Optimizing UAV surveys for coastal morphodynamics: estimation of spatial uncertainty as a function of flight acquisition and post-processing factors

Recent developments in unmanned aerial vehicles (UAVs) and photogrammetry software enable the rapid collection of aerial photography and video over study areas of varying sizes, thereby providing ease of use and accessibility for studies of coastal geomorphology. However, there remains uncertainty over UAV survey techniques, with disagreement on specific flight patterns, flight altitudes, photograph amounts, ground control point (GCP) amounts, GCP spacing schemes, drone models, and which SfM software to use, amongst other study-specific parameters. A controlled field test (of 1.2 hectares) was performed to determine SfM’s sensitivity to the following flight parameters: altitude (60 m, 80 m, 120 m), photo overlap (70%, 75%, 80%), drone model (DJI Phantom quadcopter, Sensefly eBee RTK fixed-wing), SfM software (PhotoScan, Pix4D), number of GCPs (4-34), and GCP spacing scheme (even, random). Through comparisons of the root mean squared error (RMSE) relative to the GCPs, altitude affected error significantly (\u3e1 cm RMSE difference between 60 m and 120 m) while photo overlap was the least significant parameter (only 4 mm RMSE difference between 70% and 80% overlap). Different drone models, along with varying photogrammetry software, affected RMSE significantly (\u3e3 cm RMSE differences). Surprisingly, GCP spacing schemes were insignificant to error sensitivity (differences). The most efficient survey parameters were six GCPs per hectare of land surveyed, 80 m flight altitudes, and 70% photo overlap. This study can be immediately referenced in future studies for its insight on conducting efficient and low-error UAV surveys

Using Convolutional Neural Networks for Detection and Morphometric Analysis of Carolina Bays from Publicly Available Digital Elevation Models

Carolina Bays are oriented and sandy-rimmed depressions that are ubiquitous throughout the Atlantic Coastal Plain (ACP). Their origin has been a highly debated topic since the 1800s and remains unsolved. Past population estimates of Carolina Bays have varied vastly, ranging between as few as 10,000 to as many as 500,000. With such a large uncertainty around the actual population size, mapping these enigmatic features is a problem that requires an automated detection scheme. Using publicly available LiDAR-derived digital elevation models (DEMs) of the ACP as training images, various types of convolutional neural networks (CNNs) were trained to detect Carolina bays. The detection results were assessed for accuracy and scalability, as well as analyzed for various morphologic, land-use and land cover, and hydrologic characteristics. Overall, the detector found over 23,000 Carolina Bays from southern New Jersey to northern Florida, with highest densities along interfluves. Carolina Bays in Delmarva were found to be smaller and shallower than Bays in the southeastern ACP. At least a third of Carolina Bays have been converted to agricultural lands and almost half of all Carolina Bays are forested. Few Carolina Bays are classified as open water basins, yet almost all of the detected Bays were within 2 km of a water body. In addition, field investigations based upon detection results were performed to describe the sedimentology of Carolina Bays. Sedimentological investigations showed that Bays typically have 1.5 m to 2.5 m thick sand rims that show a gradient in texture, with coarser sand at the bottom and finer sand and silt towards the top. Their basins were found to be 0.5 m to 2 m thick and showed a mix of clayey, silty, and sandy deposits. Last, the results compiled during this study were compared to similar depressional features (i.e., playa-lunette systems) to pinpoint any similarities in origin processes. Altogether, this study shows that CNNs are valuable tools for automated geomorphic feature detection and can lead to new insights when coupled with various forms of remotely sensed and field-based datasets

Clintonville Sustainability Plan : Urban Ecology [poster]

This is part of a project submitted to fulfill the requirements of the course "City and Regional Planning 724 : Introduction to Planning for Sustainable Development", completed Fall Quarter of 2007 at The Ohio State University in Columbus, Ohio

Evidence-based Systematic Review of Cognitive Rehabilitation, Emotional, and Family Treatment Studies for Children with Acquired Brain Injury Literature: From 2006 to 2017

This paper updates guidelines for effective treatments of children with specific types of acquired brain injury (ABI) published in 2007 with more recent evidence. A systematic search was conducted for articles published from 2006 to 2017. Full manuscripts describing treatments of children (post-birth to 18) with acquired brain injury were included if study was published in peer-reviewed journals and written in English. Two independent reviewers and a third, if conflicts existed, evaluated the methodological quality of studies with an Individual Study Review Form and a Joanna Briggs Institute (JBI) Critical Appraisal Checklist. Strength of study characteristics was used in development of practice guidelines. Fifty-six peer-reviewed articles, including 27 Class I studies, were included in the final analysis. Established guidelines for writing practice recommendations were used and 22 practice recommendations were written with details of potential treatment limitations. There was strong evidence for family/caregiver-focused interventions, as well as direct interventions to improve attention, memory, executive functioning, and emotional/behavioural functioning. A majority of the practice standards and guidelines provided evidence for the use of technology in delivery of interventions, representing an important trend in the field

Clintonville Sustainability Plan : Final Report

This is part of a project submitted to fulfill the requirements of the course "City and Regional Planning 724 : Introduction to Planning for Sustainable Development", completed Fall Quarter of 2007 at The Ohio State University in Columbus, Ohio