Modelling coastal land use change by incorporating spatial autocorrelation into cellular automata models   总被引：1，自引：0，他引：1  

Yongjiu Feng  Qianqian Yang  Zhonghua Hong  Li Cui 《国际地球制图》2018,33(5):470-488

This paper presents a spatial autoregressive (SAR) method-based cellular automata (termed SAR-CA) model to simulate coastal land use change, by incorporating spatial autocorrelation into transition rules. The model captures the spatial relationships between explained and explanatory variables and then integrates them into CA transition rules. A conventional CA model (LogCA) based on logistic regression (LR) was studied as a comparison. These two CA models were applied to simulate urban land use change of coastal regions in Ningbo of China from 2000 to 2015. Compared to the LR method, the SAR model yielded smaller accumulated residuals that showed a random distribution in fitting the CA transition rules. The better-fitting SAR model performed well in simulating urban land use change and scored an overall accuracy of 85.3%, improving on the LogCA model by 3.6%. Landscape metrics showed that the pattern generated by the SAR-CA model has less difference with the observed pattern.  相似文献   

Dynamic land use change simulation using cellular automata with spatially nonstationary transition rules   总被引：2，自引：0，他引：2  

Yongjiu Feng  Xiaohua Tong 《地理信息系统科学与遥感》2018,55(5):678-698

The dynamic relationships between land use change and its driving forces vary spatially and can be identified by geographically weighted regression (GWR). We present a novel cellular automata (GWR-CA) model that incorporates GWR-derived spatially varying relationships to simulate land use change. Our GWR-CA model is characterized by spatially nonstationary transition rules that fully address local interactions in land use change. More importantly, each driving factor in our GWR model contains effects that both promote and resist land use change. We applied GWR-CA to simulate rapid land use change in Suzhou City on the Yangtze River Delta from 2000 to 2015. The GWR coefficients were visualized to highlight their spatial patterns and local variation, which are closely associated with their effects on land use change. The transition rules indicate low land conversion potential in the city’s center and outer suburbs, but higher land conversion potential in the inner near suburbs along the belt expressway. Residual statistics show that GWR fits the input data better than logistic regression (LR). Compared with an LR-based CA model, GWR-CA improves overall accuracy by 4.1% and captures 5.5% more urban growth, suggesting that GWR-CA may be superior in modeling land use change. Our results demonstrate that the GWR-CA model is effective in capturing spatially varying land transition rules to produce more realistic results, and is suitable for simulating land use change and urban expansion in rapidly urbanizing regions.  相似文献   

Simulation of urban expansion via integrating artificial neural network with Markov chain – cellular automata     

Tingting Xu  Jay Gao  Giovanni Coco 《International journal of geographical information science》2019,33(10):1960-1983

Accurate simulations and predictions of urban expansion are critical to manage urbanization and explicitly address the spatiotemporal trends and distributions of urban expansion. Cellular Automata integrated Markov Chain (CA-MC) is one of the most frequently used models for this purpose. However, the urban suitability index (USI) map produced from the conventional CA-MC is either affected by human bias or cannot accurately reflect the possible nonlinear relations between driving factors and urban expansion. To overcome these limitations, a machine learning model (Artificial Neural Network, ANN) was integrated with CA-MC instead of the commonly used Analytical Hierarchy Process (AHP) and Logistic Regression (LR) CA-MC models. The ANN was optimized to create the USI map and then integrated with CA-MC to spatially allocate urban expansion cells. The validated results of kappa and fuzzy kappa simulation indicate that ANN-CA-MC outperformed other variously coupled CA-MC modelling approaches. Based on the ANN-CA-MC model, the urban area in South Auckland is predicted to expand to 1340.55 ha in 2026 at the expense of non-urban areas, mostly grassland and open-bare land. Most of the future expansion will take place within the planned new urban growth zone.  相似文献   

Examining the sensitivity of spatial scale in cellular automata Markov chain simulation of land use change   总被引：2，自引：0，他引：2  

Hao Wu  Zhen Li  Keith C. Clarke  Wenzhong Shi  Anqi Lin 《International journal of geographical information science》2019,33(5):1040-1061

Understanding the spatial scale sensitivity of cellular automata is crucial for improving the accuracy of land use change simulation. We propose a framework based on a response surface method to comprehensively explore spatial scale sensitivity of the cellular automata Markov chain (CA-Markov) model, and present a hybrid evaluation model for expressing simulation accuracy that merges the strengths of the Kappa coefficient and of Contagion index. Three Landsat-Thematic Mapper remote sensing images of Wuhan in 1987, 1996, and 2005 were used to extract land use information. The results demonstrate that the spatial scale sensitivity of the CA-Markov model resulting from individual components and their combinations are both worthy of attention. The utility of our proposed hybrid evaluation model and response surface method to investigate the sensitivity has proven to be more accurate than the single Kappa coefficient method and more efficient than traditional methods. The findings also show that the CA-Markov model is more sensitive to neighborhood size than to cell size or neighborhood type considering individual component effects. Particularly, the bilateral and trilateral interactions between neighborhood and cell size result in a more remarkable scale effect than that of a single cell size.  相似文献   

Simulation of landscape spatial layout evolution in rural-urban fringe areas: a case study of Ganjingzi District   总被引：2，自引：0，他引：2  

Jun Yang  Andong Guo  Yonghua Li  Yuqing Zhang  Xueming Li 《地理信息系统科学与遥感》2019,56(3):388-405

In recent years, the rapid expansion of urban spaces has accelerated the mutual evolution of landscape types. Analyzing and simulating spatio-temporal dynamic features of urban landscape can help to reveal its driving mechanisms and facilitate reasonable planning of urban land resources. The purpose of this study was to design a hybrid cellular automata model to simulate dynamic change in urban landscapes. The model consists of four parts: a geospatial partition, a Markov chain (MC), a multi-layer perceptron artificial neural network (MLP-ANN), and cellular automata (CA). This study employed multivariate land use data for the period 2000–2015 to conduct spatial clustering for the Ganjingzi District and to simulate landscape status evolution via a divisional composite cellular automaton model. During the period of 2000–2015, construction land and forest land areas in Ganjingzi District increased by 19.43% and 15.19%, respectively, whereas farmland, garden lands, and other land areas decreased by 43.42%, 52.14%, and 75.97%, respectively. Land use conversion potentials in different sub-regions show different characteristics in space. The overall land-change prediction accuracy for the subarea-composite model is 3% higher than that of the non-partitioned model, and misses are reduced by 3.1%. Therefore, by integrating geospatial zoning and the MLP-ANN hybrid method, the land type conversion rules of different zonings can be obtained, allowing for more effective simulations of future urban land use change. The hybrid cellular automata model developed here will provide a reference for urban planning and policy formulation.  相似文献   

鄂尔多斯盆地构造应力场特征及其构造背景   总被引：29，自引：9，他引：20  

徐黎明  周立发  张义楷  党犇 《大地构造与成矿学》2006,30(4):455-462

通过大量断层和褶皱的野外观测以及构造形成序列的确定,同时考虑盆地形成演化过程中板块动力学背景,并结合前人的研究成果,开展了鄂尔多斯盆地古构造应力场的研究。研究表明盆地区域主压应力场方向在加里东期呈NNE-SSW向和近SN向,主要是晚奥陶世以来秦岭洋盆向北俯冲并与华北板块碰撞的结果;印支期主要呈NW-SE向和NNE-SSW向、SN向,主要受中特提斯构造动力体系中羌塘地块与欧亚大陆碰撞拼贴产生的远程构造效应影响;燕山期主要呈NW-SE向,主要受古太平洋大陆板块与欧亚大陆板块碰撞远程构造效应影响,盆地西南缘呈NE-SW向;喜马拉雅期呈NNE-SSW向,主要受新特提斯构造动力体系和今太平洋构造动力体系联合作用影响,即今太平洋板块和印度板块与欧亚板块俯冲碰撞有关。  相似文献   

基于多智能体的土地利用模拟与规划模型   总被引：31，自引：5，他引：26  

刘小平  黎夏  艾彬  陶海燕  伍少坤  刘涛 《地理学报》2006,61(10):1101-1112

利用多智能体和元胞自动机对城市土地资源的可持续利用进行了规划。根据环境经济学资源分配原理和可持续发展理论,提出结合多智能体及元胞自动机的微观规划模型,在时间和空间上合理分配及规划城市土地资源的利用,以避免浪费不可再生的土地资源。该模型由相互作用的多智能体层、元胞自动机层和环境因素层组成,可方便地探索不同土地利用政策下城市土地利用发展情景,能够为城市规划提供有用的决策依据。以广州市海珠区为实验区,在可持续发展为前提的规划下,模拟了1995-2010年的城市扩展的动态变化,并讨论了在不同规划情景下城市土地资源的利用效率及合理性。  相似文献   

冲绳海槽中部岩心沉积物中浮岩的物理性质和地球化学特征差异及其对岩浆活动的指示     

方雪  曾志刚  胡思谊  朱博文  齐海燕  万世明  徐兆凯 《海洋科学》2020,44(5):1-11

为进一步了解冲绳海槽浮岩的物理性质和地球化学特征差异,对冲绳海槽中部岩心沉积物S9中的浮岩进行了微观结构和地球化学组成分析。结果显示,冲绳海槽中部存在白色、灰白色及棕色3种浮岩,其中灰白色浮岩又可以根据构造特征分为气孔构造和流动构造浮岩两个亚类。浮岩的地球化学组成表明白色、灰白色及棕色浮岩都是由玄武质岩浆经过充分的分离结晶作用形成的流纹质或流纹英安质火山岩。玄武质岩浆在演化的过程中发生了斜长石、角闪石、辉石、Fe-Ti氧化物、磷灰石等矿物的结晶分离。结合有孔虫14C年龄,认为浮岩是冲绳海槽中部距今13.1 ka左右的长英质火山活动的产物。演化程度相对较低的棕色浮岩具有比白色浮岩高的TiO2, Al2O3, Fe2O3, MgO, CaO含量,且棕色浮岩具有相对低的稀土总量和轻稀土总量。根据浮岩的物理性质及地球化学组成差异推测,岩浆的黏度和压力是影响浮岩构造特征的主要因素。黏度大、连续减压的岩浆易于形成具有流动构造和密集气孔的浮岩,黏度小、阶段性减压的岩浆易于形成气孔大而疏松的浮岩。  相似文献   

城市扩展元胞自动机多结构卷积神经网络模型   总被引：2，自引：0，他引：2  

谢志文  王海军  张彬  黄鑫鑫 《测绘学报》2020,49(3):375-385

传统的城市扩展元胞自动机(CA)模型是基于单个元胞的变量信息挖掘来构建转换规则的。针对这一问题,本文基于多结构卷积神经网络提出从区域特征出发且顾及区域多尺度特征挖掘转换规则的城市扩展元胞自动机模型(MSCNN-CA),并以武汉主城区和上海浦东新区为例,模拟了两个试验区2005—2015年期间城市扩展过程。模型验证表明:与逻辑回归和神经网络相比,本文构建的3个单一结构的卷积神经网络元胞自动机(CNN-CA)模型在4个指标(Kappa系数、FoM(figure of merit)值、命中率(h)和错误率(m))上都有不同程度的提高。特别是FoM指数,在武汉主城区提高了23.3%~29.4%,在上海浦东新区提高了20.3%~28.5%。此外,MSCNN-CA模型与3个单一结构的CNN-CA模型相比,在各个指标上也有所改善,FoM指数在武汉主城区提高了0.8%~4.8%,上海浦东新区提高了2.8%~7.8%。两个试验区的模拟结果表明:相比传统CA模型,基于多结构卷积神经网络的城市扩展元胞自动机模型(MSCNN-CA)能够有效提高城市扩展模拟的精度,更真实地反映城市扩展空间演变过程。相比单结构的卷积神经网络CA模型,多结构卷积神经网络CA模型的稳定性和模拟结果准确性有所提升。  相似文献   

顾及地类转换差异的城市空间扩展元胞自动机模型及应用研究     

于明明  曾永年 《地球信息科学学报》2018,20(1):48-56

元胞自动机模型已经成为城市空间扩展模拟研究的重要方法之一,并得到广泛应用。然而,现有的城市扩展元胞自动机模型仍存在不足。由于元胞状态设置较为简单,从而使模型转换规则中对不同用地类型向城市用地转换的差异与强度考虑不够。基于此本文在元胞自动机模型的框架下,设计了多元结构的元胞状态及转换规则,提出了顾及地类转换差异与强度的城市扩展元胞自动机模型。在计算非城市用地向城市用地转换的转换概率时,该模型考虑了3个方面的概率：① 地形地貌、经济发展等城市发展的驱动因素对城市用地扩展的影响概率,该概率采用logistics方法进行计算;② 邻域元胞的用地类型对中心元胞转换概率的影响,该概率采用扩展摩尔型方法进行计算;③ 不同类型的非城市用地（本研究中包括耕地、林地和裸地3种类型）向城市用地转换的强度,该概率由模拟基期土地利用数据与目标年份土地利用数据的叠加,得出不同类型的非城市用地在此时间段内向城市用地转换的规模,进而确定不同类型的非城市用地向城市用地转换的强度。最后,将以上3种概率的乘积作为元胞转换的概率。通过转换概率与转换阈值的对比判断中心元胞是否在下一个阶段转换为城市用地。经过迭代计算,不断增加城市用地元胞的数量。当模拟城市用地的结果与目标年份的城市用地规模差值在一定的范围内时停止模拟,得出最终结果。模型构建完成后,本文以长株潭城市群核心区为例进行了模拟实验。以2001年该地区的土地利用数据为基期数据,模拟2010年该地区的城市用地规模和空间分布。研究结果表明,根据本文提出的模型模拟的城市扩展结果与真实数据相比具有较高的一致性。模拟结果正确率达到68.66%,比基于传统logistics回归的元胞自动机模型的模拟精度提高了4.25%,Kappa系数为0.675。该模型较好地模拟了长株潭城市群核心区城市扩展,在城市空间扩展模拟中具有较好的适应性与有效性。  相似文献