High
spatial resolution multi-spectral imagery - aerial photographs, 1-m
ADAR, 1-m Quickbird, 4-m IKONOS - are useful in numerous natural
resource and
environmental
applications, but the
imagery presents technical challenges for us. The detail found
in these imagery can overwhelm often different from those produced
by coarse resolution imagery. For example, traditional
pixel-based classifiers do not work well with high spatial
resolution imagery when the mapping target (e.g. an oak tree) is
larger than the
pixel size.
Object-Based
Image Analysis (OBIA) is a tentative name for a sub-discipline of
GIScience devoted to partitioning remote sensing imagery into
meaningful image-objects, and assessing their characteristics through
spatial, spectral and temporal scale. At its most fundamental level,
OBIA requires image segmentation, attribution, classification and the
ability to query and link individual image-objects in space
and time. In order to achieve this, OBIA incorporates knowledge from
a vast array of disciplines involved in the generation and use of geographic
information.
We are using these tools for a range of applications, including wetlands,
sudden oak death, and fire mapping.
- Tidal wetlands: how can we better capture multi-scale functioning
of restored and historic wetland vegetation patterns? Karin
Tuxen is
involved in this effort.
- Sudden Oak Death: OBIA methods help us better capture tree mortality
through time, as well as monitor dynamics of disease related changes
to forest structure (e.g. gaps). Tim
DeChant is involved
in this effort.
- Fire Mapping: we are using OBIA methods with free NAIP imagery to
map urban land use primatives for fire modeling. Casey
Cleve and the Fire Center are involved in this effort.
- Monitoring Forests: Marek
Jakubowski is evaluating different image segmentation algorithms
for appliction in land use change and forest monitoring.
More information:
OBIA Wiki
OBIA@Berkeley 2007
GEOBIA 2008
