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Phenology is the study of periodic biological occurrences and can provide important insights into the influence of climatic variability and change on ecosystems. Understanding Australia's vegetation phenology is a challenge due to its diverse range of ecosystems, from savannas and tropical rainforests to temperate eucalypt woodlands, semiarid scrublands, and alpine grasslands. These ecosystems exhibit marked differences in seasonal patterns of canopy development and plant life-cycle events, much of which deviates from the predictable seasonal phenological pulse of temperate deciduous and boreal biomes. Many Australian ecosystems are subject to irregular events (i.e. drought, flooding, cyclones, and fire) that can alter ecosystem composition, structure, and functioning just as much as seasonal change. We show how satellite remote sensing and ground-based digital repeat photography (i.e. phenocams) can be used to improve understanding of phenology in Australian ecosystems. First, we examine temporal variation in phenology on the continental scale using the enhanced vegetation index (EVI), calculated from MODerate resolution Imaging Spectroradiometer (MODIS) data. Spatial gradients are revealed, ranging from regions with pronounced seasonality in canopy development (i.e. tropical savannas) to regions where seasonal variation is minimal (i.e. tropical rainforests) or high but irregular (i.e. arid ecosystems). Next, we use time series colour information extracted from phenocam imagery to illustrate a range of phenological signals in four contrasting Australian ecosystems. These include greening and senescing events in tropical savannas and temperate eucalypt understorey, as well as strong seasonal dynamics of individual trees in a seemingly static evergreen rainforest. We also demonstrate how phenology links with ecosystem gross primary productivity (from eddy covariance) and discuss why these processes are linked in some ecosystems but not others. We conclude that phenocams have the potential to greatly improve the current understanding of Australian ecosystems. To facilitate the sharing of this information, we have formed the Australian Phenocam Network (http://phenocam.org.au/).

Phenology is the study of periodic biological occurrences and can provide important insights into the influence of climatic variability and change on ecosystems. Understanding Australia's vegetation phenology is a challenge due to its diverse range of ecosystems, from savannas and tropical rainforests to temperate eucalypt woodlands, semi-arid scrublands and alpine grasslands. These ecosystems exhibit marked differences in seasonal patterns of canopy development and plant life-cycle events, much of which deviates from the predictable seasonal phenological pulse of temperate deciduous and boreal biomes. Many Australian ecosystems are subject to irregular events (i.e., drought, flooding, cyclones and fire) that can alter ecosystem composition, structure and functioning just as much as seasonal change. We show how satellite remote sensing and ground-based digital repeat photography (i.e. phenocams) can be used to improve understanding of phenology in Australian ecosystems. First, we examine temporal variation in phenology at the continental scale using the Enhanced Vegetation Index (EVI), calculated from MODerate resolution Imaging Spectroradiomter (MODIS) data. Spatial gradients are revealed, ranging from regions with pronounced seasonality in canopy development (i.e., tropical savannas) to regions where seasonal variation is minimal (i.e., tropical rainforests) or high but irregular (i.e., arid ecosystems). Next, we use time series colour information extracted from phenocam imagery to illustrate a range of phenological signals in four contrasting Australian ecosystems. These include greening and senescing events in tropical savannas and temperate eucalypt understory, as well as strong seasonal dynamics of individual trees in a seemingly static evergreen rainforest. We also demonstrate how phenology links with ecosystem gross primary productivity (from eddy covariance) and discuss why these processes are linked in some ecosystems but not others. We conclude that phenocams have the potential to greatly improve current understanding of Australian ecosystems. To facilitate sharing of this information, we have formed the Australian Phenocam Network (http://phenocam.org.au/). [ABSTRACT FROM AUTHOR]

Allometric equations are currently used to estimate above-ground biomass (AGB) based on the indirect relationship with tree parameters. Terrestrial laser scanning (TLS) can measure the canopy structure in 3D with high detail. In this study, we develop an approach to estimate AGB from TLS data, which does not need any prior information about allometry. We compare these estimates against destructively harvested AGB estimates and AGB derived from allometric equations. We also evaluate tree parameters, diameter at breast height (DBH) and tree height, estimated from traditional field inventory and TLS data., Tree height, DBH and AGB data are collected through traditional forest inventory, TLS and destructive sampling of 65 trees in a native Eucalypt Open Forest in Victoria, Australia. Single trees are extracted from the TLS data and quantitative structure models are used to estimate the tree volume directly from the point cloud data. AGB is inferred from these volumes and basic density information and is then compared with the estimates derived from allometric equations and destructive sampling., AGB estimates derived from TLS show a high agreement with the reference values from destructive sampling, with a concordance correlation coefficient (CCC) of 0·98. The agreement between AGB estimates from allometric equations and the reference is lower (CCC = 0·68-0·78). Our TLS approach shows a total AGB overestimation of 9·68% compared to an underestimation of 36·57-29·85% for the allometric equations., The error for AGB estimates using allometric equations increases exponentially with increasing DBH, whereas the error for AGB estimates from TLS is not dependent on DBH. The TLS method does not rely on indirect relationships with tree parameters or calibration data and shows better agreement with the reference data compared to estimates from allometric equations. Using 3D data also enables us to look at the height distributions of AGB, and we demonstrate that 80% of the AGB at plot level is located in the lower 60% of the trees for a Eucalypt Open Forest. This method can be applied in many forest types and can assist in the calibration and validation of broad-scale biomass maps. [ABSTRACT FROM AUTHOR]

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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 (R850/R710 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 R850/R710 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 R850/R710 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. [ABSTRACT FROM PUBLISHER]