A comparison of kriging, co-kriging and kriging combined with regression for spatial interpolation of horizon depth with censored observations

Knotters, M.; Brus, D.J.; Voshaar, J.H. Oude

doi:10.1016/0016-7061(95)00011-c

Cited by 275 publications

(151 citation statements)

References 19 publications

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“…Here, two main paths can be recognised: co-kriging and kriging combined with regression (McBratney et al, 2000). The latter path was shown to be more attractive for combination of kriging and CLORPT techniques, among others because fewer model parameters need to be estimated (Knotters et al, 1995). In many cases, kriging combined with regression has proven to be superior to the plain geostatistical techniques yielding more detailed results and higher accuracy of prediction.…”

Section: Introductionmentioning

confidence: 99%

“…Hudson and Wackernagel (1994) showed that kriging with use of elevation data improves mapping of temperature. Knotters et al (1995) compared ordinary kriging with co-kriging and regression-kriging for soil mapping purposes, favouring the latter. Bourennane et al (1996Bourennane et al ( , 2000 showed that prediction of horizon thickness is more accurate with the use of a slope map as external drift.…”

Section: Introductionmentioning

confidence: 99%

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A generic framework for spatial prediction of soil variables based on regression-kriging

Hengl¹,

Heuvelink

Stein³

2004

Geoderma

914

558

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A methodological framework for spatial prediction based on regression-kriging is described and compared with ordinary kriging and plain regression. The data are first transformed using logit transformation for target variables and factor analysis for continuous predictors (auxiliary maps). The target variables are then fitted using step-wise regression and residuals interpolated using kriging. A generic visualisation method is used to simultaneously display predictions and associated uncertainty. The framework was tested using 135 profile observations from the national survey in Croatia, divided into interpolation (100) and validation sets (35). Three target variables: organic matter, pH in topsoil and topsoil thickness were predicted from six relief parameters and nine soil mapping units. Prediction efficiency was evaluated using the mean error and root mean square error (RMSE) of prediction at validation points. The results show that the proposed framework improves efficiency of predictions. Moreover, it ensured normality of residuals and enforced prediction values to be within the physical range of a variable. For organic matter, it achieved lower relative RMSE than ordinary kriging (53.3% versus 66.5%). For topsoil thickness, it achieved a lower relative RMSE (66.5% versus 83.3%) and a lower bias than ordinary kriging (0.15 versus 0.69 cm). The prediction of pH in topsoil was difficult with all three methods. This framework can adopt both continuous and categorical soil variables in a semi-automated or automated manner. It opens a possibility to develop a bundle algorithm that can be implemented in a GIS to interpolate soil profile data from existing datasets. D

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A generic framework for spatial prediction of soil variables based on regression-kriging

Hengl¹,

Heuvelink

Stein³

2004

Geoderma

914

558

View full text Add to dashboard Cite

show abstract

“…EM instruments measure the apparent soil electrical conductivity (EC a ), which is a function of various soil properties including salinity, clay content, moisture content, and mineralogy (Triantafilis et al, 2002;Corwin et al, 2003). In the Netherlands, EM survey data have been used to identify the depth to (i) boulder clay and (ii) a soft layer in the western marine districts (Knotters et al, 1995). In the USA, EM data have been used to estimate (i) depth to clay pan (Sudduth et al, 1995) and (ii) depositional depth of sand after a large flooding event in the Midwest of the USA (Kitchen et al, 1996).…”

Section: Introductionmentioning

confidence: 99%

Mapping clay content variation using electromagnetic induction techniques

Triantafilis

Lesch

2005

Computers and Electronics in Agriculture

183

110

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“…Se construyeron dos modelos con cokriging, variante que permite modelar varias variables simultáneamente (24)(25)(26): uno correspondiente a la potencialidad y otro a la positividad. Se hizo validación cruzada de cada modelo y se registraron la media de los errores de estimación, el error cuadrático medio y el error cuadrático medio adimensional (27).…”

Section: Análisis Espacialunclassified

Spatial distribution of potential and positive Aedes aegypti breeding sites

et al. 2017

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Introducción. La distribución espacial de Aedes aegypti es heterogénea, y la interacción entre criaderos positivos y potenciales en el intradomicilio y el extradomicilio es uno de los aspectos más difíciles de caracterizar en los programas de control vectorial. Objetivo. Describir la relación espacial entre los criaderos potenciales y positivos de A. aegypti en el intradomicilio y en el extradomicilio en un sector de Cali, Colombia. Materiales y métodos. Se hizo una encuesta entomológica con el objetivo de recolectar datos de los criaderos en el intradomicilio y el extradomicilio. El análisis exploratorio de los datos espaciales incluyó la localización, la tendencia espacial, la autocorrelación espacial local, la continuidad espacial y la correlación espacial de los criaderos positivos y potenciales según el hábitat. Resultados. Se determinaron las tendencias espaciales. Mediante el análisis de autocorrelación espacial local se ubicaron los conglomerados de criaderos potenciales y positivos del extradomicilio. Se encontró una correlación positiva entre los criaderos potenciales y los positivos y una correlación negativa entre el intradomicilio y el extradomicilio. Conclusiones. La relación espacial entre criaderos positivos y potenciales de A. aegypti en el intradomicilio y el extradomicilio es dinámica y muy sensible a las características de cada territorio, por lo que establecer su distribución en el espacio contribuye a la priorización de recursos y acciones en los programas de control vectorial.Palabras clave: Aedes aegypti, dengue, Zika, chikungunya, análisis espacial. doi: https://doi.org/10.7705/biomedica.v34i2.3471 Spatial distribution of potential and positive Aedes aegypti breeding sitesIntroduction: The spatial distribution of Aedes aegypti is heterogeneous, and the interaction between positive and potential breeding sites located both inside and outside homes is one of the most difficult aspects to characterize in vector control programs. Objective: To describe the spatial relationship between potential and positive breeding sites of A. aegypti inside and outside homes in Cali, Colombia. Materials and methods:We conducted an entomological survey to collect data from both indoor and outdoor breeding sites. The exploratory analysis of spatial data included location, spatial trends, local spatial autocorrelation, spatial continuity and spatial correlation of positive and potential breeding sites according to habitat. Results: Spatial trends were identified, as well as clusters of potential and positive breeding sites outdoors using local spatial autocorrelation analysis. A positive correlation was found between potential and positive breeding sites, and a negative correlation existed between indoor and outdoor sites. Conclusions:The spatial relationship between positive and potential A. aegypti breeding sites both indoors and outdoors is dynamic and highly sensitive to the characteristics of each territory. Knowing how positive and potential breeding sites are distributed contributes to the prioritizati...

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A comparison of kriging, co-kriging and kriging combined with regression for spatial interpolation of horizon depth with censored observations

Cited by 275 publications

References 19 publications

A generic framework for spatial prediction of soil variables based on regression-kriging

A generic framework for spatial prediction of soil variables based on regression-kriging

Mapping clay content variation using electromagnetic induction techniques

Spatial distribution of potential and positive Aedes aegypti breeding sites

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