〈Research Reports〉Spontaneous simulation of land surface temperature in Tianjin city, China

Monitoring and simulating land surface temperature (LST) by using satellite images is an essential approach to understand land use/cover changes, especially in developing countries where the availability of ground truth and statistical data is limited. This study analyzed the relationship between LST and land use/cover types in Tianjin city from 2005 to 2015. Then, based on the LST distribution maps, we simulated LST in 2025 by employing a hybrid model of the artificial neural network and the cellular automata. The results show that the LST is gradually increasing from 2005 to 2025 with the changes in the land use/cover. This study provides significative information for sustainable development and environmental protection in the future

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

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

東京大都市圏におけるマルコフモデルとLand Change Modelerに基づく土地利用/被覆変化のシナリオシミュレーション：将来の持続的な発展をめざして

筑波大学 (University of Tsukuba)201

Scenario-Based Simulation of Tianjin City Using a Cellular Automata–Markov Model

Rapid urbanization is occurring throughout China, especially in megacities. Using a land use model to obtain future land use/cover conditions is an essential method to prevent chaotic urban sprawl and imbalanced development. This study utilized historical Landsat images to create land use/cover maps to predict the land use/cover changes of Tianjin city in 2025 and 2035. The cellular automata–Markov (CA–Markov) model was applied in the simulation under three scenarios: the environmental protection scenario (EPS), crop protection scenario (CPS), and spontaneous scenario (SS). The model achieved a kappa value of 86.6% with a figure of merit (FoM) of 12.18% when compared to the empirical land use/cover map in 2015. The results showed that the occupation of built-up areas increased from 29.13% in 2015 to 38.68% (EPS), 36.18% (CPS), and 47.94% (SS) in 2035. In this context, current urbanization would bring unprecedented stress on agricultural resources and forest ecosystems, which could be attenuated by implementing protection policies along with decelerating urban expansion. The findings provide valuable information for urban planners to achieve sustainable development goals

Scenario-Based Simulation of Tianjin City Using a Cellular Automata–Markov Model

Rapid urbanization is occurring throughout China, especially in megacities. Using a land use model to obtain future land use/cover conditions is an essential method to prevent chaotic urban sprawl and imbalanced development. This study utilized historical Landsat images to create land use/cover maps to predict the land use/cover changes of Tianjin city in 2025 and 2035. The cellular automata–Markov (CA–Markov) model was applied in the simulation under three scenarios: the environmental protection scenario (EPS), crop protection scenario (CPS), and spontaneous scenario (SS). The model achieved a kappa value of 86.6% with a figure of merit (FoM) of 12.18% when compared to the empirical land use/cover map in 2015. The results showed that the occupation of built-up areas increased from 29.13% in 2015 to 38.68% (EPS), 36.18% (CPS), and 47.94% (SS) in 2035. In this context, current urbanization would bring unprecedented stress on agricultural resources and forest ecosystems, which could be attenuated by implementing protection policies along with decelerating urban expansion. The findings provide valuable information for urban planners to achieve sustainable development goals

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

In recent years, urban areas have been expanding rapidly in the world, especially in developing countries. With this rapid urban growth, several environmental and social problems have appeared. Better understanding of land use and land cover (LULC) change will facilitate urban planning and constrain these potential problems. As one of the four municipalities in China, Tianjin has experienced rapid urbanization and such trend is expected to continue. Relying on remote sensing (RS) and geographical information system (GIS) tools, this study investigates LULC change in Tianjin city. First, we used RS to generate classification maps for 1995, 2005, and 2015. Then, simulation models were applied to evaluate the LULC changes. Analysis of the 1995, 2005, and 2015 LULC maps shows that more than 10% of the cropland areas were transformed into built-up areas. Finally, by employing the Markov model and cellular automata (CA) model, the LULC in 2025 and 2035 were simulated and forecasted. Our analysis contributes to the understanding of the development process in the Tianjin area, which will facilitate future planning, as well as constraining the potential negative consequences brought by future LULC changes

東京大都市圏におけるマルコフモデルとLand Change Modelerに基づく土地利用/被覆変化のシナリオシミュレーション：将来の持続的な発展をめざして

この博士論文は内容の要約のみの公開（または一部非公開）になっています筑波大学 (University of Tsukuba)201

Scenario-Based Simulation of Tianjin City Using a Cellular Automata–Markov Model

Rapid urbanization is occurring throughout China, especially in megacities. Using a land use model to obtain future land use/cover conditions is an essential method to prevent chaotic urban sprawl and imbalanced development. This study utilized historical Landsat images to create land use/cover maps to predict the land use/cover changes of Tianjin city in 2025 and 2035. The cellular automata–Markov (CA–Markov) model was applied in the simulation under three scenarios: the environmental protection scenario (EPS), crop protection scenario (CPS), and spontaneous scenario (SS). The model achieved a kappa value of 86.6% with a figure of merit (FoM) of 12.18% when compared to the empirical land use/cover map in 2015. The results showed that the occupation of built-up areas increased from 29.13% in 2015 to 38.68% (EPS), 36.18% (CPS), and 47.94% (SS) in 2035. In this context, current urbanization would bring unprecedented stress on agricultural resources and forest ecosystems, which could be attenuated by implementing protection policies along with decelerating urban expansion. The findings provide valuable information for urban planners to achieve sustainable development goals

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

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