The physiological status of forest canopy foliage is influenced by a range of factors that affect leaf pigment content and function. Recently, several indices have been developed from remotely sensed data that attempt to provide robust estimates of leaf chlorophyll content. These indices have been developed from either hand-held spectroradiometer spectra or high spectral resolution (or hyperspectral) imagery. We determined if two previously published indices (Datt 1999), which were specifically developed to predict chlorophyll content in eucalypt vegetation by remote sensing at the leaf scale, can be extrapolated accurately to the canopy. We derived the two indices from hand-held spectroradiometer data of eucalypt leaves exhibiting a range of insect damage symptoms. We also derived the indices from spectra obtained from high spectral and spatial resolution Compact Airborne Spectrographic Imager 2 (CASI-2) imagery to determine if reasonable estimates at a scale of < 1 m can be achieved. One of the indices (R 850/R 710 index, where R is reflectance) derived from hand-held spectroradiometer data showed a moderate correlation with relative leaf chlorophyll content (r = 0.59, P < 0.05) for all dominant eucalypt species in the study area. The R (850)/R (710) index derived from CASI-2 imagery yielded slightly lower correlations over the entire data set (r = 0.42, P < 0.05), but correlations for individual species were high (r = 0.77, P < 0.05). A scaling analysis indicated that the R (850)/R (710) index was strongly affected by soil and water cover types when pixels were mixed, but appeared to be invariant to changes in proportions of understory, which may limit its application.

Mapping the spatial distribution of individual species is an important ecological and forestry issue that requires continued research to coincide with advances in remote-sensing technologies. In this study, we investigated the application of high spatial resolution (80 cm) Compact Airborne Spectrographic Imager 2 (CASI-2) data for mapping both spectrally complex species and species groups (subgenus grouping) in an Australian eucalypt forest. The relationships between spectral reflectance curves of individual tree species and identified statistical differences among species were analysed with ANOVA. Supervised maximum likelihood classifications were then performed to assess tree species separability in CASI-2 imagery. Results indicated that turpentine (Syncarpia glomulifera Smith), mesic vegetation (primarily rainforest species), and an amalgamated group of eucalypts could be readily distinguished. The discrimination of S. glomulifera was particularly robust, with consistently high classification accuracies. Eucalypt classification as a broader species group, rather than individual species, greatly improved classification performance. However, separating sunlit and shaded aspects of tree crowns did not increase classification accuracy.

Since the beginning of remote sensing observation, scientists have created a ?toolbox? with which to observe the varying dimensions of the Earth?s dynamic surface. Hyperspectral imaging represents one of the later additions to this toolbox, emerging from the fields of aerial photography, ground spectroscopy and multi-spectral imaging. This new tool provides capacity to characterise and quantify, in considerable detail, the Earth?s diverse environments

Remote sensing technology has increased in both application and significance over the past decade, and now pronnises an improved observation strategy for enhanced mine environmental management. However, its utilisation as a common monitoring tool within the mining industry is less prevalent than for other primary production seotors such as agriculture and forestry. This research investigates the application of multispectral WorldView-2 (WV2) imagery to map surface rehabilitation at the Ulan Coal Mines Limited (UCML), Australia, This new concept for monitoring a rehabilitation process focuses on the assessment of discrete mine site environments by mapping vegetation health using the Normalised Difference Vegetation Index (NDVI) as an indicator. This satellite based approach clearly identifies subtle changes in vegetation composition and health across an otherwise homogenous revegetated surface and proves a valuable addition for mine rehabilitation management.