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Environ Monit Assess. 2018 May 8;190(6):332. doi: 10.1007/s10661-018-6709-0.

Land use change modeling through scenario-based cellular automata Markov: improving spatial forecasting.

Author information

1
Faculty of Natural Resources and Environmental Studies, University of Birjand, South Khorasan Province, Birjand, Iran. jahanishakib@birjand.ac.ir.
2
College of Fisheries and Environmental Sciences, Gorgan University of Agricultural Sciences and Natural Resources, Golestan Province, Gorgan, Iran.
3
Department of Architecture, Shahid Chamran University of Ahvaz, Ahvaz, Iran.

Abstract

Efficient land use management requires awareness of past changes, present actions, and plans for future developments. Part of these requirements is achieved using scenarios that describe a future situation and the course of changes. This research aims to link scenario results with spatially explicit and quantitative forecasting of land use development. To develop land use scenarios, SMIC PROB-EXPERT and MORPHOL methods were used. It revealed eight scenarios as the most probable. To apply the scenarios, we considered population growth rate and used a cellular automata-Markov chain (CA-MC) model to implement the quantified changes described by each scenario. For each scenario, a set of landscape metrics was used to assess the ecological integrity of land use classes in terms of fragmentation and structural connectivity. The approach enabled us to develop spatial scenarios of land use change and detect their differences for choosing the most integrated landscape pattern in terms of landscape metrics. Finally, the comparison between paired forecasted scenarios based on landscape metrics indicates that scenarios 1-1, 2-2, 3-2, and 4-1 have a more suitable integrity. The proposed methodology for developing spatial scenarios helps executive managers to create scenarios with many repetitions and customize spatial patterns in real world applications and policies.

KEYWORDS:

CA-Markov chain; Cross impact analysis; Landscape metrics; MORPHOL; Spatial scenarios

PMID:
29736559
DOI:
10.1007/s10661-018-6709-0
[Indexed for MEDLINE]

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