Classification of the wildland–urban interface: A comparison of pixel- and object-based classifications using high-resolution aerial photography

Abstract

The expansion of urban development into wildland areas can have significant consequences, including an increase in the risk of structural damage from wildfire. Land-use and land-cover maps can assist decision-makers in targeting and prioritizing risk mitigation activities, and remote sensing techniques provide effective and efficient methods to create such maps. However, some image processing approaches may be more appropriate than others in distinguishing land-use and land-cover categories, particularly when classifying high spatial resolution imagery for urbanizing environments. Here we explore the accuracy of pixel-based and object-based classification methods used for mapping in the wildland–urban interface (WUI) with free, readily available, high spatial resolution urban imagery, which is available in many places to municipal and local fire management agencies. Results indicate that an object-based classification approach provides a higher accuracy than a pixel-based classification approach when distinguishing between the selected land-use and land-cover categories. For example, an object-based approach resulted in a 41.73% greater accuracy for the built area category, which is of particular importance to WUI wildfire mitigation.