Ließ, M. (2015): <b>Sampling for regression-based digital soil mapping: Closing the gap between statistical desires and operational applicability</b>. <i>Spatial Statistics</i> <b>13</b>, 106-122.
Resource Description
Title:
Sampling for regression-based digital soil mapping: Closing the gap between statistical desires and operational applicability
FOR816dw ID:
1409
Publication Date:
2015-06-17
License and Usage Rights:
IPR Ließ<br/>
Copyright Elsevier
Resource Owner(s):
Individual:
Mareike Ließ
Contact:
email:
mareike.liess <at> ufz.de
Helmholtz Centre for Environmental Research - UFZ
Department of Soil Physics
D-06120 Halle (Saale), Theodor-Lieser-Str. 4
Germany
Abstract:
With respect to sampling for regression-based digital soil mapping<br/>
(DSM), the above all aim is to ensure that the spatial variability<br/>
of the soil is well-captured without introducing any bias, while<br/>
the design remains feasible according to operational constraints<br/>
such as accessibility, man power and cost. Representativeness of<br/>
the sample concerning the population to be sampled needs to be<br/>
guaranteed in any regression-based modelling approach. Four selected<br/>
sampling designs were adapted to show that basically any<br/>
design may be optimised to represent the n-dimensional predictor<br/>
space of a particular area, while selecting points is only permitted<br/>
from a small accessible sub-area or from outside the area. Sampling<br/>
efficiency may be evaluated based on the representation of<br/>
the predictor space. However, not only each predictor’s probability<br/>
function but also the interaction between predictors may have to<br/>
be considered, to select a representative sample. Instead of sampling<br/>
a previously un-sampled area with limited accessibility, the<br/>
four sampling designs may also be used to subsample an existing<br/>
dataset and, thereby, optimise a suboptimal dataset based on the<br/>
predictor space of the area which shall be mapped by DSM.
Keywords:
| sampling design | digital soil mapping | regression |
Literature type specific fields:
ARTICLE
Journal:
Spatial Statistics
Volume:
13
Page Range:
106-122
Publisher:
Elsevier
Metadata Provider:
Individual:
Mareike Ließ
Contact:
email:
mareike.liess <at> ufz.de
Helmholtz Centre for Environmental Research - UFZ
Department of Soil Physics
D-06120 Halle (Saale), Theodor-Lieser-Str. 4
Germany
