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FGARA Digital Soil Mapping Output - Rockiness
Rockiness is one of 19 attributes of soils chosen to underpin the land suitability assessment of the Flinders and Gilbert Agricultural Resource Assessment (FGARA) project through the digital soil mapping process (DSM). This raster data (in GeoTIFF format) represents a modelled surface of probability of rockiness defined by amount of coarse fragment... mores, hard segregations or rock outcrop in the plough zone (see definition in below reference to technical report). The values are from 0 being least probable to 1 being most probable. This data is derived from measured site data and environmental covariates. The data is used as a restriction to use of agricultural machinery. For this project the areas of >0.7 probability are said to be rocky.
The attribute data file is named "Rockyprobability.tif"
Also included are data reflecting confidence of the main dataset. This file is named "Rockiness_CI69.tif". "CI" represents "confusion index".
The DSM process is described in the technical report: Bartley R, Thomas MF, Clifford D, Phillip S, Brough D, Harms D, Willis R, Gregory L, Glover M, Moodie K, Sugars M, Eyre L, Smith DJ, Hicks W and Petheram C (2013) Land suitability: technical methods. A technical report to the Australian Government for the Flinders and Gilbert Agricultural Resource Assessment (FGARA) project, CSIRO.
This raster data provides improved soil information to identify opportunities and promote detailed investigation for a range of sustainable development options and was created within the “Land Suitability” component of FGARA projects. less
Agricultural Land Management
Agricultural Land Planning
Agricultural Spatial Analysis and Modelling
01 Sep 2013
Digital Soil Mapping
This data has been created from a range of inputs and processing steps. Below is an overview. Broadly, the steps were to:
1. Collate existing data (data related to: climate, topography, soils, natural resources, remotely sensed etc of various formats; reports, spatial vector, spatial raster etc.).
2. Select additional soil and attribute site data by Latin hypercube statistical sampling method applied across the covariate space.
3. Carry out fieldwork to collect additional soil and attribute data and understand geomorphology and landscapes.
4. Build models from selected input data and covariate data using predictive learning via rule ensembles in the RuleFit3 software.
5. Create Rockiness Digital Soil Mapping (DSM) key attribute output data. DSM is the creation and population of a geo-referenced database, generated using field and laboratory observations, coupled with environmental data through quantitative relationships. It applies pedometrics - the use of mathematical and statistical models that combine information from soil observations with information contained in correlated environmental variables, remote sensing images and some geophysical measurements.
Quality assessment of the attribute data is mapped spatially as a function of the model output by evaluating the rigour of the DSM attribute data using non-parametric bootstrapping of the DSM modelling. For more information refer to “Land suitability: technical methods. A technical report to the Australian Government for the Flinders and Gilbert Agricultural Resource Assessment (FGARA) project”.
This work was prepared for the Office of Northern Australia in the Australian Government Department of Infrastructure and Regional Development under the North Queensland Irrigated Agriculture Strategy <http://www.regional.gov.au/regional/ona/nqis.aspx>. The Strategy is a collaborative initiative between the Office of Northern Australia, the Queensland Government and CSIRO. One part of the Strategy is the Flinders and Gilbert Agricultural Resource Assessment, which was led by CSIRO. Important aspects of the Assessment were undertaken by the Queensland Government and TropWATER (James Cook University).
CSIRO Data Licence
CSIRO (Australia), Office of Northern Australia (Australia), Queensland Department of Natural Resources and Mines (Australia), Queensland Department of Science, Information Technology, Innovation and the Arts (DSITIA) (Australia)
Bartley, Rebecca; Thomas, Mark; Clifford, David; Philip, Seonaid; Brough, Dan; Harms, Ben; Willis, Reanna; Gregory, Linda; Glover, Mark; Moodie, Keith; Sugars, Mark; Eyre, Lauren; Smith, Doug; Hicks, Warren; Petheram, Cuan (2013): FGARA Digital Soil Mapping Output - Rockiness. v2. CSIRO. Data Collection.
All Rights (including copyright) CSIRO Australia 2013.
The metadata and files (if any) are available to the public.
Research Projects Officer
1211.1 Gulf Agricultural Resource Assess
The Flinders and Gilbert catchments in north Queensland have been identified as potential areas for further agricultural development. The Flinders and Gilbert Agricultural Resource Assessment (the Assessment) provides a comprehensive and integrated evaluation of the feasibility, economic viability and sustainability of agricultural development in t... morehese two catchments as part of the North Queensland Irrigated Agricultural Strategy (NQIAS). The Assessment seeks to:
- identify and evaluate water capture and storage options
- identify and test the commercial viability of irrigated agricultural opportunities
- assess potential environmental, social and economic impacts and risks.
By this means it seeks to support deliberation and decisions concerning sustainable regional development. less
Digital Soil Mapping
Soil survey methods have evolved with the expansion in computing power in recent decades allowing soil attributes (e.g. pH) collected at a specific point in space, to be related to comprehensive data on physical attributes or covariates (e.g. slope, geology) that are available over the whole spatial extent of the area. The relationship between thes... moree variables can then be extrapolated over much larger areas, which was not possible using traditional approaches. The process of coupling spatial data through quantitative relationships is known as digital soil mapping (DSM). It applies pedometrics, which is the use of mathematical and statistical models that combine information from soil observations with information contained in correlated spatial variables and remote sensing images. A benefit of DSM (when compared to traditional soil mapping processes) is that it is possible to quantify the statistical (un)-certainty associated with the estimate of the soil attributes at a given point. In this study we used DSM to derive the key soil attribute layers required for land suitability assessment and to develop generic soil maps for the catchments. Such an approach allowed the assessment of a greater spatial area than in previous soil evaluations in this region with a better understanding of the quality of that assessment. less
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