武汉城市圈耕地时空演变及布局模拟

发布时间：2018-03-10 00:00

本文选题：武汉城市圈　切入点：耕地　出处：《武汉大学》2017年硕士论文　论文类型：学位论文

【摘要】：耕地是地球上人类生活所需的必不可少的基本资源,但进入21世纪之后,人口不断增加,耕地逐渐减少,不管是从近期我国工农业经济发展对粮食的需要,还是从保证国家经济安全的长远战略目标来看,我们都必须合理利用有限的耕地资源。党中央、国务院素来注重耕地的保护工作,把"十分珍惜、合理利用土地和切实保护耕地"确立为我国的基本国策之一,以立法的形式正式确认了土地基本国策的法律位置。本文主要借助中国科学院资源环境科学数据中心提供的中国1:10万比例尺土地利用现状遥感监测数据库,以1995年、2000年、2005年、2010年和2015年五个时期的Landsat TM/ETM遥感影像为主要的数据来源,对武汉城市圈耕地的时空分布及演变进行深入分析,挖掘耕地变化驱动力,并基于地理加权回归(GWR)及空间数据场模型(SDF)对传统的元胞自动机(CA)模型进行改进,构建了耕地布局模拟模型GWRCA-SDF,对武汉城市圈内各个县域的耕地资源按照自身的资源禀赋以及社会经济发展水平进行有差异的分配,以保证粮食安全以及建设用地扩张的双重需要。本文主要结果和结论如下:(1)在1995年到2015年间,武汉城市圈耕地总量总体呈现减少趋势,20年内耕地减少总量为230707.08 hm2,其中变化数量最大的为2005-2010年间,耕地面积减少104626hm2,变化强度高达3.55%;1995-2015年间,武汉城市圈耕地减少中水田减少量为旱地的2倍多;武汉城市圈可开垦及可复垦的土地总量为83000.98 hm2,占湖北省可开垦及可复垦土地总量的42.40%。(2)武汉城市圈9市耕地变化强度最大的时期集中在2005-2010年、2010-2015年两个时间段,在2005-2010年间黄冈市、黄石市、潜江市、武汉市、仙桃市、咸宁市和孝感市耕地变化强度达四个时间段内最大,鄂州市、天门市在2010-2015年耕地变化强度为四个时间段内最大。土地利用结构信息熵,可以表示区域不同类型用地比例的均衡程度或者有序程度,其值越大说明土地利用系统就越无序,相反,值越小则表示土地利用结构均质性越高,武汉城市圈土地利用结构信息熵指数在1995-2015年呈缓慢增加趋势,城市圈城市中信息熵指数高于平均值的市有黄石市和鄂州市,其他城市信息熵指数低于城市圈平均水平,其中潜江市和天门市显著低于平均水平。1995-2015年,武汉市主城区和黄石市耕地年均变化速率最快,并呈现由城市圈中部向边缘变化速率逐渐降低趋势,在城市圈东部边缘耕地年均变化速率最慢,西部应城市也相对较慢。武汉城市圈各县耕地的变化程度值呈现中部和西部边缘耕地利用程度高向周围递减的趋势。(3)在景观总体构型与变化方面,在1995-2015年间蔓延度指数(CONTAG)从42.7073降低至38.6284,聚集度指数(AI)由69.744降低至67.2128,说明城市圈景观整体异质性增加;散布与并列指数(IJI)由61.7446增至64.0422,说明城市圈景观受自然影响制约呈变弱趋势;Shannon多样性指数(SHDI)和Shannon均匀度指数(SHEI)均不断增加则进一步验证了优势景观类型的优势度在逐渐降低,同时景观的空间异质性和破碎度反而逐渐趋高。耕地景观指数方面,斑块个数(NP)和斑块密度(PD)均呈增加趋势,在20年内两指数的数值分别增加10.16%和10.31%,说明武汉城市圈耕地的破碎化程度在不断增加;同时最大斑块指数(LPI)从35.0966减少至32.2399,说明耕地景观受最大耕地斑块的影响在减弱,优势斑块优势度降低;边界密度(ED)增加率为5.64%,说明耕地斑块复杂度趋于增加,耕地景观不稳定性增加;AI从75.6252下降到72.1154,降低率为4.64%,说明虽然耕地景观布局相对整体景观布局而言更为聚集,但是这种聚集程度却在不断下降。(4)针对现有的以元胞自动机为基础的土地利用优化模型中存在的上述不足,本文提出了基于地理加权回归(GWR)与元胞自动机(CA)及空间数据场模型(SDF)相结合的土地利用协同配置模型GWRCA-SDF,并对武汉"1+8"城市圈内各个县域的耕地利用布局进行协同优化布局模拟。模拟结果显示,相对传统元胞自动机模型(Traditional-CA)模拟结果,GWRCA-SDF可以根据区域的资源禀赋,社会经济发展条件差异对耕地资源进行合理的分配。
[Abstract]:Cultivated land is the indispensable basic resources necessary to human life on earth, but after entering twenty-first Century, the continuous increase of population, cultivated land decreased gradually, whether it is from the recent economic development of our country agriculture for food, or from the guarantee of national economic security's long-term strategic goal, we must make rational use of limited land resources. The Party Central Committee, the State Council has always been focusing on the protection of cultivated land, the "treasure, the rational use of land and to protect arable land" was established as one of China's basic national policy, in the form of legislation officially confirmed the legal position of the basic national policy of land. This paper mainly by providing China Academy of Sciences Data Center for resources and Environmental Sciences China 000 1:10 the scale of land use status monitoring database, in 1995, 2000, 2005, the Landsat TM/ETM remote sensing images in 2010 and 2015 five period as the main The source of the data, and the evolution of the temporal and spatial distribution of cultivated land in Wuhan city circle in-depth analysis, mining and driving forces of cultivated land change, and based on the geographical weighted regression (GWR) and spatial data model (SDF) to the traditional cellular automaton (CA) model is improved, the layout of arable land to build the simulation model of GWRCA-SDF, there are differences in the distribution of the cultivated land resources in Wuhan city circle in each county were in accordance with their own natural resources and social economic development level, to ensure the food security and the double expansion of construction land. The main results and conclusions are as follows: (1) during the period from 1995 to 2015, the total amount of cultivated land in Wuhan city circle showing an overall decreasing trend in 20 years. Reduce the amount of cultivated land within 230707.08 Hm2, which changes the maximum number of 2005-2010 years, the area of cultivated land reduced 104626hm2, changes in intensity of up to 3.55%; 1995-2015 years, reducing cultivated land in Wuhan city circle Less in paddy field reduction is more than 2 times the total dry land; Wuhan city circle can be reclaimed and reclamation is 83000.98 Hm2, accounting for the total land reclamation and reclamation in Hubei province 42.40%. (2) in 2005-2010 years period of cultivated land in Wuhan city circle 9 city changes the largest intensity, 2010-2015 year time period two in 2005-2010 years, Huanggang City, Huangshi City, Qianjiang City, Wuhan City, Xiantao, Xianning city and Xiaogan city cultivated land change strength of four time, Ezhou City, Tianmen in 2010-2015 years for the strength of cultivated land change in the four period. The information entropy of land use structure, can be expressed in regions of different types with the balanced degree ratio or the degree of order, the greater the value that the land use system is disordered, on the contrary, the smaller the value of that land use structure heterogeneity is high, the Wuhan city circle of land use structure information entropy index in 199 5-2015 years was a slowly increasing trend, the information entropy index of city city circle of Huangshi city and Ezhou city is higher than the average value of the city and other city information entropy index is lower than the average level of the city circle, Qianjiang city and Tianmen was significantly lower than the average level of.1995-2015 years, the main city of Wuhan city and Huangshi city land average annual change rate, and presented by the city circle to the edge of central rate gradually decreased, in the city circle of the eastern edge of the annual change rate of cultivated land should be the slowest, western city is relatively slow. The degree of change of Wuhan city circle county farmland values present in central and western edge of land use to the surrounding high degree of decreasing trend. (3) in the overall configuration and the landscape change in 1995-2015 years, the contagion index (CONTAG) decreased from 42.7073 to 38.6284, the aggregation index (AI) decreased from 69.744 to 67.2128, indicating that the overall landscape heterogeneity of city circle Increased; interspersion and juxtaposition index (IJI) increased from 61.7446 to 64.0422, the landscape city circle by natural constraints is weak trend; Shannon diversity index (SHDI) and Shannon evenness index (SHEI) increased further verify the advantages of landscape types and the dominance decreased gradually, and the landscape spatial heterogeneity and fragmentation but gradually. The farmland landscape index, the number of patches (NP) and patch density (PD) increased, numerical in 20 years two index increased 10.16% and 10.31%, indicating the degree of fragmentation of cultivated land in Wuhan city circle is increasing; at the same time, the largest patch index (LPI) reduced from 35.0966 to 32.2399, indicating that farmland landscape most affected farmland patches in less dominant patch dominance decreased; the boundary density (ED) increase rate is 5.64%, that tends to increase the complexity of cultivated land, cultivated land landscape Increased stability; AI decreased from 75.6252 to 72.1154, the decrease rate was 4.64%, indicating that although the farmland landscape layout relative overall landscape layout is more aggregation, but the aggregation degree was decreased. (4) based on the existing in the cellular automaton based land use optimization model of the above shortcomings, is proposed in this paper. Based on the geographical weighted regression (GWR) and cellular automaton (CA) and spatial data model (SDF) combined with land use coordinated allocation model GWRCA-SDF, and cultivated land of Wuhan "1+8" city circle in each county with the layout of collaborative optimization layout simulation. The simulation results show that compared with the traditional cellular automaton model (Traditional-CA) simulation results, GWRCA-SDF can according to the regional resource endowments, different conditions for economic and social development and reasonable allocation of land resources.

【学位授予单位】：武汉大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：F323.211

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