Invasive pests have far-reaching ecological and economic impacts, affecting global forest communities and carbon storage (Seidl et al., 2014). Globally, Quirion et al. (2021) found that forests affected by pest invasion sequester 69% less carbon, on average, compared to unaffected forests. In the U.S. alone, forest pest activity results in annual biomass losses of 5.5 TgC (Fei et al., 2019), which is roughly equivalent to 11,800 hectares of temperate forest land (based on Murray et al., 2011). One important forest pest of the U.S. is Spongy moth (Lymantria dispar dispar) which can cause severe tree defoliation that leads to mortality. From 1994–2010, Spongy moth caused an estimated 898 TgC of biomass loss in the U.S. (Fei et al., 2019), which is equivalent to roughly 1.9 M hectares of temperate forest (Murray et al., 2011). In a single U.S. city (i.e. Baltimore, MD), the damages caused by Spongy moth was estimated to range from $5.5–63.7 M per year of outbreak, depending on environmental and management scenarios (Bigsby et al., 2014).
Satellite and aerial imagery allow for a regular monitoring of forest health and tree mortality (Verbesselt et al., 2009). Sentinel-2 and Landsat satellites provide moderate-resolution imagery over large areas. Summertime aerial imagery is acquired regularly by various agencies including the USDA’s National Agriculture Inventory Program (NAIP). Thus, remote sensing provides an important tool for monitoring forest health which can allow foresters to mitigate the impact of insect invasions through forest management (Hudgins et al., 2017). Mitigation can include direct prevention strategies such as biological controls and insecticides; indirect controls such as forest thinning, prescribed fire, and quarantines (Ivantsova et al., 2019); or replanting with pest-resistant varieties of the affected species (Kinahan et al., 2020). Tree mortality maps can guide tree removal programs to protect public safety and infrastructure, maintain aesthetics (Poulos et al., 2010; Guggenmoose et al., 2003), improve forest regeneration (Parotta et al., 1997), or convert forests with high mortality to early successional wildlife habitat.
Defoliation by Spongy moth and other leaf-eating insects, has a strong impact on tree mortality (Gottschalk et al., 2007; Baker et al., 1941). Davidson et al. (1999) reported that Spongy moth invasion caused a die-off of up to 47% of deciduous trees in outbreak areas in the Eastern U.S. When more than 75% of a tree crown is defoliated, a tree may produce a second set of leaves in the same growing season which depletes energy reserves and decreases chances of surviving the invasion (Davidson et al., 1999), especially if the tree is defoliated multiple times (Campbell, 1979). Other pests, such as borers, are attracted to stressed trees which further increases chances of tree mortality (Davidson 1999).
Researchers have mapped defoliation, based on satellite and aerial imagery, and used these maps to model tree mortality (Meddens et al., 2014; Goodwin et al., 2008; Long et al., 2016; Bergmüller et al., 2022; Meng et al., 2022; Zhan et al., 2020). In the Rocky Mountains, Meddens et al. (2014) mapped dead pine trees with Landsat-based vegetation indexes and linear regression models (r2 = 0.77). In boreal forests, Goodwin et al. (2008) classified dead pine trees, with 75% accuracy, using Normalized Difference Moisture Index derived from Landsat imagery. In spruce-dominated forests, Long et al. (2016) classified dead trees, with 90% accuracy, using a combination of high-resolution aerial imagery and moderate-resolution satellite imagery. In the forests of western Canada, Bergmuller et al. (2022) used multispectral drone imagery to map tree mortality with approximately 70% and 80% accuracy for deciduous and coniferous trees, respectively. In China, Meng et al. (2022) used multispectral satellite imagery to map Southern Pine Beetle infestations with an accuracy > 80%. In pine forests in northern China, Zhan et al. (2020) used GF-2 and Sentinel-2 imagery to map tree mortality with 78% accuracy. Pasquarella et al. (2018) mapped defoliation from a Spongy moth outbreak with Landsat imagery using a tasseled-cap greenness index, but they did not model actual mortality.
Defoliation is a driving factor in tree mortality which can be compounded by various environmental stressors. Soil and geology characteristics may directly impact water availability, root depth, and nutrient availability, which can stress trees and decrease survival during defoliation events. Topographic position (i.e. hilltop, mid-slope or valley) can affect light and precipitation distribution, ground surface temperature, soil conditions, and depth of the groundwater which collectively determine water availability (Zhang et al., 2022; Øystein et al, 2015; Dunn et al., 1987). When analyzing factors related to the cause of death of individual trees in tropical moist forests of northern Amazonia, Toledo (2011) found that soil characteristics and topographic relief together accounted for 20% of the mortality variation. They also found that tree mortality was higher on steep slopes and on sandy soils in valleys while mortality was lower on plateaus with clay soils. Drought conditions reduce water availability and adds stress to defoliated tree (Bottero et al., 2017, Ramsfield et al., 2016; Choat et al., 2018). Guarin et al. (2005) found correlations between multiple years of drought and tree mortality. Tree in close proximity to roads and urban areas may experience greater stress and mortality due to exposure to de-icing salt (Kayama et al., 2003, Horsley et al., 2002) and air pollution (Trumbore et al., 2015, Percy et al., 2004). Forests near urban areas may have higher exposure to invasive plants or pests (Stravinskienė et al., 2018). In New England (U.S.), Davidson et al. (1999) reported that overall tree health significantly affected survival from Spongy moth outbreaks with mortality rates of 36% and 7% for unhealthy and healthy trees, respectively.
Forest characteristics can influence tree mortality during pest outbreaks. Some invasive pests have species-specific feeding preferences and, thus, preferred species tend to be more heavily affected (Davidson et al., 1999; Barbosa et al., 1978). Canopy cover has been found to be an important factor in mortality prediction, although the effect was not consistent across different forest types and environments (Campbell et al., 2020, Gunst et al., 2016, Dorman et al., 2015, Das et al., 2011). In woodland areas of Utah, Campbell et al. (2020) found that high mortality is more likely to occur in areas with low-to-moderate canopy cover (5–25%). Gunst et al. (2016) found that higher canopy cover was positively associated with tree mortality in xeric pine forests but negatively associated with tree mortality in red fir forests, during wet years. Das et al. (2011) found that canopy cover did not have a major effect on mortality in forests in the western U.S. where mortality was driven by insects and diseases. Dorman et al. (2015) found no association between canopy cover and mortality in pine forests.
A few studies have included both tree mortality and environmental geospatial data as predictors in tree mortality models. In open canopy semi-arid woodlands, Campbell et al. (2020) found that the inclusion of environmental factors significantly improved predictions of tree mortality when combined with defoliation mapped from high-resolution aerial imagery and Landsat imagery. Navarro-Cerrillo et al. (2019) mapped dead holm oak trees (Quercus ilex) with 87% accuracy using WorldView-2 satellite imagery and light detection and ranging (LiDAR) data. They identified a link between high mortality and specific soil properties in Spanish woodlands impacted by root rot decline.
Most tree mortality models have been based solely on defoliation metrics and they tended to be conducted in coniferous forests which were dominated by few tree species (Meddens et al., 2013; Meddens et al., 2014; Bergmüller et al., 2022; Goodwin et al., 2008; Shearman et al., 2019). Studies that modeled tree mortality for mixed deciduous forests, with a wide variety of species, tended use very high-resolution drone imagery (Bergmüller et al., 2022, Long et al., 2016), which is not practical to collect for large study areas. Some studies have included both environmental factors and satellite-based defoliation maps in tree mortality models (Campbell et al. 2020, Navarro-Cerrillo et al., 2019); however, we are not aware of any studies that have applied these methods in temperate deciduous forests.
The objective of this paper is to model tree mortality resulting from a 2015–2017 Spongy moth outbreak in the temperate deciduous forest in Rhode Island, located in the northeastern U.S. We use a random forest approach to model mortality based on defoliation and environmental factors. We map defoliation from Landsat imagery and include geospatial data representing topography, climate, soil, and vegetation characteristics in modeling mortality. This research explores the importance of defoliation and environmental factors, represented by geospatial data, in predicting tree mortality from forest pest outbreaks.