Remote sensing provides methods to infer vegetation information over large areas at a variety of spatial and temporal resolutions that is of great use for terrestrial carbon cycle modeling. Understory vegetation and foliage clumping in forests present a challenge for accurate estimates of vegetation structural information. Multi-angle remote sensing was used to derive and refine new information about the vegetation structure for the purpose of improving global leaf area index mapping. A field experiment with multi-angle, high resolution airborne observations over modified and natural backgrounds (understory, moss, litter, soil) was conducted in 2007 near Sudbury, Ontario to test a methodology for the background reflectivity retrieval. The experiment showed that it is feasible to retrieve the background information, especially over the crucial low to intermediate canopy density range where the effect of the understory vegetation is the largest. The tested methodology was then applied to background reflectivity mapping over conterminous United States, Canada, Mexico, and Caribbean land mass using space-borne Multi-angle Imaging SpectroRadiometer (MISR) data. Important seasonal development of the forest background vegetation was observed across a wide longitudinal and latitudinal span of the study area. The previous first ever global mapping of the vegetation clumping index with a limited eight-month multi-angular POLDER 1 dataset was expanded by integrating new, complete year-round observations from POLDER 3. A simple topographic compensation function was devised to correct negative bias in the data set cause by topographic effects. The clumping index reductions can reach up to 30% from the topographically non-compensated values, depending on terrain complexity and land cover type. The new global clumping index map is compared with an assembled set of field measurements, covering four continents and diverse biomes. Finally, inclusion of the new vegetation structural information, including background reflectivity and clumping index, gained from the multi-angle remote sensing was then shown to improve the performance of LAI retrieval algorithms over forests.

This thesis estimated vegetation biochemical properties at multiple spatial scales and investigate vegetation temporal dynamics under climate influences in a heterogeneous tallgrass ecosystem in Southern Ontario using remote sensing data. Ground hyperspectral and space multispectral remote sensing data derived Normalized Difference Vegetation Index (NDVI) and Simple Ratio (SR) were used to estimate biochemical properties at the species, canopy and landscape level. Both vegetation indices explained 32% to 56% of the variations in biochemical properties at the species level, 16% to 53% at the canopy level, and over 60% at the landscape level. MODIS NDVI and climate data were also collected to investigate the vegetation-climate relationships during the growing season and the lag effects of climate factors on vegetation at the peak growing season. The findings indicate that temperature is the key climate factor that drives the annual cycle, and there is a time lag effect of climate factors on vegetation.

Remote sensing provides methods to infer vegetation information over large areas at a variety of spatial and temporal resolutions that is of great use for terrestrial carbon cycle modeling. Understory vegetation and foliage clumping in forests present a challenge for accurate estimates of vegetation structural information. Multi-angle remote sensing was used to derive and refine new information about the vegetation structure for the purpose of improving global leaf area index mapping. A field experiment with multi-angle, high resolution airborne observations over modified and natural backgrounds (understory, moss, litter, soil) was conducted in 2007 near Sudbury, Ontario to test a methodology for the background reflectivity retrieval. The experiment showed that it is feasible to retrieve the background information, especially over the crucial low to intermediate canopy density range where the effect of the understory vegetation is the largest. The tested methodology was then applied to background reflectivity mapping over conterminous United States, Canada, Mexico, and Caribbean land mass using space-borne Multi-angle Imaging SpectroRadiometer (MISR) data. Important seasonal development of the forest background vegetation was observed across a wide longitudinal and latitudinal span of the study area. The previous first ever global mapping of the vegetation clumping index with a limited eight-month multi-angular POLDER 1 dataset was expanded by integrating new, complete year-round observations from POLDER 3. A simple topographic compensation function was devised to correct negative bias in the data set cause by topographic effects. The clumping index reductions can reach up to 30% from the topographically non-compensated values, depending on terrain complexity and land cover type. The new global clumping index map is compared with an assembled set of field measurements, covering four continents and diverse biomes. Finally, inclusion of the new vegetation structural information, including background reflectivity and clumping index, gained from the multi-angle remote sensing was then shown to improve the performance of LAI retrieval algorithms over forests.

This thesis estimated vegetation biochemical properties at multiple spatial scales and investigate vegetation temporal dynamics under climate influences in a heterogeneous tallgrass ecosystem in Southern Ontario using remote sensing data. Ground hyperspectral and space multispectral remote sensing data derived Normalized Difference Vegetation Index (NDVI) and Simple Ratio (SR) were used to estimate biochemical properties at the species, canopy and landscape level. Both vegetation indices explained 32% to 56% of the variations in biochemical properties at the species level, 16% to 53% at the canopy level, and over 60% at the landscape level. MODIS NDVI and climate data were also collected to investigate the vegetation-climate relationships during the growing season and the lag effects of climate factors on vegetation at the peak growing season. The findings indicate that temperature is the key climate factor that drives the annual cycle, and there is a time lag effect of climate factors on vegetation.

Natural fire return intervals are relatively long in eastern Canadian boreal forests and often allow for the development of stands with multiple, successive cohorts of trees. Multi-cohort forest management (MCM) provides a strategy to maintain such multi-cohort stands that focuses on three broad phases of increasingly complex, post-fire stand development, termed “cohorts”, and recommends different silvicultural approaches be applied to emulate different cohort types. Previous research on structural cohort typing has relied upon primarily subjective classification methods; in this thesis, I develop more comprehensive and objective methods for three common boreal mixedwood and black spruce forest types in northeastern Ontario. Additionally, I examine relationships between cohort types and stand age, productivity, and disturbance history and the utility of airborne LiDAR to retrieve ground based classifications and to extend structural cohort typing from plot to stand-levels. In both mixedwood and black spruce forest types, stand age and age related deadwood features varied systematically with cohort classes in support of an age-based interpretation of increasing cohort complexity. However, correlations of stand age with cohort classes were surprisingly weak. Differences in site productivity had a significant effect on the accrual of increasingly complex multi-cohort stand structure in both forest types, especially in black spruce stands. The effects of past harvesting in predictive models of class membership were only significant when considered in isolation of age. As an age emulation strategy, the three cohort model appeared to be poorly suited to black spruce forests where the accrual of structural complexity appeared to be more a function of site productivity than age. Airborne LiDAR data appear to be particularly useful in recovering plot-based cohort types and extending them to the stand-level. The main gradients of structural variability detected using LiDAR were similar between boreal mixedwood and black spruce forest types; the best LiDAR-based models of cohort type relied upon combinations of tree size, size heterogeneity, and tree density related variables. The methods described here to measure, classify, and predict cohort-related structural complexity assist in translating the conceptual three cohort model to a more precise, measurement based management system. In addition, the approaches presented here to measure and classify stand structural complexity promise to significantly enhance the detail of structural information in operational forest inventories in support of a wide array of forest management and conservation applications.

This thesis addresses the problem of refocusing smeared SAR images caused by ionosphere phase scintillation noise. A SAR data is smeared when the received signal experiences phase irregularities due to platform orbit deviation, target movement, or, in this thesis, ionospheric scintillation noise due to trans-ionosphere propagation is analyzed. A SAR simulator is constructed to generate stripmap and spotlight data that satisfy theoretically predicted performances under ideal conditions. The simulator is incorporated with ionospheric phase scintillation models to analyze the broadening effect on system’s PSF. Degraded simulation spotlight data are used to test the proposed compensation algorithm. This thesis proposes a two-dimensional polynomial phase fitting algorithm to compensate scintillation noise. This work discusses some requirements of the scene in order to carry out the compensation and what is gained and lost in the process. A successful application of the proposed algorithm to TerraSAR-X data is also presented.

This thesis investigated multispectral detection of the invasive floating macrophyte, European Frog-bit, using Quickbird imagery and fuzzy image classification. To determine if the spectral signature of European Frog-bit were separable from other wetland vegetation, a species level land cover classification was conducted on a 6km section of the South Nation River in Ontario, Canada. Supervised and unsupervised imagery classification approaches were evaluated using the fuzzy classifiers, Fuzzy Segmentation for Object Based Image Classification (FS) and Fuzzy C-Means (FCM). Both approaches were sufficiently robust to detect European Frog-bit. User’s and producer’s accuracies for the European Frog-bit class were 81.0% and 77.9% for the FS classifier and 63.5% and 73.0% for the FCM classifier. These accuracies indicated that the spectral signature of EFB was sufficiently different to permit detection and separation from other wetland vegetation and fuzzy image classifiers were capable of detecting EFB in Quickbird imagery.

The orbit of Phobos exhibits an along-track acceleration, which suggests energy dissipation in the Mars-Phobos system. We hypothesize that the inferred dissipation occurs within Mars. We explore the response of a layered, incompressible Maxwell viscoelastic Mars to tidal forcing by Phobos using normal mode relaxation theory. Our results elucidate the general behavior of a tidally forced viscoelastic body, and have implications for the viscoelastic structure of Mars. We find the real and imaginary part of the degree-two tidal Love number for Mars to be 0.168 and -9.32x10^−4 respectively. Models which satisfy these and other constraints have either: a fluid core with radius 2040 km and density 5410 kg/m^3; or an elastic inner core with radius 1200 km and density 6700 kg/m^3, along with a fluid outer core with thickness 850 km and density 4850 kg/m^3. These findings support previous hypotheses that Mars has at least a fluid outer core.

This thesis addresses the problem of refocusing smeared SAR images caused by ionosphere phase scintillation noise. A SAR data is smeared when the received signal experiences phase irregularities due to platform orbit deviation, target movement, or, in this thesis, ionospheric scintillation noise due to trans-ionosphere propagation is analyzed. A SAR simulator is constructed to generate stripmap and spotlight data that satisfy theoretically predicted performances under ideal conditions. The simulator is incorporated with ionospheric phase scintillation models to analyze the broadening effect on system’s PSF. Degraded simulation spotlight data are used to test the proposed compensation algorithm. This thesis proposes a two-dimensional polynomial phase fitting algorithm to compensate scintillation noise. This work discusses some requirements of the scene in order to carry out the compensation and what is gained and lost in the process. A successful application of the proposed algorithm to TerraSAR-X data is also presented.

The orbit of Phobos exhibits an along-track acceleration, which suggests energy dissipation in the Mars-Phobos system. We hypothesize that the inferred dissipation occurs within Mars. We explore the response of a layered, incompressible Maxwell viscoelastic Mars to tidal forcing by Phobos using normal mode relaxation theory. Our results elucidate the general behavior of a tidally forced viscoelastic body, and have implications for the viscoelastic structure of Mars. We find the real and imaginary part of the degree-two tidal Love number for Mars to be 0.168 and -9.32x10^−4 respectively. Models which satisfy these and other constraints have either: a fluid core with radius 2040 km and density 5410 kg/m^3; or an elastic inner core with radius 1200 km and density 6700 kg/m^3, along with a fluid outer core with thickness 850 km and density 4850 kg/m^3. These findings support previous hypotheses that Mars has at least a fluid outer core.