Showing results for: [ McKenzie, Neil ]
These data provide consistent rasterised layers of edaphic (physical and chemical conditions of the soil) and land surface physiography (landform and geomorphology) variables hypothesised to explain s... morepatial patterns in biological diversity at continental scales for immediate use with statistical modelling tools. These data are intended to be used along with a similarly compiled and spatially standardised set of climatic layers. Consistent "stacks" of raster variables are needed for spatially-explicit biodiversity modelling using tools such as MAXENT or Generalised Dissimilarity Modelling (GDM). Full details of each dataset, with a list of data sources and bibliography, are provided in a table as part of the data collection. Additional information provided with the 1km gridded raster is relevant to some these data and provided here also. Each dataset will need to be separately cited. These data have also been made available for use in the Atlas of Living Australia's Spatial Portal. less
Closed DIISR ALA Exp-Geosptl Data Mngmnt - - Published 21 Oct 2019
The Physiographic Regions of Australia (Pain, Gregory, Wilson and McKenzie 2011) are a modification of those compiled by Jennings and Mabbutt (1977), and are based on a visual interpretation of landfo... morerms as expressed on the Shuttle Radar Terrain Mission (SRTM) digital elevation model (DEM). Apart from its descriptive role, a map of physiographic regions provides a regional system of reference for geomorphological and related physical geographical accounts. Through the groupings of physiographic regional characteristics at different levels, the action of underlying controls, for instance geologic or climatic, may be made apparent. Further, the map can provide a regional basis for an understanding of land characteristics that are dependent upon landforms, for example the distribution of soils or natural vegetation. Jennings J.N. and Mabbutt J.A. (1977) Physiographic outlines and regions. In 'Australia, a geography. Volume 1. The natural environment.' (Ed. DN Jeans) (Sydney University Press: Sydney).less
CLSD DAFF/ACLEP 2009-10/(C2009/10948) - ASRIS Data - Published 01 Aug 2016
Iron (Fe) oxide mineralogy in most Australian soils is poorly characterized, even though Fe oxides play an important role in soil function. Fe oxides reflect the conditions of pH, redox potential, moi... moresture, and temperature in the soil environment. The strong pigmenting effect of Fe oxides gives most soils their color, which is largely a reflection of the soil’s Fe mineralogy. Visible-near-infrared (vis-NIR) spectroscopy can be used to identify and measure the abundance of certain Fe oxides in soil, and the visible range can be used to derive tristimuli soil color information. We measured the spectra of 4606 surface soil samples from across Australia using a vis-NIR spectrometer with a wavelength range of 350-2500 nm. We determined the Fe oxide abundance for each sample using the diagnostic absorption features of hematite (near 880 nm) and goethite (near 920 nm) and derived a normalized iron oxide difference index (NIODI) to better discriminate between them. The NIODI was generalized across Australia with its spatial uncertainty using sequential indicator simulation, which resulted in a map of the probability of the occurrence of hematite and goethite. We also derived soil RGB color from the spectra and mapped its distribution and uncertainty across the country using sequential Gaussian simulations. The simulated RGB color values were made into a composite true color image and were also converted to Munsell hue, value, and chroma. These color maps were compared to the map of the NIODI, and both were used to interpret our results. The maps were validated by randomly splitting the data into training and test data sets, as well as by comparing our results to existing studies on the distribution of Fe oxides in Australian soils.
Units of measurement:
1. Munsell Hue;
2. Munsell Chroma;
3. Munsell value;
5. NIODI uncertainty.
For details please see Viscarra Rossel et al. (2010).
Data Type: Float Grid.
Map projection: Lambert Conformal Conic.
Map units: Decimal degrees.
Resolution: 10,000 metres.
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CLSD DAFF ACLEP DSM Activity 2 - ACLEP digital soil mapping - Published 28 Aug 2015