Date of Award
5-2026
Degree Name
Master of Science
Department
Geography
First Advisor
Nicholas L. Padilla, Ph.D.
Second Advisor
Julio Pedrassoli, Ph.D.
Third Advisor
Rudy Bartels, Ph.D.
Access Setting
Masters Thesis-Open Access
Abstract
Local climatic zone (LCZ) classifications are traditionally done using reflectance, population data, and often climatic data. The goal of this research is to utilize Google’s new “Open Buildings Temporal v1” building data set as well as “spectral unmixing”. With the objective of testing, assessing, and implementing this data set into the workflow of LCZ mapping. After testing the quality of the data against a validated LiDAR data set from the same year as a given “Open Buildings Temporal v1” band, I implemented it into the Random Forest machine learning process. Secondly, I justified the use of spectral unmixing as a variable in classification. Finally, I assessed the impact of building height values and unmixed bands as variables in the model in the form of “variable importance”. Those results show that these two variables combined were 22.6% of the importance for the final model. While the final map showed an overall accuracy of 75.67%.
Recommended Citation
Stauffer, Ian Douglas, "Increasing Accuracy of Mapping Local Climatic Zones Using Building Height and Spectral Unmixing" (2026). Masters Theses. 5520.
https://scholarworks.wmich.edu/masters_theses/5520
Included in
Geographic Information Sciences Commons, Physical and Environmental Geography Commons, Remote Sensing Commons