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Forests 2013, 4(4), 808-829; doi:10.3390/f4040808

Monitoring Post Disturbance Forest Regeneration with Hierarchical Object-Based Image Analysis

1,*  and 2
1 Remote Sensing and Geospatial Analysis Laboratory, School of Environmental and Forest Sciences, College of the Environment, University of Washington, Box 352100, Seattle, WA 98195, USA 2 Kansas Applied Remote Sensing Program, University of Kansas, Higuchi Hall, 2101 Constant Avenue, Lawrence, KS 66047, USA
* Author to whom correspondence should be addressed.
Received: 13 August 2013 / Revised: 12 September 2013 / Accepted: 23 September 2013 / Published: 11 October 2013
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The main goal of this exploratory project was to quantify seedling density in post fire regeneration sites, with the following objectives: to evaluate the application of second order image texture (SOIT) in image segmentation, and to apply the object-based image analysis (OBIA) approach to develop a hierarchical classification. With the utilization of image texture we successfully developed a methodology to classify hyperspatial (high-spatial) imagery to fine detail level of tree crowns, shadows and understory, while still allowing discrimination between density classes and mature forest versus burn classes. At the most detailed hierarchical Level I classification accuracies reached 78.8%, a Level II stand density classification produced accuracies of 89.1% and the same accuracy was achieved by the coarse general classification at Level III. Our interpretation of these results suggests hyperspatial imagery can be applied to post-fire forest density and regeneration mapping.
Keywords: seedling regeneration; object based image analysis; hierarchical classification seedling regeneration; object based image analysis; hierarchical classification
This is an open access article distributed under the Creative Commons Attribution License (CC BY) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Moskal, L.M.; Jakubauskas, M.E. Monitoring Post Disturbance Forest Regeneration with Hierarchical Object-Based Image Analysis. Forests 2013, 4, 808-829.

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