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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.

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.

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.