Spatial analysis in a Markov random field framework: The case of burning oil wells in Kuwait

Raymond J. Dezzani and Ahmad Al-Dousari
Additional contact information
Raymond J. Dezzani: Department of Geography, Boston University, 675 Commonwealth Avenue, Boston, Massachusetts 02215, USA (e-mail: dezzani@crsa.bu.edu)
Ahmad Al-Dousari: Department of Geography, Boston University, 675 Commonwealth Avenue, Boston, Massachusetts 02215, USA (e-mail: dezzani@crsa.bu.edu)

Journal of Geographical Systems, 2001, vol. 3, issue 4, 387-409

Abstract: Abstract. This paper discusses a modeling approach for spatial-temporal prediction of environmental phenomena using classified satellite images. This research was prompted by the analysis of change and landscape redistribution of petroleum residues formed from the residue of the burning oil wells in Kuwait (1991). These surface residues have been termed “tarcrete” (El-Baz et al. 1994). The tarcrete forms a thick layer over sand and desert pavement covering a significant portion of south-central Kuwait. The purpose of this study is to develop a method that utilizes satellite images from different time steps to examine the rate-of-change of the oil residue deposits and determine where redistribution is are likely to occur. This problem exhibits general characteristics of environmental diffusion and dispersion phenomena so a theoretical framework for a general solution is sought. The use of a lagged-clique, Markov random field framework and entropy measures is deduced to be an effective solution to satisfy the criteria of determination of time-rate-of-change of the surface deposits and to forecast likely locations of redistribution of dispersed, aggraded residues. The method minimally requires image classification, the determination of time stationarity of classes and the measurement of the level of organization of the state-space information derived from the images. Analysis occurs at levels of both the individual pixels and the system to determine specific states and suites of states in space and time. Convergence of the observed landscape disorder with respect to an analytical maximum provide information on the total dispersion of the residual system.

Keywords: Key words: Environmental modeling; diffusion; Markov random fields; logistic model; entropy measures; JEL classification: C1; N9; N5 (search for similar items in EconPapers)
Date: 2001
References: Add references at CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://link.springer.com/10.1007/s101090100068 Abstract (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:kap:jgeosy:v:3:y:2001:i:4:d:10.1007_s101090100068

Ordering information: This journal article can be ordered from
http://www.springer. ... ce/journal/10109/PS2

DOI: 10.1007/s101090100068

Access Statistics for this article

Journal of Geographical Systems is currently edited by Manfred M. Fischer and Antonio Páez

More articles in Journal of Geographical Systems from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().