Mapping optimization based on sampling size in earth related and environmental phenomena

Modis, K; Papantonopoulos, G; Komnitsas, K; Papaodysseus, K

dc.contributor.author	Modis, K	en
dc.contributor.author	Papantonopoulos, G	en
dc.contributor.author	Komnitsas, K	en
dc.contributor.author	Papaodysseus, K	en
dc.date.accessioned	2014-03-01T01:28:44Z
dc.date.available	2014-03-01T01:28:44Z
dc.date.issued	2008	en
dc.identifier.issn	1436-3240	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/18949
dc.subject	Environment	en
dc.subject	Geostatistics	en
dc.subject	Interpolation	en
dc.subject	Mapping	en
dc.subject	Sampling size	en
dc.subject.classification	Engineering, Environmental	en
dc.subject.classification	Engineering, Civil	en
dc.subject.classification	Environmental Sciences	en
dc.subject.classification	Statistics & Probability	en
dc.subject.classification	Water Resources	en
dc.subject.other	contaminated land	en
dc.subject.other	environmental planning	en
dc.subject.other	estimation method	en
dc.subject.other	geostatistics	en
dc.subject.other	interpolation	en
dc.subject.other	mapping	en
dc.subject.other	optimization	en
dc.subject.other	sampling	en
dc.subject.other	stochasticity	en
dc.subject.other	waste disposal	en
dc.subject.other	Eurasia	en
dc.subject.other	Russian Federation	en
dc.title	Mapping optimization based on sampling size in earth related and environmental phenomena	en
heal.type	journalArticle	en
heal.identifier.primary	10.1007/s00477-006-0096-8	en
heal.identifier.secondary	http://dx.doi.org/10.1007/s00477-006-0096-8	en
heal.language	English	en
heal.publicationDate	2008	en
heal.abstract	A critical sampling grid can be defined for an earth related natural variable distributed in space, according to established theoretical results and under certain mathematical conditions. Sampling above this critical limit does not substantially improve mapping results, while based on this limit the ideal process of reproducing the original phenomenon is theoretically defined. The aim of the present paper is, by using an innovative approach; to investigate the validity of commonly used interpolation algorithms, both stochastic and deterministic, below and above this critical sampling limit. When sampling is dense, application to a simulated spatial random field shows that the results are equally accurate with those derived with more sophisticated stochastic methods. On the other hand, when the sampling grid is sparse, deterministic methods produce less accurate results, therefore stochastic algorithms with minimum estimation error are a much better option. To further demonstrate these points, the interpolation algorithms were applied in three different sampling grid densities in a contaminated waste disposal site in Russia. © Springer-Verlag 2006.	en
heal.publisher	SPRINGER	en
heal.journalName	Stochastic Environmental Research and Risk Assessment	en
dc.identifier.doi	10.1007/s00477-006-0096-8	en
dc.identifier.isi	ISI:000251001100007	en
dc.identifier.volume	22	en
dc.identifier.issue	1	en
dc.identifier.spage	83	en
dc.identifier.epage	93	en