1,639 research outputs found
Geodetic monitoring of complex shaped infrastructures using Ground-Based InSAR
In the context of climate change, alternatives to fossil energies need to be used as much as possible to produce electricity. Hydroelectric power generation through the utilisation of dams stands out as an exemplar of highly effective methodologies in this endeavour. Various monitoring sensors can be installed with different characteristics w.r.t. spatial resolution, temporal resolution and accuracy to assess their safe usage. Among the array of techniques available, it is noteworthy that ground-based synthetic aperture radar (GB-SAR) has not yet been widely adopted for this purpose. Despite its remarkable equilibrium between the aforementioned attributes, its sensitivity to atmospheric disruptions, specific acquisition geometry, and the requisite for phase unwrapping collectively contribute to constraining its usage. Several processing strategies are developed in this thesis to capitalise on all the opportunities of GB-SAR systems, such as continuous, flexible and autonomous observation combined with high resolutions and accuracy.
The first challenge that needs to be solved is to accurately localise and estimate the azimuth of the GB-SAR to improve the geocoding of the image in the subsequent step. A ray tracing algorithm and tomographic techniques are used to recover these external parameters of the sensors. The introduction of corner reflectors for validation purposes confirms a significant error reduction. However, for the subsequent geocoding, challenges persist in scenarios involving vertical structures due to foreshortening and layover, which notably compromise the geocoding quality of the observed points. These issues arise when multiple points at varying elevations are encapsulated within a singular resolution cell, posing difficulties in pinpointing the precise location of the scattering point responsible for signal return. To surmount these hurdles, a Bayesian approach grounded in intensity models is formulated, offering a tool to enhance the accuracy of the geocoding process. The validation is assessed on a dam in the black forest in Germany, characterised by a very specific structure.
The second part of this thesis is focused on the feasibility of using GB-SAR systems for long-term geodetic monitoring of large structures. A first assessment is made by testing large temporal baselines between acquisitions for epoch-wise monitoring. Due to large displacements, the phase unwrapping can not recover all the information. An improvement is made by adapting the geometry of the signal processing with the principal component analysis. The main case study consists of several campaigns from different stations at Enguri Dam in Georgia. The consistency of the estimated displacement map is assessed by comparing it to a numerical model calibrated on the plumblines data. It exhibits a strong agreement between the two results and comforts the usage of GB-SAR for epoch-wise monitoring, as it can measure several thousand points on the dam. It also exhibits the possibility of detecting local anomalies in the numerical model. Finally, the instrument has been installed for continuous monitoring for over two years at Enguri Dam. An adequate flowchart is developed to eliminate the drift happening with classical interferometric algorithms to achieve the accuracy required for geodetic monitoring. The analysis of the obtained time series confirms a very plausible result with classical parametric models of dam deformations. Moreover, the results of this processing strategy are also confronted with the numerical model and demonstrate a high consistency. The final comforting result is the comparison of the GB-SAR time series with the output from four GNSS stations installed on the dam crest.
The developed algorithms and methods increase the capabilities of the GB-SAR for dam monitoring in different configurations. It can be a valuable and precious supplement to other classical sensors for long-term geodetic observation purposes as well as short-term monitoring in cases of particular dam operations
Recommended from our members
Design for Accessible Collaborative Engagement: Making online synchronous collaborative learning more accessible for students with sensory impairments.
This thesis looks at the accessibility of collaborative learning and the barriers to engagement experienced by blind/visually impaired (BVI) students and deaf/hard of hearing (DHH) students. It focuses specifically on online synchronous collaborative learning after establishing that this format presented the greatest barriers, and that these student groups were not engaging.
Taking a design-based research (DBR) approach, five studies were undertaken to identify these barriers and determine potential interventions. The product of the research, a result of collaborative design by the participants in the study, is a framework for accessible collaborative engagement represented in the form of an interactive website model, the Model for Accessible Collaborative Engagement (MACE).
The studies involved representatives of all stakeholders in the collaborative learning process at the institution (the Open University): students, tutors, modules teams, academics, support staff, and the student union Disabled Students Group. These studies took the form of an online survey of 327 students, 10 interviews with staff and students, 6 staff workshops and a collaborative design focus group. With significant representation of the target groups (BVI and DHH) in all studies, and taking an iterative approach to the design, evaluation and construction of the framework model, the studies established that barriers existed in four main categories covering different themes:
1. Communications: aural, visual, screen reading and navigation, text and captioning, lip reading and non-verbal communications, interpretation and third-party communications, mode control, and synchronisation.
2. Emotional and Social Factors: familiarisation, support networks, self-advocacy, opting out, cognitive load, and stress and anxiety.
3. Provisioning and Technical Factors: dissemination, speed and pacing of sessions, staff training, participation control, group size, technical provisioning, and recordings.
4. Activity and Session Design: Volume of materials, advance materials, accessible materials, accessible activities, and session formats.
Interventions were designed that could reduce the barriers in each of these categories and themes by adjustments and changes from both the student and institutional standpoints. MACE is designed to be utilised by both students and staff to provide guidance and suggestions on how to identify and acknowledge these barriers and implement interventions to reduce them.
This research represents an original and essential contribution to the field of investigation. As well as informing future research inquiry, the model can be used by all participants and stakeholders in online collaborative learning to help reduce barriers for BVI and DHH students and improve inclusivity in synchronous online events
Impacts of coffee fragmented landscapes on biodiversity and microclimate with emerging monitoring technologies
Habitat fragmentation and loss are causing biodiversity declines across the globe. As biodiversity is unevenly distributed, with many hotspots located in the tropics, conserving and protecting these areas is important to preserve as many species as possible. Chapter 2 presents an overview of the Ecology of the Atlantic Forest, a highly fragmented biodiversity hotspot. A major driver of habitat fragmentation is agriculture, and in the tropics coffee is major cash crop. Developing methods to monitor biodiversity effectively without labour intensive surveys can help us understand how communities are using fragmented landscapes and better inform management practices that promote biodiversity. Acoustic monitoring offers a promising set of tools to remotely monitor biodiversity. Developments in machine learning offer automatic species detection and classification in certain taxa. Chapters 3 and 4 use acoustic monitoring surveys conducted on fragmented landscapes in the Atlantic Forest to quantify bird and bat communities in forest and coffee matrix, respectively. Chapter 3 shows that acoustic composition can reflect local avian communities. Chapter 4 applies a convolutional neural network (CNN) optimised on UK bat calls to a Brazilian bat dataset to estimate bat diversity and show how bats preferentially use coffee habitats. In addition to monitoring biodiversity, monitoring microclimate forms a key part of climate smart agriculture for climate change mitigation. Coffee agriculture is limited to the tropics, overlapping with biodiverse regions, but is threatened by climate change. This presents a challenge to countries strongly reliant on coffee exports such as Brazil and Nicaragua. Chapter 5 uses data from microclimate weather stations in Nicaragua to demonstrate that sun-coffee management is vulnerable to supraoptimal temperature exposure regardless of local forest cover or elevation.Open Acces
- …