Date of Award
2024
Document Type
Open Access Master's Thesis
Degree Name
Master of Science in Forest Ecology and Management (MS)
Administrative Home Department
College of Forest Resources and Environmental Science
Advisor 1
Tara L. Bal
Committee Member 1
Mickey P. Jarvi
Committee Member 2
Julia I. Burton
Abstract
Forest health data collected in a two-phased survey of maple dieback was used to build on previous work, extending analysis into mapping implications. Using the National Insect and Disease Risk Maps (NIDRM) published by the US Forest Service as a baseline, we compared our collected data from 2009-2012 and 2021-2022 with the published 240m resolution sugar maple decline risk map, finding the NIDRM map to be a poor predictor of sugar maple dieback measured at the plots. Measured sugar maple crown dieback was also compared to measured soil characteristics and nutrient levels, and augmented with soil survey maps and long-term climatic trends. We developed a linear regression framework utilizing multiple individual variables and composite variables generated through Principal Component Analysis. Only models created with covariates measured at each plot resulted in significant predictive models, but lacked temporal repeatability between study phases. The scale discrepancy between plot size and the 240m map pixel size limited the effectiveness of the NIDRM map and other spatially derived variables as predictors of maple canopy health in our study area, but these findings also highlight opportunities for improvement. As data sets improve with higher resolution and machine learning algorithms, it will be increasingly important to validate large-scale derivative models. Enhancing our understanding of forest health at a national level can inform regional decision-making and enable forest managers to tailor their management strategies to the specific threats facing their forest stands.
Recommended Citation
Beeson, Joseph R., "Validation and Improvement of Forest Health Risk Maps: A Case Study with Maple Decline/Dieback", Open Access Master's Thesis, Michigan Technological University, 2024.
