Authors: Kangsan Lee*, University of Arizona - Geography & Development
Topics: Remote Sensing, Agricultural Geography, Environmental Science
Keywords: UAS, Lidar, Microtopography, Photogrammetry
Session Type: Guided Poster
Presentation File: No File Uploaded
The purpose of this study is comparing the accuracy of elevation models constructed by Terrestrial laser scanner (TLS) and Unmanned aerial system (UAS) photogrammetry in the harvested crop field, especially focusing on microtopography detection. We try to circumnavigate the soil roughness associated with sustainable practices and physical characteristics of fields by collecting soil image datasets. The amount of soil roughness was observed environmental conditions derived from the Terrestrial Laser Scanner (TLS) and the Unmanned Aerial System (UAS) photogrammetry within harvested fields in Eastern Central Iowa. Zooming on local relief detections and the relationship between outlier distributions and image textures, both TLS and UAS derived point clouds successfully reconstructed digital elevation models around 5cm RMSE after the registration and merge process. These models showed local reliefs of study areas with fine details; however, several outlier cluster points were detected in the comparisons between TLS and UAS derived DEMs. To discover the outlier distributions, image texture was addressed with global and local block analysis. Since there were no significant correlations, most of the study sites show that poor texture of ground may trigger high elevation errors. To enhance the texture of images, several possible solutions are described, such as local contrast enhancement using the Wallis filter.