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

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.
Forests 2013, 4(4), 808-829; https://doi.org/10.3390/f4040808
Received: 13 August 2013 / Revised: 12 September 2013 / Accepted: 23 September 2013 / Published: 11 October 2013
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. View Full-Text
Keywords: seedling regeneration; object based image analysis; hierarchical classification seedling regeneration; object based image analysis; hierarchical classification
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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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