Mapping deciduous forest ice storm damage using Landsat and environmental data

Sugar maple (Acer Saccharum Marsh.) damage resulting from a severe ice storm was modeled and mapped over eastern Ontario using pre- and post-storm Landsat 5 imagery and environmental data. Visual damage estimates in 104 plots and corresponding reflectance and environmental data were divided into multiple, mutually exclusive training and reference datasets for damage classification evaluation. Damage classification accuracy was compared among four methods: multiple regression, linear discriminant analysis, maximum likelihood, and neural networks. Using the best classifier, various stratification methods were assessed for potential inflationary effects on classification accuracy due to spatial proximity between training and reference data. Of the classifiers that were evaluated, neural networks performed best. Neural networks ‘learn’ training data accurately (94% overall), but classify proximate reference data less accurately (65%), and distant, spatially independent reference data least accurately (55%). Results indicate that, while remotely sensed and environmental data cannot discriminate among many levels of deciduous ice storm damage, they can by considered useful for differentiating areas of low to medium damage from areas of severe damage (69% accuracy). Such classification methods can provide regional damage maps more objectively than point-based visual estimates or aerial sketch mapping and aid in identification of areas of severe damage where management intervention may be advantageous.