Object-based classification of semi-arid wetlands

Meghan Halabisky; L. Monika Moskal; Sonia A. Hall

doi:10.1117/1.3563569

1 January 2011 Object-based classification of semi-arid wetlands

Meghan Halabisky, L. Monika Moskal, Sonia A. Hall

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 5, Issue 1, 053511 (January 2011). https://doi.org/10.1117/1.3563569

Abstract

Wetlands are valuable ecosystems that benefit society. However, throughout history wetlands have been converted to other land uses. For this reason, timely wetland maps are necessary for developing strategies to protect wetland habitat. The goal of this research was to develop a time-efficient, automated, low-cost method to map wetlands in a semi-arid landscape that could be scaled up for use at a county or state level, and could lay the groundwork for expanding to forested areas. Therefore, it was critical that the research project contain two components: accurate automated feature extraction and the use of low-cost imagery. For that reason, we tested the effectiveness of geographic object-based image analysis (GEOBIA) to delineate and classify wetlands using freely available true color aerial photographs provided through the National Agriculture Inventory Program. The GEOBIA method produced an overall accuracy of 89% (khat = 0.81), despite the absence of infrared spectral data. GEOBIA provides the automation that can save significant resources when scaled up while still providing sufficient spatial resolution and accuracy to be useful to state and local resource managers and policymakers.

©(2011) Society of Photo-Optical Instrumentation Engineers (SPIE)

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Meghan Halabisky, L. Monika Moskal, and Sonia A. Hall "Object-based classification of semi-arid wetlands," Journal of Applied Remote Sensing 5(1), 053511 (1 January 2011). https://doi.org/10.1117/1.3563569

Published: 1 January 2011

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