INVASIVE BUCKTHORN MAPPING: A UAV-BASED APPROACH UTILIZING MACHINE LEARNING, GIS, AND REMOTE SENSING TECHNIQUES IN THE UPPER PENINSULA OF MICHIGAN
Date of Award
Open Access Master's Report
Master of Geographic Information Science
Administrative Home Department
College of Forest Resources and Environmental Science
Michael D. Hyslop
Committee Member 1
Sigrid C. Resh
An Invasive species is a species that is alien or non-native to the ecosystem which causes harm to economic, environmental, or human health (E.O. 13112 of Feb 3, 1999). Invasive species have posed a serious threat to ecosystems across the globe. These invasive species have impacts on the biodiversity and productivity of invaded forests. Remotely sensed data is a valuable resource for understanding and addressing issues related to invasive species. This study presents a novel approach for mapping the distribution of two invasive plant species, Common and Glossy Buckthorn, using unmanned aerial vehicles (UAVs), machine learning algorithms, geographic information systems (GIS), and remote sensing techniques. The study focused on a site in the Upper Peninsula of Michigan (Houghton), where the two buckthorn species have invaded natural ecosystems and caused significant ecological and economic damage. The study collected high-resolution RGB imagery using a UAV and applied machine learning algorithms to classify the images into buckthorn and non-buckthorn areas. The classified images were then integrated into a GIS database, and spatial analysis was performed to identify the distribution and extent of the buckthorn invasion. The study found that the UAV-based approach using machine learning algorithms, GIS, and remote sensing techniques is a highly effective method for mapping invasive species, providing accurate and timely information for invasive species management and control. However, several factors must be considered to avoid misclassification which would impact the accuracy of the classification.
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Madeppa, Vikranth, "INVASIVE BUCKTHORN MAPPING: A UAV-BASED APPROACH UTILIZING MACHINE LEARNING, GIS, AND REMOTE SENSING TECHNIQUES IN THE UPPER PENINSULA OF MICHIGAN", Open Access Master's Report, Michigan Technological University, 2023.
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