Abstract
This paper analyses the potential of a high-resolution airborne remote sensing system, the Digital Multi-Spectral Imagery (DMSI), for detecting canola growth variability within a field to help farmers for future incorporation of the system into sitespecific crop management approaches for agriculture. Transect sampling within a canola field of a broad acre agricultural property in the South West of Western Australia was conducted synchronous with the capture of one-meter spatial resolution DMSI. Four individual bands (blue, green, red, and NIR) and five image transformations namely the Normalized Difference Vegetation Index (NDVI), Normalized Difference Vegetation Index ‐ Green (NDVI-green), Soil Adjusted Vegetation Index (SAVI), Photosynthetic Vigor Ratio (PVR) and Plant Pigment Ratio (PPR) of DMSI were investigated. Canola density was correlated with the four individual bands and five image transformations, while the LAI was correlated with the four individual bands. The NDVI-green, red and near-infrared bands of DMSI produced the best correlations with the density of canola, whereas the LAI had significant (� � 0.05) negative correlations with the blue (� 0.93) and red (� 0.89) DMSI bands, and a significant positive correlation were found with the nearinfrared band (0.82).
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