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Full-Text Articles in Physical Sciences and Mathematics
Uavs And Deep Neural Networks: An Alternative Approach To Monitoring Waterfowl At The Site Level, Zachary J. Loken
Uavs And Deep Neural Networks: An Alternative Approach To Monitoring Waterfowl At The Site Level, Zachary J. Loken
LSU Master's Theses
Understanding how waterfowl respond to habitat restoration and management activities is crucial for evaluating and refining conservation delivery programs. However, site-specific waterfowl monitoring is challenging, especially in heavily forested systems such as the Mississippi Alluvial Valley (MAV)—a primary wintering region for ducks in North America. I hypothesized that using uncrewed aerial vehicles (UAVs) coupled with deep learning-based methods for object detection would provide an efficient and effective means for surveying non-breeding waterfowl on difficult-to-access restored wetland sites. Accordingly, during the winters of 2021 and 2022, I surveyed wetland restoration easements in the MAV using a UAV equipped with a dual …
Pixel-Wise Machine Learning And Deep Learning Methods Implementation On Multi-Class Wildfire Mapping, Mingda Wu
Pixel-Wise Machine Learning And Deep Learning Methods Implementation On Multi-Class Wildfire Mapping, Mingda Wu
Honors Capstones
Wildfires are destructive natural hazards. Artificial Intelligence (AI) has been a trendy topic in recent years due to its powerful applicability. This study focuses on the use of artificial intelligence (AI) in hazard management, specifically in the field of wildfire mapping. Machine learning and Deep learning are two subsets of AI. This study applied pixel-wise machine learning and deep learning methods to do multi-class mapping on two wildfire events in California, USA. The purpose of this research is to demonstrate the usefulness and advantages of using AI in the field of hazard management. The machine learning methods selected are Random …
On Interpreting Eddy Covariance In Small Area Agricultural Situations With Contrasting Site Management., Joel Oetting
On Interpreting Eddy Covariance In Small Area Agricultural Situations With Contrasting Site Management., Joel Oetting
Doctoral Dissertations
This dissertation examined the carbon sequestration potential of a low C:N soil amendment and its incorporation into the soil over a rolling agricultural field. A segmented planar fit was developed to assess and correct the systematic errors the topography introduces on the carbon dioxide fluxes. The carbon dioxide fluxes were then be partitioned into gross primary productivity and soil respiration to understand the influence of the contrasting management practices, using flux variance partitioning. Concomitant with the partitioning, high resolution temporal and spatial scale remote sensing images were interpolated and standardized to conduct hypothesis testing for treatment effects.
Intra-Hour Solar Forecasting Using Cloud Dynamics Features Extracted From Ground-Based Infrared Sky Images, Guillermo Terrén-Serrano
Intra-Hour Solar Forecasting Using Cloud Dynamics Features Extracted From Ground-Based Infrared Sky Images, Guillermo Terrén-Serrano
Electrical and Computer Engineering ETDs
Due to the increasing use of photovoltaic systems, power grids are vulnerable to the projection of shadows from moving clouds. An intra-hour solar forecast provides power grids with the capability of automatically controlling the dispatch of energy, reducing the additional cost for a guaranteed, reliable supply of energy (i.e., energy storage). This dissertation introduces a novel sky imager consisting of a long-wave radiometric infrared camera and a visible light camera with a fisheye lens. The imager is mounted on a solar tracker to maintain the Sun in the center of the images throughout the day, reducing the scattering effect produced …