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Imputing forest inventory data to stands formed by image segmentation in Maryland's Green Ridge State ForestAuthor(s): Andrew Lister; Harry Kahler; Alexander Clark; Francis Zumbrun; Jack Perdue
Source: In: Bettinger, P.; Merry, K.; Fei, S.; Drake, J.; Nibbelink, N.; Hepinstall, J., eds. Proceedings of the 6th Southern Forestry and Natural Resources GIS Conference. Athens, GA: University of Georgia, Warnell School of Forestry and Natural Resources: 1-12.
Publication Series: Paper (invited, offered, keynote)
Station: Northern Research Station
PDF: Download Publication (554.0 KB)
DescriptionMaking stand based forest management decisions is difficult for landowners of large tracts because creating, managing, and updating stand maps is expensiv e and time consuming. Recently, advances in image processing in the area of image segmentation have made this task easier. The goal of the study reported here was to develop a method to delineate forest stands using image segmentation, assign to them fores t characteristics, and assess their quality. We combined Landsat satellite and National Aerial Imagery Program (NAIP) aerial imagery to create a multispectral, 5-m resolution dataset, used image segmentation to delineate homogeneous landscape polygons, and used these polygons to aggregate a pixel-based map that imputes plot data to each pixel on the Green Ridge State Forest in Maryland. We also developed a quality assurance assessment method for the resulting stands and pixel-based maps.
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CitationLister, Andrew; Kahler, Harry; Clark, Alexander; Zumbrun, Francis; Perdue, Jack. 2008. Imputing forest inventory data to stands formed by image segmentation in Maryland's Green Ridge State Forest. In: Bettinger, P.; Merry, K.; Fei, S.; Drake, J.; Nibbelink, N.; Hepinstall, J., eds. Proceedings of the 6th Southern Forestry and Natural Resources GIS Conference. Athens, GA: University of Georgia, Warnell School of Forestry and Natural Resources: 1-12.
KeywordsImage classification, eCognition, stand delineation, classification validation
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