Active microwave remote sensing is an important tool for classification of sea ice in polar regions. The aim of this research is to improve the understanding of microwave scattering that occurs during the advanced melt season, with a focus on multiyear ice (MYI). This was done using a combination of in situ C-Band scatterometer measurements, geophysical characteristics of ice, and Radarsat-2 data. Results indicate that it is difficult to differentiate between first year ice (FYI) and MYI during advanced melt but combinations of incidence angle and polarization exist that assist with this. It is known that the presence of liquid water governs microwave scattering, therefore further research investigating the variation of microwave backscattered signatures over a diurnal time period was conducted. These results indicate an inverse relationship between temperatures and microwave signatures. The overall results from this research show that summer MYI signatures are extremely variable and difficult to classify.

No description available.

This thesis investigates the potential contribution of data from the Advance Very High Resolution Radiometer (AVHRR) on-board the National Oceanic and Atmospheric Administrations (NOAA) polar-orbiting meteorological satellites and data from the High Resolution Radiometer (HRR) on-board the Meteosat geostationary meteorological satellites for predicting the distribution and abundance of the tsetse fly (Diptera: Glossinidae) in Africa. The images were processed to produce a range of monthly land surface temperature, atmospheric moisture and rainfall indices for the period 1988 to 1990. The performance of these indices, derived from several different methods, was tested using meteorological records collected during these years at stations across continental Africa and the most accurate used to form a refined dataset for subsequent analysis. The time-series of these land surface temperature, atmospheric moisture and rainfall indices and a range of Spectral Vegetation Indices (SVI) were subject to temporal Fourier analysis to parameterise the seasonal variation in these variables. These data, in combination with elevation information from a digital elevation model (DEM) were used to predict the land-cover of Nigeria determined independently by an aerial survey in 1990. The Normalised Difference Vegetation Index (NDVI) performed best and so was used in combination with the satellite proxy meteorological and DEM data to predict the distribution and abundance of eight tsetse fly species in Cote d'lvoire and Burkina Faso, West Africa. The results are discussed in relation to the ecology of the different tsetse species. Conclusions are then drawn on the potential of such meteorological satellite data for remote tsetse fly population surveillance and, in the wider context, to the study and control of arthropod vectors of disease.

<p> While there is a tradition that the population of Rapa Nui was large during prehistory, there is remarkably little evidence used to support to these claims. This study represents an empirically-based estimate of pre-contact agricultural productivity to create a sound evaluation of Rapa Nui&rsquo;s prehistoric population. In this study, I map the spatial distributions of lithic mulching using satellite imagery, RPV aerial photography, <i> in situ</i> spectral reflectance analyses, and supervised and sub-pixel image classification methods. Using the results of these analyses, I estimate the total mapped lithic mulch area and combine this estimate with previously documented distributions of <i>manavai</i>. Together these analyses provide an estimate of the extent of these two important cultivation practices and an upper-limit magnitude of prehistoric food production. The spatial data, when evaluated in conjunction with appropriate agricultural cultivation statistic proxies, are then used to conservatively quantify the island&rsquo;s carrying capacity. In my final analysis, I argue that the prehistoric productivity was insufficient to support the large populations that have been suggested. </p>

Coastal and estuarine environments are dynamic yet highly sensitive which makes them particularly susceptible to any changes dictated by external forces. The interaction between environmental forces and those imposed by humans who live and work in the area is a very delicate one and needs to be considered through an holistic management approach to ensure the maintenance of a sustainable equilibrium. The use of airborne remote sensing in the coastal zone has been employed and validated for the specific aims of suspended particulate matter (SPM) concentration and flux quantification in the Humber Estuary and sea-surface temperature and salinity determination in the Tweed Estuary. Routines for the effective radiometric, atmospheric, thermal and geometric correction of Compact Airborne Spectrographic Imager (CASQ and Airborne Thematic Matter (ATM) data were tested and enhanced. Validations at all stages were executed through comparison with sea-based optical data acquired coincident with the images. The data acquired from the sea-surface also yielded important information regarding the nature and content of the waters. Water classification techniques were addressed and a new algorithm for use in case II waters based on the Austin &amp; Petzold (1981) K^{490) routine derived. A new algorithm to determine SPM concentration in the Humber Estuary from CASI images was successfully determined and validated. SPM flux estimates were ascertained through the incorporation of image data, hydrodynamic models and depth profiles determined from hydrographic charts. In the Tweed Estuary, ATM images were used to determine sea-surface temperature and salinity using thermal image calibration and comparison with surface monitoring. The results provide an hitherto unseen insight into the dynamics of the Humber and the Tweed Estuaries. In particular, information regarding SPM concentration and fluxes in the Humber supports the so far unproved hypothesis that most of the SPM moves into and out of the mouth in elongated streaks. The use of the width of a streak (or patch) to predict the SPM concentration and / or flux and so eliminate the necessity for surface-based monitoring was addressed. Algorithms to determine SPM concentration and flux were devised using patch size and within-patch water depth alone. A model to apply these algorithms to all data was unsuccessful due to the sparse temporal coverage of the image data. The analyses exemplified in this study give an invaluable insight into the forces at play in coastal and estuarine environments and would provide key information sources for hydrodynamic modellers and coastal zone managers.

No description available.

This thesis investigates the use of combined field and satellite data for crop identification and area estimation in County Durham, Northeast England. The satellite data were obtained by the Thematic Mapper (TM) sensor onboard Landsat-5 on 31 May 1985. The TM data were geometrically corrected to the British National Grid and the county boundaries were digitized in order to apply the methodology used in this study on a county basis. The field data were obtained by applying a stratified random sampling strategy. The area was subdivided into five main strata and forty four 1km(_^2) sample units were randomly chosen and fully surveyed by the author using a pre-prepared questionnaire. The field area measurements were taken and the final hectarage estimates were obtained for each crop. The research demonstrated the ability of Landsat-TM data to discriminate between agricultural crops in the study area. Results obtained emphasised that satellite data can be used for identification of agricultural crops over large geographic areas with small field sizes and different environmental and physical features. A land-cover classification system appropriate to the study area was designed. Using the Landsat-TM data, the study produced a classification map of thirteen land-cover types with more than 80% accuracy. The classification accuracy was assessed quantitatively by using the known land-use information obtained from the sample units visited during the field survey. The study analysed the factors which influenced the degree of separability between different agricultural crops since some crops were more clearly identified than others. Using a double sampling method based on the combination of both Landsat- TM and field data in regression analysis, a hectarage estimate was produced for each crop type in County Durham. The results obtained showed that the regression estimator was always more efficient than the field estimator. Crop area estimated by regression reduced the imprecision in all strata and was more efficient in some strata than others. This indicated that a gain in precision was achieved by using Landsat- TM in conjunction with the field data. The results illustrated that stratification based on an environmental criterion was an efficient approach as far as the the application of agricultural remote sensing in County Durham is concerned. The stratified approach allowed each stratum to be analysed separately, thereby lessening the reliance on cloud free imagery for the whole county on any given date. Furthermore, the results obtained by this study suggest that it is possibile to link remote sensing data with existing county based information systems on agricultural and land-use.

Information extracted from aerial photographs has found applications in a wide range of areas including urban planning, crop and forest management, disaster relief, and climate modeling. At present, much of the extraction is still performed by human experts, making the process slow, costly, and error prone. The goal of this thesis is to develop methods for automatically extracting the locations of objects such as roads, buildings, and trees directly from aerial images. We investigate the use of machine learning methods trained on aligned aerial images and possibly outdated maps for labeling the pixels of an aerial image with semantic labels. We show how deep neural networks implemented on modern GPUs can be used to efficiently learn highly discriminative image features. We then introduce new loss functions for training neural networks that are partially robust to incomplete and poorly registered target maps. Finally, we propose two ways of improving the predictions of our system by introducing structure into the outputs of the neural networks. We evaluate our system on the largest and most-challenging road and building detection datasets considered in the literature and show that it works reliably under a wide variety of conditions. Furthermore, we are releasing the first large-scale road and building detection datasets to the public in order to facilitate future comparisons with other methods.

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.