Large-area spatial disaggregation of a mosaic of conventional soil maps: evaluation over Western Australia
K. W. Holmes A B C E , E. A. Griffin A and N. P. Odgers D
+ Author Affiliations
- Author Affiliations
A Department of Agriculture and Food Western Australia, 3 Baron-Hay Court, South Perth, WA 6151, Australia.
B CSIRO Sustainable Agriculture Flagship, Brisbane, Qld 4001, Australia.
C Soil Matrix Group, School of Earth and Environment, The University of Western Australia, Crawley, WA 6009, Australia.
D Faculty of Agriculture and Environment, C81 Biomedical Building, The University of Sydney, NSW 2006, Australia.
E Corresponding author. Email: karen.holmes@agric.wa.gov.au
Soil Research 53(8) 865-880 https://doi.org/10.1071/SR14270
Submitted: 29 September 2014 Accepted: 4 March 2015 Published: 2 November 2015
Abstract
Conventional soil maps may be the best available source for spatial soil information in data-limited areas, including individual soil properties. Spatial disaggregation of these maps, or mapping the unmapped soil components, offers potential for transforming them into spatially referenced soil class distributions. We used an automated, iterative classification tree approach to spatially disaggregate a patchwork of soil surveys covering Western Australia (2.5 × 106 km2) to produce raster surfaces of soil class occurrence. The resulting rasters capture the broad spatial patterns of dominant soils and harmonise soil class designations across most survey boundaries. More than 43 000 archived profiles were used to evaluate the accuracy of the rasters. In 20% of cases, the predicted soil class with the highest probability matched that recorded for the profile; when any of the three highest probability soil classes predicted were considered correct, the global accuracy was 40%. The accuracy increased to 71% when the rasters were reassembled to represent a higher level in the soil classification system. The predicted surfaces retained features related to the mapping intensity of the original surveys and generally had higher prediction accuracy of profile soil class where the surface geochemistry was more homogeneous. The best indicator of prediction accuracy was how common the profile soil class was in the original mapping (94% variance explained); profile observations collected during soil survey may be biased towards rare soils, making them less suitable for validation or modelling directly from point data.
Additional keywords: digital soil mapping, global digital soil map, legacy soil maps, resampled classification trees, scale, uncertainty, validation.
References
Adhikari K, Minasny B, Greve MB, Greve MH (2014) Constructing a soil class map of Denmark based on the FAO legend using digital techniques. Geoderma 214–215, 101–113.| Constructing a soil class map of Denmark based on the FAO legend using digital techniques.Crossref | GoogleScholarGoogle Scholar |
Augusiak J, Van den Brink PJ, Grimm V (2014) Merging validation and evaluation of ecological models to ‘evaludation’: a review of terminology and a practical approach. Ecological Modelling 280, 117–128.
| Merging validation and evaluation of ecological models to ‘evaludation’: a review of terminology and a practical approach.Crossref | GoogleScholarGoogle Scholar |
Brus DJ, Kempen B, Heuvelink GBM (2011) Sampling for validation of digital soil maps. European Journal of Soil Science 62, 394–407.
| Sampling for validation of digital soil maps.Crossref | GoogleScholarGoogle Scholar |
Bui EN, Moran CJ (2001) Disaggregation of polygons of surficial geology and soil maps using spatial modelling and legacy data. Geoderma 103, 79–94.
| Disaggregation of polygons of surficial geology and soil maps using spatial modelling and legacy data.Crossref | GoogleScholarGoogle Scholar |
Burrough PA, van Gaans PFM, Hootsmans R (1997) Continuous classification in soil survey: spatial correlation, confusion and boundaries. Geoderma 77, 115–135.
| Continuous classification in soil survey: spatial correlation, confusion and boundaries.Crossref | GoogleScholarGoogle Scholar |
Carré F, McBratney AB, Minasny B (2007) Estimation and potential improvement of the quality of legacy soil samples for digital soil mapping. Geoderma 141, 1–14.
| Estimation and potential improvement of the quality of legacy soil samples for digital soil mapping.Crossref | GoogleScholarGoogle Scholar |
Clarke KR (1993) Non-parametric multivariate analyses of changes in community structure. Australian Journal of Ecology 18, 117–143.
| Non-parametric multivariate analyses of changes in community structure.Crossref | GoogleScholarGoogle Scholar |
Congalton RG (1991) A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment 37, 35–46.
| A review of assessing the accuracy of classifications of remotely sensed data.Crossref | GoogleScholarGoogle Scholar |
Debella-Gilo M, Etzelmüller B (2009) Spatial prediction of soil classes using digital terrain analysis and multinomial logistic regression modeling in integrated in GIS: examples from Vestfold County, Norway. Catena 77, 8–18.
| Spatial prediction of soil classes using digital terrain analysis and multinomial logistic regression modeling in integrated in GIS: examples from Vestfold County, Norway.Crossref | GoogleScholarGoogle Scholar |
Department of Agriculture and Food Western Australia (DAFWA) (2013a) Soil-landscape map unit database. DAFWA, Perth.
Department of Agriculture and Food Western Australia (DAFWA) (2013b) Soil profile database. DAFWA, Perth.
Dewitte O, Jones A, Spaargaren O, Breuning-Madsen H, Brossard M, Dampha A, Deckers J, Gallali T, Hallett S, Jones R, Kilasara M, Le Roux P, Micheli E, Montanarella L, Thiombiano L, Van Ranst E, Yemefack M, Zougmore R (2013) Harmonisation of the soil map of Africa at the continental scale. Geoderma 211–212, 138–153.
Donohue RJ, Roderick ML, McVicar TR (2008) Deriving consistent long-term vegetation information from AVHRR reflectance data using a cover-triangle-based framework. Remote Sensing of Environment 112, 2938–2949.
| Deriving consistent long-term vegetation information from AVHRR reflectance data using a cover-triangle-based framework.Crossref | GoogleScholarGoogle Scholar |
Gallant JC, Austin JM (2015) Derivation of terrain covariates for digital soil mapping in Australia. Soil Research 53, 895–906.
Gallant JC, Dowling TI, Read AM, Wilson N, Tickle P, Inskeep C (2011) ‘1 Second SRTM derived digital elevation models user guide.’ (Geoscience Australia: Canberra, ACT)
Grunwald S (2009) Multi-criteria characterization of recent digital soil mapping and modeling approaches. Geoderma 152, 195–207.
| Multi-criteria characterization of recent digital soil mapping and modeling approaches.Crossref | GoogleScholarGoogle Scholar |
Grunwald S (2010) Current state of digital soil mapping and what is next. In ‘Digital soil mapping: bridging research, environmental application, and operation’. (Eds JL Boettinger, AC Moore, S Kienast-Brown, DW Howell, AE Hartemink) pp. 3–12. (Springer: Dordrecht)
Hansen MK, Brown DJ, Dennison PE, Graves SA, Bricklemyer RS (2009) Inductively mapping expert-derived soil-landscape units within dambo wetland catenae using multispectral and topographic data. Geoderma 150, 72–84.
Häring 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.
| Spatial disaggregation of complex soil map units: a decision-tree based approach in Bavarian forest soils.Crossref | GoogleScholarGoogle Scholar |
Hartemink AE, McBratney A (2008) A soil science renaissance. Geoderma 148, 123–129.
| A soil science renaissance.Crossref | GoogleScholarGoogle Scholar |
Hempel J, McBratney AB, Arrouys D, McKenzie NJ, Hartemink AE (2014) GlobalSoilMap project history. In ‘GlobalSoilMap: basis of the global spatial soil information system’. (Eds D Arrouys, N McKenzie, J Hempel, AC Richer de Forges, A McBratney) pp. 3–8. (CRC Press/Balkema: Leiden)
Heuvelink GBM (2014) Uncertainty quantification of GlobalSoilMap products. In ‘GlobalSoilMap: basis of the global spatial soil information system’. (Eds D Arrouys, N McKenzie, J Hempel, AC Richer de Forges, A McBratney) pp. 335–340. (Taylor & Francis Group: London)
Hewitt AE, McKenzie NJ, Grundy MJ, Slater BK (2008) Qualitative survey. In ‘Guidelines for surveying soil and land resources’. (Eds NJ McKenzie, MJ Grundy, R Webster, AJ Ringrose-Voase) pp. 285–306. (CSIRO Publishing: Melbourne)
Howell D, Kim YG, Haydu-Houdeshell CA (2008) Development and application of digital soil mapping within traditional soil survey: what will it grow into? In ‘Digital soil mapping with limited data’. (Eds AE Hartemink, A McBratney, MdL Mendoca-Santos) pp. 45–51. (Springer Science+Business Media BV)
Isbell RF (2002) ‘The Australian Soil Classification.’ revised edn. (CSIRO Publishing: Melbourne)
IUSS Working Group WRB (2014) ‘World reference base for soil resources 2014. International soil classification system for naming soils and creating legends for soil maps.’ World Soil Resources Report No. 106. (FAO: Rome)
Ju J, Gopal S, Kolaczyk ED (2005) On the choice of spatial and categorical scale in remote sensing land cover classification. Remote Sensing of Environment 96, 62–77.
| On the choice of spatial and categorical scale in remote sensing land cover classification.Crossref | GoogleScholarGoogle Scholar |
Kempen B, Brus DJ, Heuvelink GBM, Stoorvogel JJ (2009) Updating the 1 : 50,000 Dutch soil map using legacy soil data: a multinomial logistic regression approach. Geoderma 151, 311–326.
| Updating the 1 : 50,000 Dutch soil map using legacy soil data: a multinomial logistic regression approach.Crossref | GoogleScholarGoogle Scholar |
Kerry R, Goovaerts P, Rawlins BG, Marchant BP (2012) Disaggregation of legacy soil data using area to point kriging for mapping soil organic carbon at the regional scale. Geoderma 170, 347–358.
| Disaggregation of legacy soil data using area to point kriging for mapping soil organic carbon at the regional scale.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC38Xhsl2gs7c%3D&md5=164fa70ab4226d72c07ce125303e9583CAS | 25729090PubMed |
Kuhn M, Weston S, Coulter N (2014) C5.0 decision trees and rule-based models. CRAN repository. https://cran.r-project.org/web/packages/C50/index.html (accessed September 2014).
Legendre P, Legendre L (2012) Ecological resemblance. In ‘Numerical ecology’. 3rd edn. pp. 265–336. (Elsevier: Amsterdam)
Li Z, Huffman T, Zhang AN, Zhou FQ, McConkey B (2012) Spatially locating soil classes within complex soil polygons: mapping soil capability for agriculture in Saskatchewan Canada. Agriculture, Ecosystems & Environment 152, 59–67.
| Spatially locating soil classes within complex soil polygons: mapping soil capability for agriculture in Saskatchewan Canada.Crossref | GoogleScholarGoogle Scholar |
Malone B, McBratney AB, Minasny B (2013) Spatial scaling for digital soil mapping. Soil Science Society of America Journal 77, 890–902.
| Spatial scaling for digital soil mapping.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3sXnsl2hur8%3D&md5=58fdd419fcf766949c3e29f6659ae409CAS |
McBratney AB, Mendoça Santos ML, Minasny B (2003) On digital soil mapping. Geoderma 117, 3–52.
| On digital soil mapping.Crossref | GoogleScholarGoogle Scholar |
Minasny B, McBratney AB (2007) Incorporating taxonomic distance into spatial prediction and digital mapping of soil classes. Geoderma 142, 285–293.
| Incorporating taxonomic distance into spatial prediction and digital mapping of soil classes.Crossref | GoogleScholarGoogle Scholar |
Minty B, Franklin R, Milligan P, Richardson LM, Wilford J (2009) Radiometric map of Australia. Exploration Geophysics 40, 325–333.
Möller M, Koschitzki T, Hartmann KJ, Jahn R (2012) Plausibility test of conceptual soil maps using relief parameters. Catena 88, 57–67.
| Plausibility test of conceptual soil maps using relief parameters.Crossref | GoogleScholarGoogle Scholar |
Nauman TW, Thompson JA (2014) Semi-automated disaggregation of conventional soil maps using knowledge driven data mining and classification trees. Geoderma 213, 385–399.
| Semi-automated disaggregation of conventional soil maps using knowledge driven data mining and classification trees.Crossref | GoogleScholarGoogle Scholar |
Nauman TW, Thompson JA, Rasmussen C (2014) Semi-automated disaggregation of a conventional soil map using knowledge driven data mining and random forests in the Sonoran Desert, USA. Photogrammetric Engineering and Remote Sensing 80, 353–366.
| Semi-automated disaggregation of a conventional soil map using knowledge driven data mining and random forests in the Sonoran Desert, USA.Crossref | GoogleScholarGoogle Scholar |
Northcote KH (1979) ‘A factual key for the recognition of Australian soils.’ 4th edn. (Reliim Technical Publishers: Glenside, SA)
Northcote KH, Beckmann GG, Bettenay E, Churchward HM, Van Dijk DC, Dimmock GM, Hubble GD, Isbell RF, McArthur WM, Murtha GG, Nicolls KD, Paton TR, Thompson CH, Webb AA, Wright MJ (1960–1968) ‘Atlas of Australian soils. Sheets 1 to 10, with explanatory data.’ (CSIRO Australia and Melbourne University Press: Melbourne)
Odgers NP, Sun W, McBratney AB, Minasny B, Clifford D (2013) ‘DSMART version 2.7.3.’ (The University of Sydney: Sydney)
Odgers NP, McBratney AB, Minasny B (2014a) Spatial disaggregation using random toposequences. In ‘20th World Congress of Soil Science’. 8–13 June 2014, Jeju, Korea. Abstract.
Odgers NP, Sun W, McBratney AB, Minasny B, Clifford D (2014b) Disaggregating and harmonising soil map units through resampled classification trees. Geoderma 214–215, 91–100.
| Disaggregating and harmonising soil map units through resampled classification trees.Crossref | GoogleScholarGoogle Scholar |
Odgers NP, McBratney AB, Minasny B (2015) Digital soil property mapping and uncertainty estimation using soil class probability rasters. Geoderma 237–238, 190–198.
| Digital soil property mapping and uncertainty estimation using soil class probability rasters.Crossref | GoogleScholarGoogle Scholar |
Poggio L, Gimona A, Brewer MJ (2013) Regional scale mapping of soil properties and their uncertainty with a large number of satellite-derived covariates. Geoderma 209–210, 1–14.
| Regional scale mapping of soil properties and their uncertainty with a large number of satellite-derived covariates.Crossref | GoogleScholarGoogle Scholar |
Quinlan JR (1993) ‘C4.5: programs for machine learning.’ (Morgan Kaufmann: San Mateo, CA)
Sanchez PA, Ahamed S, Carre F, Hartemink AE, Hempel J, Huising J, Lagacherie P, McBratney AB, McKenzie NJ, Mendonca-Santos MD, Minasny B, Montanarella L, Okoth P, Palm CA, Sachs JD, Shepherd KD, Vagen TG, Vanlauwe B, Walsh MG, Winowiecki LA, Zhang GL (2009) Digital soil map of the world. Science 325, 680–681.
| Digital soil map of the world.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD1MXhtVSrs7bN&md5=7b7c33dd307eecdf261b292bf49633d6CAS | 19661405PubMed |
Schmidt J, Tonkin P, Hewitt A (2005) Quantitative soil–landscape models for the Haldon and Hurunui soil sets, New Zealand. Australian Journal of Soil Research 43, 127–137.
| Quantitative soil–landscape models for the Haldon and Hurunui soil sets, New Zealand.Crossref | GoogleScholarGoogle Scholar |
Schoknecht N, Pathan S (2013) ‘Soil groups of Western Australia.’ Resource Management Technical Report 380, 4th edn. (Department of Agriculture and Food Western Australia: Perth)
Schoknecht N, Tille P, Purdie B (2004) ‘Soil–landscape mapping in south-western Australia.’ Resource Management Technical Report 280. (Department of Agriculture and Food Western Australia: Perth)
Soil Survey Staff (1999) ‘Soil taxonomy: a basic system for soil classification for making and interpreting soil surveys.’ Agricultural Handbook 436, 2nd edn. (Natural Resources Conservation Service, USDA: Washington, DC)
Subburayalu SK, Slater BK (2013) Soil series mapping by knowledge discovery from an Ohio county soil map. Soil Science Society of America Journal 77, 1254–1268.
| Soil series mapping by knowledge discovery from an Ohio county soil map.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3sXhtFKqu7fN&md5=99867d30c3f99a4f635640f17366facfCAS |
Subburayalu SK, Jenhani I, Slater BK (2014) Disaggregation of component soil series on an Ohio County soil survey map using possibilistic decision trees. Geoderma 213, 334–345.
| Disaggregation of component soil series on an Ohio County soil survey map using possibilistic decision trees.Crossref | GoogleScholarGoogle Scholar |
Sun W, McBratney AB, 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 ‘Proceedings of the 4th Global Workshop on Digital Soil Mapping’. 24–26 May 2010, Rome, Italy. (CD-ROM)
Truong PN, Heuvelink GBM (2013) Uncertainty quantification of soil property maps with statistical expert elicitation. Geoderma 202–203, 142–152.
| Uncertainty quantification of soil property maps with statistical expert elicitation.Crossref | GoogleScholarGoogle Scholar |
Wilford J (2008) Remote sensing with gamma-ray spectrometry. In ‘Guidelines for surveying soil and land resources’. (Eds NJ McKenzie, MJ Grundy, R Webster, AJ Ringrose-Voase) pp. 189–202. (CSIRO Publishing: Melbourne)
Wilford J, Creasey J (2002) Landsat thematic mapper. In ‘Geophysical and remote sensing methods for regolith exploration’. (Ed. E Papp) pp. 6–12. (CRC LEME, CSIRO Exploration and Mining: Bentley, WA)
Yang L, Jiao Y, Fahmy S, Zhu AX, Hann S, Burt JE, Qi F (2011) Updating conventional soil maps through digital soil mapping. Soil Science Society of America Journal 75, 1044–1053.
| Updating conventional soil maps through digital soil mapping.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3MXnsFyltr8%3D&md5=f563103de6a52b00246b7e5b180afe0aCAS |
Zhu AX, Hudson B, Burt J, Lubich K, Simonson D (2001) Soil mapping using GIS, expert knowledge, and fuzzy logic. Soil Science Society of America Journal 65, 1463–1472.
| Soil mapping using GIS, expert knowledge, and fuzzy logic.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD38XptlWn&md5=d94209a344a59728bbaac5f64fba2ed4CAS |
