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Title: Markov Land Cover Change Modeling Using Pairs of Time-Series Satellite Images
Contributor(s): Sinha, Priyakant (author); Kumar, Lalit (author)orcid 
Publication Date: 2013
DOI: 10.14358/PERS.79.11.1037
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Abstract: Models 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.
Publication Type: Journal Article
Source of Publication: Photogrammetric Engineering and Remote Sensing, 79(11), p. 1037-1051
Publisher: American Society for Photogrammetry and Remote Sensing
Place of Publication: United States of America
ISSN: 0099-1112
Field of Research (FOR): 050299 Environmental Science and Management not elsewhere classified
Peer Reviewed: Yes
HERDC Category Description: C1 Refereed Article in a Scholarly Journal
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Appears in Collections:Journal Article
School of Environmental and Rural Science
School of Science and Technology

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