The use of radar often conjures up images of small blobs on a screen. But current synthetic aperture radar (SAR) systems are able to generate near-optical quality images with amazing benefits compared to optical sensors. These SAR sensors work in all weather conditions, day or night, and provide many advanced capabilities to detect and identify targets of interest. These amazing abilities have made SAR sensors a work-horse in remote sensing, and military applications. SAR sensors are ranging instruments that operate in a 3D environment, but unfortunately the results and interpretation of SAR images have traditionally been done in 2D. Three-dimensional SAR images could provide improved target detection and identification along with improved scene interpretability. As technology has increased, particularly regarding our ability to solve difficult optimization problems, the 3D SAR reconstruction problem has gathered more interest. This dissertation provides the SAR and mathematical background required to pose a SAR 3D reconstruction problem. The problem is posed in a way that allows prior knowledge about the target of interest to be integrated into the optimization problem when known. The developed model is demonstrated on simulated data initially in order to illustrate critical concepts in the development. Then once comprehension is achieved the processing is applied to actual SAR data. The 3D results are contrasted against the current gold- standard. The results are shown as 3D images demonstrating the improvement regarding scene interpretability that this approach provides.

Time series analysis of Synthetic Aperture Radar Interferometry (InSAR) data has become an important scientific tool for monitoring and measuring the displacement of Earth’s surface due to a wide range of phenomena, including earthquakes, volcanoes,landslides, changes in ground water levels, and wetlands. Time series analysis is a product of interferometric phase measurements, which become ambiguous when the observed motion is larger than half of the radar wavelength. Thus, phase observations must first be unwrapped in order to obtain physically meaningful results. Persistent Scatterer Interferometry (PSI), Stanford Method for Persistent Scatterers (StaMPS), Short Baselines Interferometry (SBAS) and Small Temporal Baseline Subset (STBAS)algorithms solve for this ambiguity using a series of spatio-temporal unwrapping algorithms and filters. In this dissertation, I improve upon current phase unwrapping algorithms, and apply the PSI method to study subsidence in Mexico City. PSI was used to obtain unwrapped deformation rates in Mexico City (Chapter 3),where ground water withdrawal in excess of natural recharge causes subsurface, clay-rich sediments to compact. This study is based on 23 satellite SAR scenes acquired between January 2004 and July 2006. Time series analysis of the data reveals a maximum line-of-sight subsidence rate of 300mm/yr at a high enough resolution that individual subsidence rates for large buildings can be determined. Differential motion and related structural damage along an elevated metro rail was evident from the results. Comparison of PSI subsidence rates with data from permanent GPS stations indicate root mean square(RMS) agreement of 6.9 mm/yr, about the level expected based on joint data uncertainty.The Mexico City results suggest negligible recharge, implying continuing degradation and loss of the aquifer in the third largest metropolitan area in the world. Chapters 4 and 5 illustrate the link between time series analysis and three-dimensional (3-D) phase unwrapping. Chapter 4 focuses on the unwrapping path.Unwrapping algorithms can be divided into two groups, path-dependent and path-independent algorithms. Path-dependent algorithms use local unwrapping functions applied pixel-by-pixel to the dataset. In contrast, path-independent algorithms use global optimization methods such as least squares, and return a unique solution. However, when aliasing and noise are present, path-independent algorithms can underestimate the signal in some areas due to global fitting criteria. Path-dependent algorithms do not underestimate the signal, but, as the name implies, the unwrapping path can affect the result. Comparison between existing path algorithms and a newly developed algorithm based on Fisher information theory was conducted. Results indicate that Fisher information theory does indeed produce lower misfit results for most tested cases. Chapter 5 presents a new time series analysis method based on 3-D unwrapping of SAR data using extended Kalman filters. Existing methods for time series generation using InSAR data employ special filters to combine two-dimensional (2-D) spatial unwrapping with one-dimensional (1-D) temporal unwrapping results. The new method,however, combines observations in azimuth, range and time for repeat pass interferometry. Due to the pixel-by-pixel characteristic of the filter, the unwrapping path is selected based on a quality map. This unwrapping algorithm is the first application of extended Kalman filters to the 3-D unwrapping problem. Time series analyses of InSAR data are used in a variety of applications with different characteristics. Consequently, it is difficult to develop a single algorithm that can provide optimal results in all cases, given that different algorithms possess a unique set of strengths and weaknesses. Nonetheless, filter-based unwrapping algorithms such as the one presented in this dissertation have the capability of joining multiple observations into a uniform solution, which is becoming an important feature with continuously growing datasets.

Thesis (M.Eng.)--Memorial University of Newfoundland, 2002. / Bibliography: leaves 95-100.

Dramatic land-cover changes have occurred in a broad range of spatial and temporal scales over the last decades. Satellite remote sensing, which can observe the earth's surface in a consistent manner, has been playing an important role in monitoring and evaluating land-cover changes. Meanwhile, optical remote sensing, a common approach to acquiring land-cover information, is limited by weather conditions and thus is greatly constrained in areas with frequent cloud cover and rainfall. Recent advances in polarimetric SAR (PolSAR) provide a promising means to extract timely information of land-cover changes regardless of weather conditions. SAR satellite can pass through an area from different orbits, namely ascending orbit and descending orbit. The PolSAR images from the same orbit will have similar backscattering even with different incident angles. But if images are acquired from different orbits, the backscattering will vary greatly, which causes many difficulties to land cover change detection. The proposed algorithms in this study can perform land cover change detection in three situations: 1) repeat-pass images (image from the same orbit and with same incident angle, 2) images from the same orbit but with different incident angle, and 3) images from different orbits. Using images from different orbits will largely reduce the monitoring interval which is important in the surveillance of natural disasters. The present study proposes 1) a sub-pixel automatic registration technique, 2) an automatic change detection technique and 3) an iterative framework to process a time series of PolSAR images that can be applied to the PolSAR images from different orbits. Firstly, automatic registration is crucial to the change detection task because a small positional error will largely degrade the accuracy of change detection. The automatic registration technique is based on the multi-scale Harris corner detector. To improve the efficiency and robustness, the orientation angle differencing method is proposed to reject outliers. This differencing method has been proved effective even in the experiment of using PolSAR images from different orbits when less than 5% of the feature point matches are correct. Secondly, the change detection technique can automatically detect land-cover conversions and classify the newly input image. Hierarchical segmentation has been applied in the change detection which generates objects within the constraint of the previous classification map. Multivariate kernel density estimation is applied to classify newly input PolSAR image. The experiments show that the proposed change detection technique can mitigate the effect of polarimetric orientation shift when the PolSAR images are from different orbits, and it can achieve high accuracy even when complex local deformation is appeared. Lastly, the iterative framework, which integrates the automatic registration and automatic change detection techniques, is proposed to process a time series of PolSAR images. In the iterative process, no obvious decrease of classification accuracy is observed. Therefore, the proposed framework provides a potential treatment to derive land-cover dynamics from a time series of PolSAR images from different orbits. / published_or_final_version / Urban Planning and Design / Doctoral / Doctor of Philosophy

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The Brazilian Pantanal, a continuous tropical wetland located in the center of South America, has been recognized as one of the largest and most important wetland ecosystems globally. The Pantanal exhibits a high biodiversity of flora and fauna species, and many threatened habitats. The spatial distribution of these habitats influence the distribution, abundance and interactions of animal species, and the change or destruction of habitat may cause alteration of key biological processes. The Pantanal may be divided into several distinct subregions based on geology and hydrology: flooding in these subregions is distinctly seasonal, but the timing, amplitude and duration of inundation vary considerably as a result of both the delayed release of floodwaters and regional rainfall patterns. Given the ecological importance of the Pantanal wetland ecosystem, the primary goal of this research was to utilize a dual season set of L-band (ALOS/PALSAR) and C-band (RADARSAT-2 and ENVISAT/ASAR) imagery, a comprehensive set of ground reference data, and a hierarchical object-oriented approach. This primary goal was achieved through two main research tasks. The first task was to define the diverse habitats of the Lower Nhecolândia subregion of the Pantanal at both a fine spatial resolution (12.5 m), and a relatively medium spatial resolution (50 m), thus evaluating the accuracy of the differing spatial resolutions for land cover classification of the highly spatially heterogeneous subregion. The second task was to define on a regional scale, using the 50 m spatial resolution imagery, the wetland habitats of each of the hydrological subregions of the Pantanal, thereby producing a final product covering the entire Pantanal ecosystem. The final classification maps of the Lower Nhecolândia subregion resulted in overall accuracies of 83% and 72% for the 12.5 m and 50 m spatial resolutions, respectively, and defined seven land cover classes. In general, the highest degree of confusion for both fine and medium resolution classifications related to issues of 1) scale of habitats, for instance, capões, cordilheiras, and lakes, in relation to spatial resolution of the imagery, and 2) issues relating to variable flooding patterns in the subregion, and 3) arbitrary class membership rules. The 50 m spatial resolution classification of the entire Pantanal wetland resulted in an overall accuracy of 80%, and defined ten land cover classes. Given the analysis of the comparison of fine and relatively medium spatial resolution classifications of the Lower Nhecolândia subregion, I conclude that significant improvements in accuracy can be achieved with the finer spatial resolution dataset, particularly in subregions with high spatial heterogeneity in land cover. The produced habitat spatial distribution maps will provide vital information for determining refuge zones for terrestrial species, connectivity of aquatic habitats during the dry season, and crucial baseline data to aid in monitoring changes in the region, as well as to help define conservation strategies for habitat in this critically important wetland. / Graduate / 0366 / tevans@uvic.ca

Levels of carbon dioxide in the atmosphere have been steadily increasing since the beginning of the Industrial Revolution in the 1800s. The earth's climate is sensitive to alterations in these levels of carbon dioxide and other greenhouse gases (GHG), with significant changes in climate predicted long term. The adoption of the Kyoto Protocol in 1997 heralded a new age in terms of greenhouse gas accounting and emissions responsibility, for all nations. In Australia, carbon emissions from the Land Use and Land Use Change and Forestry sector are responsible for a large proportion of the national total emissions. Radar remote sensing has demonstrated considerable potential in the estimation and mapping of vegetation biomass and subsequently carbon. The aim of this research is to investigate the potential of airborne polarimetric radar for quantifying and mapping the biomass and structural diversity of woodlands in semi-arid Australia. Initial investigation focussed on the physical structure of the woodland, which revealed that despite a diversity of woodland associations, the species diversity was relatively low. Both excurrent and decurrent growth forms were present, which subsequently resulted in varying allocation of biomass to the components (i.e., branches, trunks). In view of this, both empirical and modelling methodologies were explored. Empirical relationships were established between SAR backscatter and the total above ground biomass. Considerable scatter was present in these relationships, which was attributed to the large range of species and their associated structures. Comparison of actual and model simulations for C-, L- and P-band wavelengths, reveal that no significant difference existed for these wavelengths, except at CHH, and the cross-polarised data at L- and P-band. The study confirmed that microwaves at C-band interacted largely with the leaves and small branches, with scattering at VV polarization dominating. Compared to the lower frequencies, the return from the ground surface (as expected) was significant. The differences in scattering mechanisms (i.e., branch-ground versus trunk-ground) between excurrent and decurrent structures were due largely to the larger angular branches associated with Eucalyptus and Angophora species, which were absent from Callitris glaucophylla.

Thesis (Ph.D.)--University of Adelaide, 2004. / Photocopy. Includes bibliographical references (leaves 283-293).