High spatial resolution remotely sensed imagery can be a benefit to ecologists studying vegetation patterns and processes at a variety of scales.  Conventional pixel-based classification methods developed for course-scale imagery, however, can fall short of providing useful information from these data sources.  Object-based image analysis (OBIA) is a new paradigm of remote sensing that can solve many problems associated with pixel-based methods.  OBIA methods, which typically include both image segmentation and object classification, merge pixels into more ecologically meaningful objects.  These objects can be multiscalar and multitemporal in their relationships, aiding in the representation and interpretation of complex ecosystem processes represented in one or more remotely sensed scenes.  As a result, applications of OBIA are increasing throughout the field of ecology.  While they can be time consuming and expensive to implement at the current time, OBIA methods can be of great utility in distilling ecological pattern and process from voluminous amounts of data.
Results/Conclusions

This paper will highlight some of the successes and challenges facing ecologists using object-based image analysis, with examples from forest and wetland systems.