Abstract
The urban forest provides various ecosystem services. Urban tree canopy cover measurement is the most basic quantification of ecosystem services. There have been few studies focused on long-term high-resolution urban forest change analysis. Further, few if any of these studies have compared object based image analysis (OBIA) and random point based assessment for determination of urban forest cover. The research objective is to define the urban forest canopy area, location, and height within the City of St Peter, MN boundary between 1938 and 2019 using both the OBIA and random point based methods with high spatial-resolution aerial photographic images and Light Detection and Ranging (LiDAR) data. One facet of this project is to examine the impact of natural disasters, such as the 1998 tornado, and tree diseases on the urban canopy cover area. LiDAR data was used to determine the height and canopy cover density of the urban forest canopy. The results were used to compare and contrast the methods, with verification via ground truthing. Results show that both methods gave comparable accurate results. The total canopy cover area remained consistent until 1995, then increased post-tornado. The location of canopy cover areas has changed throughout St Peter over time due to the tornado, the increase in size of the City of St Peter, and land use change within the City of St Peter. The canopy change due to diseases was not detectable.
Advisor
Fei Yuan
Committee Member
Woo Jang
Committee Member
Cynthia Miller
Date of Degree
2019
Language
english
Document Type
Thesis
Degree
Master of Science (MS)
College
Social and Behavioral Sciences
Recommended Citation
Blackman, R. (2019). Long-term urban forest cover change detection with object based image analysis and random point based assessment [Master's thesis, Minnesota State University, Mankato]. Cornerstone: A Collection of Scholarly and Creative Works for Minnesota State University, Mankato. https://cornerstone.lib.mnsu.edu/etds/967/
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Included in
Rights Statement
