Research Papers

Enhancing a eucalypt crown condition indicator driven by high spatial and spectral resolution remote sensing imagery

[+] Author Affiliations
Bradley Evans

Macquarie University, Department of Biological Sciences, University Avenue, Macquarie Park, New South Wales, 2113, Australia

Murdoch University, Centre of Excellence for Climate Change, Woodland and Forest Health, 90 South Street, Murdoch, Western Australia, 6150, Australia

Tom Lyons, Giles Hardy

Murdoch University, Centre of Excellence for Climate Change, Woodland and Forest Health, 90 South Street, Murdoch, Western Australia, 6150, Australia

Paul Barber

Murdoch University, Centre of Excellence for Climate Change, Woodland and Forest Health, 90 South Street, Murdoch, Western Australia, 6150, Australia

Arbor Carbon Environmental and Arborcultural Consultants P.O. BOX 1065 Willagee Central, Western Australia, 6156, Australia

Christine Stone

Forest Science Centre, NSW Department of Primary Industries, P.O. Box 100, Beecroft, NSW, Australia, 2119

J. Appl. Remote Sens. 6(1), 063605 (Dec 04, 2012). doi:10.1117/1.JRS.6.063605
History: Received March 2, 2012; Revised October 22, 2012; Accepted October 25, 2012
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Abstract.  Individual crown condition of Eucalyptus gomphocephala was assessed using two classification models to understand changes in forest health through space and time. Using high resolution (0.5 m) digital multispectral imagery, predictor variables were derived from textural and spectral variance of all pixels inside the crown area. The results estimate crown condition as a surrogate for tree health against the total crown health index. Crown condition is derived from combining ground-based crown assessment techniques of density, transparency, dieback, and the regrowth of foliage. This object-based approach summarizes the pixel data into mean crown indices assigned to crown objects which became the carrier of information. Models performed above expectations, with a significant weighted Cohen’s kappa (κ>0.60 and p<0.001) using 70% of available data. Using in situ data for model development, crown condition was predicted forwards (2010) and backwards (2007) in time, capturing trends in crown condition and identifying decline in the healthiest between 2008 and 2010. The results confirm that combining spectral and textural information increased model sensitivity to small variations in crown condition. The methodology provides a cost-effective means for monitoring crown condition of this or other eucalypt species in native and plantation forests.

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© 2012 Society of Photo-Optical Instrumentation Engineers

Citation

Bradley Evans ; Tom Lyons ; Paul Barber ; Christine Stone and Giles Hardy
"Enhancing a eucalypt crown condition indicator driven by high spatial and spectral resolution remote sensing imagery", J. Appl. Remote Sens. 6(1), 063605 (Dec 04, 2012). ; http://dx.doi.org/10.1117/1.JRS.6.063605


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