Change of Land Use/Cover in Tianjin City Based on the Markov and Cellular Automata Models

Wang, Ruci; Murayama, Yuji

doi:10.3390/ijgi6050150

Cited by 33 publications

(12 citation statements)

References 40 publications

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“…A similar pattern of agricultural land loss can be seen in previous studies conducted in P.O. Lwin in Myanmar [5], Hangzhou in China [3], Tianjin in China [72], Baguio in the Philippines [5], Tehran in Iran [44], and Kandy in Sri Lanka [11].…”

Section: Spatiotemporal Lulc Changes and Driving Forcessupporting

confidence: 85%

Spatial Forecasting of the Landscape in Rapidly Urbanizing Hill Stations of South Asia: A Case Study of Nuwara Eliya, Sri Lanka (1996–2037)

et al. 2019

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Forecasting landscape changes is vital for developing and implementing sustainable urban planning. Presently, apart from lowland coastal cities, mountain cities (i.e., hill stations) are also facing the negative impacts of rapid urbanization due to their economic and social importance. However, few studies are addressing urban landscape changes in hill stations in Asia. This study aims to examine and forecast landscape changes in the rapidly urbanizing hill station of Nuwara Eliya, Sri Lanka. Landsat data and geospatial techniques including support vector machines, urban–rural gradient, and statistical analysis were used to map and examine the land use/land cover (LULC) change in Nuwara Eliya during the 1996–2006 and 2006–2017 periods. The multilayer perceptron neural network-Markov model was applied to simulate future LULC changes for 2027 and 2037. The results show that Nuwara Eliya has been directly affected by rapid urban development. During the past 21 years (1996–2017), built-up areas increased by 1791 ha while agricultural land declined by 1919 ha due to augmented urban development pressure. The pressure of urban development on forest land has been relatively low, mainly due to strict conservation government policies. The results further show that the observed landscape changes will continue in a similar pattern in the future, confirming a significant increase and decrease of built-up and agricultural land, respectively, from 2017 to 2037. The changes in agricultural land exhibit a strong negative relationship with the changes in built-up land along the urban–rural gradient (R2 were 0.86 in 1996–2006, and 0.93 in 2006–2017, respectively). The observed LULC changes could negatively affect the production of unique upcountry agricultural products such as exotic vegetables, fruits, cut flowers, and world-famous Ceylon tea. Further, unplanned development could cause several environmental issues. The study is important for understanding future LULC changes and suggesting necessary remedial measures to minimize possible undesirable environmental and socioeconomic impacts.

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Section: Spatiotemporal Lulc Changes and Driving Forcessupporting

confidence: 85%

Spatial Forecasting of the Landscape in Rapidly Urbanizing Hill Stations of South Asia: A Case Study of Nuwara Eliya, Sri Lanka (1996–2037)

et al. 2019

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“…The Markov chain model, is a stochastic modelling procedure, extensively used for land use land cover change modelling. This model forecasts the future land use land cover from time t = 1 to another time t + 1 [63], on the basis of the transition probability matrix and the transition area matrix of each land use land cover class [47]. The transition matrix represents the probability of land use land cover change in the observed time period from one land use group to another [18,64,65].…”

Section: Markov Change Modelmentioning

confidence: 99%

Future Simulation of Land Use Changes in Rapidly Urbanizing South China Based on Land Change Modeler and Remote Sensing Data

et al. 2020

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Landscape transformations in rapidly urbanizing Guangdong, Hong Kong, and Macao (GHKM) regions of South China represent the most complex and dynamic processes altering the local ecology and environment. In this study, Land Change Modeler (LCM) is applied to land use land cover (LULC) maps for the years 2005, 2010, and 2017, derived from Landsat images, with the aim of understanding land use land cover change patterns during 2005–2017 and, further, to predict the future scenario of the years 2024 and 2031. Furthermore, the changes in spatial structural patterns are quantified and analyzed using selected landscape morphological metrics. The results show that the urban area has increased at an annual rate of 4.72% during 2005–2017 and will continue to rise from 10.31% (20,228.95 km2) in 2017 to 16.30% (31,994.55 km2) in 2031. This increase in urban area will encroach further into farmland and fishponds. However, forest cover will continue to increase from 45.02% (88,391.98 km2) in 2017 to 46.88% (92,049.62 km2) in 2031. This implies a decrease in the mean Euclidian nearest neighbor distance (ENN_MN) of forest patches (from 217.57 m to 206.46 m) and urban clusters (from 285.55 m to 245.06 m) during 2017–2031, indicating an accelerated landscape transformation if the current patterns of the change continues over the next decade. Thus, knowledge of the current and predicted LULC changes will help policy and decision makers to reconsider and develop new policies for the sustainable development and protection of natural resources.

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“…Furthermore, the western development, the Three Gorges project, and the policy of returning farmlands to forests have resulted in changing land use patterns in Chongqing. In addition, the overall planning of the government and the functional orientation of each district and county have also had a profound impact on urban land use change [44][45][46][47].…”

Section: The Impact Of Government Policy On Land Use Changementioning

confidence: 99%

“…Land use changes in the four special municipalities of China, namely Beijing, Tianjin, Shanghai, and Chongqing, have received considerable attention. For example, it has been found that between 1999 and 2017, the built-up area increased and arable and forest land decreased in Beijing [44], where more than 10% of the cropland areas were transformed into built-up areas [45]. This is the main characteristic of land use change in Shanghai, where the expansion of the urban construction land occupied vast areas of farmland and forest land [46].…”

Section: Introductionmentioning

confidence: 99%

Comparison of the Spatiotemporal Dynamics of Land Use Changes in Four Municipalities of China Based on Intensity Analysis

et al. 2020

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Land use/cover change (LUCC) is becoming one of the most important and interesting problems in the study of global environmental change. Identifying the spatiotemporal behavior and associated driving forces behind changes in land use is crucial for the regional sustainable utilization of land resources. In this study, we consider the four municipalities of China (Beijing, Tianjin, Shanghai, and Chongqing) and compare their spatiotemporal changes in land use from 1990 to 2015 by employing intensity analysis and barycenter migration models. We then discuss their driving forces. The results show that the largest reduction and increase variations were mainly concentrated in arable and construction land, respectively. The decrement and increment were the largest in Shanghai, followed by Beijing and Tianjin, and the least in Chongqing. Furthermore, the results of the barycenter migration model indicate that in addition to Beijing, the migration distances of construction land were longer than those of arable land in three other cities. Moreover, the application of intensity analysis revealed that the rate of land use change was also the greatest in Shanghai and the slowest in Chongqing during the whole study period, with all of their arable land being mainly transformed into construction land. The driving force analysis results suggest that the spatial and temporal patterns of land use change were the results of the socio-economic development, national policies, and major events. In other words, where there was a high rate of economic and population growth, the intensity of land use change was relatively large.

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Change of Land Use/Cover in Tianjin City Based on the Markov and Cellular Automata Models

Cited by 33 publications

References 40 publications

Spatial Forecasting of the Landscape in Rapidly Urbanizing Hill Stations of South Asia: A Case Study of Nuwara Eliya, Sri Lanka (1996–2037)

Spatial Forecasting of the Landscape in Rapidly Urbanizing Hill Stations of South Asia: A Case Study of Nuwara Eliya, Sri Lanka (1996–2037)

Future Simulation of Land Use Changes in Rapidly Urbanizing South China Based on Land Change Modeler and Remote Sensing Data

Comparison of the Spatiotemporal Dynamics of Land Use Changes in Four Municipalities of China Based on Intensity Analysis

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