Urban Tree Height Assessment Using Pictometry Hyperspatial 4-Inch Multispectral Imagery

Unger, Daniel; Kulhavy, David; Williams, Jeffrey M.; Creech, David; Hung, I‐Kuai

doi:10.5849/jof.14-020

Cited by 6 publications

(4 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Pictometry is acquired using low-flying aircraft to obtain images to create a georeferenced orthomosaic image allowing for the estimation of surface object size within seconds through the Pictometry web-based interface (Dailey, 2008;Gerke & Kerle, 2011;Wang, Schultz, & Giuffrida, 2008). Pictometry can be used for height, linear and areal measurements on the Earth's surface (Unger, Kulhavy, Williams, Creech, & Hung, 2015;Kulhavy, Unger, Hung, & Douglass, 2014;Kulhavy, Unger, Zhang, Bedford, & Hung 2016;.…”

Section: Pictometry Datamentioning

confidence: 99%

Integrating Unmanned Aircraft Systems to Measure Linear and Areal Features into Undergraduate Forestry Education

Viegut

Kulhavy

Unger

et al. 2018

IJHE

Self Cite

View full text Add to dashboard Cite

The use of Unmanned Aircraft Systems (UAS) in undergraduate forestry education continues to expand and develop. Accuracy of data collection is an important aspect of preparation for “society-ready” foresters to meet the complex sustainable environment managing for ecological, social and economic interests. Hands-on use of a DJI Phantom 4 Pro UAS by undergraduates to measure the length and area of 30 linear features and areal features on Earth’s surface were estimated. These measurements were compared (measured within the ArcMap 10.5.2 interface) to hyperspectral Pictometry imagery measured on the web-based interface and the Google Earth Pro interface. Each remotely estimated measurement was verified with the actual ground measurements and the methods compared. An analysis of variance, conducted on the absolute length errors resulting in a p-value of 0.000057, concluded that the three length estimating techniques were statistically different at a 95% confidence interval. A Tukey pair-wise test found that the remotely sensed DJI Phantom 4 Pro data was statistically less accurate than the Pictometry and Google Earth Pro data, while both of which were found to be not different statistically in terms of accuracy. The areal feature area measurements were not normally distributed and therefore tested for equal medians using a Kruskal-Wallis test. The test found that there was no significant difference between sample medians, indicating that all three methods of estimating area are statistically equal in accuracy. The results indicate that Pictometry and Google Earth Pro could both be used to accurately estimate linear feature lengths remotely in lieu of in situ linear measurements while all three remote sensing techniques can be used to accurately estimate areal feature areas remotely in lieu of in situ areal measurements.

show abstract

Section: Pictometry Datamentioning

confidence: 99%

Integrating Unmanned Aircraft Systems to Measure Linear and Areal Features into Undergraduate Forestry Education

Viegut

Kulhavy

Unger

et al. 2018

IJHE

Self Cite

View full text Add to dashboard Cite

show abstract

“…The root mean square error (RMSE) for Pictometry derived heights was 81.98 centimeters when measuring the height of buildings with a conclusion that obtaining accurate height measurements using Pictometry data was very simple (Daily, 2008). Pictometry was not statistically different for measuring heights of baldcypress compared to a telescoping height pole with a liner correlation coefficient of 0.99 between Pictometry and in situ tree height (Unger, Kulhavy, Williams, Creech, & Hung, 2014).…”

Section: Height Measurement With Pictometrymentioning

confidence: 99%

Integrating Faculty Led Service Learning Training to Quantify Height of Natural Resources from a Spatial Science Perspective

Unger

Kulhavy

Busch-Petersen

et al. 2016

IJHE

Self Cite

View full text Add to dashboard Cite

Arthur Temple College of Forestry and Agriculture (ATCOFA) faculty members were trained how to integrate service learning activities within senior level classes at Stephen F. Austin State University (SFASU) in Nacogdoches, Texas. The service learning training, taught under the acronym Mentored Undergraduate Scholarship (MUGS), involved meeting with fellow faculty members over the course of an academic year during the fall semester to first learn how to incorporate service learning activities in a senior level class followed by its incorporation into a class the following spring semester. The service learning model was applied to students in GIS 420, a senior level Landscape Modeling, Spatial Analysis, and Quantitative Assessment course within ATCOFA. The students were instructed within a hands-on interactive environment on how to use geospatial analysis to quantify natural resources. The overall goal was for a student to demonstrate proficiency in understanding how to apply aerial photo interpretation, satellite remote sensing, global positioning system and geographic information systems technology to quantify, qualify, map, monitor and manage natural and environmental resources at the local and landscape scales. Students applied this concept within a quantitative resource assessment, whereby students compared the conventional methodology of measuring height of vertical features within a landscape using a clinometer with the newer ways of measuring height using Pictometry hyperspatial imagery and drone acquired digital imagery. Conventional results were compared to newer technological methodologies to determine the most efficient and accurate way to quantify vertical resources from a spatial perspective.

show abstract

“…Pictometry data are acquired using low-flying aircraft to obtain individual images that are merged to create a georeferenced orthomosaic image allowing for the estimation of surface object dimensions within a matter of seconds through the Pictometry web-based interface (Dailey, 2008;Wang, Schultz, & Giuffrida, 2008). Pictometry data, which can be assessed through a user-friendly interface for a small fee based on spatial location specificity, can be used to measure the height, length, slope, and area of real-world objects on the Earth's surface (Unger, Kulhavy, Williams, Creech, & Hung, 2015;Kulhavy, Unger, Hung, & Douglass, 2014;Kulhavy, Unger, Zhang, Bedford, & Hung 2016;.…”

Section: Measurements Using Pictometry Datamentioning

confidence: 99%

Measuring Real-World Ground Distance Using High-Spatial Resolution Remotely Sensed Data: A Student-Focused Hands-on Study

Rutledge,

Kulhavy,

Unger

et al. 2024

IJHE

View full text Add to dashboard Cite

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.

show abstract

Urban Tree Height Assessment Using Pictometry Hyperspatial 4-Inch Multispectral Imagery

Cited by 6 publications

References 18 publications

Integrating Unmanned Aircraft Systems to Measure Linear and Areal Features into Undergraduate Forestry Education

Integrating Unmanned Aircraft Systems to Measure Linear and Areal Features into Undergraduate Forestry Education

Integrating Faculty Led Service Learning Training to Quantify Height of Natural Resources from a Spatial Science Perspective

Measuring Real-World Ground Distance Using High-Spatial Resolution Remotely Sensed Data: A Student-Focused Hands-on Study

Contact Info

Product

Resources

About