Self-modifying CA model using dual ensemble Kalman filter for simulating urban land-use changes

2015 ◽  
Vol 29 (9) ◽  
pp. 1612-1631 ◽  
Author(s):  
Yihan Zhang ◽  
Xia Li ◽  
Xiaoping Liu ◽  
Jigang Qiao
Keyword(s):  
Land Use ◽  
Kalman Filter ◽  
Ensemble Kalman Filter ◽  
Land Use Changes ◽  
Urban Land ◽  
Urban Land Use ◽  
Ca Model

A Cellular Automata Model for Urban Land-Use Simulation

2005 ◽  
Vol 32 (2) ◽  
pp. 247-263 ◽  
Author(s):  
Kwok Hung Lau ◽  
Booi Hon Kam
Keyword(s):  
Land Use ◽  
Cellular Automata ◽  
Distance Function ◽  
Alternative Model ◽  
Resistance To Change ◽  
Land Use Changes ◽  
Urban Land ◽  
Urban Land Use ◽  
Land Use Allocation ◽  
Ca Model

This paper presents an urban land-use simulation model using cellular automata (CA). In the model urban growth is regarded as the result of a global process underpinned by local actions and land-use change as the joint action of three different effects: attribute, heterogeneity, and gravity. The attribute and heterogeneity effects are regarded as different aspects of a local driving force for change constituted by changing accessibility and other attributes resulting from the interaction of land use and transport at the neighborhood level. The gravity effect is a universal resistance to change as a result of inertia and agglomeration of compatible land uses in the vicinity. To ensure that local actions would lead to global behavior, a multipass, in addition to a single-pass, land-use-allocation algorithm is designed to mimic land-use changes. With metropolitan Melbourne in Australia as a case study, the performance of the model in replicating land-use changes is compared with that of an alternative model developed by using only a distance function. The results of the comparison show that the proposed CA model outperforms the alternative model with only a distance function, confirming the importance of incorporating local attributes in modeling land-use changes.

scholarly journals Correction to: The impacts of agricultural and urban land-use changes on plant and bird biodiversity in Costa Rica (1986–2014)

2021 ◽  
Vol 21 (3) ◽  
Author(s):  
Andrea Montero ◽  
Joan Marull ◽  
Enric Tello ◽  
Claudio Cattaneo ◽  
Francesc Coll ◽  
...  
Keyword(s):  
Land Use ◽  
Costa Rica ◽  
Land Use Changes ◽  
Urban Land ◽  
Urban Land Use

scholarly journals Optimization of a Novel Urban Growth Simulation Model Integrating an Artificial Fish Swarm Algorithm and Cellular Automata for a Smart City

2021 ◽  
Vol 13 (4) ◽  
pp. 2338
Author(s):  
Xinxin Huang ◽  
Gang Xu ◽  
Fengtao Xiao
Keyword(s):  
Land Use ◽  
Sustainable Development ◽  
Urban Growth ◽  
Smart City ◽  
Urban Land ◽  
Urban Land Use ◽  
Growth Simulation ◽  
Artificial Fish Swarm Algorithm ◽  
Artificial Fish Swarm ◽  
Ca Model

As one of the 17 Sustainable Development Goals, it is sensible to analysis historical urban land use characteristics and project the potentials of urban sustainable development for a smart city. The cellular automaton (CA) model is the widely applied in simulating urban growth, but the optimum parameters of variables driving urban growth in the model remains to be continued to improve. We propose a novel model integrating an artificial fish swarm algorithm (AFSA) and CA for optimizing parameters of variables in the urban growth model and make a comparison between AFSA-CA and other five models, which is used to study a 40-year urban land growth of Wuhan. We found that the urban growth types from 1995 to 2015 appeared relatively consistent, mainly including infilling, edge-expansion and distant-leap types in Wuhan, which a certain range of urban land growth on the periphery of the central area. Additionally, although the genetic algorithms (GA)-CA model and the AFSA-CA model among the six models due to the distance variables, the parameter value of the GA-CA model is −15.5409 according to the fact that the population (POP) variable should be positively. As a result, the AFSA-CA model regardless of the initial parameter setting is superior to the GA-CA model and the GA-CA model is superior to all the other models. Finally, it is projected that the potentials of urban growth in Wuhan for 2025 and 2035 under three scenarios (natural urban land growth without any restrictions (NULG), sustainable urban land growth with cropland protection and ecological security (SULG), and economic urban land growth with sustainable development and economic development in the core area (EULG)) focus mainly on existing urban land and some new town centers based on AFSA-CA urban growth simulation model. An increasingly precise simulation can determine the potential increase area and quantity of urban land, providing a basis to judge the layout of urban land use for urban planners.

scholarly journals Study on Land-use Changes and Their Impacts on Air Pollution in Chengdu

Atmosphere ◽  
2019 ◽  
Vol 11 (1) ◽  
pp. 42 ◽  
Author(s):  
Wei Sun ◽  
Zhihong Liu ◽  
Yang Zhang ◽  
Weixin Xu ◽  
Xiaotong Lv ◽  
...  
Keyword(s):  
Land Use ◽  
Wind Speed ◽  
Correlation Coefficient ◽  
Urban Areas ◽  
Land Use Changes ◽  
Urban Land ◽  
Urban Land Use ◽  
Simulation Results ◽  
The City ◽  
Better Than

The expansion of urban areas and the increase in the number of buildings and urbanization characteristics, such as roads, affect the meteorological environment in urban areas, resulting in weakened pollutant dispersion. First, this paper uses GIS (geographic information system) spatial analysis technology and landscape ecology analysis methods to analyze the dynamic changes in land cover and landscape patterns in Chengdu as a result of urban development. Second, the most appropriate WRF (Weather Research and Forecasting) model parameterization scheme is selected and screened. Land-use data from different development stages in the city are included in the model, and the wind speed and temperature results simulated using new and old land-use data (1980 and 2015) are evaluated and compared. Finally, the results of the numerical simulations by the WRF-Chem air quality model using new and old land-use data are coupled with 0.25° × 0.25°-resolution MEIC (Multi-resolution Emission Inventory for China) emission source data from Tsinghua University. The results of the sensitivity experiments using the WRF-Chem model for the city under different development conditions and during different periods are discussed. The meteorological conditions and pollution sources remained unchanged as the land-use data changed, which revealed the impact of urban land-use changes on the simulation results of PM2.5 atmospheric pollutants. The results show the following. (1) From 1980 to 2015, the land-use changes in Chengdu were obvious, and cultivated land exhibited the greatest changes, followed by forestland. Under the influence of urban land-use dynamics and human activities, both the richness and evenness of the landscape in Chengdu increased. (2) The microphysical scheme WSM3 (WRF Single–Moment 3 class) and land-surface scheme SLAB (5-layer diffusion scheme) were the most suitable for simulating temperatures and wind speeds in the WRF model. The wind speed and temperature simulation results using the 2015 land-use data were better than those using the 1980 land-use data when assessed according to the coincidence index and correlation coefficient. (3) The WRF-Chem simulation results obtained for PM2.5 using the 2015 land-use data were better than those obtained using the 1980 land-use data in terms of the correlation coefficient and standard deviation. The concentration of PM2.5 in urban areas was higher than that in the suburbs, and the concentration of PM2.5 was lower on Longquan Mountain in Chengdu than in the surrounding areas.

scholarly journals The Exploration of Urban Material Anabolism Based on RS and GIS Methods: Case Study in Jinchang, China

2020 ◽  
Vol 12 (3) ◽  
pp. 370
Author(s):  
Shuqi He ◽  
Xingpeng Chen ◽  
Zilong Zhang ◽  
Zhaoyue Wang ◽  
Mengran Hu
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Metabolic Response ◽  
Land Use Changes ◽  
Central Business District ◽  
Urban Land ◽  
Urban Land Use ◽  
Urban Systems ◽  
Building Stock ◽  
High Resolution Satellite Images

As an open artificial ecosystem, the development of a city requires the continuous input and output of material and energy, which is called urban metabolism, and includes catabolic (material-flow) and anabolic (material-accumulation) processes. Previous studies have focused on the catabolic and ignored the anabolic process due to data and technology problems. The combination of remote-sensing technology and high-resolution satellite images facilitates the estimation of cumulative material amounts in urban systems. This study focused on persistent accumulation, which is the metabolic response of urban land use/urban land expansion, building stock, and road stock to land-use changes. Building stock is an extremely cost-intensive and long-lived component of cumulative metabolism. The study measured building stocks of Jinchang, China’s nickel capital by using remote-sensing images and field-research data. The development of the built environment could be analyzed by comparing the stock of buildings on maps representing different time periods. The results indicated that material anabolism in Jinchang is a distance-dependent function, where the amounts and rates of material anabolism decrease with changes in distance to the central business district (CBD) and city administration center (CAC). The cumulative metabolic rate and cumulative total metabolism were observed to be increasing, however, the growth rate has decreased.

scholarly journals Applications of GIS and Very High-Resolution RS Data for Urban Land Use Change Studies in Mongolia

2011 ◽  
Vol 2011 ◽  
pp. 1-8 ◽  
Author(s):  
D. Amarsaikhan ◽  
V. Battsengel ◽  
E. Egshiglen ◽  
R. Gantuya ◽  
D. Enkhjargal
Keyword(s):  
Land Use ◽  
High Resolution ◽  
Land Use Change ◽  
Large Scale ◽  
Land Use Changes ◽  
Urban Land ◽  
Urban Land Use ◽  
Capital City ◽  
Geographical Information ◽  
Topographic Map

The aim of this study is to analyze the urban land use changes occurred in the central part of Ulaanbaatar, the capital city of Mongolia, from 1930 to 2008 with a 10-year interval using geographical information system (GIS) and very high-resolution remote sensing (RS) data sets. As data sources, a large-scale topographic map, panchromatic and multispectral Quickbird images, and TerraSAR synthetic aperture radar (SAR) data are used. The primary urban land use database is developed using the topographic map of the study area and historical data about buildings. To extract updated land use information from the RS images, Quickbird and TerraSAR images are fused. For the fusion, ordinary and special image fusion techniques are used and the results are compared. For the final land use change analysis and RS image processing, ArcGIS and Erdas imagine systems installed in a PC environment are used. Overall, the study demonstrates that within the last few decades the central part of Ulaanbaatar city is urbanized very rapidly and became very dense.

Predicting Urban Land Use Changes Using a CA–Markov Model

2014 ◽  
Vol 39 (7) ◽  
pp. 5565-5573 ◽  
Author(s):  
Jafar Nouri ◽  
Alireza Gharagozlou ◽  
Reza Arjmandi ◽  
Shahrzad Faryadi ◽  
Mahsa Adl
Keyword(s):  
Land Use ◽  
Markov Model ◽  
Land Use Changes ◽  
Urban Land ◽  
Urban Land Use

Urban Land Use Changes by the Integration of Remote Sensing, GIS, and Dynamics Modeling

2013 ◽  
Vol 726-731 ◽  
pp. 4645-4649
Author(s):  
Jia Hua Zhang ◽  
Cui Hao ◽  
Feng Mei Yao
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Urban Areas ◽  
Logistic Model ◽  
Land Use Changes ◽  
Urban Land ◽  
Urban Land Use ◽  
Ratio Test ◽  
Multinomial Logistic Model ◽  
The Impact

We developed an approach to assess urban land use changes that incorporates socio-economic and environmental factors with multinomial logistic model, remote sensing data and GIS, and to quantify the impact of macro variables on land use of urban areas for the years 1990, 2000 and 2010 in Binhai New Area, China. The Markov transition matrix was designed to integrate with multinomial logistic model to illustrate and visualize the predicted land use surface. The multinomial logistic model was evaluated by means of Likelihood ratio test and Pseudo R-Square and showed a relatively good simulation. The prediction map of 2010 showed accurate rates 78.54%, 57.25% and 70.38%, respectively.

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