6,235 research outputs found

    Undergraduate Catalog of Studies, 2023-2024

    Get PDF

    Graduate Catalog of Studies, 2023-2024

    Get PDF

    Flood dynamics derived from video remote sensing

    Get PDF
    Flooding is by far the most pervasive natural hazard, with the human impacts of floods expected to worsen in the coming decades due to climate change. Hydraulic models are a key tool for understanding flood dynamics and play a pivotal role in unravelling the processes that occur during a flood event, including inundation flow patterns and velocities. In the realm of river basin dynamics, video remote sensing is emerging as a transformative tool that can offer insights into flow dynamics and thus, together with other remotely sensed data, has the potential to be deployed to estimate discharge. Moreover, the integration of video remote sensing data with hydraulic models offers a pivotal opportunity to enhance the predictive capacity of these models. Hydraulic models are traditionally built with accurate terrain, flow and bathymetric data and are often calibrated and validated using observed data to obtain meaningful and actionable model predictions. Data for accurately calibrating and validating hydraulic models are not always available, leaving the assessment of the predictive capabilities of some models deployed in flood risk management in question. Recent advances in remote sensing have heralded the availability of vast video datasets of high resolution. The parallel evolution of computing capabilities, coupled with advancements in artificial intelligence are enabling the processing of data at unprecedented scales and complexities, allowing us to glean meaningful insights into datasets that can be integrated with hydraulic models. The aims of the research presented in this thesis were twofold. The first aim was to evaluate and explore the potential applications of video from air- and space-borne platforms to comprehensively calibrate and validate two-dimensional hydraulic models. The second aim was to estimate river discharge using satellite video combined with high resolution topographic data. In the first of three empirical chapters, non-intrusive image velocimetry techniques were employed to estimate river surface velocities in a rural catchment. For the first time, a 2D hydraulicvmodel was fully calibrated and validated using velocities derived from Unpiloted Aerial Vehicle (UAV) image velocimetry approaches. This highlighted the value of these data in mitigating the limitations associated with traditional data sources used in parameterizing two-dimensional hydraulic models. This finding inspired the subsequent chapter where river surface velocities, derived using Large Scale Particle Image Velocimetry (LSPIV), and flood extents, derived using deep neural network-based segmentation, were extracted from satellite video and used to rigorously assess the skill of a two-dimensional hydraulic model. Harnessing the ability of deep neural networks to learn complex features and deliver accurate and contextually informed flood segmentation, the potential value of satellite video for validating two dimensional hydraulic model simulations is exhibited. In the final empirical chapter, the convergence of satellite video imagery and high-resolution topographical data bridges the gap between visual observations and quantitative measurements by enabling the direct extraction of velocities from video imagery, which is used to estimate river discharge. Overall, this thesis demonstrates the significant potential of emerging video-based remote sensing datasets and offers approaches for integrating these data into hydraulic modelling and discharge estimation practice. The incorporation of LSPIV techniques into flood modelling workflows signifies a methodological progression, especially in areas lacking robust data collection infrastructure. Satellite video remote sensing heralds a major step forward in our ability to observe river dynamics in real time, with potentially significant implications in the domain of flood modelling science

    Effects of atmospheric, topographic, and BRDF correction on imaging spectroscopy-derived data products

    Full text link
    Surface reflectance is an important data product in imaging spectroscopy for obtaining surface information. The complex retrieval of surface reflectance, however, critically relies on accurate knowledge of atmospheric absorption and scattering, and the compensation of these effects. Furthermore, illumination and observation geometry in combination with surface reflectance anisotropy determine dynamics in retrieved surface reflectance not related to surface absorption properties. To the best of authors’ knowledge, no comprehensive assessment of the impact of atmospheric, topographic, and anisotropy effects on derived surface information is available so far.This study systematically evaluates the impact of these effects on reflectance, albedo, and vegetation products. Using three well-established processing schemes (ATCOR F., ATCOR R., and BREFCOR), high-resolution APEX imaging spectroscopy data, covering a large gradient of illumination and observation angles, are brought to several processing states, varyingly affected by mentioned effects. Pixel-wise differences of surface reflectance, albedo, and spectral indices of neighboring flight lines are quantitatively analyzed in their respective overlapping area. We found that compensation of atmospheric effects reveals actual anisotropy-related dynamics in surface reflectance and derived albedo, related to an increase in pixel-wise relative reflectance and albedo differences of more than 40%. Subsequent anisotropy compensation allows us to successfully reduce apparent relative reflectance and albedo differences by up to 20%. In contrast, spectral indices are less affected by atmospheric and anisotropy effects, showing relative differences of 3% to 10% in overlapping regions of flight lines.We recommend to base decisions on the use of appropriate processing schemes on individual use cases considering envisioned data products

    Graduate Catalog of Studies, 2023-2024

    Get PDF

    Evolutionary ecology of obligate fungal and microsporidian invertebrate pathogens

    Get PDF
    The interactions between hosts and their parasites and pathogens are omnipresent in the natural world. These symbioses are not only key players in ecosystem functioning, but also drive genetic diversity through co-evolutionary adaptations. Within the speciose invertebrates, a plethora of interactions with obligate fungal and microsporidian pathogens exist, however the known interactions is likely only a fraction of the true diversity. Obligate invertebrate fungal and microsporidian pathogen require a host to continue their life cycle, some of which have specialised in certain host species and require host death to transmit to new hosts. Due to their requirement to kill a host to spread to a new one, obligate fungal and microsporidian pathogens regulate invertebrate host populations. Pathogen specialisation to a single or very few hosts has led to some fungi evolving the ability to manipulate their host’s behaviour to maximise transmission. The entomopathogenic fungus, Entomophthora muscae, infects houseflies (Musca domestica) over a week-long proliferation cycle, resulting in flies climbing to elevated positions, gluing their mouthparts to the substrate surface, and raising their wings to allow for a clear exit from fungal conidia through the host abdomen. These sequential behaviours are all timed to occur within a few hours of sunset. The E. muscae mechanisms used in controlling the mind of the fly remain relatively unknown, and whether other fitness costs ensue from an infection are understudied.European Commissio

    Combining spectral and texture feature of UAV image with plant height to improve LAI estimation of winter wheat at jointing stage

    Get PDF
    IntroductionLeaf area index (LAI) is a critical physiological and biochemical parameter that profoundly affects vegetation growth. Accurately estimating the LAI for winter wheat during jointing stage is particularly important for monitoring wheat growth status and optimizing variable fertilization decisions. Recently, unmanned aerial vehicle (UAV) data and machine/depth learning methods are widely used in crop growth parameter estimation. In traditional methods, vegetation indices (VI) and texture are usually to estimate LAI. Plant Height (PH) unlike them, contains information about the vertical structure of plants, which should be consider.MethodsTaking Xixingdian Township, Cangzhou City, Hebei Province, China as the research area in this paper, and four machine learning algorithms, namely, support vector machine(SVM), back propagation neural network (BPNN), random forest (RF), extreme gradient boosting (XGBoost), and two deep learning algorithms, namely, convolutional neural network (CNN) and long short-term memory neural network (LSTM), were applied to estimate LAI of winter wheat at jointing stage by integrating the spectral and texture features as well as the plant height information from UAV multispectral images. Initially, Digital Surface Model (DSM) and Digital Orthophoto Map (DOM) were generated. Subsequently, the PH, VI and texture features were extracted, and the texture indices (TI) was further constructed. The measured LAI on the ground were collected for the same period and calculated its Pearson correlation coefficient with PH, VI and TI to pick the feature variables with high correlation. The VI, TI, PH and fusion were considered as the independent features, and the sample set partitioning based on joint x-y distance (SPXY) method was used to divide the calibration set and validation set of samples.ResultsThe ability of different inputs and algorithms to estimate winter wheat LAI were evaluated. The results showed that (1) The addition of PH as a feature variable significantly improved the accuracy of the LAI estimation, indicating that wheat plant height played a vital role as a supplementary parameter for LAI inversion modeling based on traditional indices; (2) The combination of texture features, including normalized difference texture indices (NDTI), difference texture indices (DTI), and ratio texture indices (RTI), substantially improved the correlation between texture features and LAI; Furthermore, multi-feature combinations of VI, TI, and PH exhibited superior capability in estimating LAI for winter wheat; (3) Six regression algorithms have achieved high accuracy in estimating LAI, among which the XGBoost algorithm estimated winter wheat LAI with the highest overall accuracy and best results, achieving the highest R2 (R2 = 0.88), the lowest RMSE (RMSE=0.69), and an RPD greater than 2 (RPD=2.54).DiscussionThis study provided compelling evidence that utilizing XGBoost and integrating spectral, texture, and plant height information extracted from UAV data can accurately monitor LAI during the jointing stage of winter wheat. The research results will provide a new perspective for accurate monitoring of crop parameters through remote sensing

    Fault structure and slip mechanics of the 2022 Mw 6.7 Menyuan earthquake revealed by coseismic rupture observations

    Get PDF
    \ua9 2023 The Authors. Large and shallow strike-slip earthquakes produce striking ground ruptures, damaging roads and infrastructure but providing great opportunities for examining the fault\u27s structure. After the 2022 Mw 6.7 Menyuan earthquake, we observed abundant surface fractures by a combination of optical remote sensing, radar offset and unmanned aerial vehicle measurements. These fractures reveal a complex fault structure including apparent bending geometries and bifurcating branches, which are essential to understanding the mechanisms of faulting. In this paper, we used triangular dislocations to construct the fault geometry that reflected the distribution of measured strike changes but avoided unexcepted discontinuities and overlaps where the fault bent. The modeled fault geometry revealed an extensional releasing bend which was responsible for the southward branching of the fault rupture at its western edge. Our results also demonstrated the potential to explain the occurrence of aftershock clusters and to infer their fault geometries through the correlation analysis of the aftershock distribution and the slip induced stress field. The triangular dislocation model also enabled the calculation of the fault plane roughness and its spatial variation which directly controlled the fault slip magnitude and rupture termination. These analyses reveal an unprecedented level of detail of the fault structure and slip mechanics and, to some extent, offer insights into the physical processes and structural properties of crustal faults in the Earth\u27s shallow crust

    Air Quality Research Using Remote Sensing

    Get PDF
    Air pollution is a worldwide environmental hazard that poses serious consequences not only for human health and the climate but also for agriculture, ecosystems, and cultural heritage, among other factors. According to the WHO, there are 8 million premature deaths every year as a result of exposure to ambient air pollution. In addition, more than 90% of the world’s population live in areas where the air quality is poor, exceeding the recommended limits. On the other hand, air pollution and the climate co-influence one another through complex physicochemical interactions in the atmosphere that alter the Earth’s energy balance and have implications for climate change and the air quality. It is important to measure specific atmospheric parameters and pollutant compound concentrations, monitor their variations, and analyze different scenarios with the aim of assessing the air pollution levels and developing early warning and forecast systems as a means of improving the air quality and safeguarding public health. Such measures can also form part of efforts to achieve a reduction in the number of air pollution casualties and mitigate climate change phenomena. This book contains contributions focusing on remote sensing techniques for evaluating air quality, including the use of in situ data, modeling approaches, and the synthesis of different instrumentations and techniques. The papers published in this book highlight the importance and relevance of air quality studies and the potential of remote sensing, particularly that conducted from Earth observation platforms, to shed light on this topic
    • …
    corecore