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Research On Land Use Change And Multi-Scenario Simulation In Tianjin Based On CA-Markov Model

Posted on:2023-02-12Degree:MasterType:Thesis
Country:ChinaCandidate:L ShuFull Text:PDF
GTID:2530306788953979Subject:Surveying and mapping engineering
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Since the 21 st century,China’s society and national economy have developed rapidly,and the degree of development and utilization of land resources has been greatly improved.However,the limitation,scarcity and irreplaceable of land resources have intensified the contradiction between man and land.In order to find a balance between economic development,land resource protection and ecological security,we need to study land use change,explore the characteristics and laws of land use temporal and spatial evolution,and predict its development trend,so as to reasonably allocate urban land resources,adjust and optimize urban layout and spatial structure,so as to realize regional sustainable development.Taking Tianjin as the study area,this paper comprehensively considers the difference of urban and rural development degree and the possibility of expansion to the sea,and analyzes the temporal and spatial characteristics of the changes of 9 land use types in the study area from four aspects: the overall land use pattern,land use transfer,urban expansion,cultivated land occupation and compensation;According to the law of land use change in Tianjin,a total of10 driving factors are selected from natural and social aspects to reveal the driving mechanism of land use change through Logistic regression;Referring to the expansion of construction land in contiguous difficult areas of Hebei Province under the key poverty alleviation policy,the driving effect of Rural Revitalization policy on rural expansion in Tianjin is quantified by using fuzzy function analogy;Finally,considering the regression results and policy factors,this paper constructs the conversion rules under the three scenarios,and uses CA Markov model to predict the land use pattern of Tianjin in 2030 under the "natural development scenario","sustainable development scenario" and "Rural Revitalization scenario".The results show that:(1)From 2000 to 2020,the land use in Tianjin changed dramatically,and urbanization is its main feature.The occupation of cultivated land by urban expansion reached 899.88km2,which made it difficult to maintain the balance of cultivated land occupation and compensation in Tianjin;At the same time,with a high degree of development in the city,Tianjin’s ecological space and production space are gradually expanding to the ocean.(2)Logistic regression analysis showed that the 10 driving factors had an impact on the spatial distribution of classes in different degrees.Natural factors,as congenital conditions,have played a significant role in 9 land types;Social and economic factors have a negative effect on the natural surface in the study area;Among the accessibility factors,the traffic factor plays a strong guiding role in the expansion trend of urban and rural areas.(3)The transformation rules of Ca Markov model are improved by logistic regression method,and the land use pattern in 2020 is simulated.Compared with the classification map in 2020,the kappa coefficient is 0.8495,and the reliability of the model is high.Then this method is used to predict the land use pattern of Tianjin under the natural development scenario,sustainable development scenario and Rural Revitalization scenario in 2030.(4)The prediction results show that under the influence of driving factors and restrictive policies,there is no disorderly expansion of urban and rural construction land,occupation of a large amount of cultivated land and occupation of the red line reserve in the sustainable development scenario and Rural Revitalization scenario;Moreover,in the scenario of adding the driving factors of rural revitalization,the process of rural development is accelerated compared with the scenario of sustainable development,the rural spatial layout is more compact,and the level of intensification is improved.
Keywords/Search Tags:land use change, CA-Markov model, driving forces, Rural Revitalization
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