Assessment of relationships between precipitation and satellite derived vegetation condition in South Australia

The normalised difference vegetation index (NDVI) has evolved as a primary tool for monitoring continental-scale vegetation changes and interpreting the impact of short to long-term climatic events on the biosphere. The objective of this research was to assess the nature of relationships between precipitation and vegetation condition, as measured by the satellite-derived NDVI within South Australia. The correlation, timing and magnitude of the NDVI response to precipitation were examined for different vegetation formations within the State (forest, scrubland, shrubland, woodland and grassland). Results from this study indicate that there are strong relationships between precipitation and NDVI both spatially and temporally within South Australia. Differences in the timing of the NDVI response to precipitation were evident among the five vegetation formations. The most significant relationship between rainfall and NDVI was within the forest formation. Negative correlations between NDVI and precipitation events indicated that vegetation green-up is a result of seasonal patterns in precipitation. Spatial patterns in the average NDVI over the study period closely resembled the boundaries of the five classified vegetation formations within South Australia. Spatial variability within the NDVI data set over the study period differed greatly between and within the vegetation formations examined depending on the location within the state. ACRONYMS AVHRR Advanced Very High Resolution Radiometer ENVSAEnvironments of South Australia EOS Terra-Earth Observing System EVIEnhanced Vegetation Index MODIS Moderate Resolution Imaging Spectro-radiometer MVC Maximum Value Composite NDVINormalised Difference Vegetation Index NIRNear Infra-Red NOAANational Oceanic and Atmospheric Administration SPOT Systeme Pour l’Observation de la Terre. [ABSTRACT FROM AUTHOR]

How the west was once: vegetation change in south-west Queensland from 1930 to 1995

Conflicting perceptions of past and present rangeland condition and limited historical data have led to debate regarding the management of vegetation in pastoral landscapes both internationally and in Australia. In light of this controversy we have sought to provide empirical evidence to determine the trajectory of vegetational change in a semi-arid rangeland for a significant portion of the 20th century using a suite of proxy measures. Ambathala Station, approximately 780 km west of Brisbane, in the semi-arid rangelands of south-western Queensland, Australia. We excavated stratified deposits of sheep manure which had accumulated beneath a shearing shed between the years 1930 and 1995. Multi-proxy data, including pollen and leaf cuticle analyses and analysis of historical aerial photography were coupled with a fine resolution radiocarbon chronology to generate a near annual history of vegetation on the property and local area. Aerial photography indicates that minor (< 5%) increases in the density of woody vegetation took place between 1951 and 1994 in two thirds of the study area not subjected to clearing. Areas that were selectively or entirely cleared prior to the 1950s (approximately 16% of the study area) had recovered to almost 60% of their original cover by the 1994 photo period. This slight thickening is only partially evident from pollen and leaf cuticle analyses of sheep faeces. Very little change in vegetation is revealed over the nearly 65 years based on the relative abundances of pollen taxonomic groups. Microhistological examination of sheep faeces provides evidence of dramatic changes in sheep diet. The majority of dietary changes are associated with climatic events of sustained above-average rainfall or persistent drought. Most notable in the dietary analysis is the absence of grass during the first two decades of the record. In contrast to prevailing perceptions and limited research into long-term vegetation change in the semi-arid areas of eastern Australia, the record of vegetation change at the Ambathala shearing shed indicates only a minor increase in woody vegetation cover and no decrease in grass cover on the property over the 65 years of pastoral activity covered by the study. However, there are marked changes in the abundance of grass cuticles in sheep faeces. The appearance and persistence of grass in sheep diets from the late 1940s can be attributed to the effects of periods of high rainfall and possibly some clearing and thinning of vegetation. Lower stock numbers may have allowed grass to persist through later drought years. The relative abundances of major groups of plant pollen have not changed significantly over the past 65 years.

Laboratory evaluation of the effect of alternative prey and vegetation on predation of Culex annulirostris immatures by Australian native fish species

The impact of alternative prey and simulated vegetation on Culex annulirostris Skuse predation efficacy by Australian smelt, Retropinna semoni (Retropinnidae); crimson-spotted rainbowfish, Melanotaenia duboulayi (Melanotaeniidae); empire gudgeon, Hypseleotris compressa (Eleotridae); estuary perchlet, Ambassis marianus (Ambassidae); firetail gudgeon, Hypseleotris galii (Eleotridae); fly-specked hardyhead, Craterocephalus stercusmuscarum (Atherinidae); and Pacific blue-eye, Pseudomugil signifer (Atherinidae), was evaluated in Queensland, Australia. The presence of chironomid midge larvae and tusked frog, Adelotus brevis (Leptodactylidae), tadpoles did not have a significant negative impact on the predation rates of Cx. annulirostris by these 7 fish species. Hypseleotris galii, M. duboulayi, and R. semoni demonstrated strong preference for larvae of Cx. annulirostris over both alternative prey species. In the presence of alternative prey species, the mean predation rate of M. duboulayi on larvae of Cx. annulirostris remained greater than that of other fish species tested. When evaluated at varying densities of simulated vegetation, predation rates of all fish species were similar to those reported in open conditions.

Modelling pre-clearing vegetation distribution using GIS-integrated statistical, ecological and data models: A case study from the wet tropics of Northeastern Australia

Traditional vegetation mapping methods use high cost, labour-intensive aerial photography interpretation. This approach can be subjective and is limited by factors such as the extent of remnant vegetation, and the differing scale and quality of aerial photography over time. An alternative approach is proposed which integrates a data model, a statistical model and an ecological model using sophisticated Geographic Information Systems (GIS) techniques and rule-based systems to support fine-scale vegetation community modelling. This approach is based on a more realistic representation of vegetation patterns with transitional gradients from one vegetation community to another. Arbitrary, though often unrealistic, sharp boundaries can be imposed on the model by the application of statistical methods. This GIS-integrated multivariate approach is applied to the problem of vegetation mapping in the complex vegetation communities of the Innisfail Lowlands in the Wet Tropics bioregion of Northeastern Australia. The paper presents the full cycle of this vegetation modelling approach including sampling sites, variable selection, model selection, model implementation, internal model assessment, model prediction assessments, models integration of discrete vegetation community models to generate a composite pre-clearing vegetation map, independent data set model validation and model prediction's scale assessments. An accurate pre-clearing vegetation map of the Innisfail Lowlands was generated (0.83r(2)) through GIS integration of 28 separate statistical models. This modelling approach has good potential for wider application, including provision of. vital information for conservation planning and management; a scientific basis for rehabilitation of disturbed and cleared areas; a viable method for the production of adequate vegetation maps for conservation and forestry planning of poorly-studied areas. (c) 2006 Elsevier B.V. All rights reserved.

Mapping structural parameters and species composition of riparian vegetation using IKONOS and Landsat ETM+ data in Australian tropical savannahs

Government agencies responsible for riparian environments are assessing the utility of remote sensing for mapping and monitoring vegetation structural parameters. The objective of this work was to evaluate Ikonos and Landsat-7 ETM+ imagery for mapping structural parameters and species composition of riparian vegetation in Australian tropical savannahs for a section of Keelbottom Creek, Queensland, Australia. Vegetation indices and image texture from Ikonos data were used for estimating leaf area index (R-2 = 0.13) and canopy percentage foliage cover (R-2 = 0.86). Pan-sharpened Ikonos data were used to map riparian species composition (overall accuracy = 55 percent) and riparian zone width (accuracy within +/- 3 m). Tree crowns could not be automatically delineated due to the lack of contrast between canopies and adjacent grass cover. The ETM+ imagery was suited for mapping the extent of riparian zones. Results presented demonstrate the capabilities of high and moderate spatial resolution imagery for mapping properties of riparian zones.

Linking riparian vegetation spatial structure in Australian tropical savannas to ecosystem health indicators: semi-variogram analysis of high spatial resolution satellite imagery

Government agencies responsible for riparian environments are assessing the combined utility of field survey and remote sensing for mapping and monitoring indicators of riparian zone health. The objective of this work was to determine if the structural attributes of savanna riparian zones in northern Australia can be detected from commercially available remotely sensed image data. Two QuickBird images and coincident field data covering sections of the Daly River and the South Alligator River - Barramundie Creek in the Northern Territory were used. Semi-variograms were calculated to determine the characteristic spatial scales of riparian zone features, both vegetative and landform. Interpretation of semi-variograms showed that structural dimensions of riparian environments could be detected and estimated from the QuickBird image data. The results also show that selecting the correct spatial resolution and spectral bands is essential to maximize the accuracy of mapping spatial characteristics of savanna riparian features. The distribution of foliage projective cover of riparian vegetation affected spectral reflectance variations in individual spectral bands differently. Pan-sharpened image data enabled small-scale information extraction (< 6 m) on riparian zone structural parameters. The semi-variogram analysis results provide the basis for an inversion approach using high spatial resolution satellite image data to map indicators of savanna riparian zone health.

Mapping indicators of riparian vegetation health using IKONOS and Landsat-7 ETM+ image data in Australian tropical savannas

Government agencies responsible for riparian environments are assessing the utility of remote sensing for mapping and monitoring environmental health indicators. The objective of this work was to evaluate IKONOS and Landsat-7 ETM+ imagery for mapping riparian vegetation health indicators in tropical savannas for a section of Keelbottom Creek, Queensland, Australia. Vegetation indices and image texture from IKONOS data were used for estimating percentage canopy cover (r2=0.86). Pan-sharpened IKONOS data were used to map riparian species composition (overall accuracy=55%) and riparian zone width (accuracy within 4 m). Tree crowns could not be automatically delineated due to the lack of contrast between canopies and adjacent grass cover. The ETM+ imagery was suited for mapping the extent of riparian zones. Results presented demonstrate the capabilities of high and moderate spatial resolution imagery for mapping properties of riparian zones, which may be used as riparian environmental health indicators