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Segmentation and object-oriented classification of wetlands in a karst Florida landscape using multi-season Landsat-7 ETM+ Imagery
Citation:
Frohn, R. C., B. C. AUTREY, C. R. LANE, AND M. Reif. Segmentation and object-oriented classification of wetlands in a karst Florida landscape using multi-season Landsat-7 ETM+ Imagery. International Journal of Remote Sensing. Taylor & Francis Group, London, Uk, 32(5):1471-1489, (2011).
Impact/Purpose:
The purpose of this research project is to provide methods, tools and guidance to Regions, States and Tribes to support the TMDL program. This research will investigate new measurement methods and models to link stressors to biological responses and will use existing data and knowledge to develop strategies to determine the causes of biological impairment in rivers and streams. Research will be performed across multiple spatial scales, site, subwatershed, watershed, basin, ecoregion and regional/state.
Description:
Segmentation and object-oriented processing of single-season and multi-season Landsat-7 ETM+ data was utilized for the classification of wetlands in a 1560 km2 study area of north central Florida. This segmentation and object-oriented classification outperformed the traditional maximum likelihood algorithm (MLC) in accurately mapping wetlands, with overall accuracies of 90.2% (single-season imagery) and 90.8% (multi-season imagery), compared to overall accuracies ofor the MLC classifiers of 78.4% and 79.0%, respectively. Kappa coefficients were over 1.5 times greater for the segmentation/object-oriented classifications than for the MLC classifications and producer and user accuracies were also higher. The producer accuracies of the segmentation/object-oriented classifications were 90.8% (single-season) and 91.6% (multi-season), compared to 70.6% and 74.4%, respectively, for the MLC classifications. User accuracies were 73.9% and 73.5% for the single-season and multi-season segmentation/object-oriented classifications, respectively, compared to 54.1% (single-season) and 55.0% (multi-season) for the MLC classifications. The use of multi-seasonal data resulted in only a slight increase in overall accuracy over the single-season imagery. This small increase was primarily due to better discrimination of riparian wetlands in the multi-season data. Segmentation and object-oriented processing provides a low-cost, high acc uracy method for classification of wetlands on a local, regional, or national basis.