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Mapping complex soil patterns with multiple-point geostatistics

Eef Meerschman, Marc Van Meirvenne UGent, Ellen Van De Vijver UGent, Philippe De Smedt UGent, Mohammad Monirul Islam UGent and Timothy Saey UGent (2013) EUROPEAN JOURNAL OF SOIL SCIENCE. 64(2). p.183-191
abstract
The commonly used variogram function is incapable of modelling complex spatial patterns associated with repetitive, connected or curvilinear features, because it is a two-point statistic. Because this was strongly limiting to petroleum- and hydrogeologists, they developed multiple-point geostatistics (MPG), an approach that replaces the variogram by a training image (TI). However, soil scientists also face complex spatial patterns and MPG might be of use to them as well. Therefore, this paper aims to introduce MPG to soil science and demonstrate its potential with a case study of polygonal subsoil patterns caused by Weichselian periglacial frost cracks in Belgium. A high-resolution proximal soil sensing survey provided a reference image from which a continuous (655 sensor data) and a categorical (100 point observations) dataset were extracted. As a continuous TI, we used the geophysical data of another part of the field, and as categorical TI we used a classified photograph of an ice-wedge network in Alaska. The resulting MPG maps reconstructed the polygonal patterns very well and corresponded closely to the reference image. Consequently, we identify MPG as a promising technique to map complex soil patterns and suggest that it should be added to the pedometrician's toolbox.
Please use this url to cite or link to this publication:
author
organization
year
type
journalArticle (original)
publication status
published
subject
keyword
CONDITIONAL SIMULATION, ELECTROMAGNETIC INDUCTION SENSOR, POLYGONAL NETWORK, WEDGE CASTS, STATISTICS
journal title
EUROPEAN JOURNAL OF SOIL SCIENCE
Eur. J. Soil Sci.
volume
64
issue
2
pages
183 - 191
Web of Science type
Article
Web of Science id
000316564700002
JCR category
SOIL SCIENCE
JCR impact factor
2.387 (2013)
JCR rank
8/34 (2013)
JCR quartile
1 (2013)
ISSN
1351-0754
DOI
10.1111/ejss.12033
language
English
UGent publication?
yes
classification
A1
copyright statement
I have transferred the copyright for this publication to the publisher
id
3173032
handle
http://hdl.handle.net/1854/LU-3173032
date created
2013-03-25 11:23:12
date last changed
2016-12-19 15:39:27
@article{3173032,
  abstract     = {The commonly used variogram function is incapable of modelling complex spatial patterns associated with repetitive, connected or curvilinear features, because it is a two-point statistic. Because this was strongly limiting to petroleum- and hydrogeologists, they developed multiple-point geostatistics (MPG), an approach that replaces the variogram by a training image (TI). However, soil scientists also face complex spatial patterns and MPG might be of use to them as well. Therefore, this paper aims to introduce MPG to soil science and demonstrate its potential with a case study of polygonal subsoil patterns caused by Weichselian periglacial frost cracks in Belgium. A high-resolution proximal soil sensing survey provided a reference image from which a continuous (655 sensor data) and a categorical (100 point observations) dataset were extracted. As a continuous TI, we used the geophysical data of another part of the field, and as categorical TI we used a classified photograph of an ice-wedge network in Alaska. The resulting MPG maps reconstructed the polygonal patterns very well and corresponded closely to the reference image. Consequently, we identify MPG as a promising technique to map complex soil patterns and suggest that it should be added to the pedometrician's toolbox.},
  author       = {Meerschman, Eef and Van Meirvenne, Marc and Van De Vijver, Ellen and De Smedt, Philippe and Islam, Mohammad Monirul and Saey, Timothy},
  issn         = {1351-0754},
  journal      = {EUROPEAN JOURNAL OF SOIL SCIENCE},
  keyword      = {CONDITIONAL SIMULATION,ELECTROMAGNETIC INDUCTION SENSOR,POLYGONAL NETWORK,WEDGE CASTS,STATISTICS},
  language     = {eng},
  number       = {2},
  pages        = {183--191},
  title        = {Mapping complex soil patterns with multiple-point geostatistics},
  url          = {http://dx.doi.org/10.1111/ejss.12033},
  volume       = {64},
  year         = {2013},
}

Chicago
Meerschman, Eef, Marc Van Meirvenne, Ellen Van De Vijver, Philippe De Smedt, Mohammad Monirul Islam, and Timothy Saey. 2013. “Mapping Complex Soil Patterns with Multiple-point Geostatistics.” European Journal of Soil Science 64 (2): 183–191.
APA
Meerschman, E., Van Meirvenne, M., Van De Vijver, E., De Smedt, P., Islam, M. M., & Saey, T. (2013). Mapping complex soil patterns with multiple-point geostatistics. EUROPEAN JOURNAL OF SOIL SCIENCE, 64(2), 183–191.
Vancouver
1.
Meerschman E, Van Meirvenne M, Van De Vijver E, De Smedt P, Islam MM, Saey T. Mapping complex soil patterns with multiple-point geostatistics. EUROPEAN JOURNAL OF SOIL SCIENCE. 2013;64(2):183–91.
MLA
Meerschman, Eef, Marc Van Meirvenne, Ellen Van De Vijver, et al. “Mapping Complex Soil Patterns with Multiple-point Geostatistics.” EUROPEAN JOURNAL OF SOIL SCIENCE 64.2 (2013): 183–191. Print.