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<p> There is an undisputed need to increase accuracy of snow cover estimation in regions comprised of complex terrain, especially in areas dependent on winter snow accumulation for a substantial portion of their annual water supply, such as the Western United States, Central Asia, and the Andes. Presently, the most pertinent monitoring and research needs related to alpine snow cover area (SCA) are: (1) to improve SCA monitoring by providing detailed fractional snow cover (FSC) products which perform well in temporal/spatial heterogeneous forested and/or alpine terrains; and (2) to provide accurate measurements of FSC at the watershed scale for use in snow water equivalent (SWE) estimation for regional water management. </p><p> To address the above, the presented research approach is based on Landsat Fractional Snow Cover (Landsat-FSC), as a measure of the temporal/spatial distribution of alpine SCA. A fusion methodology between remotely sensed multispectral input data from Landsat TM/ETM+, terrain information, and IKONOS are utilized at their highest respective spatial resolutions. Artificial Neural Networks (ANNs) are used to capture the multi-scale information content of the input data compositions by means of the ANN training process, followed by the ANN extracting FSC from all available information in the Landsat and terrain input data compositions. The ANN Landsat-FSC algorithm is validated (RMSE ~ 0.09; mean error ~ 0.001-0.01 FSC) in watersheds characterized by diverse environmental factors such as: terrain, slope, exposition, vegetation cover, and wide-ranging snow cover conditions. ANN input data selections are evaluated to determine the nominal data information requirements for FSC estimation. Snow/non-snow multispectral and terrain input data are found to have an important and multi-faced impact on FSC estimation. Constraining the ANN to linear modeling, as opposed to allowing unconstrained function shapes, results in a weak FSC estimation performance and therefore provides evidence of non-linear bio-geophysical and remote sensing interactions and phenomena in complex mountain terrains. The research results are presented for rugged areas located in the San Juan Mountains of Colorado, and the hilly regions of Black Hills of Wyoming, USA. </p>

Global modeling of gross primary production (GPP) is a critical component of climate change research. On local scales, GPP can be assessed from measuring CO₂ exchange above the plant canopy using tower-based eddy covariance (EC) systems. The limited footprint inherent to this method however, restricts observations to relatively few discrete areas making continuous predictions of global CO₂ fluxes difficult. Recently, the advent of high resolution optical remote sensing devices has offered new possibilities to address some of the scaling issues related to GPP using remote sensing. One key component for inferring GPP spectrally is the efficiency (ε) with which plants can use absorbed photosynthetically active radiation to produce biomass. While recent years have seen progress in measuring ε using the photochemical reflectance index (PRI), little is known about the temporal and spatial requirements for up-scaling these findings continuously throughout the landscape. Satellite observations of canopy reflectance are subject to view and illumination effects induced by the bi-directional reflectance distribution function(BRDF) which can confound the desired PRI signal. Further uncertainties include dependencies of PRI on canopy structure, understorey, species composition and leaf pigment concentration. The objective of this research was to investigate the effects of these factors on PRI to facilitate the modeling of GPP in a continuous fashion. Canopy spectra were sampled over a one-year period using an automated tower-based, multi-angular spectroradiometer platform (AMSPEC), designed to sample high spectral resolution data. The wide range of illumination and viewing geometries seen by the instrument permitted comprehensive modeling of the BRDF. Isolation of physiologically induced changes in PRI yielded a high correlation (r²=0.82, p<0.05) to EC-measured ε, thereby demonstrating the capability of PRI to model ε throughout the year. The results were extrapolated to the landscape scale using airborne laser-scanning (light detection and ranging, LiDAR) and high correlations were found between remotely-sensed and EC-measured GPP (r²>0.79, p<0.05). Permanently established tower-based canopy reflectance measurements are helpful for ongoing research aimed at up-scaling ε to landscape and global scales and facilitate a better understanding of physiological cycles of vegetation and serve as a calibration tool for broader band satellite observations.

Satellite imagery such as Landsat has been in use for decades for many landscape and regional scale mapping applications, but has been too coarse for use in detailed forest inventories where stand level structural and compositional information is desired. Recently available high spatial resolution satellite imagery may be well suited to mapping fine-scale components of ecosystems, however, this remains an area of ongoing research. The first goal of this thesis was to assess the capacity of high spatial resolution satellite imagery to detect the variability in late seral coastal temperate rainforests in British Columbia, Canada. Using an object-based classifier, two hierarchical classification schemes are evaluated: a broad classification based on structural (successional) stage and a finer classification of late seral vegetation associations. The finer-scale classification also incorporates ancillary landscape positional variables (elevation and potential soil moisture) derived from Light Detection and Ranging (LiDAR) data, and the relative contribution of spectral, textural and landscape positional data for this classification is determined. Results indicate that late seral forests can be well distinguished from younger forests using QuickBird spectral and textural data. However, discrimination among late seral forest associations is challenging, especially in the absence of landscape positional variables. Classification accuracies were particularly low for rare forest associations. Given this finding, the objective of the third chapter was to explicitly examine the caveats of using high spatial resolution imagery to map rare classes. Classification accuracy is assessed in several different ways in order to examine the impact on perceived map accuracy. In addition, the effects on habitat extent and configuration resulting from post-classification implementation of a minimum mapping unit are examined. Results indicate that classification accuracies may vary considerably depending on the assessment technique used. Specifically, ignoring the presence of fine-scale heterogeneity in a classification during accuracy assessment falsely lowered the accuracy estimates. Further, post-classification smoothing had a large effect on the spatial pattern of rare classes. These findings suggest that routinely used image classification and assessment techniques can greatly impact mapping of rare classes.

<p> The accurate estimation of forest attributes at many different spatial scales is a critical problem. Forest landowners may be interested in estimating timber volume, forest biomass, and forest structure to determine their forest's condition and value. Counties and states may be interested to learn about their forests to develop sustainable management plans and policies related to forests, wildlife, and climate change. Countries and consortiums of countries need information about their forests to set global and national targets to deal with issues of climate change and deforestation as well as to set national targets and understand the state of their forest at a given point in time. </p><p> This dissertation approaches these questions from two perspectives. The first perspective uses the process model Biome-BGC paired with inventory and remote sensing data to make inferences about a current forest state given known climate and site variables. Using a model of this type, future climate data can be used to make predictions about future forest states as well. An example of this work applied to a forest in northern California is presented. The second perspective of estimating forest attributes uses high resolution aerial imagery paired with light detection and ranging (LiDAR) remote sensing data to develop statistical estimates of forest structure. Two approaches within this perspective are presented: a pixel based approach and an object based approach. Both approaches can serve as the platform on which models (either empirical growth and yield models or process models) can be run to generate inferences about future forest state and current forest biogeochemical cycling.</p>

<p> Finding a subject's heart rate from a distance without any contact is a difficult and very practical problem. This kind of technology would allow more comfortable patient monitoring in hospitals or in home settings. It would also allow another level of security screening, as a person's heart rate increases in stressful situations, such as when lying or hiding malicious intent. In addition, the fact that the heart rate is obtained remotely means that the subject would not have to know he/she is being monitored at all, adding to the efficacy of the measurement. </p><p> Using millimeter-wave interferometry, a signal can be obtained that contains composite chest wall motion made up of component motions due to cardiac activity, respiration, and interference. To be of use, these components have to be separated from each other by signal processing. To do this, the quadrature and in-phase components of the received signal are analyzed to get a displacement waveform. After that, processing can be done on that waveform in either the time or frequency domains to find the individual heartbeats. The first method is to find the power spectrum of the displacement waveform and to look for peaks corresponding to heartbeats and respiration. Another approach is to examine the signal in the time domain using wavelets for multiresolution analysis. One more method involves studying the statistics of the wavelet-processed signal. The final method uses a heartbeat model along with probabilistic processing to find heartbeats. </p><p> For any of the above methods to work, the millimeter-wave sensor has to be accurately pointed at the subject's chest. However, even small subject motions can render the rest of the gathered data useless as the antenna may have lost its aim. To combat this, a color and a depth camera are used with a servo-pan/tilt base. My program finds a face in the image and subsequently tracks that face through upcoming frames. The pan/tilt base adjusts the aim of the antenna depending on the subject's position. This makes the entire system self-aiming and also allows the subject to move to a new location and to have data acquisition continue.</p>

The magnitude, spatial extent, growth, and seasonal and diurnal behaviors of the urban heat island of Houston, Texas are characterized using both in situ air temperature and remotely sensed surface temperature data. Between 1990 and 2000, the air temperature heat island of Houston had an average magnitude of 1.25 K at night but was largely absent during the day. This behavior is reflected in a survey of extreme temperature events, which reveals a dramatic increase in the number of extremely warm nights relative to the surrounding rural areas. Thermal satellite imagery acquired between 1985 and 2001 demonstrate a surface temperature heat island of approximately 3 K at night and up to 10 K during the day. Climatological analysis reveals an inverse dependence of air temperature heat island magnitude on rural temperature. Conversely, daytime surface temperature heat islands grow with rural temperature, while nighttime surface temperature heat islands show no relationship to rural temperature. Examination of temperature maps reveals an urban heat island area of 1200 km2 at night and 2100 km2 during the day. Comparison of satellite imagery taken twelve years apart exhibits a growth in the nighttime heat island of 0.8 K in magnitude and 650 km2 in area. High-resolution temperature data are also examined and show an urban temperature dependence on population density.

Guidance from theory for a more holistic approach to achieving greater sustainability in urban landscapes has yet to be derived for many settings. Often extensions of their surrounding cities, campuses provide a finer scale for experimental design. This study developed a quantitative assessment to guide the transformation of campus landscapes into more instructive demonstrations of social and ecological concern. A Framework for Assessing Sustainability over Time (FAST) was created through an integrative research review and synthesis of validated models: Normalized Difference Vegetation Index, Local Climate Zones, and Impervious Cover Model, and measurable indicators: patch size and connectivity. This framework was applied to the University of Guelph to test the relative quality of landscape components, where principles prescribed by urban ecology were identified and operationalized to improve the environmental sustainability of the campus design. The framework will inform ecological sensitivity in campus and urban design that can influence user awareness.

Moss dominated ecosystems are an important part of the global terrestrial carbon cycle. Over large areas, remote sensing can be useful to provide an improved understanding of these ecosystems. Two boreal mossess (Pleurozium and Sphagnum) were assessed using remote sensing based spectral vegetation indices for estimating biochemical capacity and photosynthetic efficiency by varying net photosynthesis rate via changes in water content. In the laboratory, changes in the normalized difference vegetation index (NDVI) and chlorophyll index coincided with declining photosynthetic capacity due to desiccation. This effect was more dramatic in Sphagnum. The photochemical reflectance index (PRI) did not vary with changes in CO2 supply as anticipated, possibly due to overriding effects of changing water content. The water band index (WBI) was strongly related to water content but this relationship showed an uncoupling in the field. Bi-directional reflectance measurements indicated what WBI was sensitive to sensor, sun, and moss surface slope angles. / xi, 110 leaves : ill. (some col.) ; 29 cm.

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