Operational sampling challenges to digital soil mapping in Tasmania, Australia

Kidd, Darren; Malone, Brendan; McBratney, Alex B.; Minasny, Budiman; Webb, Mathew

doi:10.1016/j.geodrs.2014.11.002

Cited by 47 publications

(39 citation statements)

References 16 publications

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“…The workshops are a meeting place for soil surveyors, practitioners, and pedometricians to discuss progress, research applications and operational gaps. As an example, scientists developed sampling techniques that optimise the coverage of covariate or feature space, while practitioners informed researchers on operational constraints, how the sampling approach needs to be flexible, efficient, and compatible with a project area's land use and terrain (Kidd et al, 2015). (Cipra, 1973) Digital application of environmental similarity method Legros andBonneric (1979) 1980s Soil geostatistics Hajrasuliha et al (1980), Burgess and Webster (1980) Geographical information system Burrough (1986 Pedometrics: nonlinear geostatistics, fuzzy clustering, uncertainty analysis McBratney and Gruijter (1992), Odeh et al (1992b) Digital terrain modelling Gallant and Wilson (1996) Environmental correlation & cloprt model Moore et al (1993), McKenzie and Ryan (1999) Mapping using reference area Lagacherie et al (1995) Mapping using expert knowledge and expert system Skidmore et al (1996), Zhu et al (1997) Use of data mining tools Pachepsky et al (1996) (2014) Digital soil mapping has transformed soil survey and cartography more than anything else in recent times, and it is being tested and routinely used in soil mapping programmes around the world.…”

Section: Digital Soil Mappingmentioning

confidence: 99%

Digital soil mapping: A brief history and some lessons

2016

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Section: Digital Soil Mappingmentioning

confidence: 99%

Digital soil mapping: A brief history and some lessons

2016

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“…Taking into consideration the correlation between soil and its environmental covariates, some sampling design aims to select sample locations that can represent the feature space of environmental covariates to capture soil variability (Minasny and McBratney, 2006;Mulder et al, 2013;Kidd et al, 2015). A popular sampling method based on this idea is cLHS.…”

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confidence: 99%

“…It enables the designed sample distribution to closely replicate the covariate distribution (Minasny and McBratney, 2006). Conditioned Latin hypercube sampling is thus considered as an efficient sampling method and implemented widely (Worsham et al, 2012;Louis et al, 2014;TaghizadehMehrjardi et al, 2014;Pahlavan Rad et al, 2014;Silva et al, 2014Silva et al, , 2015Kidd et al, 2015).…”

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Evaluation of Integrative Hierarchical Stepwise Sampling for Digital Soil Mapping

Yang

Feng

Zhu

et al. 2016

Soil Science Soc of Amer J

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This paper presents an integrative hierarchical stepwise sampling (IHS) method and two case studies to compare it with stratified random sampling (SRS) and conditioned Latin hypercube sampling (cLHS). The first comparison between IHS and SRS was conducted for mapping sand content of two soil layers in a study area in Anhui Province, China. Two sample sets of the same sample size were collected in the field based on IHS and SRS. The second case study is a simulation study, where we compared IHS and cLHS for mapping soil series in the Raffelson watershed in Wisconsin (USA). The study used an accurate and detailed soil series map produced previously as a proxy of the real soil distribution. Virtual samples with nine sample sizes designed by IHS and cLHS were collected on the soil map. For both case studies, an individual predictive soil mapping method was employed and independent validation samples were used to evaluate the mapping accuracies. Results indicate that IHS generally performs better than SRS for capturing distributions of the environmental variables. It obtained higher mapping accuracies than SRS at different sample sizes. On the other hand, cLHS appears to provide a better representation for distributions of the environmental variables than IHS, but the mapping accuracies with IHS are higher than those with cLHS at nearly all sample sizes. Finally, both case studies showed that IHS provides valuable information on representativeness of the samples.Abbreviations: FCM, fuzzy c-means; IHS, integrative hierarchical stepwise sampling; cLHS, conditioned Latin hypercube sampling; SRS, stratified random sampling. S oil sampling plays an essential role in soil survey. A sampling design specifies where and how many to sample in the field. Different sampling design strategies will produce different soil distribution maps and greatly affect the mapping accuracies (Brus and de Gruijter, 1997;van Groenigen et al., 2000;de Gruijter et al., 2006;Brus et al., 2006;Gregoire and Valentine, 2007;Heim et al., 2009). Because the collection of soil samples in the field is often time-consuming and resource-intensive, it is important to apply efficient sampling design strategies.When prior knowledge on an area's local soil variation is limited, simple random sampling has been a commonly used sampling strategy (Oliver and Webster, 1986;Grinand et al., 2008;Walvoort et al., 2010 • Evaluation of an integrative hierarchical stepwise (IHS) sampling method by comparing it with stratified random sampling (SRS) and conditioned Latin hypercube sampling (cLHS) through two case studies.• IHS obtained higher mapping accuracies than SRS and cLHS at nearly all sample sizes.• IHS provides valuable information on the representativeness of samples.• SRS and cLHS were found to generate unstable results on sample sets and soil maps.

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“…There is a need for studies focused on methods to reduce the negative impacts of modeling error, and this is especially important for soil science because classification definitions is can be very ambiguous, even among experts on this topic (Brus et al, 2011). The use of more statistically robust methods can contribute to increased model validity (Kidd et al, 2015). Some works have used the cross validation method (leave-one-out) to validate their models, observing that the prediction accuracies were poor (Mueller et al, 2004;Hengl et al, 2014).…”

Section: Future Of Pedologic Modelingmentioning

confidence: 99%