Bryce Rutledge, D. Kulhavy, Daniel R. Unger, I. Hung, Yanli Zhang, Victoria Williams
{"title":"Measuring Real-World Ground Distance Using High-Spatial Resolution Remotely Sensed Data: A Student-Focused Hands-on Study","authors":"Bryce Rutledge, D. Kulhavy, Daniel R. Unger, I. Hung, Yanli Zhang, Victoria Williams","doi":"10.5430/ijhe.v13n2p13","DOIUrl":null,"url":null,"abstract":"Students under the direction of geospatial science faculty, 30 real-world distances were measured on the campus of Stephen F. Austin State University in the field with tape. Students were then instructed on how to measure all 30 real-world features remotely using drone imagery, point cloud data, pictometry data and the Google Earth Pro online interface. Real-world measurements were compared to remote sensing measurements taken by the students to calculate the root mean square error (RMSE). In addition, an ANOVA was conducted on the absolute errors to determine the statistical significance of the variation among the remotely sensed methods, while a Tukey test was performed to assess the statistical significance between the methods. Students discovered that the RMSE results indicate that the pictometry measurements were the most accurate, with an RMSE of 0.68 meters, and that the point cloud data were the least accurate, with an RMSE of 1.27 meters. The ANOVA results indicate that there was a significant difference in the mean absolute error among the methods, whereas the point cloud data, with a mean absolute error of 1.0423 meters, were significantly less accurate than those of the other methods, which was confirmed by the Tukey test.","PeriodicalId":510213,"journal":{"name":"International Journal of Higher Education","volume":"215 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Higher Education","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5430/ijhe.v13n2p13","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Students under the direction of geospatial science faculty, 30 real-world distances were measured on the campus of Stephen F. Austin State University in the field with tape. Students were then instructed on how to measure all 30 real-world features remotely using drone imagery, point cloud data, pictometry data and the Google Earth Pro online interface. Real-world measurements were compared to remote sensing measurements taken by the students to calculate the root mean square error (RMSE). In addition, an ANOVA was conducted on the absolute errors to determine the statistical significance of the variation among the remotely sensed methods, while a Tukey test was performed to assess the statistical significance between the methods. Students discovered that the RMSE results indicate that the pictometry measurements were the most accurate, with an RMSE of 0.68 meters, and that the point cloud data were the least accurate, with an RMSE of 1.27 meters. The ANOVA results indicate that there was a significant difference in the mean absolute error among the methods, whereas the point cloud data, with a mean absolute error of 1.0423 meters, were significantly less accurate than those of the other methods, which was confirmed by the Tukey test.