Detection and prediction of land cover changes using Markov chain model in semi-arid rangeland in western Iran

Fathizad, Hassan; Rostami, Noredin; Faramarzi, Marzban

doi:10.1007/s10661-015-4805-y

Cited by 50 publications

(22 citation statements)

References 31 publications

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“…Using a modified version of Anderson's often-used land use classification description [38,39], the satellite images for 1991, 2002, 2011 and 2018 were classified into five land use types: waterbodies, farmland, built-up areas, rangeland and forest ( Table 2). We collected 150 random ground truth points for assessing the classification accuracy of the classified satellite images (i.e., 30 points for each land-use class identified below).…”

Section: Land Use Classificationmentioning

confidence: 99%

Modeling the Impact of Urbanization on Land-Use Change in Bahir Dar City, Ethiopia: An Integrated Cellular Automata–Markov Chain Approach

Fitawok

Derudder

Minale

et al. 2020

Land

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The fast-paced urbanization of recent decades entails that many regions are facing seemingly uncontrolled land-use changes (LUCs) that go hand in hand with a range of environmental and socio-economic challenges. In this paper, we use an integrated cellular automata–Markov chain (CA–MC) model to analyze and predict the urban expansion of and its impact on LUC in the city of Bahir Dar, Ethiopia. To this end, the research marshals high-resolution Landsat images of 1991, 2002, 2011, and 2018. An analytical hierarchy process (AHP) method is then used to identify the biophysical and socioeconomic factors underlying the expansion in the research area. It is shown that, during the period of study, built-up areas are rapidly expanding in the face of an overall decline of the farmland and vegetation cover. Drawing on a model calibration for 2018, the research predicts the possible geographies of LUC in the Bahir Dar area for 2025, 2034, and 2045. It is predicted that the conversions of other land-use types into built-up areas will persist in the southern, southwestern, and northeastern areas of the sprawling city, which can mainly be traced back to the uneven geographies of road accessibility, proximity to the city center, and slope variables. We reflect on how our findings can be used to facilitate sustainable urban development and land-use policies in the Bahir Dar area.

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Section: Land Use Classificationmentioning

confidence: 99%

Modeling the Impact of Urbanization on Land-Use Change in Bahir Dar City, Ethiopia: An Integrated Cellular Automata–Markov Chain Approach

Fitawok

Derudder

Minale

et al. 2020

Land

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“…A large number of studies examined the rate of green space change using multitemporal remotely sensed data [17] and showed spatial variation in green space patterns in different cities [18]. Furthermore, land-use transitions among different types were also quantified in the previous studies [19]. These studies quantified the land-use/land-cover changes and their patterns, while few examined the spatial variation in green spaces dominated by topographic features.…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Dynamics of Green Spaces in the Beijing–Tianjin–Hebei Region in the Past 20 Years

2018

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Rapid urbanization has caused the reduction of green spaces in most cities, disrupting the structure and process of urban and rural ecosystems. The accurate identification of spatiotemporal changes in green spaces is important to delineate future management and planning. We investigated green space types of the Beijing–Tianjin–Hebei region in 1995, 2000, 2005, 2010, and 2015 based on the elevation data and land use/cover for those years. Spatiotemporal changes in these identified green spaces between 1995 and 2015 were evaluated as well as the spatial hotspots of disappeared and unstable green patches. The results indicate that the cultivated land in plains and forests and cultivated land in medium-high mountainous areas were the main green space types in the Beijing–Tianjin–Hebei region during the period from 1995 to 2015. A large number of green spaces, in particular cultivated lands, in the peripheral areas of big cities were replaced by construction sites over the past 20 years. Hotspots of unstable green spaces were mainly distributed in the western and northern mountainous areas of the Beijing–Tianjin–Hebei region, where green spaces changed from one type to another. These findings provide an important reference for the management and planning of land and green spaces towards an integrative and collaborative development of the Beijing-Tianjin-Hebei region.

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“…Predicting land use changes can assist in determining the extent of degradation and enabling the managers to control changes in the proper directions [16][17][18]. From a planning and management perspective, it is of great significance to have an explicit understanding of predicted land use change as well as the underlying drivers [19][20][21]. The processes and mechanisms of land use change are complex and largely influenced by natural and socio-economic driving factors [22].…”

Section: Introductionmentioning

confidence: 99%

Scenarios Simulation of Spatio-Temporal Land Use Changes for Exploring Sustainable Management Strategies

Wang

Cai

et al. 2018

Sustainability

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Land use and land cover change have received considerable attention from global researchers in recent decades. The conflicts between different development strategies for land uses have become a problem that urgently needs to be solved, especially in those regions with a fragile ecological environment. The development of scenario simulations is essential in order to highlight possible alternative pathways for the future under the backgrounds of urbanization, economic growth and ecological protection. This study simulated land use changes for Tekes in 2020 with the Conversion of Land Use and its Effects at Small regional extent (CLUE-S) model under a 'business as usual' scenario, cropland protection scenario, ecological security scenario, and artificial modification scenario. The results indicated that the spatial patterns of the land use types were explained well by the environment variables, and the selected models had a satisfactory accuracy in this study. The requirements and the patterns were quite different owing to the variation of the major objectives of the four scenarios. In addition to the constraint rules of the land use transformation, the hot point for land use change was its spatial coherency. Areas near to an existing land use type were more likely to transform to that type than those farther away. The increased cropland and urban land were mainly located around the current cropland and urban land while forests and grassland were more likely to occur in places with flat terrain and good hydrological conditions. The results could contribute to better insight into the relationships between land use changes and their driving factors and provide a scientific basis for regional management strategies and sustainable land use development.

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Detection and prediction of land cover changes using Markov chain model in semi-arid rangeland in western Iran

Cited by 50 publications

References 31 publications

Modeling the Impact of Urbanization on Land-Use Change in Bahir Dar City, Ethiopia: An Integrated Cellular Automata–Markov Chain Approach

Modeling the Impact of Urbanization on Land-Use Change in Bahir Dar City, Ethiopia: An Integrated Cellular Automata–Markov Chain Approach

Spatiotemporal Dynamics of Green Spaces in the Beijing–Tianjin–Hebei Region in the Past 20 Years

Scenarios Simulation of Spatio-Temporal Land Use Changes for Exploring Sustainable Management Strategies

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