The purpose of this study is comparing elevation models from Terrestrial laser scanner (TLS) and Unmanned aerial system (UAS) photogrammetry focusing on detecting microtopography and the relationship between elevation differences and image textures. The soils on agricultural lands are permanently modified by intensive farming activities almost every year. The microtopography of the soil, that plays an important role in the surface runoff and infiltration, depends on cultivation practices and the field environment. By way of example: crop residues, furrows, tillage direction, and slope may impact the soil nutrient and erosion. To better understand and prevent soil degradation via erosion, 3-D reconstructions of high-resolution soil monitoring are required.
In this study, we try to circumnavigate the soil roughness associated with sustainable practices and physical characteristics of fields by collecting soil datasets from non-contacted remote sensing platforms. 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. Additionally, by focusing on local relief detections and the relationship between outlier distributions and image textures, the two datasets were compared.
Both TLS and UAS derived point clouds successfully reconstructed digital elevation models ~ 5cm RMSE after the registration and merge process, and 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.
| Identifer | oai:union.ndltd.org:uiowa.edu/oai:ir.uiowa.edu:etd-8288 |
| Date | 01 May 2019 |
| Creators | Lee, Kang San |
| Contributors | Linderman, Marc A. |
| Publisher | University of Iowa |
| Source Sets | University of Iowa |
| Language | English |
| Detected Language | English |
| Type | thesis |
| Format | application/pdf |
| Source | Theses and Dissertations |
| Rights | Copyright © 2019 Kang San Lee |
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