Derivation of terrain covariates for digital soil mapping in Australia
John C. Gallant A B and Jenet M. Austin A
+ Author Affiliations
- Author Affiliations
A CSIRO Land and Water, Black Mountain Laboratories, Canberra, ACT 2600, Australia.
B Corresponding author. Email: John.Gallant@csiro.au
Soil Research 53(8) 895-906 https://doi.org/10.1071/SR14271
Submitted: 30 September 2014 Accepted: 9 September 2015 Published: 2 November 2015
Abstract
Digital soil mapping is founded on the availability of covariates that are used as surrogates for the spatial patterns in soil properties. One important subset of covariates represents the patterns due to terrain, and these are typically derived from a digital elevation model at a suitable resolution. When each digital soil mapping exercise requires the calculation of terrain covariates, there is a clear potential for inconsistent methods and for choosing the covariates that are easiest to derive rather than those that are most relevant. The creation of open repositories of relevant terrain covariates that are correctly derived avoids these problems and fosters the application of digital soil mapping and other modelling activities that benefit from landscape properties.
This paper describes the creation of a suite of commonly used terrain covariates from the 1-arcsecond (~30 m) resolution digital elevation models for Australia that were released through CSIRO’s Data Access Portal and the TERN Data Discovery Portal. The methods used to derive the terrain covariates are described and their characteristics are identified.
References
Abrams M, Bailey B, Tsu H, Hato M (2010) The ASTER Global DEM. Photogrammetric Engineering and Remote Sensing 76, 344–348.Airbus Defence and Space (2015) WorldDEM. Airbus Defence and Space. Available at: www.geo-airbusds.com/worlddem/ (accessed 11 August 2015).
Austin JM, Gallant JC, Van Niel T (2013) Mean monthly radiation surfaces for Australia at 1 arcsecond resolution. In ‘MODSIM2013, 20th International Congress on Modelling and Simulation’. December 2013. (Eds J Piantadosi, RS Anderssen, J Boland) pp. 2506–2512. (Modelling and Simulation Society of Australia and New Zealand) Available at: www.mssanz.org.au/modsim2013/H2/austin.pdf
Beven KJ, Kirkby MJ (1979) A physically based variable contributing area model of basin hydrology. Hydrological Sciences Bulletin 24, 43–69.
| A physically based variable contributing area model of basin hydrology.Crossref | GoogleScholarGoogle Scholar |
BoM (2013) Spatial climate data sets for Australia. Bureau of Meteorology. Available at: www.bom.gov.au/climate (accessed 22 August 2013).
BoM (2014) Spatial climate data sets for Australia. Bureau of Meteorology. Available at: www.bom.gov.au/climate (accessed 14 June 2014).
Bui EN, Henderson BL, Viergever K (2006) Knowledge discovery from models of soil properties developed through data mining. Ecological Modelling 191, 431–446.
| Knowledge discovery from models of soil properties developed through data mining.Crossref | GoogleScholarGoogle Scholar |
Dobos E, Hengl T (2009) Soil mapping applications. In ‘Geomorphometry: Concepts, software, applications’. (Eds T Hengl, HI Reuter) pp. 461–479. (Elsevier: Amsterdam)
Donohue RJ, McVicar TR, Roderick ML (2010a) Assessing the ability of potential evaporation formulations to capture the dynamics in evaporative demand within a changing climate. Journal of Hydrology 386, 186–197.
| Assessing the ability of potential evaporation formulations to capture the dynamics in evaporative demand within a changing climate.Crossref | GoogleScholarGoogle Scholar |
Donohue RJ, McVicar TR, Lingtao L, Roderick ML (2010b) A data resource for analysing dynamics in Australian ecohydrological conditions. Austral Ecology 35, 593–594.
| A data resource for analysing dynamics in Australian ecohydrological conditions.Crossref | GoogleScholarGoogle Scholar |
Farr TG, Rosen PA, Caro E, Crippen R, Duren R, Hensley S, Kobrick M, Paller M, Rodriguez E, Roth L, Seal D, Shaffer S, Shimada J, Umland J, Werner M, Oskin M, Burbank D, Alsdorf D (2007) The shuttle radar topographic mission. Reviews of Geophysics 45, RG2004
| The shuttle radar topographic mission.Crossref | GoogleScholarGoogle Scholar |
Franklin J, McCullough P, Gray C (2000) Terrain variables used for predictive mapping of vegetation communities in Southern California. In ‘Terrain analysis: Principles and applications’. (Eds JP Wilson, JC Gallant) pp. 331–353. (John Wiley and Sons: New York)
Freeman TG (1991) Calculating catchment area with divergent flow based on a regular grid. Computers & Geosciences 17, 413–422.
| Calculating catchment area with divergent flow based on a regular grid.Crossref | GoogleScholarGoogle Scholar |
Gallant JC (2011) Adaptive smoothing for noisy DEMs. In ‘Geomorphometry 2011. Proceedings of Geomorphometry Conference’. 7–11 September 2011, Redlands, CA, USA. (Eds JP Wilson, M Gould, IS Evans, T Hengl) http://geomorphometry.org/Gallant2011
Gallant JC, Dowling TI (2003) A multiresolution index of valley bottom flatness for mapping depositional areas. Water Resources Research 39, 1347
| A multiresolution index of valley bottom flatness for mapping depositional areas.Crossref | GoogleScholarGoogle Scholar |
Gallant JC, Wilson JP (2000) Primary topographic attributes. In ‘Terrain analysis: Principles and applications’. (Eds JP Wilson, JC Gallant) pp. 51–85. (John Wiley and Sons: New York)
Gallant JC, Dowling TI, Read AM, Wilson N, Tickle PK, Inskeep C (2011) 1 second SRTM-derived Digital Elevation Models User Guide. Geoscience Australia. Available at: www.ga.gov.au/topographic-mapping/digital-elevation-data.html
Gallant JC, Austin JM, Van Niel T (2014) Mean monthly net radiation modelled using the 1ʹʹ DEM-S. v2. CSIRO Data Collection.
Gessler PE, Moore ID, McKenzie NJ, Ryan PJ (1995) Soil-landscape modeling and spatial prediction of soil attributes. International Journal of Geographical Information Systems 9, 421–432.
| Soil-landscape modeling and spatial prediction of soil attributes.Crossref | GoogleScholarGoogle Scholar |
Grundy MJ, Viscarra Rossel RA, Searle RD, Wilson PL, Chen C, Gregory LJ (2015) Soil and Landscape Grid of Australia. Soil Research 53, 835–844.
Hey T, Tansley S, Tolle K (Eds) (2009) ‘The fourth paradigm: Data-intensive scientific discovery.’ (Microsoft Research: Redmond, WA, USA)
Holmes KW, Griffin EA, Odgers NP (2015) Large-area spatial disaggregation of a mosaic of conventional soil maps: evaluation over Western Australia. Soil Research 53, 865–880.
Hutchinson MF (2009) ANUDEM Version 5.2. Fenner School of Environment and Society, Australian National University. Available at: http://fennerschool.anu.edu.au/publications/software/anudem.php (accessed January 2011)
Hutchinson MF, Stein JA, Stein JL, Xu T (2009) Locally adaptive gridding of noisy high resolution topographic data. In ‘18th World IMACS Congress and MODSIM’09 International Congress on Modelling and Simulation’. (Eds RS Anderssen, RD Braddock, LTH Newham) pp. 2493–2499. (Modelling and Simulation Society of Australia and New Zealand and International Association for Mathematics and Computers in Simulation) Available at: www.mssanz.org.au/modsim09/F13/hutchinson.pdf
Jenness J (2006) Topographic Position Index. Jenness Enterprises. Available at: http://jennessent.com/arcview/tpi.htm (accessed 10 February 2012)
Jones DA, Wang W, Fawcett R (2009) High-quality spatial climate data-sets for Australia. Australian Meteorological and Oceanographic Journal 58, 233–248.
Jovanovic B, Collins D, Braganza K, Jakob D, Jones DA (2011) A high-quality monthly total cloud amount dataset for Australia. Climatic Change 108, 485–517.
| A high-quality monthly total cloud amount dataset for Australia.Crossref | GoogleScholarGoogle Scholar |
Lagacherie P, McBratney A, Voltz M (Eds) (2007) ‘Digital soil mapping: An introductory perspective.’ Developments in Soil Science 31. (Elsevier: Amsterdam)
Liddicoat C, Maschmedt D, Clifford D, Searle R, Herrmann T, Macdonald LM, Baldock J (2015) Predictive mapping of soil organic carbon stocks in South Australia’s agricultural zone. Soil Research 53, 956–973.
Mackey BG, Mullen IC, Baldwin KA, Gallant JC, Sims RA, McKenney DW (2000) Toward a spatial model of boreal forest ecosystems: The role of digital terrain analysis. In ‘Terrain analysis: Principles and applications’. (Eds JP Wilson, JC Gallant) pp. 391–422. (John Wiley and Sons: New York)
Magierowski R, Wild A, Anderson G, Gaynor S, Lefroy T, Davies PE (2014) MCAS-S datapack for alpine bogs of the Australian Alps bioregion. Landscapes and Policy Research Hub. Available at: www.lifeatlarge.edu.au/__data/assets/pdf_file/0005/654197/Alpine-bogs-MCAS-S-Manual.pdf (accessed 1 September 2015)
McKenzie NJ, Gallant JC (2007) Digital soil mapping with improved environmental predictors and models of pedogenesis. In ‘Digital soil mapping: An introductory perspective’. (Eds P Lagacherie, AB McBratney, M Voltz) pp. 327–349. (Elsevier: Amsterdam)
McKenzie NJ, Gessler PE, Ryan PJ, O’Connell DA (2000) The role of terrain analysis in soil mapping. In ‘Terrain analysis: Principles and applications’. (Eds JP Wilson, JC Gallant) pp. 245–265. (John Wiley and Sons: New York)
McKenzie NJ, Gallant JC, Gregory L (2003) Estimating water storage capacities in soil at catchment scales. Technical Report 03/3, Cooperative Research Centre for Catchment Hydrology, Melbourne.
NASA LP DAAC (Land Processes Distributed Active Archive Center) (2013) MODIS Albedo products MCD43A3, B3. USGS Earth Resources Observation and Science (EROS) Center, Sioux Falls, SD, USA. http://lpdaac.usgs.gov/dataset_discovery/modis/modis_products_table/mcd43a3 (accessed 23 September 2015).
Paget MJ, King EA (2008) MODIS land data sets for the Australian region. Internal Report No. 004, CSIRO Marine and Atmospheric Research, Canberra. Available at: https://remote-sensing.nci.org.au/u39/public/html/modis/lpdaac-mosaics-cmar
Prata AJ (1996) A new long-wave formula for estimating downward clear-sky radiation at the surface. Quarterly Journal of the Royal Meteorological Society 122, 1127–1151.
| A new long-wave formula for estimating downward clear-sky radiation at the surface.Crossref | GoogleScholarGoogle Scholar |
Prescott JA (1950) A climatic index for the leaching factor in soil formation. Journal of Soil Science 1, 9–19.
| A climatic index for the leaching factor in soil formation.Crossref | GoogleScholarGoogle Scholar |
Priestley CHB, Taylor RJ (1972) On the assessment of surface heat flux and evaporation using large-scale parameters. Monthly Weather Review 100, 81–92.
| On the assessment of surface heat flux and evaporation using large-scale parameters.Crossref | GoogleScholarGoogle Scholar |
Reside AE, VanDerWal J, Phillips BL, Shoo LP, Rosauer DF, Anderson BJ, Welbergen JA, Moritz C, Ferrier S, Harwood TD, Williams KJ, Mackey B, Hugh S, Williams YM, Williams SE (2013) Climate change refugia for terrestrial biodiversity: Defining areas that promote species persistence and ecosystem resilience in the face of global climate change. National Climate Change Adaptation Research Facility, Gold Coast, Qld.
Sayre R, Comer P, Warner H, Cress J (2009) A new map of standardized terrestrial ecosystems of the conterminous United States. Professional Paper 1768, U.S. Geological Survey. Available at: http://pubs.usgs.gov/pp/1768/pp1768.pdf
Speight JG (2009) Landform. In ‘Australian soil and land survey field handbook’. (Ed. National Committee on Soil and Terrain) pp. 15–72. (CSIRO Publishing: Melbourne)
Viscarra Rossel RA, Chen C, Grundy MJ, Searle R, Clifford D, Campbell PH (2015) The Australian three-dimensional soil grid: Australia’s contribution to the GlobalSoilMap project. Soil Research 53, 845–864.
Wilson JP, Gallant JC (Eds) (2000a) ‘Terrain analysis: Principles and applications.’ (John Wiley and Sons: New York)
Wilson JP, Gallant JC (2000b) Secondary topographic attributes. In ‘Terrain analysis: Principles and applications’. (Eds JP Wilson, JC Gallant) pp. 87–131. (John Wiley and Sons: New York)
Zund PR (2014) ‘Disaggregation of land systems mapping—Fitzroy Drainage Basin.’ (Department of Science, Information Technology, Innovation and the Arts: Brisbane, Qld)
