Assessment of Pinus sylvestris L. tree health in urban forests at highway sides in Lithuania

Stravinskienė, Vida; Bartkevičius, Edmundas; Abraitienė, Jolita; Dautartė, Anželika

doi:10.1016/j.gecco.2018.e00517

Cited by 2 publications

(1 citation statement)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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 signi cantly affected survival from Spongy moth outbreaks with mortality rates of 36% and 7% for unhealthy and healthy trees, respectively.…”

Section: Introductionmentioning

confidence: 99%

Modeling Spongy Moth Forest Mortality in Rhode Island Temperate Deciduous Forest

Dumarevskaya,

Parent

2024

Preprint

View full text Add to dashboard Cite

Invasive pests cause major ecological and economic damages to forests around the world. Satellite imagery is an important tool for monitoring defoliation at broad scales, but environmental conditions can affect whether defoliation leads to mortality. In this study, we modeled forest mortality resulting from a 2015-2017 Spongy moth outbreak in the temperate deciduous forests of Rhode Island (northeastern U.S.). We used Landsat-based defoliation mapping and geospatial environmental data with Random Forest to model mortality severity of canopy trees at a 100 m spatial resolution. Defoliation was mapped based on declines in Normalized Difference Vegetation Index (NDVI) in outbreak years compared to baseline NDVI from pre-outbreak years. Other predictors included geospatial data representing soil characteristics, drought condition, and forest characteristics as well as proximity to coast, development, and water. The Random Forest tool in Python Sklearn was used to model forest mortality with 2 classes (low/high) and 3 classes (low/med/high). The best models had overall accuracies of 82% and 65% for the 2-class and 3-class models, respectively. The most important predictors of forest mortality were defoliation, distance to coast, and canopy cover. Soils and topography had minimal importance in the models possibly due to limitations of the data and limited variability within our study area. Repeated defoliations were relatively rare during the outbreak. Model performance improved only slightly with the inclusion of more than 3 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.

show abstract