Harnessing the Power of Geospatial Data with Random Forest to Forecast Gypsy Moth Defoliation

Authors: Zhiyue Xia*, University of Maryland, Department of Geographical Science, Laura Leites, The Pennsylvania State University, Department of Ecosystem Science and Management , Douglas Miller, The Pennsylvania State University, Department of Ecosystem Science and Management
Topics: Natural Resources, Environment, Spatial Analysis & Modeling
Keywords: Forest Disturbance, Ecological Model
Session Type: Guided Poster
Day: 4/4/2019
Start / End Time: 9:55 AM / 11:35 AM
Room: Roosevelt 3.5, Marriott, Exhibition Level
Presentation File: No File Uploaded

The Gypsy moth, Lymantria dispar, is an exotic forest pest that was introduced to the USA in 1869. Since then it has spread continuously across the majority of the northeastern US. Larvae of this insect prefer feeding on oak species, although other species also serve as host trees. During an outbreak, larvae defoliate forests across large regions and repeated defoliation can predispose the trees to attacks by secondary insect pests or fungal infections causing tree mortality. Development of forecasting models remains a challenge despite their potential usefulness in effectively mobilizing resources to deal with the outbreaks. Previous studies indicate that vegetation attributes measured through remote sensing, as well as terrain, and climate characteristics influence the likelihood gypsy moth outbreaks. In addition, temporal and spatial variables describing the cyclic and spatial patterns of the outbreaks could be very valuable in forecasting outbreaks. We develop a model that forecasts gypsy moth outbreaks using Pennsylvania as a case study. A large suite of temporal and spatial predictor variables are derived from remote sensing, climate, topographic and inventory data, while the occurrence of the outbreak is obtained from annual outbreak sketch maps. We use the machine learning modeling algorithm Random Forests which has a well-documented predictive ability and can deal with a large number of variables. We present modeling results and an assessment of the model performance. An accurate forecasting model will be of critical importance for projecting the spatial extent of future outbreaks and for forest management planning.

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