Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/13814
Full metadata record
DC FieldValueLanguage
dc.contributor.authorSinha, Priyakanten
dc.contributor.authorKumar, Laliten
dc.date.accessioned2013-12-23T13:42:00Z
dc.date.issued2013en
dc.identifier.citationPhotogrammetric Engineering and Remote Sensing, 79(11), p. 1037-1051en
dc.identifier.issn0099-1112en
dc.identifier.urihttps://hdl.handle.net/1959.11/13814en
dc.description.abstractModels of change processes created with the Markov chain model (MCM) can be used in the interpolation of temporal data and in short-term change projections. However, there are two major issues associated with the use of Markov models for land-cover change projections: the stationarity of change and the impact of neighboring cells on the change areas. This study addressed these two issues using an investigation of five time-series land-cover datasets generated between 1972 and 2009 for the Liverpool region of NSW, Australia. Four short term transition matrices were computed, and the results were used to predict land-cover distributions for the near future. The issue of neighborhood effects was addressed by incorporating spatial components in a Cellular Automata (CA)-based MCM, and the results were compared with those derived from a normal MCM. Given the marginal improvements in the simulation achieved with CA-MCM rather than MCM, and because of the ability of CA-MCM to incorporate spatial variants, CA-MCM was determined to be the more suitable method for predicting land-cover changes for the year 2019. The land-cover projection indicated that future land-cover changes will likely continue to affect the natural vegetation, which will in turn likely decrease through the continued conversion of natural to agricultural lands over the years.en
dc.languageenen
dc.publisherAmerican Society for Photogrammetry and Remote Sensingen
dc.relation.ispartofPhotogrammetric Engineering and Remote Sensingen
dc.titleMarkov Land Cover Change Modeling Using Pairs of Time-Series Satellite Imagesen
dc.typeJournal Articleen
dc.identifier.doi10.14358/PERS.79.11.1037en
dc.subject.keywordsEnvironmental Science and Managementen
local.contributor.firstnamePriyakanten
local.contributor.firstnameLaliten
local.subject.for2008050299 Environmental Science and Management not elsewhere classifieden
local.subject.seo2008960699 Environmental and Natural Resource Evaluation not elsewhere classifieden
local.profile.schoolSchool of Science and Technologyen
local.profile.schoolSchool of Environmental and Rural Scienceen
local.profile.emailpsinha2@une.edu.auen
local.profile.emaillkumar@une.edu.auen
local.output.categoryC1en
local.record.placeauen
local.record.institutionUniversity of New Englanden
local.identifier.epublicationsrecordune-20131204-114150en
local.publisher.placeUnited States of Americaen
local.format.startpage1037en
local.format.endpage1051en
local.peerreviewedYesen
local.identifier.volume79en
local.identifier.issue11en
local.contributor.lastnameSinhaen
local.contributor.lastnameKumaren
dc.identifier.staffune-id:psinha2en
dc.identifier.staffune-id:lkumaren
local.profile.orcid0000-0002-9205-756Xen
local.profile.roleauthoren
local.profile.roleauthoren
local.identifier.unepublicationidune:14027en
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
local.title.maintitleMarkov Land Cover Change Modeling Using Pairs of Time-Series Satellite Imagesen
local.output.categorydescriptionC1 Refereed Article in a Scholarly Journalen
local.description.statisticsepubsVisitors: 337<br />Views: 336<br />Downloads: 0en
local.search.authorSinha, Priyakanten
local.search.authorKumar, Laliten
Appears in Collections:Journal Article
School of Environmental and Rural Science
School of Science and Technology
Files in This Item:
2 files
File Description SizeFormat 
Show simple item record

SCOPUSTM   
Citations

7
checked on Nov 30, 2018

Page view(s)

136
checked on Mar 4, 2019
Google Media

Google ScholarTM

Check

Altmetric

SCOPUSTM   
Citations

 

WEB OF SCIENCETM
Citations

 

Items in Research UNE are protected by copyright, with all rights reserved, unless otherwise indicated.