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Full-Text Articles in Geography
Application Of An Imputation Method For Geospatial Inventory Of Forest Structural Attributes Across Multiple Spatial Scales In The Lake States, U.S.A., Ram K. Deo
Dissertations, Master's Theses and Master's Reports - Open
Credible spatial information characterizing the structure and site quality of forests is critical to sustainable forest management and planning, especially given the increasing demands and threats to forest products and services. Forest managers and planners are required to evaluate forest conditions over a broad range of scales, contingent on operational or reporting requirements. Traditionally, forest inventory estimates are generated via a design-based approach that involves generalizing sample plot measurements to characterize an unknown population across a larger area of interest. However, field plot measurements are costly and as a consequence spatial coverage is limited. Remote sensing technologies have shown remarkable …
Implementation Of Photogrammetry To Improve Proactive Assessment Of Retaining Walls Along Transportation Corridors, Daniel J. Cerminaro
Implementation Of Photogrammetry To Improve Proactive Assessment Of Retaining Walls Along Transportation Corridors, Daniel J. Cerminaro
Dissertations, Master's Theses and Master's Reports - Open
Retaining walls are important assets in the transportation infrastructure and assessing their condition is important to prolong their performance and ultimately their design life. Retaining walls are often overlooked and only a few transportation asset management programs consider them in their inventory. Because these programs are few, the techniques used to assess their condition focus on a qualitative assessment as opposed to a quantitative approach. The work presented in this thesis focuses on using photogrammetry to quantitatively assess the condition of retaining walls. Multitemporal photogrammetry is used to develop 3D models of the retaining walls, from which offset displacements are …
Beyond Roots Alone: Novel Methodologies For Analyzing Complex Soil And Minirhizotron Imagery Using Image Processing And Gis Tools, Justina A. Silva
Beyond Roots Alone: Novel Methodologies For Analyzing Complex Soil And Minirhizotron Imagery Using Image Processing And Gis Tools, Justina A. Silva
Dissertations, Master's Theses and Master's Reports - Open
Quantifying belowground dynamics is critical to our understanding of plant and ecosystem function and belowground carbon cycling, yet currently available tools for complex belowground image analyses are insufficient. We introduce novel techniques combining digital image processing tools and geographic information systems (GIS) analysis to permit semi-automated analysis of complex root and soil dynamics. We illustrate methodologies with imagery from microcosms, minirhizotrons, and a rhizotron, in upland and peatland soils. We provide guidelines for correct image capture, a method that automatically stitches together numerous minirhizotron images into one seamless image, and image analysis using image segmentation and classification in SPRING or …
Deep Learning Methods For Multiband Explosive Hazard Detection Using L-Band And X-Band Forward-Looking Ground-Penetrating Radar, John T. Becker
Deep Learning Methods For Multiband Explosive Hazard Detection Using L-Band And X-Band Forward-Looking Ground-Penetrating Radar, John T. Becker
Dissertations, Master's Theses and Master's Reports - Open
Explosive hazards are one of the most deadly threats in modern conflicts. The U.S. Army is interested in a reliable way to detect these hazards at range. A promising way of accomplishing this task is using a forward-looking ground-penetrating radar (FLGPR) system. Recently, the Army has been testing a system that utilizes both L-band and X-band radar arrays on a vehicle mounted platform. Using data from this system, we sought to improve the performance of a constant false-alarm-rate (CFAR) prescreener through the use of three deep learning architechtures; deep belief networks (DBNs), stacked denoising autoencoders (SDAEs), and convolutional neural networks …