A fuzzy-constrained cellular automata model of forest insect infestations

Geographical and ecological processes are complex systems where individual elements interact to create complex behaviour. These systems can be examined with spatially explicit models such as cellular automata (CA) that explain how interactions at the local level lead to global patterns. Tree mortality patterns caused by forest insects provide a good case for CA as local interactions lead to changes at the landscape level. However, problems exist with defining aspects of insect–host relationships that explain the susceptibility of a tree to insect attack. The main objective of this study is to develop a GIS-based CA model of forest insect infestations that incorporates fuzzy set theory in order to obtain information from high resolution remote sensing (RS) images. The model is based on tree mortality patterns caused by outbreaks of mountain pine beetle (MPB), Dendroctonus ponderosae Hopkins, in the central interior of British Columbia, Canada. Fuzzy sets are used in order to represent the susceptibility of trees to MPB attack and to acknowledge the uncertainty inherent in dealing with geospatial data. Fuzzy values provide the input for the CA Sub-Model where MPB attack behaviour is constrained by the susceptibility level of trees. The results from the model reflect the process of MPB infestations as described in the literature and exhibit non-linear dynamics expected in ecological processes. This study reveals that fuzzy-constrained CA modelling can provide useful information for forest management in the presence of insect outbreaks.