Integrating high resolution remote sensing, GIS and fuzzy set theory for identifying susceptibility areas of forest insect infestations

The use of fuzzy set theory has become common in remote sensing and geographical information system (GIS) applications to deal with issues surrounding the uncertainty of geospatial datasets. The objective of this study is to develop a model that integrates the concept of fuzzy set theory with remote sensing and GIS in order to produce susceptibility maps of insect infestations in forest landscapes. Fuzzy set theory was applied to information extracted from multiple-year high resolution remote sensing data and integrated in a raster-based GIS to create a map indicating the spatial variation of insect susceptibility in a landscape. Variable-specific fuzzy membership functions were developed based on expert knowledge and existing data, and integrated through a semantic import model. The results from a case study on mountain pine beetle (Dendroctonus ponderosae Hopkins) illustrate that the model provides a method to successfully estimate areas of varying susceptibility to insect infestation from high resolution remote sensing images. It was concluded that fuzzy sets are an adequate method for dealing with uncertainty in defining susceptibility variables. The susceptibility maps can be utilized for guiding management decisions based on the spatial aspects of insect-host relationships.