Journal article
Dieback classification modelling using high-resolution digital multispectral imagery and in situ assessments of crown condition
Remote Sensing Letters, Vol.3(6), pp.541-550
2012
Abstract
Quantifying dieback in forests is useful for land managers and decision makers seeking to explain spatial disturbances and understand the cyclic nature of forest health. Crown condition is assessed as reference to dieback in terms of the density, transparency, extent and in-crown distribution of foliage. At 20 sites in the Yalgorup National Park, Western Australia, a total of 80 Eucalyptus gomphocephala crowns were assessed both in situ (2008) and using two acquisitions (2008 and 2010) of airborne imagery. Each tree was assessed using four crown-condition indices: Crown Density, Foliage Transparency, the Crown Dieback Ratio and Epicormic Index combined into a single index called the Total Crown Health Index (TCHI). The airborne imagery is like value calibrated then classified and modelled using in situ canopy condition assessments resulting in a quantification of crown-condition change over time. Comparison of Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI) and a novel Red-Edge Extrema Index (REEI) suggests that the latter is more suited to classification applications of this type.
Details
- Title
- Dieback classification modelling using high-resolution digital multispectral imagery and in situ assessments of crown condition
- Authors/Creators
- B. Evans (Author/Creator) - Murdoch UniversityT.J. Lyons (Author/Creator) - Murdoch UniversityP.A. Barber (Author/Creator) - Murdoch UniversityC. Stone (Author/Creator) - Centre of Excellence for Climate Change Woodland and Forest Health, Industry & Investment NSW , PO Box 100, Beecroft , NSW , 2125 , AustraliaG. Hardy (Author/Creator)
- Publication Details
- Remote Sensing Letters, Vol.3(6), pp.541-550
- Publisher
- Taylor & Francis
- Identifiers
- 991005542805307891
- Murdoch Affiliation
- School of Biological Sciences and Biotechnology; Centre of Excellence for Climate Change and Forest and Woodland Health
- Language
- English
- Resource Type
- Journal article
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- Citation topics
- 4 Electrical Engineering, Electronics & Computer Science
- 4.169 Remote Sensing
- 4.169.91 Vegetation Mapping
- Web Of Science research areas
- Imaging Science & Photographic Technology
- Remote Sensing
- ESI research areas
- Geosciences