LiDAR as a Means of Predicatively Modeling Archaeological Mound Sites
Academic Level at Time of Presentation
Graduate
Major
Archaeology
List all Project Mentors & Advisor(s)
Dr. Robin Q. Zhang
Presentation Format
Poster Presentation
Abstract/Description
The goal of this research is to evaluate Lidar imagery as a potential source for predictive modeling of prehistoric mounds that have been largely eroded. Cahokia was used as a test site to design this model since the site contains multiple mounds of fairly large size that would help with developing the model. The process involves converting the Lidar data into a DEM and then inverting the data so that the high elevations become low elevations. This would turn the mounds and any other feature with local elevation maximum into artificial sinks. Then, various hydrological tools in the ArcMap software are applied to identify where those sinks are. The downside is that unless a Fill tool is used to filter out the smaller sinks prior to identification, the resulting image will contain a significant number of potential sinks that will make it hard to identify potential mounds without using other criteria/methods. Another solution is to use statistical methods to filter out the sinks that are either too big or too small to be potential mounds. However, both methods require known mounds to be in the research area in order to use as a control for identifying unknown mounds. The method has some potential at identifying mounds. However, its accuracy and reliance on having known mounds to use as a control means it shouldn’t be a primary method of predictive modeling.
Spring Scholars Week 2019 Event
Sigma Xi Poster Competition (Juried)
LiDAR as a Means of Predicatively Modeling Archaeological Mound Sites
The goal of this research is to evaluate Lidar imagery as a potential source for predictive modeling of prehistoric mounds that have been largely eroded. Cahokia was used as a test site to design this model since the site contains multiple mounds of fairly large size that would help with developing the model. The process involves converting the Lidar data into a DEM and then inverting the data so that the high elevations become low elevations. This would turn the mounds and any other feature with local elevation maximum into artificial sinks. Then, various hydrological tools in the ArcMap software are applied to identify where those sinks are. The downside is that unless a Fill tool is used to filter out the smaller sinks prior to identification, the resulting image will contain a significant number of potential sinks that will make it hard to identify potential mounds without using other criteria/methods. Another solution is to use statistical methods to filter out the sinks that are either too big or too small to be potential mounds. However, both methods require known mounds to be in the research area in order to use as a control for identifying unknown mounds. The method has some potential at identifying mounds. However, its accuracy and reliance on having known mounds to use as a control means it shouldn’t be a primary method of predictive modeling.