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Understanding the contribution of the Antarctic Ice Sheet (AIS) to past and future sea-level rise has emerged as a scientific priority over the last four decades. Whilst our knowledge of ice-dynamical changes occurring as a result of current anthropogenic forcing has improved considerably since the start of the satellite era, significantly less is known about the evolution of the AIS during the pre-industrial Holocene (the last ~11.7 thousand years; ka). Quantifying these changes is crucial, however, as this time period corresponds to a time when the ice sheet was retreating from its maximal extent at the Last Glacial Maximum (LGM; ~20 ka) and environmental conditions were similar to today. Therefore, improving our understanding of this period may provide a long-term context to the decadal changes observed in recent times and how these may evolve in the future. Whilst point-based geochronological measurements of ice and sediment cores, or surface exposure dating, can be used to assess past ice-sheet changes over the AIS, it remains unclear how representative they are of a wider region. A complementary and spatially extensive resource across the ice sheet are Internal Reflecting Horizons (IRHs) as imaged by Radio-Echo Sounding (RES) techniques, which provide a cumulative record of accumulation, basal melt and ice dynamics that, if dated precisely at ice cores, can be used to inform numerical ice-sheet models projecting past and future changes on large spatial scales. The aim of this thesis is therefore to develop and extend age-depth models from IRHs across the AIS to assess the past stability of the ice sheet. In this thesis, an age-depth model of Pine Island Glacier spanning the LGM and Holocene periods is derived from spatially extensive IRHs. The connection between RES profiles and the WAIS Divide ice core enables the direct dating of the IRHs, and reveals that they match large peaks in sulphate concentrations which are unparalleled in the 68,000 year-old record, thus suggesting that the cause of these IRHs is from past explosive volcanic eruptions. By connecting this IRH stratigraphy with a previously developed age-depth model across the Institute and Möller Ice Streams (IMIS), I show that a precisely dated age-depth model now exists over 20% of the West Antarctic Ice Sheet (WAIS). One of these IRHs, precisely dated at ~4.7 ka, is then used as input into a one-dimensional ice-flow model to estimate past accumulation rates during the mid-Holocene over the catchments encompassing Pine Island Glacier, Thwaites Glacier, and IMIS, together representing 30% of the WAIS. The inferred mid-Holocene accumulation estimates are then compared with modern rates derived from climate models and observational measurements to show that accumulation rates were 18% greater during the last five millennia compared to the present over the Amundsen-Weddell-Ross Divide. These results match previous findings from isolated ice-core measurements and spatially targeted studies over the divide, and correspond to periods of grounding line retreat and readvance during the Holocene over the WAIS. Together, these show the potential for extracting further IRH information from other sectors of the AIS in order to build an age-depth model of the ice sheet. However, the underlying RES data necessary for this work were, until recently, relatively inaccessible to the wider scientific community, thus restricting the extraction and interpretation of age-depth models across the AIS. This motivated the release of ~300,000 line-km of RES profiles acquired by the British Antarctic Survey between 2004 and 2020. In addition to standardising and releasing these data, this thesis shows that large sections of continuous englacial layering exist widely across both East and West Antarctica, suggesting that, together with previously developed age-depth models of both regions and nearby ice-core stratigraphies, these newly released RES datasets will be critical in our aim to build an ice-sheet wide age-depth model of Antarctica, as motivated by the AntArchitecture Initiative. Together, the findings from this thesis reveal the spatially extensive nature of IRHs across West and East Antarctica and demonstrate how these can be used to infer past ice-sheet changes. This thesis also highlights the need to extract further age-depth models, particularly across East Antarctica, in order to provide important boundary conditions such as past accumulation rates and ice-elevation change which can be used by numerical ice-sheet models to help improve predictions of past and future ice-sheet change and ensuing sea-level rise contributions.

The work presented in this thesis contributes to researching honeybee monitoring applications, with the aim of providing an unobtrusive monitoring solution to extend our understanding of bee behaviour and help preserve the globally threatened honeybee population. This study presents the design and assembly of a 5.8 GHz continuous wave (CW) radar used for unobtrusive insect monitoring from a 2 to 3 meter distance. The system was designed based on matching the radar cross section (RCS) of a 4 mm steel sphere to that of a honeybee model, which estimated the RCS of the bee using a full-wave electromagnetic (EM) simulator. A honeybee hive was monitored, resulting in readouts containing accurate Doppler shifts of flying honeybees. These were used to extract the RCS of 164 free-flying honeybees and was found to be within the range of −55 to −60 dBsm ± 3 dBsm, which was within the RCS EM simulations. The radar was integrated on a cost-effective 45 mm × 40 mm 4-layer PCB and was later for in-phase and quadrature output, supporting the identification of positive and negative Doppler shifts. It allowed the extraction of micro-Doppler signatures and provided readouts of honeybee and bumblebee wing beats that matched their expected wing beat frequency, demonstrating the capability of operating as a radar based insect classification system. The radar was integrated with machine learning (ML) to allow automatic classification of bee behaviour. The first ML model classified a hive's outgoing and incoming bees with an accuracy of 87.83%. The second ML model added hovering bees as a third classification group, which achieved a classification accuracy of 93.37%, thereby demonstrating the performance of the radar. Finally, a feasibility study of coating honeybee was performed, where the upper part of the thorax and abdomen were identified as ideal coating locations. The study estimated a maximum detection range enhancement of 4.2 metres from the original 2.4 metres through the application of 100 μm coating. This work led to several publications and demonstrated the ability of unobtrusive insect monitoring using widely available 5.8 GHz components. It addressed a research gap in honeybee RCS at 5.8 GHz and the feasibility of increased radar range monitoring using coating that will pave the way for further insect monitoring studies.

This thesis develops the technique of millimetre-wave radar at 94 GHz for close-range remote sensing of glaciers and terrestrial snow cover (the cryosphere). The capabilities of 94 GHz radar for cryosphere mapping are demonstrated using the 2ⁿᵈ generation All-weather Volcano Topography Imaging Sensor (AVTIS2), which maps 3D terrain from real-beam scanning. AVTIS2 acquires 3D point clouds of terrain and a comparison to co-located high density point clouds derived from Terrestrial Laser Scanner (TLS) data showed that AVTIS2 point cloud uncertainties were 1.5 m at 1.5 km and 3 m at 3 km. These values are smaller than other close-range radar systems used to map cryospheric terrain in 3D. Next, the distribution of Normalised Radar Cross Section (σ⁰) values over glacier ice at 94 GHz was found to be −17.0 < σ⁰ < −3.4; σ⁰ₘₑₐₙ = −9.9 dB and followed a log-normal distribution. These values are comparable to other terrain types at 94 GHz such as refrozen snow, wet snow and wet soil, hence glacier surfaces were found to be suitable targets for terrain mapping at 94 GHz. These fundamental results were used to apply the AVTIS2 94 GHz radar for monitoring snow cover and glacier calving. Field trials in Scotland showed that the transition of wet snow to a refrozen snowpack in response to reductions in air temperature could be detected from changes in 94 GHz σ⁰. Snow hazard features could also be identified, such as post-avalanche debris accumulations which manifest as localised increases in radar backscatter. Next, 3D AVTIS2 data was used to quantify glacier calving rates at the Hansbreen tidewater glacier in Svalbard. A time series of ice cliff morphology derived from the AVTIS2 3D data sets exposed the role of melt undercutting and demonstrated the critical role of the ocean on calving processes in Svalbard. The high accuracy 3D data acquired from AVTIS2 and the sensitivity of radar backscatter to surface changes has demonstrated the unique capabilities of 94 GHz radar for cryosphere mapping, paving the way forward for new applications and opportunities for close-range remote sensing of the cryosphere.

Polarimetric synthetic aperture radar (PolSAR) has been widely used in ocean and cryospheric applications. This is because, PolSAR can be used in all-day operations and in areas of cloud cover, and therefore can provide valuable large-scale monitoring in polar regions, which is very helpful to shipping and offshore maritime operations. In the last decades, attention has turned to the potential of PolSAR to detect icebergs in the Arctic since they are a major hazard to vessels. However, there is a substantial lack of literature exploring the potentialities of PolSAR and the understanding of iceberg scattering mechanisms. Additionally, it is not known if high resolution PolSAR can be used to detect icebergs smaller than 120 metres. This thesis aims to improve the knowledge of the use of PolSAR scattering mechanisms of icebergs, and detection of small icebergs. First, an introduction to PolSAR is outlined in chapter two, and monitoring of icebergs is presented in chapter three. The first data chapter (Chapter 4) is focused on developing a multi-scale analysis of icebergs using parameters from the Cloude-Pottier and the Yamaguchi decompositions, the polarimetric span and the Pauli scattering vector. This method is carried out using ALOS-2 PALSAR quad polarimetric L-band SAR on icebergs in Greenland. This approach outlines the good potential for using PolSAR for future iceberg classification. One of the main important outcomes is that icebergs are composed by a combination of single targets, which therefore may require a more complex way of processing SAR data to properly extract physical information. In chapter five, the problem of detecting icebergs is addressed by introducing six state-of-the-art detectors previously applied to vessel monitoring. These detectors are the Dual Intensity Polarisation Ratio Anomaly Detector (iDPolRAD), Polarimetric Notch Filter (PNF), Polarimetric Matched Filter (PMF), reflection symmetry (sym), Optimal Polarimetric Detector (OPD) and the Polarimetric Whitening Filter (PWF). Cloude-Pottier entropy, and first and third eigenvalues (eig1 and eig3) of the coherency matrix are also utilised as parameters for comparison. This approach uses the same ALOS-2 dataset, but also evaluates detection performance in two scenarios: icebergs in open ocean, and in sea ice. Polarimetric modes (quad-pol, dual-pol, and single intensities) are also considered for comparison. Currently it is very difficult to detect icebergs less than 120 metres in length using this approach, due to the scattering mechanisms of icebergs and sea ice being very similar. However, it was possible to obtain detection performances of the OPD and PWF, which both showed a Probability of Detection (PF) of 0.99 when the Probability of False Alarms (PF) was set to 10-5 in open ocean. Similarly, in dual pol images, the PWF gave the best performance with a PD of 0.90. Results in sea ice found eig3 to be the best detector with a PD of 0.90 while in dual-pol mode, iDPolRAD gave a PD of 0.978. Single intensity detector performance found the HV channel gave the best detection with a PD of 0.99 in open ocean and 0.87 in sea ice. In the previous two approaches, only satellite data is used. However, in chapter six, data from a ground-based Ku-band Gamma Portable Radio Interferometer (GPRI) instrument is introduced, providing images that are synchronised with the satellite acquisitions. In this approach, the same six detectors are applied to three multitemporal RADARSAT-2 quad pol C-band SAR images on icebergs in Kongsfjorden, Svalbard to evaluate the detection performance within a changing fjord environment. As before, we also make use of Cloude-Pottier entropy, eig1 and eig3. Finally, we evaluate the target-to-clutter ratio (TCR) of the icebergs and check for correlation between the backscattering coefficients and the iceberg dimension. The results obtained from this thesis present original additions to the literature that contributes to the understanding of PolSAR in cryospheric applications. Although these methods are applied to PolSAR and ground-based radar on vessels, they have been applied for the first time on icebergs in this thesis. To summarise, the main findings are that icebergs cannot be represented as single or partial targets, but they do exhibit a collection of single targets clustered together. This result leads to the fact that entropy is not sufficient as a parameter to detect icebergs. Detection results show that the OPD and PWF detectors perform best in an open ocean setting and using quad-pol mode. These results are degraded in dual-pol mode, while single intensity detection is best in the HV cross polarisation channel. When these detectors are applied to the RADARSAT-2 in Svalbard, the OPD and PWF detectors also perform best with PD values ranging between 0.5-0.75 for a PF of 0.01-0.05. However, the sea ice present in the fjord degrades performance across all detectors. Correlation plots with iceberg size show that a regression is not straightforward and Computer Vision methodologies may work best for this.

Forests play a vital role in the wellbeing of our planet. Large and small scale deforestation across the globe is threatening the stability of our climate, forest biodiversity, and therefore the preservation of fragile ecosystems and our natural habitat as a whole. With increasing public interest in climate change issues and forest preservation, a large demand for carbon offsetting, carbon footprint ratings, and environmental impact assessments is emerging. Satellite remote sensing is the only method that can provide global coverage at frequent revisit times and is therefore the standard method for global forest monitoring. Most often, deforestation maps are created from optical data such as Landsat and MODIS. Although such maps are of generally good quality, they cannot quantify biomass and are not typically available at less than annual intervals due to persistent cloud cover in many parts of the world, especially the tropics where most of the world's forest biomass is concentrated. Synthetic Aperture Radar (SAR) can fill this gap as it penetrates clouds and interacts with the three-dimensional structure on the ground in a way that scales with volume and therefore biomass. While longer wavelengths are better for deeper penetration of the canopy and therefore biomass estimation, one of the most readily available data sources is Sentinel-1, a shorter wavelength C-band radar. In this thesis, the theory behind SAR is discussed to demonstrate its usefulness for forest monitoring. The potential for C-band SAR data to distinguish forest and non-forest is then assessed empirically in different regions of the world and existing change detection algorithms for deforestation are reviewed. The effect of spatial and temporal context on change detection accuracy is investigated, leading to the development of a robust method for deforestation detection in the absence of reliable reference data which often constitutes the largest practical hurdle. This method achieves a change detection sensitivity (producer's accuracy) above 99%, although false positives lead to a low user's accuracy of about 60%. The mean change detection delay amounts to about two to three months. While further work is required to reduce the false positive rate, improve detection delay, and validate this method in different biomes, the results show that Sentinel-1 data have the potential to advance global deforestation monitoring.

Ego-motion estimation in robotics is typically performed with cameras or laser sensors using established techniques that have proven successful in a variety of applications. While these advances have expanded the capabilities of au- tonomous systems, they are limited by sensor hardware that is adversely affected by poor weather and lighting conditions. Sensing with radar offers a robust means to perceive the environment in even the harshest conditions where these optical sensors fail. This thesis presents strategic improvements to a radar-only ego-motion estimation pipeline to extend its utility as a core component in an autonomous system. By leveraging information pertaining to the context in which the sensor is used, we offer a means to reduce processing time by filtering out spurious returns, predict failure using an introspective component, and estimate ego-motion with much greater accuracy by considering motion constraints. The first contribution uses a weakly-supervised approach to train a neural network that filters out radar returns not likely to correspond to static objects in the scene. By passing only those returns that are useful for ego-motion estimation, processing times are reduced by a factor of 2.36. Secondly, we propose a system that interprets the information encoded within the data association algorithm such that the overall confidence in a match between two radar landmark sets can be determined. A classifier is trained to distinguish between strong and weak correspondences and flag poor performance prior to taking action, reducing failure frequency by 24.7 %. Finally, we present a method which leverages information regarding the physical constraints of the robot platform to refine landmark correspondences. We discard those that suggest extreme ego-motion when under the assumption that the vehicle traverses paths of constant curvature. This includes a way to estimate relative pose from a single landmark observed twice, and reduces translational error in odometry by 45.94%. In each instance, validation is performed on real data captured from a vehicle- mounted scanning radar to measure the performance improvement of the proposed subsystem against a baseline Radar Odometry (RO) system. Through these data- driven advances, we seek to further the use of radar as a navigation sensor, so as to enable our robots to roam with greater efficiency, safety, and accuracy than before.

Structure from motion and multi-view Stereo (SfM+MVS) is a technique for creating land surface models from optical remote sensing images. SfM+MVS is often coupled with aerial drones and has advanced considerably within spatial ecology for assessing: landslide progression, hydrological pathways, coastal erosion, arable crop yields and woody vegetation. However, there are few examples of its application to understanding grassland functions and services. Grasslands are important as they deliver a range of functions and services, including: biodiversity provision, regulation of hydrology and food and fibre provision. This thesis advances understanding of the spatial and temporal uncertainty of drone and SfM+MVS-based workflows in grassland ecology. It details new solutions which address key limitations of SfM+MVS-based workflows, and explores novel applications within conservation management in grassy habitats. A combination of traditional agronomic techniques, airborne Light Detection and Ranging (LiDAR) and drone and SfM+MVS-coupled approaches were used to develop a greater understanding of the capabilities of current techniques and technologies. This thesis developed and evaluated novel methods to advance quantitative understanding of spatial variation in vegetation canopy characteristics and biodiversity provision across temperate grasslands. The research presented herein clearly details how traditional agronomic techniques alongside a drone, SfM+MVS and LiDAR-coupled workflow can support grassland conservation monitoring schemes through resource efficient measurements of key sward canopy characteristics. This thesis extends the understanding of the capabilities, limitations and confidence in drone-based SfM+MVS for understanding grassland functions and services, aiding sound and timely management of protected features. This was achieved through answering four research questions: i) How does the choice of SfM+MVS software (including settings and user experience/time) impact spatial and temporal uncertainty of sward height measurements? ii) Can drone data capture sward height variability, and are drone and SfM+MVS-based measurements reproducible across replicate image datasets? iii) Can airborne LiDAR-derived data products be used to address the lack of sufficiently accurate and fine-grain bare-Earth elevation reference data required for generating sward height measurements from a drone and SfM+MVS-based workflow? iv) Can a drone and SfM+MVS-based workflow be used to measure key habitat quality indicators for observed patterns in nectar feeding insects in high nature value grassland habitats? Summaries of how these four research questions were addressed: I Chapter 3 details the need to consider choice of software in SfM+MVS studies. Up until now, most studies carried out employing an SfM+MVS workflow were not statistically reproducible. When designing a drone and SfM+MVS-based study it is crucial to consider differences between software and how robust the workflow, including software, are by considering the variation in the SfM+MVS-derived vegetation canopy height measurements between replicate image datasets. To address the latter point I proposed that an SfM+MVS workflow for time series analyses should capture at least one replicate image dataset. This would, at a small cost, improve the reproducibility of the results, which is crucial when monitoring fine-grained indicators of environmental change over time. The findings presented in this part of the thesis have important implications for the application of SfM+MVS in ecology as well as in other fields of Earth and environmental science. II The extent to which aerial photographs taken from a drone could deliver new insights into the spatial heterogeneity of an intensively managed grassland field were determined in Chapter 4. It is argued that fine-grained monitoring of temperate grasslands at management relevant extents is either technically or practically not possible with traditional manual approaches. Thus, an easily applied workflow which can support decision-making of grassland farmers and conservation managers, allowing the optimisation of sward management for production and/or biodiversity aims is demonstrated. The need for this workflow is evidenced in scientific literature on the conservation of grassland invertebrates and bird communities which reveals the intrinsic challenge of accurately and precisely quantifying grassland habitats, let alone at the temporal resolution required to capture the complex nature of population dynamics. III The extent to which the quality of information derived from LiDAR-based data products could be improved, and the extent to which LiDAR-based data products can be used alongside drone-based aerial photographic data to deliver new insights into the spatial heterogeneity of short-sward grassland habitats were determined in Chapter 5. It is argued that not only can a workflow centred around LiDAR-derived data products deliver accurate ground height measurements, it also addresses concerns pertaining to the lack of sufficiently accurate and fine-grain bare-Earth elevation reference data. The proposed workflow extends previous attempts at calibrating LiDAR-derived height measurements into pastures and meadows. IV Chapter 6 is set on the backdrop of the rapid decline in key grassland habitats experienced around large parts of the globe since the early 1900s which stress the importance of assessing the wider implications and often complex dynamics of change. While in-situ species population counts have and will continue to be an important asset for conservation management schemes and policy making, they are time consuming. Instead, spatial and temporal assessment of habitat quality offers resource efficient means of tracking patterns and trends in key habitat quality indicators. However, current approaches are time consuming and often fail to capture patterns of important features. Hence, in Chapter 6 a novel, resource efficient image- and LiDAR and machine learning-coupled workflow capable of delivering structural measurements at the grain size and over the extent required in conservation management was developed. Thus, there is arguably now a strong case for re-evaluating existing conservation monitoring schemes in the light of new technologies and techniques.

This thesis concerns the development of an adaptive waveform design scheme for synthetic aperture radar (SAR) to support its operation in the increasingly crowded radio frequency (RF) spectrum, focusing on mitigating the effects of external RF interference. The RF spectrum is a finite resource and the rapid expansion of the telecommunications industry has seen radar users face a significant restriction in the range of available operational frequencies. This crowded spectrum scenario leads to increased likelihood of RF interference either due to energy leakage from neighbouring spectral users or from unlicensed transmitters. SAR is a wide bandwidth radar imaging mode which exploits the motion of the radar platform to form an image using multiple one dimensional profiles of the scene of interest known as the range profile. Due to its wideband nature, SAR is particularly vulnerable to RF interference which causes image impairments and overall reduction in quality. Altering the approach for radar energy transmission across the RF spectrum is now imperative to continue effective operation. Adaptive waveforms have recently become feasible for implementation and offer the much needed flexibility in the choice and control over radar transmission. However, there is a critically small processing time frame between waveform reception and transmission, which necessitates the use of computationally efficient processing algorithms to use adaptivity effectively. This simulation-based study provides a first look at adaptive waveform design for SAR to mitigate the detrimental effects of RF interference on a pulse-to-pulse basis. Standard SAR systems rely on a fixed waveform processing format on reception which restricts its potential to reap the benefits of adaptive waveform design. Firstly, to support waveform design for SAR, system identification techniques are applied to construct an alternative receive processing method which allows flexibility in waveform type. This leads to the main contribution of the thesis which is the formation of an adaptive spectral waveform design scheme. A computationally efficient closed-form expression for the waveform spectrum that minimizes the error in the estimate of the SAR range profile on a pulse to pulse basis is derived. The range profile and the spectrum of the interference are estimated at each pulse. The interference estimate is then used to redesign the proceeding waveform for estimation of the range profile at the next radar platform position. The solution necessitates that the energy is spread across the spectrum such that it competes with the interferer. The scenario where the waveform admits gaps in the spectrum in order to mitigate the effects of the interference is also detailed and is the secondary major thesis contribution. A series of test SAR images demonstrate the efficacy of these techniques and yield reduced interference effects compared to the standard SAR waveform.

The dynamic response of a faster-flowing Greenland Ice Sheet to climate change is modulated by feedbacks between ice flow and surface meltwater delivery to the basal environment. While supraglacial melt processes have been thoroughly examined and are well constrained, the response of the englacial and subglacial environment to these seasonal perturbations still represent the least-studied, understood, and parameterised processes of glacier dynamics due to a paucity of direct observation. To better understand these processes in the wake of a changing climate, novel in-situ geophysical experiments were undertaken on Store Glacier in west Greenland to quantify rates of englacial deformation and basal melting. The records produced from these experiments yield new insights into the thermodynamic setting of a major outlet glacier, and the physical mechanisms underlying and resulting from fast glacier motion. The deployment of autonomous phase-sensitive radio-echo sounders (ApRES) $\SI{30}{\kilo\metre}$ from the calving terminus of Store Glacier between 2014 and 2016 revealed high rates of both englacial deformation and basal melting, driven primarily by the dynamic response of the basal hydrological system to seasonal surface meltwater influxes. Thermodynamic modelling of this process revealed a convergence of large-scale basal hydrological pathways that aggregated large amounts of water towards the field site. The warm, turbulent water routed from surface melt contained and dissipated enough energy at the ice-bed interface to explain the observed high melt rates. Simultaneously, changes in the local strain field, involving seasonal variations in the morphology of internal layers, were found to be the result of far-field perturbations in downstream ice flow which propagated tens of kilometres upglacier through longitudinal stress coupling. When observed in multiple dimensions, the layer structure revealed complex internal reflection geometries, demonstrating ApRES as not just a monitor of depth changes in ice thickness, but also as an imaging instrument capable of characterising the subsurface environment within and beneath ice sheets. Altogether, the observations and analyses comprising this thesis provide new and significant insight and understanding into the structural, thermal, and mechanical processes tied to Store Glacier and its fast, complex, and dynamic ice flow.

Ice streams are fast flowing outlet glaciers through which over 90% of the ice stored within the Antarctic Ice Sheet drains. The dynamic behaviour of ice streams is therefore crucial in controlling the mass balance of the ice sheet. Over the past few decades, Antarctica has been losing mass. Much of this mass loss has been focussed around coastal regions of the Antarctic Ice Sheet. Some of the most dramatic changes such as grounding-line retreat, acceleration and surface elevation change have been observed in Pine Island Glacier (PIG) and its neighbouring ice streams. This is of particular concern because these ice streams account for 10% of the discharge from the west Antarctic Ice Sheet and therefore have the potential to contribute significantly to global sea-level rise. One of the key challenges in accurately forecasting this future sea-level rise is improving understanding of processes occurring at the beds of ice streams. This requires detailed knowledge of the properties and dynamics of the bed. This thesis aims to address this knowledge gap by investigating the spatial and temporal characteristics of the bed of PIG using high-resolution geophysical data acquired in its trunk and tributaries and beneath the ice shelf. The thesis begins by analysing radar-derived high-resolution maps of subglacial topography. These data show a contrasting topography across the ice-bed interface. These diverse subglacial landscapes have an impact on ice flow through form drag, controlled by the size and orientation of bedrock undulations and protuberances. The next chapter provides a quantitative analysis of these landscapes using Fast Fourier analysis of subglacial roughness. This analysis investigates the roughness signature of subglacial bedforms and the how the orientation and wavelength of roughness elements determine their correlation with ice dynamic parameters. The slow-flowing inter-tributary site is found to have a distinct signature comparable to 'ribbed' patterns of modelled basal shear stress and transverse 'mega rib' bedforms. Roughness oriented parallel to ice flow with wavelengths approaching mean ice thickness are found to have the highest correlation with ice dynamic parameters. The temporal stability of PIG is analysed using repeat radar measurements. No significant change is observed over a period of 3-6 years with no evidence of rapid erosion or the evolution of subglacial bedforms as observed in previous repeat measurements of ice-stream beds elsewhere in Antarctica. This suggests that the widespread deforming till layer detected in extensive seismic reflection surveys is in steady state. Lastly, the thesis explores geomorphological evidence of twentieth-century grounding-line retreat beneath PIG Ice Shelf using high-resolution geophysical data acquired from autonomous underwater vehicle surveys. Evidence of erosion, deposition, meltwater flow and post-glacial modification is observed in fine detail. The observed distribution of sediment supported previous surveys indicating a geological transition coinciding with the ridge that acted as a former stable grounding-line location. Metre-scale resolution images of recently deglaciated ice stream beds were found to reveal bedforms that are not detectable with traditional offshore bathymetric surveys. Together these findings reveal the role of short wavelength topography as both an influence on, and product of fast ice stream flow. It also highlights the spatial diversity of subglacial environments and the need to focus future research on tying detailed observations of ice-stream beds with knowledge of basal properties over time.

The future of global telecommunications looks even more promising with the advent of Free Space Optics (FSO) to complement Fibre Optics technology. With the main impairments to Free Space Optics known to be diffraction and atmospheric turbulence, it is critical to adequately characterise the atmospheric medium for effective FSO system design. Most laser sources can be designed to produce Gaussian-like beam profiles, which suffer from diffraction issues. To address this, a non-diffracting beam called the Bessel beam is introduced; its central core has been proven to be resistant to diffractive spreading whilst propagating. However, both Gaussian and Bessel beams will experience distortion when propagating through atmospheric turbulence. The strength of atmospheric turbulence Cn2 is considered constant for ground-to-ground (terrestrial) applications, but proven variable and gradually-weakening for ground-to-space (satellite) applications. In this research, we investigate the propagation of the two beams both in the ground-to-ground scenario and in the ground-to-space scenario. For the ground-to-space scenario, we define a maximum height of 22 km above which the effect of atmospheric turbulence is considered negligible. We also investigate the propagation of the beams from the ground, beyond the 22 km limit, into deep space. We analyse and compare the performance of the beams for all the scenarios based on predefined performance measures. The Bessel beam offers enhanced performance and is shown to outperform the Gaussian on a number of the performance measures.

Through-the-wall Imaging (TWI) is crucial for various applications such as law enforcement, rescue missions and defense. TWI methods aim to provide detailed information of spaces that cannot be seen directly. Current state-of-the-art TWI systems utilise ultra-wideband (UWB) signals to simultaneously achieve wall penetration and high resolution. These TWI systems transmit signals and mathematically back-project the reflected signals received to image the scenario of interest. However, these systems are diffraction-limited and encounter problems due to multipath signals in the presence of multiple scatterers. Time reversal (TR) methods have become popular for remote sensing because they can take advantage of multipath signals to achieve superresolution (resolution that beats the diffraction limit). The Decomposition Of the Time-Reversal Operator (DORT in its French acronym) and MUltiple SIgnal Classification (MUSIC) methods are both TR techniques which involve taking the Singular Value Decomposition (SVD) of the Multistatic Data Matrix (MDM) which contains the signals received from the target(s) to be located. The DORT and MUSIC imaging methods have generated a lot of interests due to their robustness and ability to locate multiple targets. However these TR-based methods encounter problems when the targets are behind an obstruction, particularly when the properties of the obstruction is unknown as is often the case in TWI applications. This dissertation introduces a novel total sub-MDM algorithm that uses the highly acclaimed MUSIC method to image targets hidden behind an obstruction and achieve superresolution. The algorithm utilises spatio-temporal windows to divide the full-MDM into sub-MDMs. The summation of all images obtained from each sub-MDM give a clearer image of a scenario than we can obtain using the full-MDM. Furthermore, we propose a total sub-differential MDM algorithm that uses the MUSIC method to obtain images of moving targets that are hiddenbehind an obstructing material.

The propagation of electromagnetic (EM) waves of frequencies above 100MHz is affected by the existence and properties of the atmospheric duct, i.e. a horizontal layer in the lower atmosphere in which radio signals propagate more efficiently. Atmospheric ducts can be found in many parts of the world ocean including the Arabian Gulf. Ambient winds blowing from different directions bring air masses of different properties into the area and hence have a significant impact on the formation and strength of the atmospheric duct. However, little information is available on the long-term and intra-annual variability of wind and its effect on the ducting phenomenon in the Arabian Gulf region. This study addresses this gap by characterising the local meteorology, with a special emphasis on its effect on electromagnetic wave propagation. This study uses a new methodology to measure the persistence of Shamal wind, by considering the number of days associated with the specific wind pattern in addition to commonly used parameters such as the wind speed. In this study, thirty years (1981-2010) of observations and NCEP/NCAR reanalysis data have been analyzed to identify a long-term trend and the intra-annual variability of various wind systems. Results clearly indicate that the Shamal (the northwesterly wind) is the most frequent meteorological feature over the region; therefore it has been investigated in greater detail. The Suhaili (southerly wind) is the second important wind which can occur any time of the year but it is less frequent than the Shamal. The Al-Nashi (cold and dry northeasterly wind) wind occurs only in December, January and February. The analysis shows that the wind strength and the frequency of Shamal days over the region have decreased over the last thirty years. Variations in the occurrence of summer and winter Shamal days were studied in relation to global atmospheric phenomena, and relationships have been established, synoptically and statistically between the frequency of Shamal days and large-scale atmospheric fluctuations. These links include atmospheric fluctuations over the Caspian Sea (a correlation coefficient of 0.66) and Siberia (0.69) in summer and Greenland (0.51) and Western Europe (0.65) in winter. The frequency of winter Shamal days during December, January and February are shown to be statistically related (a correlation coefficient of 0.41) to the North Atlantic Oscillation (NAO) and (0.49) the Arctic Oscillation (AO), as they influence the pathway of the westerly depression over the north Atlantic Ocean during the winter season. It is also shown that the decline in the number of Shamal days is linked to a decrease in the number of westerly depressions. The EM wave propagation has been examined using the Advanced Refraction Effects Prediction System (AREPS) model for different representative air masses. The radiosonde data from Abu Dhabi airport used in AREPS provided evidence of the general influence of each air mass. It was found that atmospheric ducting conditions and characteristics (height, thickness, and type) were variable in the lower part of the atmosphere (surface to 6000m) as a result of changing air masses. The influence of the Shamal conditions develop an elevated duct at approximately 850mb level. The Suhaili increases the thickness of the evaporation duct. In regards to the surface based and elevated duct, Suhaili and Al-Nashi provide standard atmospheric conditions. Land and sea breezes were mostly associated with the surface based duct and sometimes elevated the duct. Atmospheric ducting could extend the range of electromagnetic wave propagation above the usual range. Good knowledge of atmospheric duct characteristics enables the efficient assessment of the range of EM propagation, which is important for a number of practical applications, for example air traffic control and rescue operations. This could include the selection of the appropriate frequency and altitude of the electromagnetic wave device (e.g. radar and/or communication systems) operating with a frequency above 100MHz to be trapped in the duct to cover long distances.

Peat moorlands represent a nationally significant carbon store. Wildfires in peat moorlands release CO2 into the atmosphere, reducing the carbon store and burn into the seed bank preventing vegetation recovery. Burned areas of bare peat remain, known as 'burn scars' which are eroded by freeze thaw and desiccation, then weathered by precipitation and wind to cause discolouration of the water supply. A technique for the systematic monitoring of peat moorland burn scars is essential for informing land management and moorland restoration. Satellite data enables peat moorland burn scars to be monitored at the landscape scale for operational services e.g. European Forest Fire Information System (EFFIS). However, in the UK cloud is highly problematic for optical satellites and thermal data provides only a short window of opportunity for active fire detection. This thesis provides a unique line of enquiry by exploring the potential of Synthetic Aperture Radar (SAR) intensity and Interferometric Synthetic Aperture Radar (InSAR) coherence for burn scar characterisation and persistence, using a multi-temporal and multi-sensor approach for degraded peat moorland. The Peak District National Park (PDNP) was selected because it is a marginal moorland environment, which experiences high rates of peat erosion and will experience more wildfires, based on future projections of increased temperature, due to global warming. Initial SAR intensity results for the Bleaklow 2003 burn scar showed a clear post-fire increase of 7 dB for burned peat bog when acquired under wet conditions. Post-fire, dry-wet InSAR pairs were characterised by vegetation removal caused by combustion within the burn scar area, whereas wet-wet InSAR pairs characterised the burn scar, but also degraded peat moorland caused by previous wildfires blurring the new burn scar perimeter. Intensity differed significantly with slope for the PDNP 2003 wildfires, reducing the effectiveness of the technique for characterising burn scars on slopes facing away from the sensor, although these wildfires showed no significant difference on coherence for the inland bare ground class. When using coherence as a burn scar discriminator, this research found that it is essential to acquire InSAR pairs immediately post-fire with B⊥ < 550 m. Using a combination of intensity and coherence data a multi-difference colour composite was produced and an ISODATA classification applied. Results were reclassified to produce a burned area map with an overall map accuracy of 94% and Kappa Coefficient of 0.69 covering the Bleaklow and Kinder 2003 burn scars. Burn scars > 6 km2 provided a persistently higher burned area intensity signal for up to six months after the wildfire but only 2 - 3 months for coherence. The smaller Edale burn scar (0.10 km2) was characterised by 2 - 3 dB greater intensity for the burned area over a year after the wildfire. The Edale 2008 case study showed that L-band PALSAR data is less sensitive to characterising peat moorland burn scars compared to C-band data. This study therefore strongly recommends C-band data for peat moorland burn scar characterisation and monitoring. Future research will explore the new C-band Sentinel-1 data which offers improved spatial resolution and repeat-pass time.

The aim of this thesis is to evaluate the capability of radio detection and ranging (radar) data collected by the Advanced Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture radar (PALSAR), supported by field measurements obtained through ground survey, to predict and map Above Ground Woody Biomass (AGWB) in the tropical savannas of the developing country of Belize, and to understand how the forest structure may influence the backscatter observed. Firstly, an extensive inventory of the woody vegetation of the tropical savannas of Belize was created by measuring the diameter at breast height (dbh), the total height (ht) and the location of 6547 trees in plots covering a total woodland area of 30.8 hectares, located within four protected areas (the Rio Bravo Conservation and Management Area (11×1ha), Deep River (108×0.1ha) and Manatee Forest Reserve (1ha) and the Bladen Nature Reserve (1ha) and also from plots located in unprotected areas (7×1ha). These measurements of forest structure, when combined with information about forest management practices obtained from local organisations revealed that different forms of protection and management may lead to the development of pine woodlands with different structural characteristics in these savannas. Secondly, a case-study was conducted to establish the sensitivity of the ALOS PALSAR backscatter data to AGWB and determine the effect of sample plot size to their relationship. The findings of this case-study show that the L-band backscatter in these low density pine woodlands is a possible predictor of AGWB and confirm that the appropriate sample plot size for predicting AGWB is one hectare; while the sensitivity degrades significantly with decreasing sample plot size. Taken together, the findings described above were combined to assess the capability of ALOS PALSAR backscatter to predict AGWB in these woodlands. A semi-empirical Water Cloud Model (WCM) describing the interaction between the backscatter and vegetation was re-arranged to enable the prediction of AGWB. Non-linear regression analysis revealed that the ALOS PALSAR backscatter predicted AGWB with an R2=0.92; an external validation conducted with additional ground reference data estimated this AGWB prediction to have an RMSE ~13 t/ha. The form of the regression model linking backscatter to AGWB appears to be particularly influenced by sample plots with higher tree numbers and by plots in which the trees were more homogeneous. The presence of many similar sized individuals within some plots is postulated as one explanation for the elevated saturation level for predictions in this study (> 100 t/ha) compared to other models. The model developed here predicts complete saturation in the backscatter - AGWB relationship to occur primarily as a result of increases in the tree number density and often concurrently in basal area, two parameters which are usually strongly correlated with AGWB in these woodlands. Thirdly, the locally validated relationship between ALOS PALSAR backscatter and AGWB is used to map AGWB for the lowland pine savannas of Belize at a spatial resolution of 100m. The mapping estimates that over 90% of these pine woodlands have an AGWB below 60 t/ha, with the average woody biomass estimated at 23.5 t/ha. When these new predictions are mapped and aggregated over the extents of two protected areas (Rio Bravo and Deep River), the totals obtained agree closely (error ≤20%) with previous estimates of AGWB obtained from ground data and previous research. The combined evidence suggests that woodland protection may produce a small, positive effect upon AGWB, with the mean of the AGWB/ha predictions higher in areas that are protected and managed for biodiversity (29.55 ± 0.84 t/ha) than in other areas that are not protected (23.29 ± 0.19 t/ha). When the fine scale local AGWB mapping produced using ALOS PALSAR is compared cell-by-cell with global biomass products at coarser spatial resolutions (500m and 1000m), the AGWB differences observed range from 115-120%. When the coarser AGWB estimates are aggregated over the extents of Deep River and Rio Bravo, the AGWB totals obtained differ significantly (~280 – 300%) from AGWB estimates from ground data and previous research. Overall, these findings suggest that where sufficient ground data exists to build a reliable local relationship to radar backscatter, more detailed biomass mapping can be produced from ALOS and similar satellite sensor data at resolutions of ~100m. This more accurate and spatially detailed information about the distribution of woody biomass within tropical lowland savannas is more appropriate for monitoring local changes in forest cover and for supporting management decisions for forested areas of around ~10,000ha than estimates based upon previously available, but coarser scale, global biomass products.

A prevailing urban phenomenon of the 21st century is that more people than ever before are living in informal settlements. As residents of informal settlements, the majority of shack dwellers can be considered socially, economically and politically marginalised citizens. The combination of poverty, marginalisation, and precarious living conditions has in many cases given rise to a vicious cycle. In this cycle, shack dwellers lack an effective voice and are unable to participate in political decision-making processes that affect their lives, leading to deepening deprivation and marginalisation. To break this cycle, the development of a genuine political voice of shack dwellers is essential. However, the process of developing a political voice in shack dwellers has only received scant academic attention and is poorly understood. One aspect of this process is the role of information communication technologies (ICTs) in enabling political voice. This has received some attention and has become a salient topic in academic study and development policy. Despite growing adoption of certain ICT tools by marginalised individuals, there is little evidence of their meaningful use for political voice. Access to ICTs is not the same as meaningful use of ICTs for political voice. Only a few studies have examined the issue of meaningful use of ICTs for political voice. This dissertation explores the factors influencing the processes by which marginalised individuals are able to develop a political voice, with a particular focus on the role of two increasingly ubiquitous ICTs – mobile phones and internet. A qualitative case study of a shack dweller grassroots organisation in South Africa (Abahlali baseMjondolo (AbM)) is used to explore different types of political voice. The case study looks at the circumstances under which members of AbM are able to develop individual and collective forms of political expression, and the role that the use of ICTs play in this development. Data collected from semi-structured interviews and participant observation for this study suggests that, individual differences in combination with entrenched traditions and social structures based on patronage may undermine the development and or expression of political voice. However, active engagement in a grassroots organisation was found to be useful to overcome these limitations and for some individuals to develop their political voices. AbM members were able to engage in collective processes which led to the development of social bonds, trust, self-confidence, and critical reflection. Both the internet and mobile phones were found to play an important role in the development of political voice of AbM members. However, interaction between the use of ICTs and the development of a political voice is complex. In many instances the technology has enabled mobilization, as well as given individuals a feeling of security. Where this has happened, the appropriation and re-purposing of ICTs to fit the needs of AbM members has come about as a result of attaching meaning to these technologies, which did not exist before AbM. ICTs can facilitate the development of political voice, in particular by facilitating collective processes (e.g. mobilization), channelling support and trust, as well as raising self-confidence. Yet, as the case of AbM demonstrates, ICTs have not operated as a political equalizer within AbM. The use of ICTs for political voice might have even created new barriers for the development of political voice of some members. This dissertation brings together disparate stands of literature dealing with ICTs, political voice, social movements, empowerment, community psychology, and participation to conceptualise the development of a political voice. Moreover, a framework is devised to analyse the nature and the process of this development in marginalised individuals, as well as the role played by ICTs in this process. This dissertation aims to bring an understanding of the complex relationship between ICTs and political voice of marginalised individuals. An understanding of the process can provide important inputs to devise more effective design and implementation of policies and projects aimed at increasing political participation of an ever-growing population of disenfranchised and marginalised people living in informal settlements.

This thesis presents a body of work on the theme of millimetre-wave FMCW radar, for the purposes of security screening and remote sensing. First, the development of an optimised software radar signal processor will be outlined. Through use of threading and GPU acceleration, high data processing rates were achieved using standard PC hardware. The flexibility of this approach, compared to specialised hardware (e.g. DSP, FPGA etc…), allowed the processor to be rapidly adapted and has produced a significant performance increase in a number of advanced real-time radar systems. An efficient tracker was developed and was successfully deployed in live trials for the purpose of real-time wave detection in an autonomous boat control system. Automated radar operation and remote data telemetry functions were implemented in a terrain mapping radar to allow continuous monitoring of the Soufrière Hills volcano on the Caribbean island of Montserrat. This work concluded with the installation of the system 3 km from the volcano. Hardware modifications were made to enable coherent measurement in a number of existing radar systems, allowing phase sensitive measurements, including range-Doppler, to be performed. Sensitivity to displacements of less than 200 nm was demonstrated, which is limited by the phase noise of the system. Efficient compensation techniques are presented which correct for quadrature mixer imbalance, FMCW chirp non-linearity, and scanner drive distortions. In collaboration with the Home Office, two radar systems were evaluated for the stand-off detection of concealed objects. Automatic detection capability, based on polarimetric signatures, was developed using data gathered under controlled conditions. Algorithm performance was assessed through blind testing across a statistically significant number of subjects. A detailed analysis is presented, which evaluates the effect of clothing and object type on detection efficiency.

Robust estimates of forest above-ground biomass (AGB) are needed in order to constrain the uncertainty in regional and global carbon budgets, predictions of global climate change and remote sensing efforts to monitor large scale changes in forest cover and biomass. Estimates of AGB and their associated uncertainty are also essential for international forest-based climate change mitigation strategies such as REDD+. Mangrove forests are widely recognised as globally important carbon stores. Continuing high rates of global mangrove deforestation represent a loss of future carbon sequestration potential and could result in significant release into the atmosphere of the carbon currently being stored within mangroves. The main aims of this thesis are 1) to provide information on the current AGB stocks of mangrove forests in Kenya at spatial scales relevant for climate change research, forest management and REDD+ and 2) to evaluate and constrain the uncertainty associated with these AGB estimates. This thesis adopted both a ground-based statistical approach and a remote sensing based approach to estimating mangrove AGB in Kenya. Allometric equations were developed for Kenyan mangroves using mixed-effects regression analysis and uncertainties were fully propagated (using a Monte Carlo based approach) to estimates of AGB at all spatial scales (tree, plot, region and landscape). In this study, species and site effects accounted for a large proportion (41%) of the total variability in mangrove AGB. The generic biomass equation produced for Kenyan mangroves has the potential for broad application as it can be used to estimate the AGB of new trees where there is no pre-existing knowledge of the specific species-site allometric relationship. The 95% prediction intervals for landscape scale estimates of total AGB suggest that between 5.4 and 7.2 megatonnes (Mt) of AGB is currently held in Kenyan mangrove forests. An in-depth evaluation of the relative contribution of various components of uncertainty (measurement, parameter and residual uncertainty) to the magnitude of the total uncertainty of AGB estimates was carried out. This evaluation was undertaken using both the mixed-effects regression model and a standard ordinary least squares (OLS) regression model. The exclusion of measurement uncertainty during the biomass estimation process had negligible impact on the magnitude of the uncertainty regardless of spatial scale or tree size. Excluding the uncertainty due to species and site effects (from the mixed-effects model) consistently resulted in a large reduction (~ 70%) in the overall uncertainty. Estimates of the uncertainty produced by the OLS model were unrealistically low which is illustrative of the general need to account for group effects in biomass regression models. L-band Synthetic Aperture Radar (SAR) was used to estimate the AGB of Kenyan mangroves. There was an observable relationship (R2 = 0.45) between L-band HH and AGB with HH backscatter found to decrease as a function of increasing AGB. There was no significant relationship found between L-band HV and AGB. The negative relationship between HH and AGB in this study can possibly be attributed to enhanced backscatter at lower AGB due to strong double-bounce and direct surface scattering from short stature/open forests and attenuation of the SAR signal at higher AGB. The SAR-derived estimate of total AGB for Kenyan mangroves was 5.32 Mt ± 18.6%. However, due to the unexpected nature of the HH-AGB relationship found in this study the SAR-derived estimates of mangrove AGB in this study should be considered with caution.

Distributed video coding (DVC) is a new video coding methodology that shifts the highly complex motion search components from the encoder to the decoder, such a video coder would have a great advantage in encoding speed and it is still able to achieve similar rate-distortion performance as the conventional coding solutions. Applications include wireless video sensor networks, mobile video cameras and wireless video surveillance, etc. Although many progresses have been made in DVC over the past ten years, there is still a gap in RD performance between conventional video coding solutions and DVC. The latest development of DVC is still far from standardization and practical use. The key problems remain in the areas such as accurate and efficient side information generation and refinement, quality control between Wyner-Ziv frames and key frames, correlation noise modelling and decoder complexity, etc. Under this context, this thesis proposes solutions to improve the state-of-the-art side information refinement schemes, enable consistent quality control over decoded frames during coding process and implement highly efficient DVC codec. This thesis investigates the impact of reference frames on side information generation and reveals that reference frames have the potential to be better side information than the extensively used interpolated frames. Based on this investigation, we also propose a motion range prediction (MRP) method to exploit reference frames and precisely guide the statistical motion learning process. Extensive simulation results show that choosing reference frames as SI performs competitively, and sometimes even better than interpolated frames. Furthermore, the proposed MRP method is shown to significantly reduce the decoding complexity without degrading any RD performance. To minimize the block artifacts and achieve consistent improvement in both subjective and objective quality of side information, we propose a novel side information synthesis framework working on pixel granularity. We synthesize the SI at pixel level to minimize the block artifacts and adaptively change the correlation noise model according to the new SI. Furthermore, we have fully implemented a state-of-the-art DVC decoder with the proposed framework using serial and parallel processing technologies to identify bottlenecks and areas to further reduce the decoding complexity, which is another major challenge for future practical DVC system deployments. The performance is evaluated based on the latest transform domain DVC codec and compared with different standard codecs. Extensive experimental results show substantial and consistent rate-distortion gains over standard video codecs and significant speedup over serial implementation. In order to bring the state-of-the-art DVC one step closer to practical use, we address the problem of distortion variation introduced by typical rate control algorithms, especially in a variable bit rate environment. Simulation results show that the proposed quality control algorithm is capable to meet user defined target distortion and maintain a rather small variation for sequence with slow motion and performs similar to fixed quantization for fast motion sequence at the cost of some RD performance. Finally, we propose the first implementation of a distributed video encoder on a Texas Instruments TMS320DM6437 digital signal processor. The WZ encoder is efficiently implemented, using rate adaptive low-density-parity-check accumulative (LDPCA) codes, exploiting the hardware features and optimization techniques to improve the overall performance. Implementation results show that the WZ encoder is able to encode at 134M instruction cycles per QCIF frame on a TMS320DM6437 DSP running at 700MHz. This results in encoder speed 29 times faster than non-optimized encoder implementation. We also implemented a highly efficient DVC decoder using both serial and parallel technology based on a PC-HPC (high performance cluster) architecture, where the encoder is running in a general purpose PC and the decoder is running in a multicore HPC. The experimental results show that the parallelized decoder can achieve about 10 times speedup under various bit-rates and GOP sizes compared to the serial implementation and significant RD gains with regards to the state-of-the-art DISCOVER codec.

This thesis analyses satellite-based radar data to improve our understanding of the interactions between the Antarctic Ice Sheet and the ocean in the Amundsen Sea Sector of West Antarctica. Over the last two decades, the European Remote Sensing (ERS) Satellites have provided extensive observations of the marine and cryospheric environments of this region. Here I use this data record to develop new datasets and methods for studying the nature and drivers of ongoing change in this sector. Firstly, I develop a new bathymetric map of the Amundsen Sea, which serves to provide improved boundary conditions for models of (1) ocean heat transfer to the ice sheet margin, and (2) past ice sheet behaviour and extent. This new map augments sparse ship-based depth soundings with dense gravity data acquired from ERS altimetry and achieves an RMS depth accuracy of 120 meters. An evaluation of this technique indicates that the inclusion of gravity data improves the depth accuracy by up to 17 % and reveals glaciologically-important features in regions devoid of ship surveys. Secondly, I use ERS synthetic aperture radar observations of the tidal motion of ice shelves to assess the accuracy of tide models in the Amundsen Sea. Tide models contribute to simulations of ocean circulation and are used to remove unwanted signals from estimates of ice shelf flow velocities. The quality of tide models directly affects the accuracy of such estimates yet, due to a lack of in situ records, tide model accuracy in this region is poorly constrained. Here I use two methods to determine that tide model accuracy in the Amundsen Sea is of the order of 10 cm. Finally, I develop a method to map 2-d ice shelf flow velocity from stacked conventional and multiple aperture radar interferograms. Estimates of ice shelf flow provide detail of catchment stability, and the processes driving glaciological change in the Amundsen Sea. However, velocity estimates can be contaminated by ocean tide and atmospheric pressure signals. I minimise these signals by stacking interferograms, a process which synthesises a longer observation period, and enhances long-period (flow) displacement signals, relative to rapidly-varying (tide and atmospheric pressure) ones. This avoids the reliance upon model predictions of tide and atmospheric pressure, which can be uncertain in remote regions. Ice loss from Amundsen Sea glaciers forms the largest component of Antarctica’s total contribution to sea level, yet because present models cannot adequately characterise the processes driving this system, future glacier evolution is uncertain. Observations and models implicate the ocean as the driver of glaciological change in this region and have focussed attention on improving our understanding of the nature of ice-ocean interactions in the Amundsen Sea. This thesis contributes datasets and methods that will aid historical reconstructions, current monitoring and future modelling of these processes.

The goal of quantifying the woody cover and biomass of tropical savannas, woodlands and forests using satellite data is becoming increasingly important, but limitations in current scientific understanding reduce the utility of the considerable quantity of satellite data currently being collected. The work contained in this thesis reduces this knowledgegap, using new field data and analysis methods to quantify changes using optical, radar and LiDAR data. The first paper shows that high-resolution optical data (Landsat & ASTER) can be used to track changes in woody vegetation in the Mbam Djerem National Park in Cameroon. The method correlates a satellite-derived vegetation index with field-measured canopy cover, and the paper concludes that forest encroached rapidly into savanna in the region from 1986-2006. Using the same study area, but with radar remote sensing data from 1996 and 2007 (ALOS PALSAR & JERS-1), the second paper shows that radar backscatter correlates well with field-measured aboveground biomass (AGB). This dataset confirms the woody encroachment within the park; however, in a larger area around the park, deforestation dominates. The AGB-radar relationships described above are expanded in the next paper to include field plots from Budongo Forest (Uganda), the Niassa Reserve (north Mozambique), and the Nhambita Community Project (central Mozambique). A consistent AGB-radar relationship is found in the combined dataset, with the RMSE for predicted AGB values for a site increasing by <30 %, compared with a site-specific equation, when using an AGB-radar equation derived from the three other sites. The study of the Nhambita site is extended in the following paper to assess the ability of radar to detect change over short time periods in this environment, as will be needed for REDD (Reducing Emissions from Deforestation and Degradation). Using radar mosaics from 2007 and 2009, areas known (from detailed ground data) to have been degraded decreased in AGB in the radar change detection, whereas areas of agroforestry and forest protection showed small increases.

Development of Wireless Sensor Nodes revolutionaries sensing and control application. The size of sensor node makes it ideal to be used in variety of applications, but this brings more challenges and problems especially as the capacity of onboard battery is limited. It is due to the very reason that initial research in the field of WSN especially on MAC targets mainly on the energy conservation and gives secondary importance towards other attributes of MAC protocols. These attributes includes latency, throughput, fairness and collision. This research keeping in view of current application requirements which demands QoS as well as energy conservation in static and mobile sensor networks proposes MAC protocols to meet these challenges. In this research to improve the efficiency of the collision resolution algorithms used in mainly contention based MAC protocols, an Improved Binary Exponential Backoff Algorithm is proposed. The main target of this protocol is to resolve the problem of access collision by employing interim backoff period. The protocol targets to improve upon the performance of conventional Binary Exponential Backoff Algorithm which suffers heavily from collision. The result shows significant reduction in collision which increases the efficiency of the network in terms of QoS and energy conservation. To eliminate the problem of collision which is one of the major sources of network performance degradation a novel Delay Controlled Collision Free contention based MAC is designed. The protocol uses novel delay allocation technique. DCCF also provides mechanism to achieve fairness among the nodes. Detailed analysis and comparative result shows substantial increase in throughput and decrease in latency as compared to Industrial standard of IEEE 802.15.4 CSMA/CA MAC. The research also proposed novel MAC protocols for mobile sensor networks. These protocols uses a methodology which is based upon signal strength of the beacon sent to the node from various neighbouring coordinators that enable the nodes to seamlessly enter from one cluster to another without any link loss and unnecessary delays in the shape of association. The proposed scheme is implemented over IEEE 802.15.4 enabling the standard to perform better with dynamic topology. Result shows that mobility adaptive 802.15.4 protocol shows improvement in QoS and conserve energy far better than the existing conventional CSMA/CA MAC standard. Also the algorithm is implemented over Delay Controlled Collision Free Mac protocol and a detail comparison is carried out with other mobility adaptive MAC protocols. The result shows significant decrease in latency as well as high gain in throughput and considerable reduction in energy as compared to the mobility adaptive MAC protocols. Finally in order to resolve fundamental problem of scalable network which suffers from bottleneck as more nodes in the last hop tries to send data towards the sink, a novel protocol is proposed which allows more than one node at a time to transmit the data towards the sink. The protocol named Simultaneous Multi node CSMA/CA enables the conventional industrial standard of IEEE 802.15.4 CSMA/CA protocol to allow more than one node to transmit the data towards the coordinator or sink node. The protocol out performs the existing standard and provides significant increase in QoS of the network.

This thesis presents work done to extend and improve the operation of homodyne dechirp-on-receive linearly frequency modulated radars. First, an investigation of the effect of common phase errors on the point response function of the radar is described. The dependence on the window function of the degradation due to phase errors is investigated, and a simple, precise, and general approach for calculating the degraded Point Spread Function (PSF) is described and demonstrated. This method is shown to be particularly useful when investigating the effect of chirp nonlinearity on the PSF. Next, a method for focussing range profiles that are degraded by chirp nonlinearity is described. This method is based on two established methods, the Phase Gradient Algorithm (PGA) and a time-domain re-sampling technique. The technique is entirely hardware independent, allowing any homodyne dechirp-on-receive linearly frequency modulated radar to be focussed. Where suitable archive signal data exists, focussed imagery can even be produced from radars that no longer exist. The complete algorithm and details of the implementation are described, and the technique is demonstrated on three representative radar cases: extreme chirp nonlinearity, typical chirp nonlinearity, and a retrospective case. In all of the cases, it was shown that the PSF was dramatically improved. A technique based on down conversion by aliasing for reducing the required sampling rate is described, and a simple technique for calculating suitable sampling rates is presented. This method is demonstrated for a typical application in which sampling rate reduction might be required, namely Moving Target Indication (MTI). The MTI application is described and quantified, including a simple technique for choosing suitable radar operation parameters. The MTI technique with subsampling was demonstrated in software simulations and in a simple radar experiment. A Synthetic Aperture Radar (SAR) test bench for researching component performance and scatterer properties in the context of SAR was developed. An appropriate image formation processing algorithm was found and modified to better suit the task of a short data collection baseline and drifting centre frequencies, both of which are present in the test bench situation. Software was written to collect data, to control the hardware, and to process the signals into SAR images. A data simulator was written to test the image formation algorithm implementation; it also served as a useful tool for investigating the effect of signal errors on the quality of the resultant SAR imagery. A suitable oscillator was chosen for the task, based on phase noise and centre frequency stability considerations, both of which are quantified and discussed. Preliminary SAR imagery was produced, indicating that the system operates correctly and in agreement with comparable systems.

Synthetic Aperture Radar (SAR) is an active microwave remote sensing system able to acquire high resolution images of the scattering behaviour of an observed scene. The contribution of SAR polarimetry (POLSAR) in detection and classification of objects is described and found to add valuable information compared to previous approaches. In this thesis, a new target detection/classification methodology is developed that makes novel use of the polarimetric information of the backscattered field from a target. The detector is based on a geometrical perturbation filter which correlates the target of interest with its perturbed version. Specifically, the operation is accomplished with a polarimetric coherence representing a weighted and normalised inner product between the target and its perturbed version, where the weights are extracted from the observables. The mathematical formulation is general and can be applied to any deterministic (point) target. However, in this thesis the detection is primarily focused on multiple reflections and oriented dipoles due to their extensive availability in common scenarios. An extensive validation against real data is provided exploiting different datasets. They include one airborne system: E-SAR L-band (DLR, German Aerospace Centre); and three satellite systems: ALOS-PALSAR L-band (JAXA, Japanese Aerospace Exploration Agency), RADARSAT-2 C-band (Canadian Space Agency) and TerraSAR-X X-band (DLR). The attained detection masks reveal significant agreement with the expected results based on the theoretical description. Additionally, a comparison with another widely used detector, the Polarimetric Whitening Filter (PWF) is presented. The methodology proposed in this thesis appears to outperform the PWF in two significant ways: 1) the detector is based on the polarimetric information rather than the amplitude of the return, hence the detection is not restricted to bright targets; 2) the algorithm is able to discriminate among the detected targets (i.e. target recognition).

The major problem encountered by an airborne bistatic radar is the suppression of bistatic clutter. Unlike clutter echoes for a sidelooking airborne monostatic radar, bistatic clutter echoes are range dependent. Using training data from nearby range gates will result in widening of the clutter notch of STAP (space-time adaptive processing) processor. This will cause target returns from slow relative velocity aircraft to be suppressed or even go undetected. Some means of Doppler compensation for mitigating the clutter range dependency must be carried out. This thesis investigates the nature of the clutter echoes with different radar configurations. A novel Doppler compensation method using Doppler interpolation in the angle-Doppler domain and power correction for a JDL (joint domain localized) processor is proposed. Performing Doppler compensation in the Doppler domain, allows several different Doppler compensations to be carried out at the same time, using separate Doppler bins compensation. When using a JDL processor, a 2-D Fourier transformation is required to transform space-time domain training data into angular-Doppler domain. Performing Doppler compensation in the spacetime domain requires Fourier transformations of the Doppler compensated training data to be carried out for every training range gate. The whole process is then repeated for every range gate under test. On the other hand, Fourier transformations of the training data are required only once for all range gates under test, when using Doppler interpolation. Before carrying out any Doppler compensation, the peak clutter Doppler frequency difference between the training range gate and the range gate under test, needs to be determined. A novel way of calculating the Doppler frequency difference that is robust to error in pre-known parameters is also proposed. Reducing the computational cost of the STAP processor has always been the desire of any reduced dimension processors such as the JDL processor. Two methods of further reducing the computational cost of the JDL processor are proposed. A tuned DFT algorithm allow the size of the clutter sample covariance matrix of the JDL processor to be reduced by a factor proportional to the number of array elements, without losses in processor performance. Using alternate Doppler bins selection allows computational cost reduction, but with performance loss outside the clutter notch region. Different systems parameters are also used to evaluate the performance of the Doppler interpolation process and the JDL processor. Both clutter range and Doppler ambiguity exist in radar systems operating in medium pulse repetitive frequency mode. When suppressing range ambiguous clutter echoes, performing Doppler compensation for the clutter echoes arriving from the nearest ambiguous range alone, appear to be sufficient. Clutter sample covariance matrix is estimated using training data from the range or time or both dimension. Investigations on the number of range and time training data required for the estimation process in both space-time and angular-Doppler domain are carried out. Due to error in the Doppler compensation process, a method of using the minimum amount of range training data is proposed. The number of training data required for different clutter sample covariance matrix sizes is also evaluated. For Doppler interpolation and power correction JDL processor, the number of Doppler bins used can be increased, to reduce the amount of training data required, while maintaining certain desirable processor performance characteristics.

In the analysis of high-resolution sonar imagery of the seabed, it is important to accurately model the image texture of the sediments in order to detect objects lying on the sea floor or to segment the sediment regions within the image. The received signal is a stochastic process, as it is the summation of a large number of randomly phased echoes that interfere constructively and destructively. This thesis attempts to model the amplitude statistics of sonar imagery with models such as the K-distribution. This has been successfully applied in the past to high-resolution maritime radar clutter and synthetic aperture radar imagery of land scenes. The K-distribution provided a poor fit to images from a number of different sidescan sonar systems. This is probably due to the manufacturer's suboptimal choice of bandwidth of the post-detection filter that resulted in oversmoothing and integration of the sonar returns. Accounting for this leads to the generalised-K distribution that was developed to model weak scattering. This provided an excellent fit to all of the data tested. A small window was passed across the image and the parameters of the generalised-K distribution were estimated from the samples within the window. These parameters were input to a supervised Bayesian classifier that had previously been trained on a number of different sediments. Spatial correlation within the samples was also exploited by Maximum Likelihood spectral classification. The central pixel of the window was then classified as belonging to a particular sediment. A three-class and a four-class test image were both accurately classified. The delineation of a sediment boundary was imperfect due to the finite size of the sampling window, so a method of refining the boundary position using adaptive windows is discussed.