DELINEATING SANDHILL COMMUNITIES: THE USE OF ADVANCED TECHNIQUES TO EXTRACT FEATURES FROM SATELLITE IMAGERY
Steven J. Harper and Rebecca R. Sharitz
Savannah River Ecology Laboratory, University of Georgia, Aiken, SC 29802
ABSTRACT
High-resolution satellite imagery is now routinely acquired and used by
diverse agencies and organizations, and these remotely-sensed data often
form the foundation of natural resource layers within GIS databases. While
the mapping of environmental and ecological features (e.g., those related
to vegetation, land use, and disturbance) can provide valuable information
to natural resource managers, maintaining up-to-date databases requires
a major investment of time and labor. Historically, only highly-trained
and experienced personnel could extract useful information from remotely-sensed
imagery, which resulted in a bottleneck that prevented widespread utilization.
Advanced software applications have been developed recently that
provide users with ready access to powerful statistical techniques for
extracting object-specific features from high-resolution panchromatic
and multi-spectral imagery. For example, machine learning algorithms (e.g.,
neural networks, nearest neighbor, decision trees) allow the efficient
extraction of user-defined features by utilizing spatial context in addition
to spectral signatures. Similarly, hierarchical learning methods support
improved image classification through iterative feedback provided by users.
To demonstrate the utility of these approaches to forestry and resource
management, an example is presented in which sandhills (xeric communities
that support numerous sensitive species) are extracted from surrounding
habitats located along the interface of the Piedmont and Coastal Plain
in the southeastern U.S. Results highlight the importance of federal lands
in supporting this habitat throughout the region. Further
development of feature extraction tools may allow up-to-date GIS data
to be produced efficiently with reduced labor which, in turn, will help
resource managers make effective decisions despite limited budgets and
time constraints.
KEYWORDS. Sandhills, feature extraction, image analysis,
classification, segmentation.
SREL Reprint #2862
Harper, S. J. and R. Sharitz. 2005. Delineating sandhill communities: the use of advanced techniques to extract features from satellite imagery. p. 123-136. In Proceedings of the 4th Southern Forestry and Natural Resources GIS Conference. Warnell School of Forest Resources, University of Georgia, Athens, GA.
