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Unsupervised individual tree crown detection in high-resolution satellite imagery

[+] Author Affiliations
Alexei N. Skurikhin

Los Alamos National Laboratory, Intelligence and Space Research Division, MS-D440, P.O. Box 1663, Los Alamos, New Mexico 87545, United States

Nate G. McDowell, Richard S. Middleton

Los Alamos National Laboratory, Earth and Environmental Sciences Division, P.O. Box 1663, Los Alamos, New Mexico 87545, United States

J. Appl. Remote Sens. 10(1), 010501 (Jan 26, 2016). doi:10.1117/1.JRS.10.010501
History: Received September 24, 2015; Accepted December 23, 2015
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Abstract.  Rapidly and accurately detecting individual tree crowns in satellite imagery is a critical need for monitoring and characterizing forest resources. We present a two-stage semiautomated approach for detecting individual tree crowns using high spatial resolution (0.6 m) satellite imagery. First, active contours are used to recognize tree canopy areas in a normalized difference vegetation index image. Given the image areas corresponding to tree canopies, we then identify individual tree crowns as local extrema points in the Laplacian of Gaussian scale-space pyramid. The approach simultaneously detects tree crown centers and estimates tree crown sizes, parameters critical to multiple ecosystem models. As a demonstration, we used a ground validated, 0.6 m resolution QuickBird image of a sparse forest site. The two-stage approach produced a tree count estimate with an accuracy of 78% for a naturally regenerating forest with irregularly spaced trees, a success rate equivalent to or better than existing approaches. In addition, our approach detects tree canopy areas and individual tree crowns in an unsupervised manner and helps identify overlapping crowns. The method also demonstrates significant potential for further improvement.

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© 2016 Society of Photo-Optical Instrumentation Engineers

Citation

Alexei N. Skurikhin ; Nate G. McDowell and Richard S. Middleton
"Unsupervised individual tree crown detection in high-resolution satellite imagery", J. Appl. Remote Sens. 10(1), 010501 (Jan 26, 2016). ; http://dx.doi.org/10.1117/1.JRS.10.010501


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