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

Wang, Ruci; Derdouri, Ahmed; Murayama, Yuji

doi:10.3390/su10062056

Cited by 58 publications

(40 citation statements)

References 61 publications

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“…LULC classification is a significant challenge when it comes to obtaining complex LULC distribution information from Landsat data [18,37]. Several classification methods have been employed in different studies depending on the target area and the spatial resolution of the satellite images [38][39][40].…”

Section: Lulc Classificationmentioning

confidence: 99%

“…On the other hand, LULC change is a long-term process that is influenced by human activities, historical trends, natural disasters, and city planning. Therefore, it is essential for re-planning the LULC distribution to decrease LST to moderate levels and consequently to establish sustainable development for the future.Simulating the future LULC should be proposed under a particular environment, such as study area, future plan, economic demand, and sustainable development strategy [18]. Simulating future LULC studies have been considered as valuable because they provide critical insights to city planners [19].…”

mentioning

confidence: 99%

“…Simulating the future LULC should be proposed under a particular environment, such as study area, future plan, economic demand, and sustainable development strategy [18]. Simulating future LULC studies have been considered as valuable because they provide critical insights to city planners [19].…”

mentioning

confidence: 99%

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Spatiotemporal Analysis of Land Use/Cover Patterns and Their Relationship with Land Surface Temperature in Nanjing, China

et al. 2020

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Rapid urbanization is one of the most concerning issues in the 21st century because of its significant impacts on various fields, including agriculture, forestry, ecology, and climate. The urban heat island (UHI) phenomenon, highly related to the rapid urbanization, has attracted considerable attention from both academic scholars and governmental policymakers because of its direct influence on citizens’ daily life. Land surface temperature (LST) is a widely used indicator to assess the intensity of UHI significantly affected by the local land use/cover (LULC). In this study, we used the Landsat time-series data to derive the LULC composition and LST distribution maps of Nanjing in 2000, 2014, and 2018. A correlation analysis was carried out to check the relationship between LST and the density of each class of LULC. We found out that cropland and forest in Nanjing are helping to cool the city with different degrees of cooling effects depending on the location and LULC composition. Then, a Cellar Automata (CA)-Markov model was applied to predict the LULC conditions of Nanjing in 2030 and 2050. Based on the simulated LULC maps and the relationship between LST and LULC, we delineated high- and moderate-LST related risk areas in the city of Nanjing. Our findings are valuable for the local government to reorganize the future development zones in a way to control the urban climate environment and to keep a healthy social life within the city.

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Section: Lulc Classificationmentioning

confidence: 99%

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confidence: 99%

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Spatiotemporal Analysis of Land Use/Cover Patterns and Their Relationship with Land Surface Temperature in Nanjing, China

et al. 2020

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“…The expansion of rural settlements and the occupation of arable land during different periods (from 1972 to 1991 and 1991 to 2015) can be assessed based on the land use conversion matrix. The conversion matrix is able to demonstrate the amount and direction of regional land use conversion and has been widely used for the analysis of dynamic land use change [45,48,67].…”

Section: Analysis Of the Consumption Of High-quality Arable Land As Amentioning

confidence: 99%

Expansion of Rural Settlements on High-Quality Arable Land in Tongzhou District in Beijing, China

Song

2019

Sustainability

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Settlement expansion caused by urbanization is an important factor leading to the loss of arable land across the world. Due to various factors in China, such as institutional problems, the total number of rural settlements is decreasing, while the total area continues to increase. Rural settlements expand mainly into arable land, resulting in a significant loss of high-quality farmland, thus threatening long-term food security. However, research on this subject is relatively scarce. In this study, using KeyHole and RESURS F1 satellite remote sensing images, we examined the spatial expansion of rural settlements in Tongzhou District, Beijing, in 1972 and 1991. Then, the consumption of high-quality arable land by rural settlements expansion was assessed. It was found that the overall accuracy of the produced maps for 1972 and 1991 were 93% and 90%, respectively. The accuracy of mapped changes from 1972 to 1991 was as high as 90%. From 1972 to 1991 and from 1991 to 2015, the rural settlements in Tongzhou District expanded by 51.54% and 79.91% respectively, with 53.72% and 60.64% of the expanded rural settlements being on arable land. Rural settlements expanded mainly into high-quality arable land at the beginning of the study period, whereas later on, medium- and low-quality farmland was also occupied, albeit to a lesser degree.

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“…In order to provide technical support for land use planning and to establish interactive planning tools, researchers have used different methods to simulate future land use not only in terms of quantitative characteristics but also spatial landscape patterns [13,14]. Different approaches to simulating quantitative characteristics have been used to arrange tasks in order of complexity such as linear programming [15][16][17], multiple objective programming [18][19][20][21], and system dynamics models [22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Simulation of Land Use Change of the Upper and Middle Streams of the Luan River, Northern China

Guan

Jiang

et al. 2019

Sustainability

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Climatic, socio-economic, geophysical, and human activity factors, among others, influence land use patterns. However, these driving factors also have different relationships with each other. Combining machine learning methods and statistical models is a good way to simulate the dominant land use types. The Luan River basin is located in a farming-pastoral transitional zone and is an important ecological barrier between Beijing and Tianjin. In this study, we predicted future land use and land cover changes from 2010 to 2020 in the Luan River’s upper and middle reaches under three scenarios—the natural scenario, the ecological scenario, and the sustainable scenario. The results indicate that cultivated land will decrease while the forested areas will increase quantitatively in the future. Built-up areas would increase quickly in the natural scenario, and augmented expansion of forest would be the main features of land use changes in both the ecological scenario and the sustainable scenario. Regarding the spatial pattern, different land use patterns will be aggregated and patches will become larger. Our findings for the scenario analysis of land use changes can provide a reference case for sustainable land use planning and management in the upper and middle Luan River basin.

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

Cited by 58 publications

References 61 publications

Spatiotemporal Analysis of Land Use/Cover Patterns and Their Relationship with Land Surface Temperature in Nanjing, China

Spatiotemporal Analysis of Land Use/Cover Patterns and Their Relationship with Land Surface Temperature in Nanjing, China

Expansion of Rural Settlements on High-Quality Arable Land in Tongzhou District in Beijing, China

Dynamic Simulation of Land Use Change of the Upper and Middle Streams of the Luan River, Northern China

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