Unprecedented rates of modern species extinction present a serious challenge in the field of conservation biology. While protected areas (PAs) are regarded as key tools to reduce rates of biodiversity loss, it is unclear to what degree PAs can maintain their ecological integrity while experiencing external pressures from outside of their boundaries. Satellite remote sensing essential biodiversity variables (SRS-EBVs) are indicators of biodiversity that can be produced with large spatial coverages and can be used to measure PAs’ capacity to preserve important ecological elements for biodiversity. In this study, I used SRS-EBVs representative of ecosystem structure and function, including productivity, disturbance regimes, ecosystem extent, and ecosystem composition. I tested if PAs preserved these determinants of species survival through time, whether any changes in these variables in PAs were independent of changes in their surrounding areas (buffer zones), and if the management type of PAs influenced either of these patterns. I found that PAs maintained elements of ecosystem structure, including habitat heterogeneity and extent, inside of their boundaries, regardless of changes that occurred in their surroundings. In contrast, PAs were less effective at sustaining elements of ecosystem function and mitigating other forms of human disturbance. Productivity within PAs was the same as that of their surroundings, underscoring the inability of PAs to track shifts in climate regimes that put some species at greater risk of extinction. Fire disturbance trends were maintained across PA boundaries; however, the causes of these fires are unknown, highlighting the importance of supplemental fire census data to tease apart the trends of natural fire regimes compared to harmful burns. Finally, other human pressures thought to be the indirect effects of linear transportation features (ex. edge effects from roads) were observed to have spilled over from buffer zones into PAs. Planning for future development of the global PA network can benefit greatly from the application of SRS-EBVs. Pairing these data products with foundational ecological conservation principles can build a stronger, more efficient PA network for the preservation of Earth’s species.

Global Climate Models (GCMs) are indispensable tools for modeling climate change projections. Due to approximations, errors are introduced in the GCM computations of atmospheric radiation. The existing methodologies for the comparison of the GCM-computed shortwave fluxes (SWF) exiting the top of the atmosphere (TOA) against satellite observations do not separate the model errors in terms of the atmospheric and surface components. A new methodology has been developed for estimating the GCM systematic errors in the SWF at the TOA under clear-sky (CS) conditions. The new methodology is based on physical principles and utilizes in-situ measurements of SWF at the surface. This error adjustment methodology (EAM) has been validated by comparing GCM results against satellite measurements from the Clouds and the Earth’s Radiant Energy System (CERES) mission. The EAM was implemented in an error estimation model for solar radiation (EEMSR), and then applied to examine the hypothesis that the Community Climate System Model (CCSM), one of the most widely used GCMs, was deficient in representing the annual phenology of vegetation in many areas, and that satellite measurements of vegetation characteristics offered the means to rectify the problem. The CCSM computed monthly climatologies of TOA-CS-SWF were compared to the CERES climatology. The incorporation of satellite-derived land surface parameters improved the TOA SWF in many regions. However, for more meaningful interpretations of the comparisons, it was necessary to account for the uncertainties arising from the radiation calculations of CCSM. In-situ measurements from two sites were used by EMBC to relate the observations and model estimates via a predictive equation to derive the errors in TOA CS-SWF for monthly climatologies. The model climatologies were adjusted using the computed error and then compared to CERES climatology at the two sites. The new results showed that at one of the sites, CCSM consistently overestimated the atmospheric transmissivity whereas at the other site the CCSM overestimated during the spring, summer and early fall and underestimated during late fall and winter. The bias adjustment using the EMBC helped determine more clearly that at the two sites the utilization of satellite-derived land surface parameters improved the TOA CS-SWF.

Knowledge of land cover is important in a variety of natural resources applications. This knowledge becomes more powerful within the spatial analysis capabilities of a geographic information system (GIS). This thesis presents a digital land cover map of Virginia, produced through interpretation of 14 Landsat Thematic Mapper (TM) scenes, circa 1991-1993. The land cover map, which has a 30m pixel size, was produced entirely with personal computers. Hypercluster aggregation, an unsupervised classification method, was used when hazy and mountainous conditions were not present. A haze correction procedure by Lavreau (1991) was used, followed by a supervised classification on coastal areas. An enhanced supervised classification, focusing on topographic shading, was performed in the mountains. Color infrared photographs, digital maplets, expert knowledge, and other maps were used as training data. Aerial videography transects were flown to acquire reference data. Due to the spatial inaccuracies inherent in the videography reference data, only homogeneous land cover areas were used in the accuracy assessment. The results of the overall accuracy for each scene determined the ordering of scenes within the statewide land cover mosaic (i.e., scenes with higher accuracy had a higher proportion of area represented). An accuracy assessment was then performed on the statewide land cover mosaic. An overall accuracy of 81.8% and a Kappa statistic of 0.81 resulted. A discussion of potential reasons for land cover class confusion and suggestions for classification improvements are presented. Overall deciduous forest was the most common land cover in Virginia. Herbaceous areas accounted for 20% of the land area, which was the second largest. Mixed forest and coastal wetlands were the cover types with the least area, each under 3%. / Master of Science

Floods are a significant threat to communities around the world and require substantial resources and infrastructure to predict. Limited local resources in developing nations make it difficult to build and maintain dense sensor networks like those present in the United States, creating a large disparity in flood prediction across borders. To address this disparity, I operated the Iowa Flood Center Top Layer model to predict floods in Puerto Rico without relying on in-situ data measurements. Instead, all model forcing was provided by satellite remote sensing datasets that offer near-global coverage. I used three datasets gathered via satellite remote sensing to build and operate watershed streamflow models: elevation data obtained by the Space Shuttle Endeavour through the Shuttle Radar Topography Mission (SRTM), rainfall estimates gathered by a constellation of satellites through the Global Precipitation Measurement Mission (GPM), and evapotranspiration rate estimates collected by Moderate Resolution Imaging Spectroradiometer (MODIS) sensors aboard the Aqua and Terra satellites. While these satellite remote sensing datasets make observations of nearly the entire world, their spatiotemporal resolution is coarse compared to conventional on-the-ground measurements. Hydrologic models were assembled for 75 basins upstream of streamflow gages monitored by the United States Geologic Survey (USGS). Model simulations were compared to real-time measurements at these gages. Continuous simulations spanning 58 months achieve poor Nash Sutcliffe Efficiency and Klinge Gupta Efficiency of -112.0 and -0.5, respectively. The sources of error that influence model performance were investigated, underlining some limitations of relying solely on satellite data for operational flood prediction efforts.

Knowledge of NOx (NO2 + NO) emissions is useful to understand processes affecting air quality and climate change. Emission inventories of surface NOx have high uncertainties. Satellite remote sensing has enabled measurements of trace gases in the atmosphere over a large regional and temporal scale. Inverse modeling of NO2 observations from satellites can be used to improve existing emissions inventories. This study seeks to understand the difference in two methods of inverse modeling: the mass balance approach and the adjoint approach using the GEOS-Chem chemical transport model and its adjoint. Using both synthetic satellite observations and those derived from the SCIAMACHY satellite instrument, this paper found that the performance of these two inversions was affected by pixel smearing and observational error. Smearing reduced the accuracy of the mass balance approach, while high observational error reduced the accuracy of the adjoint approach. However, both approaches improved the a priori emissions estimate.

The Antarctic grounding zone has a disproportionately large effect on glacier dynamics and ice sheet stability relative to its size but remains poorly characterised across much of the continent. Accurate ice velocity and thickness information is needed in the grounding zone to determine glacier outflow and establish to what extent changing ocean and atmospheric conditions are affecting the mass balance of individual glacier catchments. This thesis describes new satellite remote sensing techniques for measuring ice velocity and ice thickness, validated using ground measurements collected on the Beardmore, Skelton and Darwin Glaciers and applied to other Transantarctic Mountain outlet glaciers to determine ice discharge. Outlet glaciers in the Transantarctic Mountains provide an important link between the East and West Antarctic Ice Sheets but remain inadequately studied. While long-term velocities in this region are shown here to be stable, instantaneous velocities are sensitive to stresses induced by ocean tides, with fluctuations of up to 50% of the mean observed in GPS measurements. The potential error induced in averaged satellite velocity measurements due to these effects is shown to be resolvable above background noise in the grounding zone but to decrease rapidly upstream. Using a new inverse finite-element modelling approach based on regularization of the elastic-plate bending equations, tidal flexure information from differential InSAR is used to calculate ice stiffness and infer thickness in the grounding zone. This technique is shown to be successful at reproducing the thickness distribution for the Beardmore Glacier, eliminating current issues in the calculation of thickness from freeboard close to the grounding line where ice is not in hydrostatic equilibrium. Modelled thickness agrees to within 10% of ground penetrating radar measurements. Calibrated freeboard measurements and tide-free velocities in the grounding zones of glaciers in the western Ross Sea are used to calculate grounding zone basal melt rates, with values between 1.4 and 11.8 m/a⁻¹ in this region. While strongly dependent on grounding line ice thickness and velocity, melt rates show no latitudinal trend between glaciers, although detailed error analysis highlights the need for much improved estimates of firn density distribution in regions of variable accumulation such as the Transantarctic Mountains.

Through the Global Gas Flaring Reduction (GGFR) initiative a substantial amount of effort and international attention has been focused on the reduction of gas flaring since 2002 (Elvidge et al., 2009). Nigeria is rated as the second country in the world for gas flaring, after Russia. In an attempt to reduce and eliminate gas flaring the federal government of Nigeria has implemented a number of gas flaring reduction projects, but poor governmental regulatory policies have been mostly unsuccessful in phasing it out. This study examines the effects of pollution from gas flaring using multiple satellite based sensors (Landsat 5 TM and Landsat 7 ETM+) with a focus on vegetation health in the Niger Delta. Over 131 flaring sites in all 9 states (Abia, Akwa Ibom, Bayelsa, Cross Rivers, Delta, Edo, Imo, Ondo and Rivers) of the Niger Delta region have been identified, out of which 11 sites in Rivers State were examined using a case study approach. Land Surface Temperature data were derived using a novel procedure drawing in visible band information to mask out clouds and identify appropriate emissivity values for different land cover types. In 2503 out of 3001 Landsat subscenes analysed, Land Surface Temperature was elevated by at least 1 ℃ within 450 m of the flare. The results from fieldwork, carried out at the Eleme Refinery II Petroleum Company and Onne Flow Station, are compared to the Landsat 5 TM and Landsat 7 ETM+ data. Results indicate that Landsat data can detect gas flares and their associated pollution on vegetation health with acceptable accuracy for both Land Surface Temperature (range: 0.120 to 1.907 K) and Normalized Differential Vegetation Index (sd ± 0.004). Available environmental factors such as size of facility, height of stack, and time were considered. Finally, the assessment of the impact of pollution on a time series analysis (1984 to 2013) of vegetation health shows a decrease in NDVI annually within 120 m from the flare and that the spatio-temporal variability of NDVI for each site is influenced by local factors. This research demonstrated that only 5 % of the variability in δLST and only 12 % of the variability in δNDVI, with distance from the flare stack, could be accounted for by the available variables considered in this study. This suggests that other missing factors (the gas flaring volume and vegetation speciation) play a significant role in the variability in δLST and δNDVI respectively.

The integration of information from multiple geophysical and other prospection surveys of archaeological sites and regions leads to a richer and more complete understanding of subsurface content, structure, and physical relationships. Such fusions of information occur within a single geophysical data set or between two or more geophysical and other prospection sources in one, two, or three dimensions. An absolute requirement is the accurate coregistration of all information to the same coordinate space. Data integrations occur at two levels. At the feature level, discrete objects that denote archaeological features are defined, usually subjectively, through the manual digitization of features interpreted in the data, although there is growing interest in automated feature identification and extraction. At the pixel level, distributional issues of skewness and outliers, high levels of noise that obfuscate targets of interest, and a lack of correlation between largely independent dimensions must be confronted. Nevertheless, successful fusions occur using computer graphic methods, simple arithmetic combinations, and advanced multivariate methods, including principal components analysis and supervised and unsupervised classifications. Four case studies are presented that illustrate some of these approaches and offer advancement into new domains.

No description available.

<p>The objectives of this thesis are to assess: to what degree have natural resources deteriorated in a grazing area in northern Namibia, how do perceptions of environmental change held by local stakeholders there, correspond to scientific assessments, and how do these relate to national estimates? Analysis of the process of developing national indicators for monitoring of land degradation concluded that specific indicators should be developed on national level, and in some instances even on local level as there are no universal causes of land degradation. According to farmers overgrazing and low rainfall since the early 1990s cause negative environmental changes in the study area, partly confirming findings from national monitoring. Results also suggest that: less grazing outside the study area, improved access, permanent water supply, and fencing of large areas, also contributed. Results show that improved water supply was the most important factor. Investigation of the influence of permanent water points on grazing resources showed that perennial grasses are replaced by less palatable annual grasses as far as 6 km from water points along a water pipeline. No significant grazing induced changes in grass composition were observed around privately owned wells. Private ownership seems to be a key factor preventing over-utilization of grazing resources around the latter. A remote sensing study using Landsat TM imagery identified bare ground, saltpans and grassland with a fair accuracy. Separation of woodland from shrubland and shrubland from grassland was less accurate using supervised classification. The results show that the soil adjusted vegetation index provides valuable information about variations of green biomass over time in semi-arid environments. However, it is suggested that satellite based investigations should be supported by thorough ground based assessment due to the influence of underlying soil in this environment.</p>