Markov analysis of land use change—an application of stochastic processes to remotely sensed data

Bell, Earl J.

doi:10.1016/0038-0121(74)90034-2

Cited by 119 publications

(73 citation statements)

References 2 publications

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“…Markov chains have been widely used to model land use changes including both urban and nonurban areas at large spatial scales (Drewett, 1969;Bourne, 1971;Bell, 1974;Robinson, 1978;Jahan, 1986;Muller and Middleton, 1994). Stationary and first-order have usually been assumed except in a few studies where the stationary or the order of Markov chains was tested (Bell, 1974;Robinson, 1978).…”

Section: Markov Analysis Of the Land Use Change Processmentioning

confidence: 99%

Monitoring and predicting land use change in Beijing using remote sensing and GIS

Wu¹,

Li²,

Wang³

et al. 2006

Landscape and Urban Planning

332

170

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Rapid land use change has taken place in many mega cities of China such as Beijing over the past two decades. In this paper, land use change dynamics were investigated by the combined use of satellite remote sensing, geographic information systems (GIS). The results indicated that there had been a notable and uneven urban growth and a major loss of cropland loss between 1986 and 2001. Most of the urban growth and loss of agriculture land occurred in inner and outer suburbs. Land use change was projected for the next 20 years using Markov chains and regression analyses. The further integration of remote sensing and GIS technologies with Markov model and regression model was found to be useful for describing, analyzing and predicting the process of land use change.

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Section: Markov Analysis Of the Land Use Change Processmentioning

confidence: 99%

Monitoring and predicting land use change in Beijing using remote sensing and GIS

Wu¹,

Li²,

Wang³

et al. 2006

Landscape and Urban Planning

332

170

View full text Add to dashboard Cite

show abstract

“…For example, many early computer simulations of land-use change were based on Markov formulations (Burnham 1973, Bell 1974. A variety of cellular models have enhanced these Markov formulations by incorporating spatial interactions into the calculation of probabilities of change at locations (e.g.…”

Section: Gis-based Change Modelsmentioning

confidence: 99%

Spatial process and data models: Toward integration of agent-based models and GIS

et al. 2005

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Abstract. The use of object-orientation for both spatial data and spatial process models facilitates their integration, which can allow exploration and explanation of spatial-temporal phenomena. In order to better understand how tight coupling might proceed and to evaluate the possible functional and efficiency gains from such a tight coupling, we identify four key relationships affecting how geographic data (fields and objects) and agent-based process models can interact: identity, causal, temporal and topological. We discuss approaches to implementing tight integration, focusing on a middleware approach that links existing GIS and ABM development platforms, and illustrate the need and approaches with example agent-based models.Key words: Object-orientation, Agent-based models, Spatial-temporal modeling, Topology IntroductionThis paper addresses the representation of both form and process, as spatial data models and spatial process models, respectively, and how relationships between these representations might be structured to better facilitate scientific inquiry and application. Raper and Livingstone (1995)

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“…Here, we present background information on the forest-cover modeling approach employed. Transition probabilities have been used extensively for analysis and modeling of land-use and land-cover change (Burnham, 1973;Bell, 1974;Turner, 1987;Muller and Middleton, 1994). The approach treats state transitions as Markovian random processes that are conditional on the initial state only.…”

Section: Land-use and Land-cover Change Modelingmentioning

confidence: 99%

“…land-use or land-cover classes) can be used, provided their definitions do not change over time, at any scale of analysis. The change matrix is often derived from multiple temporal classifications of land use or land cover (Bell, 1974;Turner, 1987).…”

Section: Land-use and Land-cover Change Modelingmentioning

confidence: 99%

Modeling the relationships between land use and land cover on private lands in the Upper Midwest, USA

Brown

Pijanowski

Duh

2000

Journal of Environmental Management

240

126

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Markov analysis of land use change—an application of stochastic processes to remotely sensed data

Cited by 119 publications

References 2 publications

Monitoring and predicting land use change in Beijing using remote sensing and GIS

Monitoring and predicting land use change in Beijing using remote sensing and GIS

Spatial process and data models: Toward integration of agent-based models and GIS

Modeling the relationships between land use and land cover on private lands in the Upper Midwest, USA

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