Spatio-temporal analysis of land use/land cover pattern changes in Arasbaran Biosphere Reserve: Iran

Parsa, Vahid Amini; Yavari, A.R.; Nejadi, A.

doi:10.1007/s40808-016-0227-2

Cited by 87 publications

(36 citation statements)

References 59 publications

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“…Numerous previous studies have examined the simulation of land use changes pattern by using a CA-Markov model. Parsa et al [24] successfully used the CA-Markov model in Arasbaran biosphere reserve-Iran to predict the future LULC, which serves land use planners and policy makers in order to make appropriate decisions for future land use challenges. They indicated that using a CA-Markov model can be useful in land use policy design and it may also be used as an early warning system.…”

Section: Background and Analysis Of The Literaturementioning

confidence: 99%

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Predicting Land Use/Land Cover Changes Using a CA-Markov Model under Two Different Scenarios

2018

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Multi-temporal Landsat images from Landsat 5 Thematic Mapper (TM) acquired in 1993, 1998, 2003 and 2008 and Landsat 8 Operational Land Imager (OLI) from 2017, are used for analysing and predicting the spatio-temporal distributions of land use/land cover (LULC) categories in the Halgurd-Sakran Core Zone (HSCZ) of the National Park in the Kurdistan region of Iraq. The aim of this article was to explore the LULC dynamics in the HSCZ to assess where LULC changes are expected to occur under two different business-as-usual (BAU) assumptions. Two scenarios have been assumed in the present study. The first scenario, addresses the BAU assumption to show what would happen if the past trend in 1993–1998–2003 has continued until 2023 under continuing the United Nations (UN) sanctions against Iraq and particularly Kurdistan region, which extended from 1990 to 2003. Whereas, the second scenario represents the BAU assumption to show what would happen if the past trend in 2003–2008–2017 has to continue until 2023, viz. after the end of UN sanctions. Future land use changes are simulated to the year 2023 using a Cellular Automata (CA)-Markov chain model under two different scenarios (Iraq under siege and Iraq after siege). Four LULC classes were classified from Landsat using Random Forest (RF). Their accuracy was evaluated using κ and overall accuracy. The CA-Markov chain method in TerrSet is applied based on the past trends of the land use changes from 1993 to 1998 for the first scenario and from 2003 to 2008 for the second scenario. Based on this model, predicted land use maps for the 2023 are generated. Changes between two BAU scenarios under two different conditions have been quantitatively as well as spatially analysed. Overall, the results suggest a trend towards stable and homogeneous areas in the next 6 years as shown in the second scenario. This situation will have positive implication on the park.

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Section: Background and Analysis Of The Literaturementioning

confidence: 99%

“…This model is often used in monitoring, ecological modelling, simulation changes, trends of the LULC and to predict the amount of the land use change and the stability of future land development in the area of interest [24,31,36]. Burnham [37] first used this model for land use modelling.…”

Section: The Markov Modelmentioning

confidence: 99%

Predicting Land Use/Land Cover Changes Using a CA-Markov Model under Two Different Scenarios

2018

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“…The application of a CA-Markov model in predicting the growth of most buildings considers the following points. The integration of the CA-Markov model is considered to be valuable for modelling land-use changes and is able to simulate and predict changes [42,43]. The CA-Markov model is the combination of Cellular Automata and transition probability matrix generated by the cross tabulation of two different images [42].…”

Section: Optimal Iterations Of Ca-markov Model For Built-up Growth Prmentioning

confidence: 99%

“…Furthermore, the land-use changes for any location (cells) can be clarified by the existing state and changes in neighbouring cells and can simulate the growth of things in two directions. This model is broadly used in spatial model for predicting future land-use [43,46].…”

Section: Optimal Iterations Of Ca-markov Model For Built-up Growth Prmentioning

confidence: 99%

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Built-Up Growth Impacts on Digital Elevation Model and Flood Risk Susceptibility Prediction in Muaeng District, Nakhon Ratchasima (Thailand)

Littidej¹,

Buasri²

2019

Water

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The transformation of land-use and land cover in Nakhon Ratchasima province, Thailand has rapidly changed over the last few years. The major factors affecting the growth in the province arise from the huge expansion of developing areas, according to the government’s development plans that aim to promote the province as a central business-hub in the region. This development expansion has eventually intruded upon and interfered with sub-basin areas, which has led to environmental problems in the region. The scope of this study comprises three objectives, i.e., (i) to optimize the Cellular Automata (CA) model for predicting the expansion of built-up sites by 2022; (ii) to model a linear regression method for deriving the transition of the digital elevation model (DEM); and (iii) to apply Geographic Weighted Regression (GWR) for analyzing the risk of the stativity of flood areas in the province. The results of this study show that the optimized CA demonstrates accurate prediction of the expansion of built-up areas in 2022 using Land use (LU) data of 2-year intervals. In addition, the predicting model is generalized and converged at the iteration no. 4. The prediction outcomes, including spatial locations and ground-water touch points of the construction, are used to estimate and model the DEM to extract independent hydrology variables that are used in the determination of Flood Risk Susceptibility (FRS). In GWR in the research called FRS-GWR, this integration of quantitative GIS and the spatial model is anticipated to produce promising results in predicting the growth and expansion of built-up areas and land-use change that lead to an effective analysis of the impacts on spatial change in water sub-basin areas. This research may be beneficial in the process of urban planning with respect to the study of environmental impacts. In addition, it can indicate and impose important directions for development plans in cities to avoid and minimize flood area problems.

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Urban Landscape Change Detection Using GIS and RS

Ali

Hasnat

2022

Urban Ecology and Global Climate Change

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Spatio-temporal analysis of land use/land cover pattern changes in Arasbaran Biosphere Reserve: Iran

Cited by 87 publications

References 59 publications

Predicting Land Use/Land Cover Changes Using a CA-Markov Model under Two Different Scenarios

Predicting Land Use/Land Cover Changes Using a CA-Markov Model under Two Different Scenarios

Built-Up Growth Impacts on Digital Elevation Model and Flood Risk Susceptibility Prediction in Muaeng District, Nakhon Ratchasima (Thailand)

Urban Landscape Change Detection Using GIS and RS

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