北京市土地利用空间格局演化模拟及预测

土地利用空间格局的演化模拟可定量地从空间尺度揭示区域土地利用变化的驱动因素,是厘清未来时期内土地变化的重要途径。基于CLUE-S模型,以北京市为研究案例,结合1985、2000和2010年三期土地利用数据,运用Logistic逐步回归方法识别了北京市各种土地利用类型演化的驱动因素,对北京市土地利用空间格局进行模拟。在此基础上,基于北京市社会经济发展、土地利用规划、资源禀赋及生态保护等不同情景,对北京市2020年土地利用空间分布格局进行模拟及预测。结果表明：①不同的时期内,驱动因子对不同土地利用类型的影响呈现差异性,其中交通因素及社会经济因素对土地利用类型的转化率影响较显著,坡度对各个土地利用类型的影响较大。②通过对2010年北京市土地利用变化的模拟结果来看,Kappa指数为87.03%,说明预测结果与实际土地利用情况有较好的一致性。③预测结果显示,北京市的城市发展均表现为继续向外扩展,且以东南、东北为主要扩展方向,但扩张的程度存在差异。     

土地利用变化空间模拟的进展--CLUE-S模型及其应用

土地利用变化的空间模拟是进行土地利用情景分析的重要基础。本文在介绍了国际上常用的细胞自控模型(CA)、土地利用变化及效应模型(CLUE)的基础上,重点分析了小尺度土地利用变化及效应模型(CLUE-S)的方法,并以邯郸地区为例进行了案例研究。认为CLUE-S模型采取经验模型的方法,通过建立土地利用空间分配和驱动因子之间的统计关系模拟近期土地利用变化的情景。同时也考虑了不同土地利用方式之间的竞争关系,因此可以较好地模拟小尺度地区的近期土地利用变化情景;考虑到短期或近期土地利用变化的因子主要与人类的社会经济活动有关,而社会经济因子的空间化尚存在一定难度。因此,突破这一瓶颈成为CLUE-S模型发展和应用的关键;CLUE-S模型主要解决的是不同空间尺度上的土地利用空间分配问题,在土地生产潜力评价、土地利用规划等方面具有广阔的应用前景。     

CLUE-S模型应用与研究进展

土地利用和覆被变化(LUCC)是全球环境变化研究的主要议题,对于环境管理领域十分重要。土地利用和覆被变化模型有助于人们更好地理解土地利用和覆被变化过程,了解人类决策对土地利用变化的影响,并对未来情景作出预测,确定环境变化的关键地点,是综合环境管理的重要工具。CLUE-S模型是一种适用于中小尺度的、动态的、空间显性的LUCC模型,能够实现基于土地利用需求分析下的全局土地利用类型的同步模拟,自2002年推出以来已在国内外多个研究区得到广泛应用。对CLUE-S模型在LUCC、环境管理、城市空间扩展等领域的应用进行了梳理,并从模型改进、模型耦合、模型尺度与模型参数研究等方面对CLUE-S模型研究进展进行探讨,认为模型存在尺度依赖性、缺乏反馈机制、人类决策因素考虑不足等问题,总结出未来研究应着重考虑开展多尺度模拟,探索CLUE-S模型与其他模型的耦合研究,加深在环境管理领域的应用。     

基于动态邻域思想的ACO-CA城市动态模拟——以重庆市沙坪坝区为例

邻域因子是城市动态变化的重要驱动因子,该文提出了动态邻域约束思想,在借助蚁群优化(ACO)算法提取城市用地转换规则的基础上,结合元胞自动机(CA)模型构建了基于动态邻域约束的ACO-CA城市动态模拟模型,实现了对城市用地的动态模拟,并以重庆市沙坪坝区为例,设计不同方案验证了该模型的有效性。研究结果显示:当采用动态邻域方案时,总的Kappa系数比静态邻域方案高1.70%;城市用地的Kappa系数比采用静态邻域方案时的模拟精度高出6.37%。研究结果表明:构建的基于动态邻域思想的ACO-CA模型能够有效模拟城市用地的动态变化;采用动态邻域约束条件时,尽管算法的复杂度有所增加,但与静态邻域约束方案相比,城市用地模拟精度要高,且更符合城市发展演变规律。     

CLUE-S模型原理与结构及其应用进展

空间模型是土地利用/土地覆被变化研究的重要内容和方法,CLUE-S模型是这类模型中应用较多的一种.该文分析了CLUE-S模型的运行原理和结构组成,指出:CLUE-S模型运用系统论的方法处理不同土地利用类型间的竞争关系,在综合考虑诸多限制因素和转换规则的前提下,通过反复迭代运算,把土地利用变化类型逐步分配到一定的空间单元中,将模拟结果精确直观地反映到空间位置,是一个较好的土地利用变化空间模拟模型.在此基础上,系统阐述了模型5个组成模块的机理结构和6个参数文件的特征,并对参数内涵和设置规则进行了介绍.最后概述了CLUE-S模型在国内外的应用进展,分析了CLUE-S模型的应用前景和领域及模型的不足之处.     

城市元胞自动机扩展邻域效应的测量与校准研究

城市元胞模型由于在定量分析与预测城市动态的潜力而受到众多研究者的持续关注.邻域规则是主导城市元胞模型模拟过程的关键组件.研究表明,不同土地利用组合间存在显著的邻域效应,且邻域效应具有惯性、排斥和吸引等影响.然而,传统城市元胞模型主要考虑的是特定分辨率下较小窗口的邻域范围.本文尝试刻画更大窗口的邻域效应及其对元胞模型的影响.基于测量的扩展邻域因子,应用粒子群优化算法校准大窗口邻域规则,并创建了考虑扩展邻域效应的城市元胞模型.为验证模型有效性,将其应用于模拟厦门市1995-2010年期间的城市扩张动态.与3×3摩尔邻域的逻辑回归模型相比较,1995-2010年期间的建设用地模拟精度从80.7％提高到83.9％,总体精度从87.8％提高到89.6％,Kappa系数从70.0％提高到74.5％,表明考虑扩展邻域效应的城市模型取得了更好的模拟效果.     

基于邻域相关的漓江流域土地利用多情景模拟与景观格局变化

建模和情景分析是土地利用变化研究的核心内容。选择旅游业高速发展的漓江流域为研究区,利用基于邻域相关构建空间权重的Auto-logistic 模型代替传统的Logistic 回归,构建CLUE-S 模型,对研究区2020 年土地利用格局进行多情景模拟,并通过景观格局指数对比分析不同发展情景下的景观格局特征。结果表明：① 利用邻域相关构建空间权重的Auto-logistic模型在预测土地空间分布概率方面较传统的Logistic 模型具有更好的预测能力;② 建设用地和草地高度破碎化、水域面积不稳定是漓江流域景观脆弱性的主要来源;③ 旅游地发展需要稳定、多样的景观格局。一方面要合理控制人为活动主导的用地类型对景观格局的冲击,另一方面应兼顾景观多样性,实现用地在社会经济和生态环境之间的协调。     

基于CLUE_S模型的矿区土地利用变化情景模拟——以徐州市贾汪矿区为例

以徐州市贾汪矿区1986、1996、2006和2016年4期遥感影像为数据源,基于CLUE-S模型,在传统Logistics回归模型的基础上引入空间自相关因子形成Autologistic回归模型,选取政策、自然环境、社会经济和空间约束等因素,对贾汪矿区2016年土地利用空间分布格局进行模拟以检验精度。在此基础上对研究区2026年趋势发展、经济发展和生态保护3种情景下的土地利用空间分布格局进行了模拟。结果表明：1）Autologistic回归模型在土地利用情景模拟过程中能够更好地反映真实的土地利用格局;2）研究区2016－2026年不同情景下,建设用地在3种情景下均呈现明显的增加趋势,未利用地面积持续减少,其中经济发展情景下建设用的增幅最大,生态保护用地情景下建设用地增幅最小,在生态保护情景下,林地、耕地等生态用地受到保护,建设用地的扩展速度被抑制。 关键词：土地利用变化;CLUE-S模型;Autologistic回归模型;情景模拟;贾汪矿区     

胶东地区主要土地利用变化类型与影响因子的关系

土地利用变化驱动机制研究是地理学研究的热点,而研究土地利用变化与影响因子关系是揭示土地利用变化驱动机制的关键。胶东地区是社会经济发展迅速的地区,选取该地区作为土地利用变化与影响因子关系研究区域具有重要的理论和现实意义。把胶东地区20世纪80年代和2000年的土地利用矢量数据相叠加,提取出主要土地利用类型发生变化的区域,统计出各变化类型的面积及其占总变化面积的比例,并构建出土地利用变化样本数据矩阵。把土地利用变化数据与胶东地区的城镇位置、道路、DEM、人口密度、GDP和海岸线位置等数据进行叠加分析,提取出土地利用变化图斑的影响因子,构建出土地利用变化的影响因子矩阵。在此基础上,应用典范对应分析方法(CCA)研究胶东地区土地利用变化与其影响因子的关系。结果显示,城镇化、建设用地扩展和耕地大量减少是该时期胶东地区土地利用变化的主要特点。人口密度、到城镇距离、高程和到主要公路距离是决定胶东地区主要土地利用变化类型分布的主要影响因子,其次为GDP和到海岸线距离。耕地转化为城镇用地和农村居民点转化为城镇用地多分布在人口密度大、GDP高、到城镇距离近和到主要公路距离近的区域;耕地转化为农村居民点多分布在海拔低、到城镇距离较近和到主要公路距离较近的区域。     

不同非空间模拟方法下CLUE-S模型土地利用预测——以秦淮河流域为例

基于快速城镇化背景下秦淮河流域土地利用历史状况,选择CLUE-S模型对其2020年土地利用情况进行模拟预测。分别使用线性回归、Markov模型、灰色GM（1,1）模型预测CLUE-S模型非空间模块的土地利用需求量,再嵌入CLUE-S中得到3种预测结果,对预测结果进行比较。另外设定“自然发展”情景与考虑规划政策影响的“优化格局”情景,模拟2020年不同情景下秦淮河流域土地利用格局情况,并进行景观格局分析。结果表明：线性回归模型、Markov模型、灰色GM（1,1）模型的Kappa指数分别为0.866、0.849、0.867,3种方法均满足模型精度要求;自然发展情景中2020年水域、水田、林地、城镇用地、旱地面积相对于2010年分别变化21.5%、-15.3%、-9.0%、51.5%、-28.9%,而优化格局情景下水域、水田、林地、城镇用地、旱地面积分别变化3.1%、-1.6%、10.8%、6.3%、-10.6%,相比于自然发展情景,优化情景土地利用状况更符合保护基本农田、增加生态用地连通性、提高雨水下渗能力以及缓解城市热岛效应的要求,为后期土地利用规划提供了依据。     

Characteristics of neighborhood interaction in urban land-use changes: A comparative study between three metropolitan areas of Japan

Local spatial interaction between neighborhood land-use categories (i.e. neighborhood interaction) is an important factor which affects urban land-use change patterns. Therefore, it is a key component in cellular automata (CA)-based urban geosimulation models towards the simulation and forecast of urban land-use changes. Purpose of this paper is to interpret the similarities and differences of the characteristics of neighborhood interaction in urban land-use changes of different metropolitan areas in Japan for providing empirical materials to understand the mechanism of urban land-use changes and construct urban geosimulation models. Characteristics of neighborhood interaction in urban land-use changes of three metropolitan areas in Japan, i.e. Tokyo, Osaka, and Nagoya, were compared using such aids as the neighborhood interaction model and similarity measure function. As a result, urban land-use in the three metropolitan areas was found to have had similar structure and patterns during the study period. Characteristics of neighborhood interaction in urban land-use changes are quite different from land-use categories, meaning that the mechanism of urban land-use changes comparatively differs among land-use categories. Characteristics of neighborhood interaction reveal the effect of spatial autocorrelation in the spatial process of urban land-use changes in the three metropolitan areas, which correspond with the characteristics of agglomeration of urban land-use allocation in Japan. Neighborhood interaction amidst urban land-use changes between the three metropolitan areas generally showed similar characteristics. The regressed neighborhood interaction coefficients in the models may represent the general characteristics of neighborhood effect on urban land-use changes in the cities of Japan. The results provide very significant materials for exploring the mechanism of urban land-use changes and the construction of universal urban geosimulation models which may be applied to any city in Japan.     

城市土地利用变化中邻域相互作用的特征——日本三大都市圈的比较研究

Local spatial interaction between neighborhood land-use categories (i.e. neighborhood interaction) is an important factor which affects urban land-use change patterns. Therefore,it is a key component in cellular automata (CA)-based urban geosimulation models towards the simulation and forecast of urban land-use changes. Purpose of this paper is to interpret the similarities and differences of the characteristics of neighborhood interaction in urban land-use changes of different metropolitan areas in Japan for providing empirical materials to understand the mechanism of urban land-use changes and construct urban geosimulation models. Characteristics of neighborhood interaction in urban land-use changes of three metropolitan areas in Japan,i.e. Tokyo,Osaka,and Nagoya,were compared using such aids as the neighborhood interaction model and similarity measure function. As a result,urban land-use in the three metropolitan areas was found to have had similar structure and patterns during the study period. Characteristics of neighborhood interaction in urban land-use changes are quite different from land-use categories,meaning that the mechanism of urban land-use changes comparatively differs among land-use categories. Characteristics of neighborhood interaction reveal the effect of spatial autocorrelation in the spatial process of urban land-use changes in the three metropolitan areas,which correspond with the characteristics of agglomeration of urban land-use allocation in Japan. Neighborhood interaction amidst urban land-use changes between the three metropolitan areas generally showed similar characteristics. The regressed neighborhood interaction coefficients in the models may represent the general characteristics of neighborhood effect on urban land-use changes in the cities of Japan. The results provide very significant materials for exploring the mechanism of urban land-use changes and the construction of universal urban geosimulation models which may be applied to any city in Japan.     

基于GIS的城市用地布局多情景模拟与方案评价——以江苏省太仓市为例

用地布局一直是城市总体规划中的关键难题,以往的规划方案多凭借规划师的经验判断、简单的图层叠加和有限的公众参与生成。然而新的城乡规划法要求从多角度综合考虑城乡用地布局问题,更加注重规划过程的科学性和准确性,也就特别需要有一种适应多情景分析下的城市用地布局模拟与方案评价方法。文章以江苏省太仓市为例,借助GIS技术的强大空间分析功能,探讨了这种方法的技术流程：① 参考已有的大尺度城市未来模型,结合我国特别是研究区的区域特点,构建一个实用的城市未来模型(Urban Future Model,UFM);② 通过用地评价、战略归纳、情景模拟等步骤,生成若干个可选的用地布局模拟方案;③ 应用多目标达成矩阵法从粮食、生态、灾害等多个角度对这些方案进行综合评价,并根据评价结果选择一个最佳方案作为本轮总规用地布局的规划参考方案。     

Simulating urban land-use changes at a large scale by integrating dynamic land parcel subdivision and vector-based cellular automata

Cellular automata (CA) have been widely used to simulate complex urban development processes. Previous studies indicated that vector-based cellular automata (VCA) could be applied to simulate urban land-use changes at a realistic land parcel level. Because of the complexity of VCA, these studies were conducted at small scales or did not adequately consider the highly fragmented processes of urban development. This study aims to build an effective framework called dynamic land parcel subdivision (DLPS)-VCA to accurately simulate urban land-use change processes at the land parcel level. We introduce this model in urban land-use change simulations to reasonably divide land parcels and introduce a random forest algorithm (RFA) model to explore the transition rules of urban land-use changes. Finally, we simulate the land-use changes in Shenzhen between 2009 and 2014 via the proposed DLPS-VCA model. Compared to the advanced Patch-CA and RFA-VCA models, the DLPS-VCA model achieves the highest simulation accuracy (Figure-of-Merit = 0.232), which is 32.57% and 18.97% higher respectively, and is most similar to the actual land-use scenario (similarity = 94.73%) at the pattern level. These results indicate that the DLPS-VCA model can both accurately split the land during urban land-use changes and significantly simulate urban expansion and urban land-use changes at a fine scale. Furthermore, the land-use change rules that are based on DPLS-VCA mining and the simulation results of several future urban development scenarios can act as guides for future urban planning policy formulation.     

基于logistic回归模型的农牧交错区土地利用变化驱动力分析——以内蒙古翁牛特旗为例

本文以农牧交错带的典型区域——内蒙古翁牛特旗为例,考虑土地利用变化过程的空间变量,建立了不同土地利用变化过程的logistic回归模型。结果表明:模型中转为耕地的主要解释变量是到农村居民点的距离和农业气候区;转为草地的主要解释变量是到农村居民点的距离、土壤表层有机质含量和到乡镇中心的距离;转为林地的主要解释变量是到农村居民点的距离和海拔;空间异质性和土地利用变化过程的时间变量共同影响着使用logistic回归模型来解释土地利用变化驱动力的能力;通过对草地logistic回归模型的检验,得出空间统计模型能较好地揭示不同土地利用变化过程的主要驱动力及其作用机理。     

Mining transition rules of cellular automata for simulating urban expansion by using the deep learning techniques

Along with the gradually accelerated urbanization process, simulating and predicting the future pattern of the city is of great importance to the prediction and prevention of some environmental, economic and urban issues. Previous studies have generally integrated traditional machine learning with cellular automaton (CA) models to simulate urban development. Nevertheless, difficulties still exist in the process of obtaining more accurate results with CA models; such difficulties are mainly due to the insufficient consideration of neighborhood effects during urban transition rule mining. In this paper, we used an effective deep learning method, named convolution neural network for united mining (UMCNN), to solve the problem. UMCNN has substantial potential to get neighborhood information from its receptive field. Thus, a novel CA model coupled with UMCNN and Markov chain was designed to improve the performance of simulating urban expansion processes. Choosing the Pearl River Delta of China as the study area, we excavate the driving factors and the transformational relations revealed by the urban land-use patterns in 2000, 2005 and 2010 and further simulate the urban expansion status in 2020 and 2030. Additionally, three traditional machine-learning-based CA models (LR, ANN and RFA) are built to attest the practicality of the proposed model. In the comparison, the proposed method reaches the highest simulation accuracy and landscape index similarity. The predicted urban expansion results reveal that the economy will continue to be the primary factor in the study area from 2010 to 2030. The proposed model can serve as guidance in urban planning and government decision-making.     

LUCC驱动力模型研究综述

驱动力研究是土地利用变化研究中的核心问题。土地利用变化驱动力模型是分析土地利用变化原因和结果的有力工具，模型通过情景分析可为土地利用规划与决策提供依据。基于不同理论的驱动力研究方法很多，论文选取了几种国内外应用较多的LUCC驱动力模型进行综述，分析了每个模型的优缺点及适用范围，最后得出结论：1) 基于过程的动态模型更适于研究复杂的土地利用系统。2) 基于经验的统计模型能弥补基于过程的动态模型的不足。3) 基于不同学科背景的模型进一步集成将是LUCC驱动力模型未来的发展趋势。     

Modeling urban land-use dynamics in a fast developing city using the modified logistic cellular automaton with a patch-based simulation strategy

Cellular automata (CA) have been used to understand the complexity and dynamics of cities. The logistic cellular automaton (Logistic-CA) is a popular urban CA model for simulating urban growth based on logistic regression. However, this model usually employs a cell-based simulation strategy without considering the spatial evolution of land-use patches. This drawback largely constrains the Logistic-CA for simulating realistic urban development. We proposed a Patch-Logistic-CA to deal with this problem by incorporating a patch-based simulation strategy into the conventional cell-based Logistic-CA. The Patch-Logistic-CA differentiates new developments into spontaneous growth and organic growth, and uses a moving-window approach to simulate the evolution of urban patches. The Patch-Logistic-CA is tested through the simulation of urban growth in Guangzhou, China, during 2005–2012. The cell-based Logistic-CA was also implemented using the same set of data to make a comparison. The simulation results reflect that the Patch-Logistic-CA has slightly lower cell-level agreement than the cell-based Logistic-CA. However, visual inspection of the results reveals that the cell-based Logistic-CA fails to reflect the actual patterns of urban growth, because this model can only simulate urbanized cells around the edges of initial urban patches. Actually, the pattern-level similarities of the Patch-Logistic-CA are over 18% higher than those of the cell-based Logistic-CA. This indicates that the Patch-Logistic-CA has much better performance of simulating actual development patterns than the cell-based Logistic-CA. In addition, the Patch-Logistic-CA can correctly simulate the fractal structure of actual urban development patterns. By varying the control parameters, the Patch-Logistic-CA can also be used to assist urban planning through the exploration of different development alternatives.     

Allocation of urban land uses by Multi-Objective Particle Swarm Optimization algorithm

Considering the ever-increasing urban population, it appears that land management is of major importance. Land uses must be properly arranged so that they do not interfere with one another and can meet each other's needs as much as possible; this goal is a challenge of urban land-use planning. The main objective of this research is to use Multi-Objective Particle Swarm Optimization algorithm to find the optimum arrangement of urban land uses in parcel level, considering multiple objectives and constraints simultaneously. Geospatial Information System is used to prepare the data and to study different spatial scenarios when developing the model. To optimize the land-use arrangement, four objectives are defined: maximizing compatibility, maximizing dependency, maximizing suitability, and maximizing compactness of land uses. These objectives are characterized based on the requirements of planners. As a result of optimization, the user is provided with a set of optimum land-use arrangements, the Pareto-front solutions. The user can select the most appropriate solutions according to his/her priorities. The method was tested using the data of region 7, district 1 of Tehran. The results showed an acceptable level of repeatability and stability for the optimization algorithm. The model uses parcel instead of urban blocks, as the spatial unit. Moreover, it considers a variety of land uses and tries to optimize several objectives simultaneously.     

Self-modifying CA model using dual ensemble Kalman filter for simulating urban land-use changes

There are many different methods to calibrate cellular automata (CA) models for better simulation results of urban land-use changes. However, few studies have been reported on combination of parameter update and error control using local data in CA calibration procedures. This paper presents a self-modifying CA model (SM-CA) that uses the dual ensemble Kalman filter (dual EnKF), which enables the CA model to simultaneously update model parameters and simulation results by merging observation data (local data). We applied the proposed model to simulate urban land-use changes in a 13-year period (1993–2005) in Dongguan City, a rapidly urbanizing region in south China. Simulation results indicate that this model yields better simulation results than the conventional logistic-regression CA and decision-tree CA models. For example, the validation is carried out using cross-tabulation matrix. The simulation results of SM-CA have allocation disagreement of 10.18%, 19.64%, and 30.03% in 1997, 2001, and 2005, respectively, which are 2.12%, 2.47%, and 6% lower than conventional logistic-regression CA models.