Title: Monitoring urban tree cover using object-based image
analysis and public domain remotely sensed data Author: Moskal,
L. Monika; Styers, Diane M.; Halabisky, Meghan. Source: Remote
Sensing. 3(10): 2243-2262 Description: Urban forest ecosystems
provide a range of social and ecological services, but due to
the heterogeneity of these canopies their spatial extent is
difficult to quantify and monitor. Traditional per-pixel
classification methods have been used to map urban canopies,
however, such techniques are not generally appropriate for
assessing these highly variable landscapes. Landsat imagery has
historically been used for per-pixel driven land use/land cover
(LULC) classifications, but the spatial resolution limits our
ability to map small urban features. In such cases, hyperspatial
resolution imagery such as aerial or satellite imagery with a
resolution of 1 meter or below is preferred. Object-based image
analysis (OBIA) allows for use of additional variables such as
texture, shape, context, and ot