Underlying the majority of remotely-sensed data analysis is the assumption that geographical phenomena, such as rivers, heather-moors and the dynamics associated with such objects, can be adequately detected and identified through the use of spectral and other visual information alone. There is a common misconception that any major deficiencies of quantitative analyses are "hardware problems": that by increasing the spectral, spatial, radiometric and temporal resolutions of sensors, geographical phenomena will be identified with similarly increasing accuracy and reliability. This, however, is an unrealistic viewpoint. This thesis has developed a prototype of an automated system based on the principle that by considering the "real-world" properties of the land, a more effective and robust analysis of its dynamic nature can ensue. SYMOLAC is an automated SYstem for MOnitoring LAnd Cover based upon theories of artificial intelligence. It has been developed within a specifically designed hybrid software environment called ETORA, an Environment for Task-Orientated Analysis. This prototype environment allows SYMOLAC to utilise disparate sources of spatial data, to reason with both quantitative and qualitative knowledge, to model disparate domain uncertainties, and to exploit the functionality of third-party software components. Unlike standard approaches, it allows an automated analysis to focus on each particular domain task and how it may best be performed with the available data, knowledge and software resources. The detection of forest felling and the subsequent update of the Land Cover of Scotland (1988) dataset forms the initial application of SYMOLAC. It is concluded that the system's approach is flexible, extensible and adaptable, and demonstrates one way in which satellite imagery can offer <I>potential </I>to the future monitoring of complex land cover change without the need for human intervention.

<p> High-spatial resolution measurements of vegetation structure are needed for improving understanding of ecosystem carbon, water and nutrient dynamics, the response of ecosystems to a changing climate, and for biodiversity mapping and conservation, among many research areas. Our ability to make such measurements has been greatly enhanced by continuing developments in remote sensing technology&mdash;allowing researchers the ability to measure numerous forest traits at varying spatial and temporal scales and over large spatial extents with minimal to no field work, which is costly for large spatial areas or logistically difficult in some locations. Despite these advances, there remain several research challenges related to the methods by which three-dimensional (3D) and spectral datasets are joined (remote sensing fusion) and the availability and portability of systems for frequent data collections at small scale sampling locations. Recent advances in the areas of computer vision structure from motion (SFM) and consumer unmanned aerial systems (UAS) offer the potential to address these challenges by enabling repeatable measurements of vegetation structural and spectral traits at the scale of individual trees. However, the potential advances offered by computer vision remote sensing also present unique challenges and questions that need to be addressed before this approach can be used to improve understanding of forest ecosystems. For computer vision remote sensing to be a valuable tool for studying forests, bounding information about the characteristics of the data produced by the system will help researchers understand and interpret results in the context of the forest being studied and of other remote sensing techniques. This research advances understanding of how forest canopy and tree 3D structure and color are accurately measured by a relatively low-cost and portable computer vision personal remote sensing system: 'Ecosynth'. Recommendations are made for optimal conditions under which forest structure measurements should be obtained with UAS-SFM remote sensing. Ultimately remote sensing of vegetation by computer vision offers the potential to provide an 'ecologist's eye view', capturing not only canopy 3D and spectral properties, but also seeing the trees in the forest and the leaves on the trees.</p>

No description available.

<p> This study investigated two important aspects of hydrologic effects of low impact development (LID) practices at the watershed scale by (1) examining the potential benefits of scaling up of LID design, and (2) evaluating downstream effects of LID design and its spatial translation within a watershed. The Personal Computer Storm Water Management Model (PCSWMM) was used to model runoff reduction with the implementation of LID practices in Deer Creek watershed (DCW), Missouri. The model was calibrated from 2003 to 2007 (R² = 0.58 and NSE = 0.57), and validated from 2008 to 2012 (R² = 0.64 and NSE = 0.65) for daily direct runoff. Runoff simulated for the study period, 2003 to 2012 (NSE = 0.61; R² = 0.63), was used as the baseline for comparison to LID scenarios. Using 1958 areal imagery to assign land cover, a predevelopment scenario was constructed and simulated to assess LID scenarios' ability to restore predevelopment hydrologic conditions. The baseline and all LID scenarios were simulated using 2006 National Land Cover Dataset.</p><p> The watershed was divided in 117 subcatchments, which were clustered in six groups of approximately equal areas and two scaling concepts consisting of incremental scaling and spatial scaling were modelled. Incremental scaling was investigated using three LID practices (rain barrel, porous pavement, and rain garden). Each LID practice was simulated at four implementation levels (25%, 50%, 75%, and 100%) in all subcatchments for the study period (2003 to 2012). Results showed an increased runoff reduction, ranging from 3% to 31%, with increased implementation level. Spatial scaling was investigated by increasing the spatial extent of LID practices using the subcatchment groups and all three LID practices (combined) implemented at 50% level. Results indicated that as the spatial extent of LID practices increased the runoff reduction at the outlet also increased, ranging from 3% to 19%. Spatial variability of LID implementation was examined by normalizing LID treated area to impervious area for each subcatchment group. The normalized LID implementation levels for each group revealed a reduction in runoff at the outlet of the watershed, ranging from 0.6% to 3.7%. This study showed that over a long-term period LID practices could restore pre-development hydrologic conditions. The optimal location for LID practice implementation within the study area was found to be near the outlet; however, these results cannot be generalized for all watersheds. </p>

<p> Health effects attributed to outdoor fine particulate matter (PM_2.5) rank it among the risk factors with the highest health burdens in the world, annually accounting for over 3.2 million premature deaths and over 76 million lost disability-adjusted life years. Existing PM_2.5 monitoring infrastructure cannot, however, be used to resolve variations in ambient PM_2.5 concentrations with adequate spatial and temporal density, or with adequate coverage of human time-activity patterns, such that the needs of modern exposure science and control can be met. Small, inexpensive, and portable devices, relying on newly available off-the-shelf sensors, may facilitate the creation of PM_2.5 datasets with improved resolution and coverage, especially if many such devices can be deployed concurrently with low system cost. </p><p> Datasets generated with such technology could be used to overcome many important problems associated with exposure misclassification in air pollution epidemiology. Chapter 2 presents an epidemiological study of PM_2.5 that used data from ambient monitoring stations in the Los Angeles basin to observe a decrease of 6.1 g (95% CI: 3.5, 8.7) in population mean birthweight following <i>in utero</i> exposure to the Southern California wildfires of 2003, but was otherwise limited by the sparsity of the empirical basis for exposure assessment. Chapter 3 demonstrates technical potential for remedying PM_2.5 monitoring deficiencies, beginning with the generation of low-cost yet useful estimates of hourly and daily PM_2.5 concentrations at a regulatory monitoring site. The context (an urban neighborhood proximate to a major goods-movement corridor) and the method (an off-the-shelf sensor costing approximately USD $10, combined with other low-cost, open-source, readily available hardware) were selected to have special significance among researchers and practitioners affiliated with contemporary communities of practice in public health and citizen science. As operationalized by correlation with 1h data from a Federal Equivalent Method (FEM) &beta;-attenuation data, prototype instruments performed as well as commercially available equipment costing considerably more, and as well as another reference instrument under similar conditions at the same timescale (R² = 0.6). Correlations were stronger when 24 h integrating times were used instead (R² = 0.72). </p><p> Chapter 4 replicates and extends the results of Chapter 3, showing that similar calibrations may be reasonably exchangeable between near-roadway and background monitoring sites. Chapter 4 also employs triplicate sensors to obtain data consistent with near-field (&lt; 50 m) observations of plumes from a major highway (I-880). At 1 minute timescales, maximum PM_2.5 concentrations on the order of 100 &mu;g m^&ndash;3 to 200 &mu;g m^&ndash;3 were observed, commensurate with the magnitude of plumes from wildfires on longer timescales, as well as the magnitude of plumes that might be expected near other major highways on the same timescale. Finally, Chapter 4 quantifies variance among calibration parameters for a large sample of the sensors, as well as the error associated with the remote transfer of calibrations between two sufficiently large sets (&plusmn; 10 % for <i> n</i> = 12). These findings suggest that datasets generated with similar sensors could also improve upstream scientific understandings of fluxes resulting from indoor and outdoor emissions, atmospheric transformations, and transport, and may also facilitate timely and empirical verification of interventions to reduce emissions and exposures, in many important contexts (e.g., the provision of improved cookstoves; congestion pricing; mitigation policies attached to infill development; etc.). They also demonstrate that calibrations against continuous reference monitoring equipment could be remotely transferred, within practical tolerances, to reasonably sized and adequately resourced participatory monitoring campaigns, with minimal risk of disruption to existing monitoring infrastructure (i.e., established monitoring sites). Given a collaborator with a short window of access to a reference monitoring site, this would overcome a nominally important barrier associated with non-gravimetric, <i>in-situ </i> calibration of continuous PM_2.5 monitors. Progressive and disruptive prospects linked to a proliferation of comparable sensing technologies based on commodity hardware are discussed in Chapter 5.</p>

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.

<p> The assessment of herbivore habitat quality is traditionally based on quantifying the forages available to the animal across their home range through ground-based techniques. While these methods are highly accurate, they can be time-consuming and highly expensive, especially for herbivores that occupy vast spatial landscapes. The Unimak Island caribou herd has been decreasing in the last decade at rates that have prompted discussion of management intervention. Frequent inclement weather in this region of Alaska has provided for little opportunity to study the caribou forage habitat on Unimak Island. The overall objectives of this study were two-fold 1) to assess the feasibility of using high-resolution color and near-infrared aerial imagery to map the forage distribution of caribou habitat on Unimak Island and 2) to assess the use of a new high-resolution multispectral satellite imagery platform, RapidEye, and use of the "red-edge" spectral band on vegetation classification accuracy. Maximum likelihood classification algorithms were used to create land cover maps in aerial and satellite imagery. Accuracy assessments and transformed divergence values were produced to assess vegetative spectral information and classification accuracy. By using RapidEye and aerial digital imagery in a hierarchical supervised classification technique, we were able to produce a high resolution land cover map of Unimak Island. We obtained overall accuracy rates of 71.4 percent which are comparable to other land cover maps using RapidEye imagery. The "red-edge" spectral band included in the RapidEye imagery provides additional spectral information that allows for a more accurate overall classification, raising overall accuracy 5.2 percent.</p>

This study involves stepwise application of Unconstrained Linear Mixer Model (ULMM) for sub-pixel classification of residential areas using Land sat 7 TM image. The image was geometrically and radiometrically corrected and spectral enhancement and classifications were done to determine the possible number of target classes. In the first step, five end-members were used as inputs and the pixels which were considered as well fit to ULMM were identified as outputs. The unidentified pixels were separated and taken to the second step with new end members. This method identified 52% of the mixed pixels were identified in the first phase and 6% in the second phase. 42% of the pixels were left as unidentified after the two steps. The pixels identified by ULMM were grouped into high and low density residential subclasses. The resulting image indicated very low RMS errors. However the percentages of pixels unidentified were high. The independent accuracy test carried out using census population density and the resulting image indicated a low relationship. A hyper-spectral imagery with finer spatial resolution may provide a better sub pixel classification. / Department of Geography

<p> Six spatial parameters, (<i>x, y, z</i>) for translation, and pitch, roll, and yaw for rotation, are used to describe the 3-dimensional position and orientation of a rigid body&mdash;the 6 degrees of freedom (DOF). The ability to measure these parameters is required in a diverse range of applications including machine tool metrology, robot calibration, motion control, motion analysis, and reconstructive surgery. However, there are limitations associated with the currently available measurement systems. Shortcomings include some of the following: short dynamic range, limited accuracy, line of sight restrictions, and capital cost. The objective of this dissertation was to develop a new metrology system that overcomes line of sight restrictions, reduces system costs, allows large dynamic range and has the potential to provide high measurement accuracy. </p><p> The new metrology system proposed in this dissertation is based on a combination of photogrammetry and optical pattern projection. This system has the potential to enable real-time measurement of a small lightweight module's location. The module generates an optical pattern that is observable on the surrounding walls, and photogrammetry is used to measure the absolute coordinates of features in the projected optical pattern with respect to a defined global coordinate system. By combining these absolute coordinates with the known angular information of the optical projection beams, a minimization algorithm can be used to extract the absolute coordinates and angular orientation of the module itself. The feasibility of the proposed metrology system was first proved through preliminary experimental tests. By using a module with a 7&times;7 dot matrix pattern, experimental agreement of 1 to 5 parts in 10³ was obtained by translating the module over 0.9 m and by rotating it through 60&deg;. The proposed metrology system was modeled through numerical simulations and factors affecting the uncertainty of the measurement were investigated. The simulation results demonstrate that optimum design of the projected pattern gives a lower associated measurement uncertainty than is possible by direct photogrammetric measurement with traditional tie points alone. Based on the simulation results, a few improvements have been made to the proposed metrology systems. These improvements include using a module with larger full view angle and larger number of dots, performing angle calibration for the module, using a virtual camera approach to determine the module location and employing multiple coordinates system for large range rotation measurement. With the new proposed virtual camera approach, experimental agreement at the level of 3 parts in 10⁴ was observed for the one dimension translation test. The virtual camera approach is faster than the algorithm and an additional minimization analysis is no longer needed. In addition, the virtual camera approach offers an additional benefit that it is no longer necessary to identify all dots in the pattern and so is more amenable to use in realistic and usually complicated environments. A preliminary rotation test over 120&deg; was conducted by tying three coordinate systems together. It was observed that the absolute values of the angle differences between the measured angle and the encoder reading are smaller than 0.23&deg; for all measurements. It is found that this proposed metrology system has the ability to measure larger angle range (up to 360&deg;) by using multiple coordinate systems. The uncertainty analysis of the proposed system was performed through Monte Carlo simulation and it was demonstrated that the experimental results are consistent with the analysis. </p>

<p> I think about the space between places. I search for ways to record the invisible, ephemeral act of movement. Through the use of complex and simply technologies I collect habitual movements that I carry out over the course of months and years. These banal movements of the commute are logged through the use of Global Positioning Systems, through intuitive drawings, video, sound, text, and performance. I gather this data and think about how to repurpose them from their original intent to create a new psychogeographic map. These maps are temporal, site-specific works created over days that reinterpret my real, intuitive, and memory traces to create the fiction of place. I am an Assistant Professor at the School of Visual Art &amp; Design at Southern Adventist University in Collegedale and participated in a group show <i> FRESH 2013 Emerging Artist Exhibit</i> at the Association of Visual Arts in Chattanooga, TN.</p>