Invasive pests cause major ecological and economic damages to forests around the world including reduced carbon sequestration and biodiversity and loss of forest revenue. In this study, we used Random Forest to model forest mortality resulting from a 2015–2017 Spongy moth outbreak in the temperate deciduous forests of Rhode Island (northeastern U.S.). Mortality was modeled with a 100 m spatial resolution based on Landsat-derived defoliation maps and geospatial data representing soil characteristics, drought condition, and forest characteristics as well as proximity to coast, development, and water. Random Forest was used to model forest mortality with two classes (low/high) and three classes (low/med/high). The best models had overall accuracies of 82% and 65% for the two-class and three-class models, respectively. The most important predictors of forest mortality were defoliation, distance to coast, and canopy cover. Model performance improved only slightly with the inclusion of more than three variables. The models classified 35% of forests as having canopy mortality >5 trees/ha and 21% of Rhode Island forests having mortality >11 trees/ha. The study shows the benefit of Random Forest models that use both defoliation maps and geospatial environmental data for classifying forest mortality caused by Spongy moth.

Mapping individual dead trees from aerial imagery is useful for assessing habitat quality, monitoring forest health, identifying risks to infrastructure, and guiding forest management strategies. Dead trees can be identified in summertime aerial imagery but computer-assisted methods are needed for efficient mapping across large areas. This study evaluated pixel- and object-based unsupervised classification and Deep Learning for mapping individual dead trees using summertime true-color aerial imagery. The study area was located in Rhode Island forests which had high rates of deciduous tree mortality due to a Spongy moth outbreak in 2015–2017. The unsupervised approaches included spectral and morphological filters to reduce commission error. The Deep Learning approach used a training/validation dataset with 22,504-point features representing dead trees. The pixel-based method had the best performance (F1 = 0.84), the object-based method had slightly lower performance (F1 = 0.79), and Deep Learning had the worst performance (F1 = 0.67). The pixel-based method had the added benefits of being automatable and not requiring training data whereas the object-based method could not be automated and Deep Learning required a large training dataset. The study showed that dead deciduous trees can be mapped from true-color aerial imagery and that an automated classification can yield high accuracy.

Forests have immense economic value, contributing to industries such as timber production, nontimber forest products, and ecotourism (Wang et al., 2022). Additionally, forest ecosystems play a crucial role in mitigating climate change by acting as carbon sinks (Yingchun et al., 2012). However, forests also pose hazards to humans and infrastructure. Fallen trees, limbs, and contact of tree branches with power lines create numerous hazards to public safety, property damage, and substantial economic losses from power outages. Studies report that the total cost of storm-related power outages in the United States range from $20-150 billion annually (Campbell et al., 2021; Swaminathan et al., 1998; Eto et al., 2001), with about 40-85% of storm-related outages caused by trees (Simpson et al., 1996; Guggemos et al., 2003). Monitoring forest areas with a high risk of mortality can guide management activities aimed at reducing hazards to infrastructure and public safety (Sonti et al., 2015). There is already a diverse assortment of remote sensing and GIS-based sensors, models, and applications that can be applied to the problems of forest management and monitoring (Somers et al., 2012; Kuenzer et al., 2011; Joshi et al., 2015; Kerr et al., 2003; Wulder et al., 2006; Wang et al., 2010; Hansen et al., 2013; Asner et al., 2015). However, there are gaps in the literature on detection and management of forest-related problems, such as the limited number of studies that use empirical data to evaluate existing management strategies for reducing power outages due to tree damage. Additionally, there is limited research exploring broad-scale tree mortality modeling in temperate deciduous forests and mapping individual trees using semi-automatic approaches. My research aims to help fill these knowledge gaps by utilizing remote sensing and GIS techniques in forest monitoring and management related to human and infrastructure risks. The overall objective of this dissertation is to guide forest management in reducing tree-related power outages and hazards by evaluating management strategies and modeling forest mortality. The specific objectives were to: Evaluate the effects of conductor coverings, enhanced tree trimming, and line characteristics on tree-related power outages. Model tree mortality resulting from Spongy moth outbreaks using Landsat satel-lite imagery and GIS-derived environmental data. Evaluate Deep Learning and ISODATA unsupervised classification for mapping dead trees using high-resolution aerial imagery. My first chapter evaluates electric utility solutions to reducing tree-related power outages, specifically, the use of covered conductors and enhanced tree trimming (ETT). Tree-related outages during storms,