Prediction of land use changes with Bayesian spatial modeling from the perspective of urban climate

Kyakuno, Takashi

doi:10.1016/j.uclim.2019.100569

Cited by 6 publications

(3 citation statements)

References 23 publications

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“…It is generally believed that when the AUC value is 0.5-0.7, the diagnostic value is low; when the AUC value is 0.7-0.9, the diagnostic value is moderate; when it is greater than 0.9, the diagnostic value is good. Cohen's kappa coefficient [40,41] is a measure of agreement between two variables. The ROC curve and kappa are used to test the simulation effect of the FLUS model.…”

Section: Flus Model and Verificationmentioning

confidence: 99%

Analysis and prediction of LUCC change in Huang-Huai-Hai river basin

Wang

Qin

et al. 2020

Open Geosciences

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Land use/cover change plays an important role in human development and environmental health and stability. Markov chain and a future land use simulation model were used to predict future change and simulate the spatial distribution of land use in the Huang-Huai-Hai river basin. The results show that cultivated land and grassland are the main land-use types in the basin, accounting for about 40% and 30%, respectively. The area of cultivated land decreased and artificial surfaces increased from 1980 to 2010. The degree of dynamic change of land use after the 1990s was greater than that before the 1990s. There is a high probability of exchange among cultivate land, forest and grassland. The area of forest decreased before 2000 and increased after 2000. Under the three emission scenarios (RCP2.6, RCP4.5, and RCP8.5) of IPSL-CM5A-LR climate model, the area of cultivated land will decrease and that of grassland will increase in the upstream area while it will decrease in the downstream area. The above methods and rules will be of great help to future land use planning.

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Section: Flus Model and Verificationmentioning

confidence: 99%

Analysis and prediction of LUCC change in Huang-Huai-Hai river basin

Wang

Qin

et al. 2020

Open Geosciences

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“…A study found that this figure will amount to 68 % by 2050 [1]. Therefore, the increased density of most cities and expansion of their areas beyond the outer boundaries destroy natural spaces such as forests or bodies of water [2]. The global pervasiveness of land cover change, predominantly as a result of human use, has created concerns about the sustainability and consequences of current land use trends, which in turn has led to the emergence of land change science [3].…”

Section: Introductionmentioning

confidence: 99%

Modelling of Expansion Changes of Vilnius City Area and Impacts on Landscape Patterns Using an Artificial Neural Network

Mirsanjari

Visockienė

Mohammadyari

et al. 2021

Ecological Chemistry and Engineering S

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The present study aimed to analyse changes in the land cover of Vilnius city and its surrounding areas and propose a scenario for their future changes using an Artificial Neural Network. The land cover dynamics modelling was based on a multilayer perceptron neural network. Landscape metrics at a class and landscape level were evaluated to determine the amount of changes in the land uses. As the results showed, the Built-up area class increased, while the forest (Semi forest and Dense forest) classes decreased during the period from 1999 to 2019. The predicted scenario showed a considerable increase of about 60 % in the Built-up area until 2039. The vegetation plant areas consist about 47 % of all the area in 2019, but it will be 36 % in 2039, if this trend (urban expansion) continues in the further. The findings further indicated the major urban expansion in the vegetation areas. However, Built-up area would expand over Semi forest land and Dense forest land, with a large part of them changed into built- up areas.

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“…Cellular Automata (CA) is one of the fastest-growing cellular models, based on GIS, and the change or transition process is based on local rules. Spatially, objects in cellular automata are usually depicted in unit cells or grids that contain various conditions and are influenced by what happens in other cells in their immediate environment[27][28]. Some elements of CA include cell, cell condition (which can be expressed in terms of land cover type), neighborhood, transition rules, and iteration time[29][30][31][32][33].…”

mentioning

confidence: 99%

Measurement of Sprawl Effect Based on Urban Growth Trends and Prediction in Kedungkandang District, Malang City

Hariyanto

Yudono

Wicaksono

2023

IOP Conf. Ser.: Earth Environ. Sci.

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Urban development generally produces a specific type of urban growth that can be identified by looking at the pattern formed by the growth of occuring built-up area. Urban growth in some theories has three types of growth, namely infill, leapfrog and sprawl. Each type has logical consequences for environmental sustainability, such as leapfrog and sprawl which can lead to inefficient use of resources. This study focuses on measuring sprawl intensity based on urban growth trends or built-up area in Kedungkandang District from 2012 to 2020 and based on the results of urban growth or built-up area predictions from 2021 to 2036. Urban growth predictions are carried out using the Cellular Automata method, that there will be 975 hectares built-up land growth from 2016 to 2036. Then to measure the intensity of sprawl as a result of urban growth, the Shannon’s Entropy method is used with the result that the urban growth process in Kedungkandang District within 24 years tends to decrease in sprawl intensity by 50%. The results of this study are expected to provide insight and future views for the Government in dealing with urban growth in Kedungkandang District in the future.

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Prediction of land use changes with Bayesian spatial modeling from the perspective of urban climate

Cited by 6 publications

References 23 publications

Analysis and prediction of LUCC change in Huang-Huai-Hai river basin

Analysis and prediction of LUCC change in Huang-Huai-Hai river basin

Modelling of Expansion Changes of Vilnius City Area and Impacts on Landscape Patterns Using an Artificial Neural Network

Measurement of Sprawl Effect Based on Urban Growth Trends and Prediction in Kedungkandang District, Malang City

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