Research Papers

Historic land cover change in the agricultural Midwest using an object-based approach for classification of high-resolution imagery

[+] Author Affiliations
Sarah Porter

United States Department of Agricultural Research Service, National Laboratory for Agriculture and the Environment, 2110 University Boulevard, Ames, Iowa 50011-3120

Marc Linderman

University of Iowa, Department of Geography, Iowa City, Iowa 52242

J. Appl. Remote Sens. 7(1), 073506 (Sep 12, 2013). doi:10.1117/1.JRS.7.073506
History: Received February 28, 2013; Revised June 12, 2013; Accepted August 15, 2013
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Abstract.  A semiautomated classification methodology was implemented using historic, high-resolution aerial photography in a dominant agricultural landscape. An object-based segmentation approach was applied to study land cover change from 1930 through 1990 in Johnson County, Iowa, in the Midwestern United States. A critical analysis of the approach is discussed, emphasizing the ability of the methodology to generate landscape metrics that can accurately characterize the quality of the landscape, particularly the high-resolution landscape features that are so important in a highly modified landscape. Landscape analysis includes a discussion of both the changes in the areal composition of land cover types and also the structural changes that are captured using both patch- and landscape-level metrics. Results were compared with countywide statistics from the United States Department of Agriculture as well as similar landscape studies, and provide evidence of agricultural intensification. Results also indicate some counterintuitive processes occurring from what is expected of a landscape undergoing this type of transformation, suggesting that altering the scale of study may provide different insight into land cover change dynamics.

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© 2013 Society of Photo-Optical Instrumentation Engineers

Citation

Sarah Porter and Marc Linderman
"Historic land cover change in the agricultural Midwest using an object-based approach for classification of high-resolution imagery", J. Appl. Remote Sens. 7(1), 073506 (Sep 12, 2013). ; http://dx.doi.org/10.1117/1.JRS.7.073506


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