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

摘要: 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 spectral variance all pixels inside the area. The results estimate as a surrogate for tree against total index. Crown is combining ground-based assessment techniques density, transparency, dieback, regrowth foliage. This object-based approach summarizes pixel data into mean indices assigned objects which became carrier information. Models performed above expectations, with significant weighted Cohen's kappa (κ > 0.60 p < 0.001) 70% available data. situ model development, predicted forwards (2010) backwards (2007) time, capturing trends identifying decline healthiest between 2008 2010. confirm that information increased sensitivity small variations condition. methodology provides cost-effective means monitoring this or other eucalypt species native plantation forests. © 2012 Society Photo-Optical Instrumentation Engineers (SPIE). (DOI: 10.1117/1.JRS.6