 This paper examined the potential of object-based image analysis, OBA, to accurately identify and classify urban forest ecosystems. The authors compared the accuracy of OBA-driven LULC classifications to those generated from traditional per-pixel driven approaches. They found that OBA was able to produce better and more consistent results than per-pixel driven methods, particularly when using hyperspatial data with a resolution of one meter or less. Additionally, they noted that spectral content was more important than spatial detail when it came to OBA-driven LULC classifications. This suggests that OBIA may be a useful tool for mapping urban forest ecosystems, especially when high spatial resolution data is unavailable. This article was authored by Megan Halibisky, Diane M. Steyers, and L. Monica Moskel.