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Assessment and Implementation of Aerial Photography, Remote Sensing, and Ground-Based Sensors for Estimating Waste Following a Wide Area Incident
Citation:
Wussow, M., D. Durden, R. James, T. Boe, AND S. Lee. Assessment and Implementation of Aerial Photography, Remote Sensing, and Ground-Based Sensors for Estimating Waste Following a Wide Area Incident. U.S. Environmental Protection Agency, Washington, DC, EPA/600/R-23/127, 2023.
Impact/Purpose:
This study investigated the use of Unmanned Aerial Systems (UAS) remote sensing in post-disaster recovery for estimating waste and debris volume, highlighting its potential to complement existing methods such as ground-based surveys, manned-aircraft, and satellite remote sensing. The adaptability, cost-effectiveness, and accuracy of UAS are emphasized, with UAS-LiDAR and UAS-Structure from Motion (SfM) showing promising results in generating detailed surface models for debris estimation. The study recommends a multi-sensor UAS platform, combining UAS-LiDAR and RGB camera, as an optimal solution for debris volume estimation under various conditions, and encourages further validation and comparison with traditional techniques to strengthen the body of literature in this field.
Description:
This study examines the current literature on aerial photography, remote sensing, and ground-based sensor use for estimating waste and debris volume. While there is a growing body of research in this area, the application of Unmanned Aerial Systems (UAS) remote sensing in post-disaster recovery is still limited compared to traditional methods such as ground-based surveys and manned-aircraft or satellite remote sensing. UAS-based surveying is highly adaptable and can produce accurate debris volume estimates and detailed maps for localized areas. However, factors such as coverage area, desired resolution, and resource availability affect the cost-effectiveness of UAS and must be considered prior to use. UAS remote sensing complements existing observational frameworks, including ground-based methods, manned-aircraft remote sensing, and satellite imagery. Key advantages of UAS include surveying inaccessible areas, low operating costs, enhanced safety, versatile payload options, rapid deployment and data acquisition, and high-resolution data. UAS-Light Detection and Ranging (LiDAR) and UAS-Structure from Motion (SfM) offer promising solutions for generating detailed surface models and deriving debris estimates. Although research on UAS-LiDAR for disaster response debris estimation is limited, related studies show that it offers significant benefits over UAS-SfM, including nighttime operation and tree canopy penetration. A multi-sensor platform combining UAS-LiDAR and UAS-SfM offers enhanced colorized point clouds, where Red, Green, Blue (RGB) raster data are used to colorize the LiDAR point cloud, for debris descriptions and identification while retaining UAS-LiDAR's advantages. UAS remote sensing is a valuable tool for improving efficiency and data accuracy in post-disaster situations. A multi-sensor UAS platform, consisting of UAS-LiDAR and a RGB camera, is optimal for debris volume estimation in various conditions. It is recommended to validate this method, ideally comparing it to traditional techniques, to contribute to the existing body of literature.