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Sustainability assessment is widely used to monitor public policy toward sustainable development (SD). However, such tools have been less developed at the local level. This research examined sustainability indicators (SIs) applied at the neighborhood scale. The indicators were developed by examination of previously developed sustainability rating systems and issues specific to the City of Birmingham, Alabama, USA. The indicators of Neighborhood Sustainability Assessment (NSA) systems addressed the three major dimensions of sustainability: economic, environmental, and social. The rating system was applied to all neighborhoods, and geographical patterns were analyzed. The ability to analyze the sustainability of all neighborhoods within the city provides information on which areas are performing well and which areas need more attention to become more sustainable.

This study investigates the potential of object-based feature extraction from Google Earth Imagery for mapping termite mounds. Termite mounds are often hotspots of plant growth (i.e. primary productivity). Accurate and timely information about termite mounds is crucial for land management decision-making and ecosystem monitoring. To address this issue, the effectiveness of object-based feature extraction that use automated image segmentation to extract meaningful ground features from imagery was tested. The study used very high resolution multispectral Google Earth images to produce termite mounds maps in Bahia, Brazil. The results from the study indicated that an object-based approach provides a better means for ground feature extraction than a pixel based method because it provides an effective way to incorporate spatial information and expert knowledge into the feature extraction process. Also the results suggest that Google earth imagery has considerable potential in mapping termite mounds.

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