Authors: Duncan MacIntosh*, California State University, Long Beach
Topics: UAS / UAV, Drones, Remote Sensing
Keywords: Remote Sensing, UAV, Drone, Pix4D, RTK, GPS, GCP, Landscape Mapping, RMSE, MAD, Accuracy, Slope, K-Means Clustering, Logistic Regression
Session Type: Poster
Start / End Time: 9:55 AM / 11:35 AM
Room: Lincoln 2, Marriott, Exhibition Level
Presentation File: No File Uploaded
In the field of Remote Sensing, Unoccupied Aerial Vehicles (UAVs) operate as powerful tools in the preservation and management of natural resources and landscapes. Using the site of River Ridge Ranch (Springville, California), this research assesses the use of UAVs for fine-scale landscape mapping. In this research over 100 GPS points were used to compare real-world elevations to values derived from UAVs by calculating the Root Mean Square Error (RMSE) and Mean Absolute Deviation (MAD). The collection of UAV imagery was done in a remote setting using a senseFly eBee Plus with S.O.D.A. photogrammetric camera employing Real-Time Kinematics (RTK) to achieve the best possible accuracy without the need for ground control points (GCPs). Pix4D mosaicking software was subsequently used to stitch imagery into orthomosaics and generate elevation models. In addition to evaluating the accuracy of UAV mapping methods, a two-part multivariate statistical analysis incorporating logistic regression and k-means clustering was performed to analyze the relationship, if any, between human perceived slope and UAV-derived slope. This research provides an assessment of the current state of surveying practices in UAV landscape mapping and explores how the use of new technologies, such as RTK, enable practitioners to phase out more costly surveying methods to better direct the practices of land managers.