2.
Tomislav Hengl Luis Rodríguez Lado, Hannes I. Reuter
Heavy metals in European soils: A geostatistical analysis of the FOREGS Geochemical database Journal Article
In: Geoderma, vol. 148, no. 2, pp. 189-199, 2008, ISSN: 0016-7061.
Abstract | Links | BibTeX | Tags: Night lights image, Regression-kriging
@article{LADO2008189,
title = {Heavy metals in European soils: A geostatistical analysis of the FOREGS Geochemical database},
author = {Luis Rodríguez Lado, Tomislav Hengl, Hannes I. Reuter},
url = {https://www.sciencedirect.com/science/article/pii/S0016706108002668},
doi = {https://doi.org/10.1016/j.geoderma.2008.09.020},
issn = {0016-7061},
year = {2008},
date = {2008-01-01},
urldate = {2008-01-01},
journal = {Geoderma},
volume = {148},
number = {2},
pages = {189-199},
abstract = {This paper presents the results of modeling the distribution of eight critical heavy metals (arsenic, cadmium, chromium, copper, mercury, nickel, lead and zinc) in topsoils using 1588 georeferenced samples from the Forum of European Geological Surveys Geochemical database (26 European countries). The concentrations were mapped using regression-kriging (RK) and accuracy of predictions evaluated using the leave-one-out cross validation method. A large number of auxiliary raster maps (topographic indexes, land cover, geology, vegetation indexes, night lights images and earth quake magnitudes) were used to improve the predictions. These were first converted to 36 principal components and then used to explain spatial distribution of heavy metals. The study revealed that this database is suitable for geostatistical analyses: the predictors explained from 21% (Cr) to 35% (Pb) of variability; the residuals showed spatial autocorrelation. The Principal Component Analysis of the mapped heavy metals revealed that the administrative units (NUTS level3) with highest overall concentrations are: (1) Liege (Arrondissement) (BE), Attiki (GR), Darlington (UK), Coventry (UK), Sunderland (UK), Kozani (GR), Grevena (GR), Hartlepool & Stockton (UK), Huy (BE), Aachen (DE) (As, Cd, Hg and Pb) and (2) central Greece and Liguria region in Italy (Cr, Cu and Ni). The evaluation of the mapping accuracy showed that the RK models for As, Ni and Pb can be considered satisfactory (prediction accuracy 45–52% of total variance), marginally satisfactory for Cr, Cu, Hg and Zn (36–41%), while the model for Cd is unsatisfactorily accurate (30%). The critical elements limiting the mapping accuracy are: (a) the problem of sporadic high values (hot-spots); and (b) relatively coarse resolution of the input maps. Automation of the geostatistical mapping and use of auxiliary spatial layers opens a possibility to develop mapping systems that can automatically update outputs by including new field observations and higher quality auxiliary maps. This approach also demonstrates the benefits of organizing standardized joint European monitoring projects, in comparison to the merging of several national monitoring projects.},
keywords = {Night lights image, Regression-kriging},
pubstate = {published},
tppubtype = {article}
}
This paper presents the results of modeling the distribution of eight critical heavy metals (arsenic, cadmium, chromium, copper, mercury, nickel, lead and zinc) in topsoils using 1588 georeferenced samples from the Forum of European Geological Surveys Geochemical database (26 European countries). The concentrations were mapped using regression-kriging (RK) and accuracy of predictions evaluated using the leave-one-out cross validation method. A large number of auxiliary raster maps (topographic indexes, land cover, geology, vegetation indexes, night lights images and earth quake magnitudes) were used to improve the predictions. These were first converted to 36 principal components and then used to explain spatial distribution of heavy metals. The study revealed that this database is suitable for geostatistical analyses: the predictors explained from 21% (Cr) to 35% (Pb) of variability; the residuals showed spatial autocorrelation. The Principal Component Analysis of the mapped heavy metals revealed that the administrative units (NUTS level3) with highest overall concentrations are: (1) Liege (Arrondissement) (BE), Attiki (GR), Darlington (UK), Coventry (UK), Sunderland (UK), Kozani (GR), Grevena (GR), Hartlepool & Stockton (UK), Huy (BE), Aachen (DE) (As, Cd, Hg and Pb) and (2) central Greece and Liguria region in Italy (Cr, Cu and Ni). The evaluation of the mapping accuracy showed that the RK models for As, Ni and Pb can be considered satisfactory (prediction accuracy 45–52% of total variance), marginally satisfactory for Cr, Cu, Hg and Zn (36–41%), while the model for Cd is unsatisfactorily accurate (30%). The critical elements limiting the mapping accuracy are: (a) the problem of sporadic high values (hot-spots); and (b) relatively coarse resolution of the input maps. Automation of the geostatistical mapping and use of auxiliary spatial layers opens a possibility to develop mapping systems that can automatically update outputs by including new field observations and higher quality auxiliary maps. This approach also demonstrates the benefits of organizing standardized joint European monitoring projects, in comparison to the merging of several national monitoring projects.
1.
Hengl, Tomislav; Toomanian, Norair; Reuter, Hannes I.; Malakouti, Mohammad J.
Methods to interpolate soil categorical variables from profile observations: Lessons from Iran Journal Article
In: Geoderma, vol. 140, no. 4, pp. 417-427, 2007, ISSN: 0016-7061, (Pedometrics 2005).
Abstract | Links | BibTeX | Tags: Categorical variables, Fuzzy -means, MODIS, Multinominal logistic regression, Regression-kriging, Terrain parameters
@article{HENGL2007417,
title = {Methods to interpolate soil categorical variables from profile observations: Lessons from Iran},
author = {Tomislav Hengl and Norair Toomanian and Hannes I. Reuter and Mohammad J. Malakouti},
url = {https://www.sciencedirect.com/science/article/pii/S0016706107001218},
doi = {https://doi.org/10.1016/j.geoderma.2007.04.022},
issn = {0016-7061},
year = {2007},
date = {2007-01-01},
journal = {Geoderma},
volume = {140},
number = {4},
pages = {417-427},
abstract = {The paper compares semi-automated interpolation methods to produce soil-class maps from profile observations and by using multiple auxiliary predictors such as terrain parameters, remote sensing indices and similar. The Soil Profile Database of Iran, consisting of 4250 profiles, was used to test different soil-class interpolators. The target variables were soil texture classes and World Reference Base soil groups. The predictors were 6 terrain parameters, 11 MODIS EVI images and 17 physiographic regions (polygon map) of Iran. Four techniques were considered: (a) supervised classification using maximum likelihoods; (b) multinominal logistic regression; (c) regression-kriging on memberships; and (d) classification of taxonomic distances. The predictive capabilities were assessed using a control subset of 30% profiles and the kappa statistics as criterion. Supervised classification and multinominal logistic regression can lead to poor results if soil-classes overlap in the feature space, or if the correlation between the soil-classes and predictors is low. The two other methods have better predictive capabilities, although both are computationally more demanding. For both mapping of texture classes and soil types, the best prediction was achieved using regression-kriging of indicators/memberships (κ=45%, κ=54%). In all cases kappa was smaller than 60%, which can be explained by the preferential sampling plan, the poor definition of soil-classes and the high variability of soils. Steps to improve interpolation of soil-class data, by taking into account the fuzziness of classes directly on the field are further discussed.},
note = {Pedometrics 2005},
keywords = {Categorical variables, Fuzzy -means, MODIS, Multinominal logistic regression, Regression-kriging, Terrain parameters},
pubstate = {published},
tppubtype = {article}
}
The paper compares semi-automated interpolation methods to produce soil-class maps from profile observations and by using multiple auxiliary predictors such as terrain parameters, remote sensing indices and similar. The Soil Profile Database of Iran, consisting of 4250 profiles, was used to test different soil-class interpolators. The target variables were soil texture classes and World Reference Base soil groups. The predictors were 6 terrain parameters, 11 MODIS EVI images and 17 physiographic regions (polygon map) of Iran. Four techniques were considered: (a) supervised classification using maximum likelihoods; (b) multinominal logistic regression; (c) regression-kriging on memberships; and (d) classification of taxonomic distances. The predictive capabilities were assessed using a control subset of 30% profiles and the kappa statistics as criterion. Supervised classification and multinominal logistic regression can lead to poor results if soil-classes overlap in the feature space, or if the correlation between the soil-classes and predictors is low. The two other methods have better predictive capabilities, although both are computationally more demanding. For both mapping of texture classes and soil types, the best prediction was achieved using regression-kriging of indicators/memberships (κ=45%, κ=54%). In all cases kappa was smaller than 60%, which can be explained by the preferential sampling plan, the poor definition of soil-classes and the high variability of soils. Steps to improve interpolation of soil-class data, by taking into account the fuzziness of classes directly on the field are further discussed.