Land use change prediction and driving forces analyses of Xinjian County
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Based on remote sensing and geographical information system,land use database of 1998 and 2002 in Xinjian County were established.Two commonly used prediction models,which are Markov model based on transformation matrix and GM(1,1) based on Grey theory respectively were applied to predict land use change direction in the future.The results indicated that coincidence degree between two prediction models was very high.Arable land and unused land will decrease persistently.Forest land and construction land showed acceleration tendency.Grassland and water body will be leveling-off in the future.Furthermore,driving forces of land use change are analyzed in this paper.The research results could provide scientific basis for land use planning and land use policy constitution.Keywords:
Arable land
Land information system
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Based on the analysis of the existing land use change simulation model, combined with macroland use change driving factors and microlocal land use competition, and through the application of Python language integrated technical approaches such as CA, GIS, AHP, and Markov, a multitarget land use change simulation model based on cellular automata(CA) is established. This model was applied to conduct scenario simulation of land use/cover change of the Jinzhou New District, based on 1:10000 map scale land use, planning, topography, statistics, and other data collected in the year of 1988, 2003, and 2012. The simulation results indicate the following: (1) this model can simulate the mutual transformation of multiple land use types in a relatively satisfactory way; it takes land use system as a whole and simultaneously takes the land use demand in the macrolevel and the land use suitability in the local scale into account; and (2) the simulation accuracy of the model reaches 72%, presenting higher creditability. The model is capable of providing auxiliary decision-making support for coastal regions with the analysis of the land use change driving mechanism, prediction of land use change tendencies, and establishment of land resource sustainable utilization policies.
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A macro-micro integrated land use change model, Grey-Cellular automata (CA)-Multi-Criterion Decision-Making (MCDM)-Geographic Information System (GIS) based model (GCMG for short) which can simulate human decision making process was proposed. The GCMG model borrows the theoretical hypothesis of CLUE-S model which supposes that regional land use change is driven by its land use requirement and the land use distribution is in dynamic balance with land use demands and regional natural resources and socio-economic conditions. The GCMG model consists of both non-spatial and spatial part. The non-spatial part, so called macro model, calculates the changes of land use demand in the future based on experiential relationship of land use and its dominating drivers using the grey model. The spatial part, also called micro model, completes the land use allocation process whose total quantity is calculated by the non-spatial part with a combined method of MCDM, GIS and CA model. In the spatial part, firstly MCDM method was used to simulate the human decision making process for land use change considering socio-economic and bio-physical conditions; the results of which was brought into conversion rule of CA model; and the integration was finally implemented in GIS to model the land use allocation. To illustrate the functioning of GCMG model and its validation, it is applied in Longhai County to simulate land use change in 2010. As one of the typical counties at coastal area of Fujian Province, great changes in land use have taken place in Longhai County over the past decades, including the garden plots expansion, town land for urban and rural housing, and land for industrial and mining purpose. Firstly the GCMG simulation results are compared with map of the actual distribution of land use in 2000 for validation. The Kappa equals to 0.93 in the simulation at 10 m×10 m grid level and has gained satisfactory results. Then the validated model is applied to simulate the land use conversion probabilities under different decision-making scenarios. The results show that the basic farmland protection policy will determine the future land use change pattern. The application of GCMG model indicated that it can both simulate the land use demand at macro level and land suitability at micro level, thus possessing the ability of studying the multi-level land use system.
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It generalized the model of land use changes and analyzed the concept of enery model,then classified them into three groups: Land resources quantity change model,land resources quality change model,Land re-sources spatial change model,then summarized the feature and shortage of every model.
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Research on the change and forecast of land use,enables better understanding of the land change and the trend of development,in order to adjust land use structure and become more reasonable land use.In this paper,using changes of land use data,1998-2006 in Xi'an,based on gray correlation analysis,we obtain main influencing factors of land use is demographic factors and investment in fixed assets.On this basis the use of gray system theory forecasting for the 2007-2011 land use for the prediction,to be cultivated land,forest land will continue to decrease and in construction sites will have a rapid increase speed.
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Land use change modeling is an important technique for the projection of alternative future pathways,and for conducting experiments that test our understanding of key processes in land use change.The stochastic model is a powerful tool to quantify and model land use change in a simple way with limited data requirements.It is based on Markov processes and transition probabilities,and describes a stochastic process that moves in a sequence of steps through a set of states.For land use change,the states of the system are defined as the amount of land covered by various land uses.The transition probabilities can be statistically estimated from a sample of transitions occurring during a particular time interval.This study integrates GIS and stochastic modeling to simulate land use change in Shiqian County,located in a karst area in southwest China,during the period 1988～2014.In the GIS-based stochastic model,Markov process and transition probabilities are used to simulate land use change.This approach treats state transitions as stochastic processes that are conditional only on the initial state.The factor of identical land use within a 3 by 3 grid-cell neighborhood receives special consideration.Neighborhood interactions between land use types are often included in spatially explicit analysis of land use change and they play an important role.The application consists of two parts.First,transition probabilities of land use change with biophysical factors such as elevation,solope,distance to stream,distance to road and adjacent cell with the same land use are calculated and integrated into a stochastic model,together with the 1988 land use map interpreted from Landsat TM imagery.Then,using the stochastic model in ARC-GIS environment,the 2001 land use pattern is simulated based on the 1988 land use pattern.The model is verified by a statistical cell-by-cell comparison with the actual 2001 land use pattern interpreted from Landsat ETM imagery.Second,three hypothetical and plausible land use transition rules are used to simulate the 2014 land use pattern in Arc/Info ASCII grid format.This simulated 2014 land use pattern would be helpful for local government policy decisions.This study provides a new tool for predicting land use pattern,and shows that GIS-based stochastic models have the capabilities of description and simple trend projection for land use change,regardless of data constraints and the complexity of land change systems.
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Taking land-use status map of Gao-xin district in Jiaozuo as data source,by processing land-use status maps of research area in the selected period and with functions of GIS such as data-editing and spatial analysis,the land-use change data in the period can be attained.Based on the data above,the model of land measurement and calculation can be used to analyze the status and the dynamic change of land use in research area,grey model and least square method can be respectively used to predict the developmental trend of research area so as to achieve grasping the law of land-use change of research area in all-around way.The conclusion can provide rational and effective land use of research area with the technical support for decision,which can promote the continual development of land use and fasten the advancement of urbanized construction of the research area.
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Simulating and predicting the development and changes in urban land change can provide valuable references for the sustainable development of cities. However, the change process of urban land-use/land-cover is a complex process involving multiple factors and multiple relationships. This dilemma makes it very challenging to accurately simulate the results and to make predictions. In response to this problem, we started with the physical characteristics of the land-use/land-cover change process and constructed a diffusion equation to simulate and predict urban land-use/land-cover changes. The diffusion equation is used to describe the diffusion characteristics of the land-use/land-cover change process, which helps to understand the urban land-use/land-cover change process. The experimental results show that (1) the diffusion equation we constructed can simulate urban land-use/land-cover changes, (2) the simulation process of the model is not limited by the time interval of the time series data itself, and (3) the model only requires one parameter without other constraints.
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Land use and land cover (LULC) models are essential for analyzing LULC change and predicting land use requirements and are valuable for guiding reasonable land use planning and management. However, each LULC model has its own advantages and constraints. In this paper, we explore the characteristics of LULC change and simulate future land use demand by combining a CLUE-S model with a Markov model to deal with some shortcomings of existing LULC models. Using Beijing as a case study, we describe the related driving factors from land-adaptive variables, regional spatial variables and socio-economic variables and then simulate future land use scenarios from 2010 to 2020, which include a development scenario (natural development and rapid development) and protection scenarios (ecological and cultivated land protection). The results indicate good consistency between predicted results and actual land use situations according to a Kappa statistic. The conversion of cultivated land to urban built-up land will form the primary features of LULC change in the future. The prediction for land use demand shows the differences under different scenarios. At higher elevations, the geographical environment limits the expansion of urban built-up land, but the conversion of cultivated land to built-up land in mountainous areas will be more prevalent by 2020; Beijing, however, still faces the most pressure in terms of ecological and cultivated land protection.
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Using the land use data of Yunxian County in 2009 as the basic data source,the driving factors with great influence on land use change were selected,GIS software was used for data processing,CLUE-S model was used to simulate the change of land use pattern in 2012,and the present land use data of 2012 was used to validate the simulate results.The validation had accurate results and indicated that this model was very powerful in predicting the future land use change.At last,the land use pattern of 2020 was simulated based on the land use data of 2012,in order to provide scientific support for the sustainable development of land use.
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Using the remote sensing data of TM satellite image data between years of 1992 and 2007,On the basis of the analysis of land use change,PLS-PP model was built to analyze and predict land use change in yanji city.the computation results showed that the relative error of PLS-PP model was smaller than traditional PLS model′s,and it had improved the prediction precision evidently.According to the result of PLS-PP,on condition that the rate of land use change was not changed,area of woodlands、grasslands would decrease,but area of cultivated land、residential and industrial or mineral land、unused land and water area will increase.the water area and unused land changed significantly.This research could provide a new way to predict land use change,and the result could provide a basis for land management department to formulate land-use policies,then attained objective to optimize and rational use the land resources.
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