Abstract
Digital soil maps are contrasted from legacy soil maps mainly in terms of the underlying spatial data model. Digital soil maps are based on the pixel data model, while legacy soil maps will typically consist of a tessellation of polygons. The advantage of the pixel model is that the information is spatially explicit. The soil map polygons are delineations of soil mapping units which consist of a defined assemblage of soil classes assumed to exist in more-or-less fixed proportions. There is great value in legacy soil mapping because a huge amount of expertise and resources went into their creation. Digital soil mapping will be the richer by using this existing knowledge-base to derive detailed and high resolution digital soil infrastructures. However the digitization of legacy soil maps is not digital soil mapping. Rather, the incorporation of legacy soil maps into a digital soil mapping workflow involves some method (usually quantitative) of data mining, to appoint spatially explicit soil information—usually a soil class or even a measurable soil attribute—upon a grid the covers the extent of the existing (legacy) mapping. In some ways, this process is akin to downscaling because there is a need to extract soil class or attribute information from aggregated soil mapping units. A better term therefore is soil map disaggregation.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Breiman L (2001) Random forests. Mach Learn 41:5–32
Bui E, Moran C (2001) Disaggregation of polygons of surficial geology and soil maps using spatial modelling and legacy data. Geoderma 103:79–94
Burrough PA, van Gaans PFM, Hootsmans R (1997) Continuous classification in soil survey: spatial correlation, confusion and boundaries. Geoderma 77:115–135
Chaney N, Hempel JW, Odgers NP, McBratney AB, Wood EF (2014) Spatial disaggregation and harmonization of gSSURGO. In: ASA, CSSA and SSSA international annual meeting, Long Beach. ASA, CSSA and SSSA
Grundy MJ, Viscarra Rossel R, Searle RD, Wilson PL, Chen C, Gregory LJ (2015) Soil and landscape grid of Australia. Soil Res. http://dx.doi.org/10.1071/SR15191
Haring T, Dietz E, Osenstetter S, Koschitzki T, Schroder B (2012) Spatial disaggregation of complex soil map units: a decision-tree based approach in Bavarian forest soils. Geoderma 185–186:37–47
McBratney A (1998) Some considerations on methods for spatially aggregating and disaggregating soil information. In: Finke P, Bouma J, Hoosbeek M (eds) Soil and water quality at different scales. Developments in plant and soil sciences, vol 80. Springer, Dordrecht, pp 51–62
Nauman TW, Thompson JA (2014) Semi-automated disaggregation of conventional soil maps using knowledge driven data mining and classification trees. Geoderma 213:385–399
Nauman TW, Thompson JA, Odgers NP, Libohova Z (2012) Fuzzy disaggregation of conventional soil maps using database knowledge extraction to produce soil property maps. In: Digital soil assessments and beyond: Proceedings of the fifth global workshop on digital soil mapping. CRC Press, London, pp 203–207
Odgers NP, McBratney AB, Minasny B (2015) Digital soil property mapping and uncertainty estimation using soil class probability rasters. Geoderma 237–238:190–198
Odgers NP, Sun W, McBratney AB, Minasny B, Clifford D (2014) Disaggregating and harmonising soil map units through resampled classification trees. Geoderma 214–215:91–100
Quinlan JR (1993) C4.5: programs for machine learning. Morgan Kaufmann, San Mateo
Rogers L, Cannon M, Barry E (1999) Land resources of the Dalrymple shire, 1. Land resources bulletin DNRQ980090. Department of Natural Resources, Brisbane, Queensland
Thompson JA, Prescott T, Moore AC, Bell J, Kautz DR, Hempel JW, Waltman SW, Perry C (2010) Regional approach to soil property mapping using legacy data and spatial disaggregation techniques. In: 19th world congress of soil science. IUSS, Brisbane
Wei S, McBratney A, Hempel J, Minasny B, Malone B, D’Avello T, Burras L, Thompson J (2010) Digital harmonisation of adjacent analogue soil survey areas – 4 Iowa counties. In: 19th world congress of soil science, IUSS, Brisbane
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Malone, B.P., Minasny, B., McBratney, A.B. (2017). Using Digital Soil Mapping to Update, Harmonize and Disaggregate Legacy Soil Maps. In: Using R for Digital Soil Mapping. Progress in Soil Science. Springer, Cham. https://doi.org/10.1007/978-3-319-44327-0_8
Download citation
DOI: https://doi.org/10.1007/978-3-319-44327-0_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-44325-6
Online ISBN: 978-3-319-44327-0
eBook Packages: Earth and Environmental ScienceEarth and Environmental Science (R0)