土地利用变化模拟模型及应用研究进展

元胞自动机CA(Cellular Automata)和多智能体ABM(Agent-Based Model)模型是土地利用格局和演化模拟的主流方法,两者在模拟自然因素影响和人文驱动机制方面具有突出优势,为LUCC研究提供了重要的工具。当前,ABM无论在模型构建还是应用研究方面,CA和ABM均取得了显著进展。论文从数据基础、模拟尺度、CA转换规则挖掘、ABM行为规则定义、CA和ABM的耦合4个方面梳理土地利用模拟模型和方法的研究进展。并总结这些模型在虚拟城市模拟与理论验证、真实城市模拟与规划预测以及多类用地模拟与辅助决策等方面的应用。最后,总结土地利用模拟模型在精细模拟和全球变化研究方面存在的局限性,认为未来发展将主要集中于解决从2维模型向3维模型发展、大数据与规则精细挖掘以及大尺度模拟与知识迁移等问题。     

Modelling coastal land use change by incorporating spatial autocorrelation into cellular automata models

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.     

Simulation of landscape spatial layout evolution in rural-urban fringe areas: a case study of Ganjingzi District

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.     

Dynamic land use change simulation using cellular automata with spatially nonstationary transition rules

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.     

基于支持向量机的元胞自动机及土地利用变化模拟

提出了利用遥感数据，并采用支持向量机来确定元胞自动机非线性转换规则的新方法。元胞自动机在模拟复杂地理现象时，需要采用非线性转换规则。目前元胞自动机主要采用线性方法来获取转换规则，在反映复杂的非线性地理现象时有一定的局限性。以城市扩张的模拟为例，将模拟城市系统的主要特征变量映射到Hilbert空间后，通过SVM建立最优分割超平面，分割超平面的分类决策函数由径向基核（Radial Basis Kernel）构造。利用历史遥感数据校正超平面的决策函数，确定城市元胞自动机的非线性转换规则，计算出城市发展概率。利用所提出的方法，对深圳市1988-2010年的城市发展进行了模拟，取得了较理想的模拟效果。研究结果表明，基于SVM-CA模型的模拟精度比传统MCE方法模拟精度高，MoranⅠ指数与实际更为接近。     

基于ECA-GIS模型的土地利用时空变化模拟方法

在城市土地利用认知基础上,本文将ECA-GIS模型的时空数据模型应用于城市土地利用演变模拟中,论述了ECA-GIS模型在模拟土地利用时空演变上的规则,并设计了基于ECA-GIS模型的城市土地利用演化模型,研究了ECA-GIS模型在土地利用演变上的模拟方法和过程。     

A grey wolf optimizer–cellular automata integrated model for urban growth simulation and optimization

This article proposes a grey wolf optimizer (GWO) and cellular automata (CA) integrated model for the simulation and spatial optimization of urban growth. A new grey wolf‐inspired approach is put forward to determine the urban growth rules of CA cells by using the GWO algorithm, which is suitable for solving optimization problems. The inspiration for GWO comes from the social leadership of wolf groups, as well as their hunting behavior. The GWO‐optimized urban growth rules for CA describe the relationship between the spatial variables and the urban land‐use status for each cell in the formation of “if–then.” The GWO algorithm and CA model are then integrated as the GWO–CA model for urban growth simulation and optimization. By taking Nanjing City as an example, the simulation accuracy in terms of urban cells is 86.6%, and the kappa coefficient is 0.715, indicating that the GWO algorithm is efficient at obtaining urban growth rules from spatial variables. The validation of the GWO–CA model also illustrates that it performs well in terms of the simulation and spatial optimization of urban growth, and can further contribute to urban planning and management.     

Calibration of a Multi-criteria Evaluation Based Cellular Automata Model for Indian Cities Having Varied Growth Patterns

The study aims to investigate the efficiency of Cellular Automata (CA) based models for simulation of urban growth in two Indian cities (Dehradun and Saharanpur) having different growth patterns. The transition rules in the CA model were defined using Multi-Criteria Evaluation technique. The model was calibrated by varying two parameters namely the neighbourhood (type and size) and model iterations. The model results were assessed using two measures, i.e., percent correct match and Moran’s Index. It was found that for Dehradun, which had a dispersed growth pattern, Von Neumann neighbourhood of small size produced the highest accuracy, in terms of pattern and location of simulated urban growth. For Saharanpur, which had a compact growth pattern, large neighbourhoods, produced the most optimum results, irrespective of the type of neighbourhood. For both study areas, large number of model iterations failed to increase the accuracy of urban growth assessment.     

Simulating urban growth in a megalopolitan area using a patch‐based cellular automata

Although traditional cellular automata (CA)‐based models can effectively simulate urban land‐use changes, they typically ignore the spatial evolution of urban patches, due to their use of cell‐based simulation strategies. This research proposes a new patch‐based CA model to incorporate a spatial constraint based on the growth patterns of urban patches into the conventional CA model for reducing the uncertainty of the distribution of simulated new urban patches. In this model, the growth pattern of urban patches is first estimated using a developed indicator that is based on the local variations in existing urban patches. The urban growth is then simulated by integrating the estimated growth pattern and land suitability using a pattern‐calibrated method. In this method, the pattern of new urban patches is gradually calibrated toward the dominant growth pattern through the steps of the CA model. The proposed model is applied to simulate urban growth in the Tehran megalopolitan area during 2000–2006–2012. The results from this model were compared with two common models: cell‐based CA and logistic‐patch CA. The proposed model yields a degree of patch‐level agreement that is 23.4 and 7.5% higher than those of these pre‐existing models, respectively. This reveals that the patch‐based CA model simulates actual development patterns much better than the two other models.     

An Artificial-Neural-Network-based,Constrained CA Model for Simulating Urban Growth

Insufficient research has been done on integrating artificial-neural-network-based cellular automata (CA) models and constrained CA models, even though both types have been studied for several years. In this paper, a constrained CA model based on an artificial neural network (ANN) was developed to simulate and forecast urban growth. Neural networks can learn from available urban land-use geospatial data and thus deal with redundancy, inaccuracy, and noise during the CA parameter calibration. In the ANN-Urban-CA model we used, a two-layer Back-Propagation (BP) neural network has been integrated into a CA model to seek suitable parameter values that match the historical data. Each cell's probability of urban transformation is determined by the neural network during simulation. A macro-scale socio-economic model was run together with the CA model to estimate demand for urban space in each period in the future. The total number of new urban cells generated by the CA model was constrained, taking such exogenous demands as population forecasts into account. Beijing urban growth between 1980 and 2000 was simulated using this model, and long-term (2001–2015) growth was forecast based on multiple socio-economic scenarios. The ANN-Urban-CA model was found capable of simulating and forecasting the complex and non-linear spatial-temporal process of urban growth in a reasonably short time, with less subjective uncertainty.     

人口密度模型与CA集成的城市化时空模拟实验

有机地集成城市经典模型与元胞自动机 (CA)是一种有意义的理论实验。本文分无约束和有约束两种条件构建了城市化CA模型 ,推导出了基于均质地理背景和孤立城市的假设的城市人口密度时空模型 ,并进行了二者集成的实验研究 ,得出了如下结论 :(1)CA是城市化时空模拟的有效方法 ;(2 )经典的地理、城市模型可以有效地集成到城市化CA模型中 ,起到控制城市化轨迹的基本作用 ,在某种程度上能够弥补CA建模过于简单的不足。     

元胞自动机（CA）模型在土地利用领域的研究综述

通过模型对区域土地利用/覆盖变化（LUCC）进行分析已经成为了当前全球的研究主要内容之一。元胞自动机（CA）模型是一种通过定义局部的简单的计算规则来模拟和表示整个系统中复杂现象的时空动态模型,其＂自下而上＂的研究思路,强大的复杂计算功能及高度动态,使得它在模拟空间复杂系统的时空动态演变方面具有很强的能力。CA模型通过与其他模型相结合,在综合考虑各种限制因素和转换规则的前提下,通过反复迭代综合空间分析与非空间分析,模拟土地利用变化情景,在国内外已经形成了较为成熟的研究模型。本文首先提出了CA模型在土地利用变化中应用的背景及其特点;然后,分析了CA模型的构成原理以及在国内外的应用进展与现状;最后,详细阐述了CA模型在土地利用变化中的发展趋势及今后研究工作中应注意的问题。     

耦合遥感观测和元胞自动机的城市扩张模拟

在传统元胞自动机(CA)模型中,静态的模型参数和模型误差不能释放是影响城市扩张模拟效果的两个重要原因。文中引入集合卡尔曼滤波方法到CA模型中,提出了基于联合状态矩阵的地理元胞自动机。该模型在模拟过程中可以通过同化遥感观测数据,动态地调整模型参数和纠正模拟结果,使模型参数能够反映转换规则的时空变化,同时也能较好地释放积累的模型误差。将模型应用于东莞市的城市扩张模拟中,实验结果表明,模型能够准确地调整模型参数使之符合城市发展模式,同时也能有效地控制模型误差,其模拟的空间格局与真实情况吻合。     

Integrating the multi-label land-use concept and cellular automata with the artificial neural network-based Land Transformation Model: an integrated ML-CA-LTM modeling framework

Cellular automata (CA) and artificial neural networks (ANNs) have been used by researchers over the last three decades to simulate land-use change (LUC). While conventional CA and ANN models assign a cell to only one land-use class, in reality, a cell may belong to several land-use classes simultaneously. The recently developed multi-label (ML) concept overcomes this limitation in land change science. Although the ML concept is a new paradigm with nonexclusive classes and has shown considerable merit in several applications, few studies in land change science have applied it. In addition, determining transition rules in conventional CA is difficult when the number of drivers is large. Since CA has been shown as a potential model to consider neighborhood effects and ANN has been shown effective in determining CA transition rules, we integrated both CA with an ANN model to overcome limitations of each tool. In this study, we specifically extended the ANN-based Land Transformation Model (LTM) with both a CA-based model and the ML concept to create an integrated ML-CA-LTM modeling framework. We also compared, using standard evaluation measures, differences between the proposed integrated model with a conventional CA-based LTM model (called the ml-CA-LTM). Parameterization was made using a learning and testing procedure common in machine learning. Results showed that the modified LUC model, ML-CA-LTM, produced consistently better goodness of fit calibration values compared to the ml-CA-LTM. The outcome of this modified model can be used by managers and decision makers for improved urban planning.     

集成GIS和细胞自动机模型进行地理时空过程模拟与预测的新方法

目前商用地理信息系统（GIS）不能完整地表达地理实体的时态信息和时空关系，缺乏时空分析和时空动态模拟的能力，这已成为GIS界的一个共识，然而，未来GIS在各应用领域的深入发展以及在实现“数字地球”战略过程中，都要求发展新的时空分析和模拟方法，细胞自动机（Cellular Automaton)是一种“自下而上”的动态模拟建模框架，具有模拟地理复要系统时空演化过程的能力，首先将标准CA模型的4元组进行扩展以满足GIS环境下时空动态模型的要求，然后以城市土地利用演化这一动态过程为例，建立了土地利用演化动态模拟与预测模型（LESP），最后运用此模型对包头市城市扩展和土地可持续利用演化进行了比较成功的模拟和预测。     

利用生物地理学优化算法获取城市扩展元胞自动机模型参数

提出了一种基于生物地理学优化算法寻找城市扩展元胞自动机（cellular automata,CA）模型最佳参数的方法。转换规则制定及相应权重参数获取是构建城市扩展CA的核心和难点。生物地理学优化算法（biogeography-based optimization,BBO）通过模拟生物物种在栖息地的分布、迁移和灭绝来求解优化问题。利用BBO算法自动获取城市扩展CA模型参数值,构建BBO-CA模型进行城市扩展模拟实验,并与粒子群算法（particle swarm optimization,PSO）、蚁群算法（ant colony optimization,ACO）、遗传算法（genetic algorithm,GA）及逻辑回归（logistic regression,LR）等方法相比较。结果表明,BBO算法具有较好的收敛性,可有效地快速自动寻找城市扩展CA模型最佳参数组合,获取的空间变量权重参数较为合理;BBO-CA模型明显提升了城市用地模拟精度,城市用地模拟精度为72.5%,相对PSO、ACO、GA、LR各算法分别提升了1.1%、1.2%、2.7%和4.0%,Kappa系数达到0.700,分别提升了0.015、0.016、0.034和0.046,且整体空间布局与实际情况更为接近,验证了应用BBO算法的可行性与优势。     

Integrating the system dynamic and cellular automata models to predict land use and land cover change

Land use and land cover change (LULCC) is a widely researched topic in related studies. A number of models have been established to simulate LULCC patterns. However, the integration of the system dynamic (SD) and the cellular automata (CA) model have been rarely employed in LULCC simulations, although it allows for combining the advantages of each approach and therefore improving the simulation accuracy. In this study, we integrated an SD model and a CA model to predict LULCC under three future development scenarios in Northern Shanxi province of China, a typical agro-pastoral transitional zone. The results indicated that our integrated approach represented the impacts of natural and socioeconomic factors on LULCC well, and could accurately simulate the magnitude and spatial pattern of LULCC. The modeling scenarios illustrated that different development pathways would lead to various LULCC patterns. This study demonstrated the advantages of the integration approach for simulating LULCC and suggests that LULCC is affected to a large degree by natural and socioeconomic factors.     

基于CA模型的佛山市建设用地动态变化研究

元胞自动机具有能模拟复杂动态系统的强大能力,本文采用了多约束条件的元胞自动机模型,以广东佛山市2000年、2006年和2012年建设用地的变化为例,从自然、社会经济发展等方面综合考虑选取了高程、坡度、人口密度、道路交通、水系等对城市建设用地发展变化起决定作用的诸多因子,利用马尔科夫概率矩阵计算2000年～2006年建设用地变化,推算建设用地转移总量。结合Logistic-CA模型和决策树-CA模型,预测模拟了2012年的建设用地分布并与实际相比较,分析其整体精度和误差来源。结果显示基于CA模型的建设用地动态发展模拟具有良好的效果,可以为城市的发展规划,过程演变提供虚拟的实验手段和科学依据。     

Cellular Automata Based Model of Urban Spatial Growth

In the study reported in this paper an attempt has been made to develop a Cellular Automata (CA) model for simulating future urban growth of an Indian city. In the model remote sensing data and GIS were used to provide the empirical data about urban growth while Markov chain process was used to predict the amount of land required for future urban use based on the empirical data. Multi-criteria evaluation (MCE) technique was used to reveal the relationships between future urban growth potential and site attributes of a site. Finally using the CA model, land for future urban development was spatially allocated based on the urban suitability image provided by MCE, neighbourhood information of a site and the amount of land predicted by Markov chain process. The model results were evaluated using Kappa Coefficient and future urban growth was simulated using the calibrated model     

利用CA模型进行城市空间增长动力的研究--以武汉市主城空间增长过程分析为例

提出了基于扩散生长的状态型CA模型、基于轴线生长的交通型CA模型和基于优势驱动的环境型CA模型等三种CA模型，设定了包围填充、扩散生长、交通延伸、交通连接、交通吸引、优势生长等6种转换规则。模拟结果表明，武汉市主城整体上呈现出“摊大饼”的发展态势，并且扩散到城市地区，近郊优势增长十分明显，导致了大规模的郊区化，呈现出城乡一体化的发展态势。