SREL Reprint #2862

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. pp. 123-136 In Proceedings of the 4th Southern Forestry and Natural Resources GIS Conference. Warnell School of Forest Resources, University of Georgia, Athens, GA.

This information was provided by the University of Georgia's Savannah River Ecology Laboratory (srel.uga.edu).