Soil and informatics science combine to develop S-map: A new generation soil information system for New Zealand

Lilburne, Linda; Hewitt, Allan E.; Webb, T.W.

doi:10.1016/j.geoderma.2011.11.012

Cited by 66 publications

(36 citation statements)

References 17 publications

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“…More efficient methods, such as DSM, will have to be used or will have to be integrated with CSM to accomplish the update with the given resources. Outside the Netherlands, integration of DSM methods within conventional soil survey is slowly gaining ground (Bui, 2007;Howell et al, 2008;Moore et al, 2010;Lilburne et al, 2012). In the Netherlands, DSM is still in the development phase (Brus et al, 2008;Kempen et al, 2009) but needs to move forward now to the operational phase.…”

Section: Relevance *•mentioning

confidence: 99%

“…Most DSM applications are research oriented and have a regional focus Vasques et al, 2010;Marchant et al, 2011;van der Klooster et al, 2011;Lemercier et al, 2012). Operational use of DSM is still limited (Bui and Moran, 2003;MacMillan et al, 2007;Lilburne et al, 2012). Nevertheless, although there are still issues that need to be resolved for operationalization, DSM is maturing (Sanchez et al, 2009;Grunwald, 2009;Grunwald et al, 2011;Einke, 2012).…”

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confidence: 99%

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Efficiency Comparison of Conventional and Digital Soil Mapping for Updating Soil Maps

Kempen

Brus

Stoorvogel

et al. 2012

Soil Science Soc of Amer J

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This study compared the efficiency of geostatistical digital soil mapping (DSM) with conventional soil mapping (CSM) for updating soil class and property maps of a cultivated peatland in the Netherlands. For digital soil class mapping, the generalized linear geostatistical model was used. Digital mapping of the soil organic matter (SOM) content and peat thickness was done by universal kriging. The conventional soil class map was created by free survey, while the property maps were created with the representative profile description (RPD) and map unit means (MUM) methods. For each method, we computed the effort invested in the mapping in terms of the sampling and cost densities. The accuracies of the created soil maps were estimated from independent probability sample data. The results showed that for DSM, the cost density could be reduced by a factor of three compared with CSM without compromising accuracy. The map purity of both maps was around 55%. For conventional soil property mapping, the MUM maps were more accurate than the RPD maps. For SOM, CSM-MUM (RMSE 7.5%) performed better than DSM (RMSF 12.1%), although accuracy differences were not significant. For peat thickness, DSM (RMSE 23.3 cm) performed slightly better than CSM-MUM (RMSE 24.9 cm). Despite the differences in accuracy being small, the digital soil property maps were produced more efficiently. The cost density was a factor of 3.5 smaller. We conclude that for updating conventional soil maps in the Dutch peatlands, geostatistical DSM can be more efficient, although not necessarily more accurate, than CSM.

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Section: Relevance *•mentioning

confidence: 99%

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confidence: 99%

Efficiency Comparison of Conventional and Digital Soil Mapping for Updating Soil Maps

Kempen

Brus

Stoorvogel

et al. 2012

Soil Science Soc of Amer J

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“…However, there is no evidence in the scientifi c literature that boundary line segments between soil polygons would have ever been analyzed and spatial association rules would have been extracted as trees from legacy soil maps. Compared to other regionalization studies (Schmidt, K. et al 2010;Lilburne, L.R. et al 2012), we used only boundary segments and soil classes on both sides of the line instead of complex data sets on soil, terrain, geology and other surface properties and analyzed the whole data set instead of subsett ing by moving window method with rasterized data (Behrens, T. et al 2009;Schmidt, K. et al 2010).…”

Section: Resultsmentioning

confidence: 99%

Method development to extract spatial association structure from soil polygon maps

et al. 2015

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“…The S-map is a contemporary digital soil spatial information system for New Zealand (Lilburne et al 2012), which provides information on the best available knowledge of classification of the Soil Order consistent with the New Zealand Soil Classification (NZSC; Hewitt 2010). Coverage by S-map is not available for all the land area, but it is available for regions of intensive agricultural use.…”

Section: Other Explanatory Layersmentioning

confidence: 99%

Development and calibration of a soil carbon inventory model for New Zealand

2014

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Abstract.A soil organic carbon (SOC) and SOC change model for New Zealand is developed for use in national SOC inventory reporting. The foundation for the model is a generalised least-squares regression, based on explanatory variables of land use, soil-climate class, and erosivity. The SOC change model is based on the assumption that changes in SOC over a decadal timescale are usually restricted to transitions in land use. Improvements to the model are then considered that are intended to reduce the uncertainty of SOC changes through reduction of the standard error of the land-use effects. Stochastic gradient boosting is used to find data layers most strongly associated with SOC. The most influential of these were then used in a general least-squares model after stepwise refinement. The stepwise-refined model significantly reduced the standard error for SOC, but did not result in a consistent reduction in the standard error for land-use classes, nor did it result in an improvement in the SOC change model. The method of calculating SOC change from a transition between two land-use classes is described, along with the significance of the transition, by use of a multi-comparison significance procedure.

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Soil and informatics science combine to develop S-map: A new generation soil information system for New Zealand

Cited by 66 publications

References 17 publications

Efficiency Comparison of Conventional and Digital Soil Mapping for Updating Soil Maps

Efficiency Comparison of Conventional and Digital Soil Mapping for Updating Soil Maps

Method development to extract spatial association structure from soil polygon maps

Development and calibration of a soil carbon inventory model for New Zealand

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