Continuous classification in soil survey: spatial correlation, confusion and boundaries

Burrough, P.A.; Gaans, P.F.M. van; Hootsmans, R. M.

doi:10.1016/s0016-7061(97)00018-9

Cited by 233 publications

(151 citation statements)

References 28 publications

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“…9 we calculated and mapped the confusion index (CI). The method is described in Burrough et al (1997) and was developed to assist in identifying where more information may be appropriate in order to better understand the nature of the overlap between classes. Fig.…”

Section: Fkm and Fkme Analysismentioning

confidence: 99%

Mapping clay content variation using electromagnetic induction techniques

Triantafilis

Lesch

2005

Computers and Electronics in Agriculture

183

110

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Section: Fkm and Fkme Analysismentioning

confidence: 99%

Mapping clay content variation using electromagnetic induction techniques

Triantafilis

Lesch

2005

Computers and Electronics in Agriculture

183

110

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“…However, if the difference in probabilities between the top predictions is small, there is a considerable chance of misclassification. To assess the prediction uncertainty at each grid cell, the confusion index (Burrough et al, 1997) is calculated from the first and second most probable components (CI=100− (P 1 − P 2 )). The CI values range from 0 to 100.…”

Section: Prediction Uncertaintymentioning

confidence: 99%

POLARIS: A 30-meter probabilistic soil series map of the contiguous United States

et al. 2016

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Chaney, Nathaniel W.; Wood, Eric F.; McBratney, Alexander B.; Hempel, Jonathan W.; Nauman, Travis W.; Brungard, Colby W.; and Odgers, Nathan P., "POLARIS: A 30-meter probabilistic soil series map of the contiguous United States" (2016 A new complete map of soil series probabilities has been produced for the contiguous United States at a 30 m spatial resolution. This innovative database, named POLARIS, is constructed using available high-resolution geospatial environmental data and a state-of-the-art machine learning algorithm (DSMART-HPC) to remap the Soil Survey Geographic (SSURGO) database. This 9 billion grid cell database is possible using available high performance computing resources. POLARIS provides a spatially continuous, internally consistent, quantitative prediction of soil series. It offers potential solutions to the primary weaknesses in SSURGO: 1) unmapped areas are gap-filled using survey data from the surrounding regions, 2) the artificial discontinuities at political boundaries are removed, and 3) the use of high resolution environmental covariate data leads to a spatial disaggregation of the coarse polygons. The geospatial environmental covariates that have the largest role in assembling POLARIS over the contiguous United States (CONUS) are fine-scale (30 m) elevation data and coarse-scale (~2 km) estimates of the geographic distribution of uranium, thorium, and potassium. A preliminary validation of POLARIS using the NRCS National Soil Information System (NASIS) database shows variable performance over CONUS. In general, the best performance is obtained at grid cells where DSMART-HPC is most able to reduce the chance of misclassification. The important role of environmental covariates in limiting prediction uncertainty suggests including additional covariates is pivotal to improving POLARIS' accuracy. This database has the potential to improve the modeling of biogeochemical, water, and energy cycles in environmental models; enhance availability of data for precision agriculture; and assist hydrologic monitoring and forecasting to ensure food and water security.

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“…It is described as Ci = 1 -(m max -m max-1 ), where m max is the maximum membership value and m max-1 is the next highest membership value in the cell. It is used to draw geographical boundaries (Burrough et al 1997) between the analysed STUs as zones of confusion.…”

Section: Model Descriptionmentioning

confidence: 99%

“…Fuzzy techniques are known to provide taxonomically interpretable data with the floating numeric format (e.g. McBratney & Moore 1985;De Gruijter & McBratney 1988;Burrough et al 1997;De Gruijter et al 1997;Hengl et al 2004;Lagacherie 2005), which can be treated as numeric indices of the spatial variability of soils. Fuzzy k-means method implements the theory of fuzzy sets (Zadeh 1965) and it partitions soil profiles into an explicit number of classes through the fuzzy k-partition, i.e.…”

mentioning

confidence: 99%

Mapping soils using the fuzzy approach and regression-kriging case study from the Považský Inovec Mountains, Slovakia

Balkovič¹,

Čemanová²,

Kollár³

et al. 2007

Soil Water Res.

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The paper introduces a method of digital mapping of spatial distribution of soil typological units. It implements fuzzy k-means to classify the soil profile data (study area from the Považský Inovec Mountains, Slovakia) and regression-kriging with the selected digital terrain and remote sensing data to draw membership maps of soil typological units. Totally three soil typological units were identified: Haplic Cambisols (Skeletic, Dystric), Albic Stagnic Luvisols, and Haplic Stagnosols (Albic, Dystric). We analysed the membership values to these units with respect to terrain and remote sensing data. The membership values appeared as spatially smoothly dependant on the terrain gradients (linearly or exponentially) whereas the residua showed spatial autocorrelation. Based on regression and kriging analyses, the regression-kriging model was successfully deployed to draw raster membership maps. These maps yield coefficients of determination between R<sup>2</sup> = 56% (Albic Stagnic Luvisols) to R<sup>2</sup>= 79% (Haplic Cambisols (Skeletic, Dystric)) when evaluated by cross validation. The grid-based continuous soil map represents an alternative to the classical polygon soil maps and can offer a wide range of interpretations for landscape studies.

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Continuous classification in soil survey: spatial correlation, confusion and boundaries

Cited by 233 publications

References 28 publications

Mapping clay content variation using electromagnetic induction techniques

Mapping clay content variation using electromagnetic induction techniques

POLARIS: A 30-meter probabilistic soil series map of the contiguous United States

Mapping soils using the fuzzy approach and regression-kriging case study from the Považský Inovec Mountains, Slovakia

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