0.8 m pixel resolution (sub-pixel satellite scale) is presented. It consists of a tethered Balloon Launched Imaging and Monitoring Platform (BLIMP) that records LSWT images and an autonomously operating catamaran (called ZiviCat) that measures in situ surface/near surface temperatures within the image area, thus permitting simultaneous ground-truthing of the BLIMP data. The BLIMP was equipped with an uncooled InfraRed (IR) camera. The ZiviCat was designed to measure along predefined trajectories on a lake. Since LSWT spatial variability in each image is expected to be low, a poor estimation of the common spatial and temporal noise of the IR camera (nonuniformity and shutter-based drift, respectively) leads to errors in the thermal maps obtained. Nonuniformity was corrected by applying a pixelwise two-point linear correction method based on laboratory experiments. A Probability Density Function (PDF) matching in regions of overlap between sequential images was used for the drift correction. A feature matching-based algorithm, combining blob and region detectors, was implemented to create composite thermal images, and a mean value of the overlapped images at each location was considered as a representative value of that pixel in the final map. The results indicate that a high overlapping field of view (~95%) is essential for image fusion and noise reduction over such low-contrast scenes. The in situ temperatures measured by the ZiviCat were then used for the radiometric calibration. This resulted in the generation of LSWT maps at sub-pixel satellite scale resolution that revealed spatial LSWT variability, organized in narrow streaks hundreds of meters long and coherent patches of different size, with unprecedented detail

Large-scale and meso-scale surface heat flux patterns of Lake Geneva

Diverse studies have confirmed the adverse impact of global climate change in lakes. In order to establish effective water quality management policies, it is essential to understand how the heat exchange between the atmosphere and the lake evolves under these conditions. Lake Surface Water Temperature (LSWT), which is the key coupling parameter at the interface of the Atmospheric Boundary Layer (ABL) and the lake surface layer is often considered the reference climate variable in this context. The temporal development of the lake heat content is mainly controlled by the net Surface Heat Flux (SurHF) at this interface. LSWT, ABL conditions and SurHF are linked and may vary in space and time. However, past studies often relied on single point measurements for SurHF estimation and this can result in significant errors in the heat budget analysis, particularly over large lakes. In this thesis, the dynamics of SurHF over Lake Geneva, the largest water body in Western Europe, were investigated with an emphasis on the effect of spatial heterogeneity of the LSWT and meteorological parameters on two different scales. A large-scale study for the whole surface of the lake was carried out using meteorological data and satellite images with a pixel size of 1 km2 that can depict large-scale thermal patterns, but not the meso- or small-scale processes. To address the SurHF aspects at the meso-scale level, an airborne system for resolving LSWT with a ~1 m pixel resolution was developed that allowed investigating the structure of the processes on scales within a satellite pixel. In a multi-annual large-scale analysis, the SurHF of Lake Geneva was estimated for a 7-y period (2008 to 2014). Data sources included hourly maps of over-the-lake reanalysis meteorological data from a numerical weather model, LSWT from satellite imagery, and long-term temperature depth profiles at two locations. The most common formulas for different heat flux components were combined and calibrated at two locations based on the heat content balance in the water column. When optimized for one lake temperature profile location, SurHF models failed to predict the temperature profile at the other location due to the spatial variability of meteorological parameters. Consequently, a procedure for calibrating the optimal SurHF models was developed using two profile locations. The combination of the modified parameterization of the Brutsaert equation for incoming atmospheric radiation and of similarity theory bulk parameterization algorithms for turbulent SurHF provided the most accurate SurHF estimates. It was found that if a calibration was not carried out optimally, the calculated change in heat content could be much higher than the observed annual climate change-induced trend. The developed calibration procedure improved parameterization of bulk transfer coefficients, mainly under low wind regimes. The optimized and calibrated set of bulk models was then used to compute the spatiotemporal SurHF. Model results indicated an average spatial range of > ± 20 Wm-2. This was mainly caused by wind-sheltering over parts of the lake, which produced spatial anomalies in sensible and latent heat fluxes. During spring, much less spatial variability was evident compared to other seasons. The spring variability was c

Sensor Signal and Information Processing II

In the current age of information explosion, newly invented technological sensors and software are now tightly integrated with our everyday lives. Many sensor processing algorithms have incorporated some forms of computational intelligence as part of their core framework in problem solving. These algorithms have the capacity to generalize and discover knowledge for themselves and learn new information whenever unseen data are captured. The primary aim of sensor processing is to develop techniques to interpret, understand, and act on information contained in the data. The interest of this book is in developing intelligent signal processing in order to pave the way for smart sensors. This involves mathematical advancement of nonlinear signal processing theory and its applications that extend far beyond traditional techniques. It bridges the boundary between theory and application, developing novel theoretically inspired methodologies targeting both longstanding and emergent signal processing applications. The topic ranges from phishing detection to integration of terrestrial laser scanning, and from fault diagnosis to bio-inspiring filtering. The book will appeal to established practitioners, along with researchers and students in the emerging field of smart sensors processing