An Ecological Analysis of the Potential for Moss-Based Green Roof Design

Green roofs are a maturing application of best management practices for controlling urban stormwater runoff. The majority of green roofs are planted with drought resistant, higher plant species, such as the genus Sedum. However, other plant varieties, such as mosses, may be equally applicable. Residential roofs and natural terrestrial communities were sampled in both Maryland and Tennessee to determine moss community structure and species water composition. This served as a natural analog for potential green roof moss communities. During sampling, 21 species of moss were identified throughout the 37 total sites. The average percent moss cover and water composition across all roof sites was 40.7% and 38.6%, respectively and across all natural sites, 76.7% and 47.7%, respectively. Additional maximum water holding capacity procedures were completed on sedum and 19 of the 21 sampled moss species to assess their individual potential for stormwater absorption. Sedum species on average held 166% of their biomass in water, while moss species held 732%. The results of this study are used as a basis to propose moss species that will improve green roof performance.

RISK-BASED MULTIOBJECTIVE PATH PLANNING AND DESIGN OPTIMIZATION FOR UNMANNED AERIAL VEHICLES

Safe operation of unmanned aerial vehicles (UAVs) over populated areas requires reducing the risk posed by a UAV if it crashed during its operation. We considered several types of UAV risk-based path planning problems and developed techniques for estimating the risk to third parties on the ground. The path planning problem requires making trade-offs between risk and flight time. Four optimization approaches for solving the problem were tested; a network-based approach that used a greedy algorithm to improve the original solution generated the best solutions with the least computational effort. Additionally, an approach for solving a combined design and path planning problems was developed and tested. This approach was extended to solve robust risk-based path planning problem in which uncertainty about wind conditions would affect the risk posed by a UAV.