A cellular automata model of land cover change to integrate urban growth with open space conservation

Mitsova, Diana; Shuster, William D.; Wang, Xinhao

doi:10.1016/j.landurbplan.2010.10.001

Cited by 286 publications

(128 citation statements)

References 29 publications

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“…The cellular automata model is a spatiotemporal model that can analyze the relationships between different cells [49]. According to neighborhood interactions and rules, the state of a cell can be decided [50]. By combining the cellular automata model and the Markov model, the spatiotemporal distribution of land use/cover can be simulated [51].…”

Section: Cellular Automata-markov Modelmentioning

confidence: 99%

Spatiotemporal Simulation of Future Land Use/Cover Change Scenarios in the Tokyo Metropolitan Area

2018

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Abstract:Simulating future land use/cover changes is of great importance for urban planners and decision-makers, especially in metropolitan areas, to maintain a sustainable environment. This study examines the changes in land use/cover in the Tokyo metropolitan area (TMA) from 2007 to 2017 as a first step in using supervised classification. Second, based on the map results, we predicted the expected patterns of change in 2027 and 2037 by employing a hybrid model composed of cellular automata and the Markov model. The next step was to decide the model inputs consisting of the modeling variables affecting the distribution of land use/cover in the study area, for instance distance to central business district (CBD) and distance to railways, in addition to the classified maps of 2007 and 2017. Finally, we considered three scenarios for simulating land use/cover changes: spontaneous, sub-region development, and green space improvement. Simulation results show varied patterns of change according to the different scenarios. The sub-region development scenario is the most promising because it balances between urban areas, resources, and green spaces. This study provides significant insight for planners about change trends in the TMA and future challenges that might be encountered to maintain a sustainable region.

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Section: Cellular Automata-markov Modelmentioning

confidence: 99%

Spatiotemporal Simulation of Future Land Use/Cover Change Scenarios in the Tokyo Metropolitan Area

2018

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“…Several prediction methods including those Markov chain models [24,25], ant colony optimization models [26], and cellular automata models [27,28] have been widely applied to analyze changes of land cover and to project future land-cover scenarios [29][30][31][32][33][34][35][36][37]. Each model has pros and cons, however.…”

Section: Introductionmentioning

confidence: 99%

Multi-Decadal Mangrove Forest Change Detection and Prediction in Honduras, Central America, with Landsat Imagery and a Markov Chain Model

et al. 2013

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Abstract:Mangrove forests play an important role in providing ecological and socioeconomic services for human society. Coastal development, which converts mangrove forests to other land uses, has often ignored the services that mangrove may provide, leading to irreversible environmental degradation. Monitoring the spatiotemporal distribution of mangrove forests is thus critical for natural resources management of mangrove ecosystems. This study investigates spatiotemporal changes in Honduran mangrove forests using Landsat imagery during the periods 1985-1996, 1996-2002, and 2002-2013. The future trend of mangrove forest changes was projected by a Markov chain model to support decision-making for coastal management. The remote sensing data were OPEN ACCESS Remote Sens. 2013, 5 6409 processed through three main steps: (1) data pre-processing to correct geometric errors between the Landsat imageries and to perform reflectance normalization; (2) image classification with the unsupervised Otsu's method and change detection; and (3) mangrove change projection using a Markov chain model. Validation of the unsupervised Otsu's method was made by comparing the classification results with the ground reference data in 2002, which yielded satisfactory agreement with an overall accuracy of 91.1% and Kappa coefficient of 0.82. When examining mangrove changes from 1985 to 2013, approximately 11.9% of the mangrove forests were transformed to other land uses, especially shrimp farming, while little effort (3.9%) was applied for mangrove rehabilitation during this 28-year period. Changes in the extent of mangrove forests were further projected until 2020, indicating that the area of mangrove forests could be continuously reduced by 1,200 ha from 2013 (approximately 36,700 ha) to 2020 (approximately 35,500 ha). Institutional interventions should be taken for sustainable management of mangrove ecosystems in this coastal region.

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“…The canopy reflectance in the infrared range (SWIR) is closely related to the water absorption. Meanwhile, the energy dispersion is also caused by the canopy structure and by the aerial space among the leaves [44,45]. The spectral separability was determined by the Jeffrey-Matusita distance (J M) [46], according to Equation (5).…”

Section: Spectral Separabilitymentioning

confidence: 99%

Detection and Projection of Forest Changes by Using the Markov Chain Model and Cellular Automata

et al. 2016

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Abstract:The spatio-temporal analysis of land use changes could provide basic information for managing the protection, conservation and production of forestlands, which promotes a sustainable resource use of temperate ecosystems. In this study we modeled and analyzed the spatial and temporal dynamics of land use of a temperate forests in the region of Pueblo Nuevo, Durango, Mexico. Data from the Landsat images Multispectral Scanner (MSS) 1973, Thematic Mapper (TM) 1990, and Operational Land Imager (OLI) 2014 were used. Supervised classification methods were then applied to generate the land use for these years. To validate the land use classifications on the images, the Kappa coefficient was used. The resulting Kappa coefficients were 91%, 92% and 90% for 1973, 1990 and 2014, respectively. The analysis of the change dynamics was assessed with Markov Chains and Cellular Automata (CA), which are based on probabilistic modeling techniques. The Markov Chains and CA show constant changes in land use. The class most affected by these changes is the pine forest. Changes in the extent of temperate forest of the study area were further projected until 2028, indicating that the area of pine forest could be continuously reduced. The results of this study could provide quantitative information, which represents a base for assessing the sustainability in the management of these temperate forest ecosystems and for taking actions to mitigate their degradation.

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A cellular automata model of land cover change to integrate urban growth with open space conservation

Cited by 286 publications

References 29 publications

Spatiotemporal Simulation of Future Land Use/Cover Change Scenarios in the Tokyo Metropolitan Area

Spatiotemporal Simulation of Future Land Use/Cover Change Scenarios in the Tokyo Metropolitan Area

Multi-Decadal Mangrove Forest Change Detection and Prediction in Honduras, Central America, with Landsat Imagery and a Markov Chain Model

Detection and Projection of Forest Changes by Using the Markov Chain Model and Cellular Automata

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