Hostname: page-component-77c89778f8-9q27g Total loading time: 0 Render date: 2024-07-17T23:32:37.687Z Has data issue: false hasContentIssue false
  • English
  • Français

A geostatistical approach for modelling and combining spatial data with different support

Published online by Cambridge University Press:  01 June 2017

A. Castrignanò ,
R. Quarto ,
A. Venezia  and
G. Buttafuoco
A. Castrignanò
Affiliation:
CREA – Research Unit for Cropping Systems in Dry Environments (SCA), Bari, Italy
R. Quarto
Affiliation:
Earth and Geoenvironmental Sciences department, University of Bari Aldo Moro, Italy
A. Venezia
Affiliation:
CREA – Centro di Ricerca per l’Orticoltura (ORT), Pontecagnano (SA), Italy
G. Buttafuoco*
Affiliation:
National Research Council of Italy – Institute for Agricultural and Forest Systems in the Mediterranean (ISAFOM), Rende (CS), Italy
*
†E-mail: gabriele.buttafuoco@cnr.it
Get access

Abstract

The paper proposes a geostatistical framework to solve the issues of heterogeneous support for spatial estimation. Apparent soil electrical conductivity (ECa) was measured in a field cropped with San Marzano tomato using a multiple frequency electromagnetic profiler with 6 operating frequencies. Mixed support kriging was used to estimate ECa taking into account the change of support. The method includes punctual kriging with the error being the dispersion variance associated with each frequency. The individual ECa maps were weighted by the dispersion variance to obtain a map which was used for field partition in management zones.

Keywords

spatial datadata fusionchange of supportmixed support kriging
Type
Data analysis and Geostatistics
Information
Advances in Animal Biosciences , Volume 8 , Special Issue 2: Papers presented at the 11th European Conference on Precision Agriculture (ECPA 2017), John McIntyre Centre, Edinburgh, UK, July 16–20 2017 , July 2017 , pp. 594 - 599
Copyright
© The Animal Consortium 2017 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Braverman, A 2008. Data fusion. In Encyclopedia of quantitative risk analysis and assessment, volume 2. Wiley, NY, USA.Google Scholar
Bush, D 2010. An overview of the estimation of kimberlite diamond deposits. In: The Southern African Institute of Mining and Metallurgy. Diamonds – Source to Use 2010. The Southern African Institute of Mining and Metallurgy, Johannesburg, South Africa. pp. 73–84. ISBN 978-1-920410-03-2.Google Scholar
Castrignanò, A and Buttafuoco, G 2004. Geostatistical Stochastic Simulation of Soil Water Content in a Forested Area of South Italy. Biosystems Engineering 87 (2), 257–266.Google Scholar
Chilès, JP and Delfiner, P 2012. Geostatistics: Modelling spatial uncertainty, 2nd edition. Wiley, New York, NY, USA.Google Scholar
Daniels, JJ, Vendl, M, Ehsani, MR and Allred, BJ 2008. Electromagnetic Induction Methods. In BJ Allred, JJ Daniels and MR Ehsani (Eds.), Handbook of agricultural geophysics (pp. 109–128). CRC Press, Taylor and Francis Group, Boca Raton, Florida, FL, USA.Google Scholar
Geovariances 2016. ISATIS Software: Technical References Release 2016.1. Geovariances & Ecole des Mines de Paris, Paris: France, 220 pp.Google Scholar
Hall, DL and McMullen, SAH 2004. Mathematical Techniques in Multisensor Data Fusion, 2nd edition. Artech House, Inc. Norwood, MA, USA.Google Scholar
Journel, AG and Huijbregts, CJ 1978. Mining Geostatistics. Academic Press, New York, NY, USA.Google Scholar
Lajaunie, C 1996. Documentation of the mixed support kriging programs, Centre de Géostatistique, Fontainebleau, FL, USA.Google Scholar
Longobardi, A, Buttafuoco, G, Caloiero, T and Coscarelli, R 2016. Spatial and temporal distribution of precipitation in a Mediterranean area (southern Italy). Environmental Earth Sciences 75 (3) 1–20, 189.Google Scholar
Marechal, A and Serra, J 1970. Random Kriging. In Merriam, DF (ed.), Geostatistics: A Colloquium. ISBN 978-1-4615-7103-2 DOI: 10.1007/978-1-4615-7103-2_9, pp. 91–112.Google Scholar
Nguyen, H, Katzfuss, M, Cressie, N and Braverman, A 2014. Spatio-Temporal Data Fusion for Very Large Remote Sensing Datasets. Technometrics 56 (2), 174–185. DOI: 10.1080/00401706.2013.2013.831774.Google Scholar
Reynolds, JM 2011. An Introduction to Applied and Environmental Geophysics, 2nd Edition. John Wiley & Sons, Ltd., Chichester, UK.Google Scholar
Wackernagel, H 2003. Multivariate Geostatistics: an introduction with applications, 3rd ed. Springer-Verlag, Berlin, Germany, pp. 388.Google Scholar
Webster, R and Oliver, MA 2007. Geostatistics for Environmental Scientists, 2nd ed., Wiley, Chichester, UK, 330 pp.Google Scholar