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Wednesday, April 11 • 2:30pm - 2:45pm
SYMPOSIA-13: Harnessing the Power of Geospatial Data with Random Forest to Forecast Gypsy Moth Outbreaks

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AUTHORS: Zhiyue Xia, Department of Ecosystem Science and Management, The Pennsylvania State University; Laura Leites, Department of Ecosystem Science and Management, The Pennsylvania State University; Douglas Miller, Department of Geography and Department of Ecosystem Science and Management, The Pennsylvania State University; Andrew Liebhold, US Forest Service Northern Research Station, Morgantown, WV

ABSTRACT: 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 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. Gypsy moth outbreaks are episodic and are difficult to predict. 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.In this study, we develop a model that forecasts gypsy moth outbreaks using Pennsylvania as a case study. We use systematic sampling and locate 8,424 sampling points across forest areas of Pennsylvania and focus on outbreaks during the time period 200-2016 to train the model. For each point, 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 that focuses on hindcasting outbreaks during the period 1980-1990. An accurate forecasting model will be of critical importance for projecting the spatial extent of future outbreaks and for forest management planning.

Wednesday April 11, 2018 2:30pm - 2:45pm CDT
Water Tower Parlor