EventsInternational Electronic Conference on Sensors and Applications
Published
This submission belongs to the session g. Applications of the event International Electronic Conference on Sensors and Applications
Published date
02 Jun, 2014
Citation
Sunhui Sim, Dongha Lee, Object-Based Feature Extraction of Google Earth Imagery for Mapping Termite Mounds in Amazon's Savannas, in Proceedings of International Electronic Conference on Sensors and Applications, 1 June–16 June 2014, MDPI: Basel, Switzerland, doi: 10.3390/ecsa-1-g003
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Object-Based Feature Extraction of Google Earth Imagery for Mapping Termite Mounds in Amazon's Savannas

Dongha Lee 1
1. University of North Alabama
Abstract
This study investigates the potential of object-based feature extraction from Google Earth Imagery for mapping termite mounds in Amazon's savannas. 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 in Amazon's savannas.
Keywords
Object-based image analysis
Feature extraction
Remote sensing
Google Earth
Termite mounds
Amazon
Manuscript
Poster
ECSA-1_Object-Based Feature Extraction of Google Earth Imagery_Presentation_Sim et al.pdf
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