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Using Landsat-Based Phenology Metrics, Terrain Variables, And Machine Learning For Mapping And Probabilistic Prediction Of Forest Community Types In West Virginia, Faith M. Hartley
Using Landsat-Based Phenology Metrics, Terrain Variables, And Machine Learning For Mapping And Probabilistic Prediction Of Forest Community Types In West Virginia, Faith M. Hartley
Graduate Theses, Dissertations, and Problem Reports
This study investigates the mapping of forest community types for the entire state of West Virginia, USA using Global Land Analysis and Discovery (GLAD) Phenology Metrics analysis ready data (ARD) derived from the Landsat time series and digital terrain variables derived from a digital terrain model (DTM). Both classifications and probabilistic predictions were made using random forest (RF) machine learning (ML) and training data derived from ground plots provided by the West Virginia Natural Heritage Program (WVNHP). The primary goal of this study is to explore the use of globally consistent ARD data for operational forest type mapping over a …
Needs Assessment Of Diabetes Self-Management Education And Support Within West Virginia From Provider Viewpoints, Kasey M. Yost
Needs Assessment Of Diabetes Self-Management Education And Support Within West Virginia From Provider Viewpoints, Kasey M. Yost
Graduate Theses, Dissertations, and Problem Reports
Diabetes is a chronic health condition that affects millions of Americans. It can have significant, lasting effects on an individual’s mental and physical wellbeing, especially when it goes unmanaged or untreated. Diabetes has been a growing problem in West Virginia (WV), which has the highest adult rate of diabetes at 15.7% in the US. Furthermore, WV has the highest mortality rate associated with diabetes at 36.2%. An effective method of management is through diabetes self-management education and support (DSMES). DSMES is a critical component of care for all individuals with diabetes. It is defined as the ongoing process of facilitating …