基于元胞自动机的崂山林火蔓延模拟

采用流行的林火蔓延模型结合元胞自动机理论,对崂山地区进行了给定气象、地形等参数条件下的林火蔓延情况模拟,模拟过程综合考虑了林火在地形、风、植被等因素影响下的蔓延扩散速度及元胞燃烧状态,最后对模拟蔓延结果的可靠性进行了分析.     

利用元胞自动机作用域构建林火蔓延模型

在现有的基于元胞自动机的林火蔓延模型的基础上,提出了元胞作用域的概念,将元胞自身属性与外界影响通过作用域的概念归纳为一个整体。建立的基于元胞作用域的林火蔓延模型不仅包括中心元胞的属性,如可燃物类型、气温等,还将邻居元胞的点燃系数、距离系数、风作用项及地形作用项纳入考虑范畴,其中距离系数以基于地图代数的障碍距离为基础,地形作用项则以DEM为基础求取。模拟结果表明,基于元胞作用域的林火蔓延模型能够真实地反映现实世界林火蔓延的过程。     

一种耦合元胞自动机的改进林火蔓延仿真算法

针对传统林火蔓延仿真模型在模拟大范围森林火灾时误差大和效率低的问题,对文献[10]的林火模型添加时间修正来提升林火蔓延模拟的准确性,提出耦合地理元胞自动机模拟林火蔓延过程的仿真算法。分析了元胞自动机时间步长对模拟精度的影响,优化时间步长选择,提高了模拟大规模森林火灾的精度及效率。以模拟2006年5月大兴安岭林区森林大火蔓延过程为例验证本算法,发现地理元胞自动机算法中时间步长取整个元胞完全燃烧所需时间的1/8效果最好,林火蔓延模拟结果与实际TM影像解译的火情时空一致性较高,Kappa系数平均为0.6352,准确率平均为87.89%。算法可用于实际林火蔓延过程的重现及趋势预测,且算法可逆。     

耦合Rothermel模型与粒子系统的林火蔓延模拟方法北大核心CSCD

科学准确地模拟分析森林火灾蔓延动态对防灾减灾救灾工作具有重要意义。现有的林火蔓延模拟方法在林火蔓延计算和可视化表达上,耦合程度低且难以将动态物理模型计算结果实时可视化表达。针对这一问题,本文在综合考虑国内外各种火灾蔓延模型优缺点的基础上,选取应用广泛的Rothermel模型作为物理模型。通过惠更斯理论优化了火灾演进范围边界点割裂的不足,采取着火点密度阈值控制种子点数量与模拟可视化效率的平衡;利用布尔运算提高多着火点蔓延范围计算效率,将火灾模型与Open Scene Graph的粒子系统进行紧密耦合,完成火灾演进可视化表达。本文方法实现了对森林火灾蔓延的精确计算和实时、逼真模拟,为灾害应急部门提供信息化支撑。     

林火风险、过程与评估遥感模型与方法研究

世界范围人口数量的急剧增长及人类社会的快速工业化,导致森林生态系统的安全性面临严重威胁。特别是由于人为因素、全球气候变化和政府政策等多种因素共同导致的森林火灾在全球范围内频繁爆发,对森林生态系统和人民生命财产的安全产生着重要影响。因此,基于遥感模型与方法,开展森林火灾风险、过程与评估的相关研究,揭示其时空特征规律,进而制定合理、有效的森林火灾防控和管理措施,已成为本领域当今全球相关研究人员共同面临的科学挑战之一。为此,本文在深入分析目前常规林火风险预测方法相关研究进展的基础上,构建了定量集成基于遥感数据多种指标变量的林火风险预测方法;在传统元胞自动机理论框架下,提出适用于多特征区域林火蔓延过程模拟的元胞自动机模型;在分析目前单时相、差分、相对差分类型光谱指数精度的基础上,构建了基于地表温度和植被特征的新型林火烈度综合评估指数,并从提高林火烈度空间分布制图精度的角度,构建了基于支持向量回归的林火烈度空间分布制图模型。以期为相关的科学研究和应用管理提供一定的理论基础和决策支持。     

平行系统支撑的林火仿真与协同灭火研究

为了能够为突发的森林火灾提供科学预案与精准决策,本文以时空大数据为基础,提出了以平行系统为骨架,以广域HLA协议为准则,在搭建林火发生、蔓延及灭火等全过程模拟与应急演练模型设计基础上,采用粒子系统和修正的Rothermel模型分别进行林火及其蔓延过程模拟,在对各对象类和交互类设计的基础上,完成了应急模拟与协同演练的原型系统开发,并以漳浦县为例,对该系统进行验证。结果表明,平行系统的引入与时空大数据的耦合可以很好地克服模拟场景与实时场景的关键脱节,实测应急响应模块的导入,能够为实时灭火决策提供高精准信息化服务,使得该协同模拟与应急演练更具实用性与推广性。     

水系元胞自动机的设计与实现

水系元胞自动机模型是应用元胞自动机理论对水系的形成与发展的随机过程进行模拟的模型。本文使用DEM作为基础数据,针对水系元胞自动机模型的建模过程和模型结构,详细阐述了将其抽象为计算机中具体的数据模型,以及使用C#程序设计语言实现该模型对水系进行模拟的具体过程,模拟了南流江流域局部地区的水系发展过程,为元胞自动机与水文学研究的结合提供了参考。     

基于SDOG-ESSG的地球系统元胞自动机框架及其应用试验

元胞自动机结构简单且具备模拟复杂系统的能力,已被广泛应用于大气、流体力学、地球物理等领域。然而,现有元胞自动机以欧氏空间为约束进行地球系统过程模拟,忽略了地球重力等天然约束,导致计算过程中元胞状态的传递方向与真实运动的趋势方向不相符,一定程度上扭曲了最终的模拟结果。本文提出了地球系统元胞自动机这一概念,并从元胞表达及构建、邻居模型等方面设计了基于SDOG-ESSG格网的地球系统元胞自动机框架。由于演化规则取决于不同的应用,因此本文进一步以地壳的热传递为例,通过对热力学方程离散化设计了相应的演化规则。最后,借助公开的数据源开展了地壳热传递元胞自动机模拟的初步试验,并与一定区域下的数值模拟结果展开了比对。试验表明,与数值模拟方法相比,本文方法的模拟结果相对误差控制在27%以内,具备一定程度的可行性,可作为地球系统过程模拟的一种新思路。     

基于CA的时空过程模拟建模方法

对现有GIS数据模型的局限性、时空过程模拟模型与GIS的结合进行了分析。介绍了元胞自动机的特点及连续型元胞自动机模型 ,并结合地震动模拟对二维连续型元胞自动机进行了实例分析     

GIS支持下森林火场蔓延的空间模拟

林火扩展具有时间和空间特征。对火场模拟必须建立在空间背景数据库之上，即在地理信息系统和遥感技术的辅助下完成。森林火灾的发生发展有内在和外在因素的影响，内在因素包括了可燃物类型、可燃物湿度、地形，考虑这些因素形成了像元尺度植被燃烧基本历时图；外在因素主要是气候的影响，文中利用生成的植被起火概率图控制像元是否起火。在火场扩展中将Ｒｏｔｈｅｒｍｅｌ模型和经验模型结合使用，并考虑了坡度和风向带方向的因素的影响。最后利用迷宫算法在计算机上实现了火场在地理信息系统支持下的空间蔓延动态模拟。     

Fire danger assessment in Iran based on geospatial information

Fire danger assessment is a vital issue to alleviate the impacts of wildland fires. In this study, a fire danger assessment system is proposed, which extensively uses geographical databases to characterize the spatial variations of fire danger conditions in Iran. This assessment requires three steps: (i) generation of the required input variables, (ii) methods to integrate those variables for creating synthetic indices and (iii) validation of those indices versus fire occurrence data. This fire danger model is based on previous works but adapted to Iranian conditions. It includes an estimation of the fire ignition potential (both considering human and climatic factors) and fire propagation potential. The former was generated from a logistic regression approach based on a wide range of input variables. The fire propagation probability was estimated from the Flammap fire behavior model. A first stage for validation of our fire danger system was based on comparing the estimated danger values to actual fire occurrence, based on satellite detected active fires and burned areas. The logistic regression model for fire ignition probability estimated 72.7% of true ignitions. Detected hotspots occurred more frequently in areas with higher fire ignition probability (average value: 0.65) than non hotspots (average value: 0.4). Propagation probability showed higher values for areas with higher proportion of burned area (r = 0.68, p < 0.001).     

顾及地理网络特征的城市消防站布局渐进优化

基于地理网络模型与GIS技术，提出了顾及地理网络特征的城市消防站布局渐进优化方法，实现了城市新建消防站的选址规划及城市消防站的整体布局优化。实例研究表明了该方法的科学性和合理性。     

基于GIS的奥运场馆消防灭火数字预案系统

分析了奥运场馆消防灭火数字预案系统的体系结构和数据库设计,阐述了系统的关键技术,即组件式GIS、火灾模型与GIS集成和模型的GIS表达,实现了火灾接警定位、火灾模拟与评估以及应急处置等功能.     

Multi-year MODIS active fire type classification over the Brazilian Tropical Moist Forest Biome

ABSTRACT

The Brazilian Tropical Moist Forest Biome (BTMFB) spans almost 4 million km² and is subject to extensive annual fires that have been categorized into deforestation, maintenance, and forest fire types. Information on fire types is important as they have different atmospheric emissions and ecological impacts. A supervised classification methodology is presented to classify the fire type of MODerate resolution Imaging Spectroradiometer (MODIS) active fire detections using training data defined by consideration of Brazilian government forest monitoring program annual land cover maps, and using predictor variables concerned with fuel flammability, fuel load, fire behavior, fire seasonality, fire annual frequency, proximity to surface transportation, and local temperature. The fire seasonality, local temperature, and fuel flammability were the most influential on the classification. Classified fire type results for all 1.6 million MODIS Terra and Aqua BTMFB active fire detections over eight years (2003–2010) are presented with an overall fire type classification accuracy of 90.9% (kappa 0.824). The fire type user’s and producer’s classification accuracies were respectively 92.4% and 94.4% (maintenance fires), 88.4% and 87.5% (forest fires), and, 88.7% and 75.0% (deforestation fires). The spatial and temporal distribution of the classified fire types are presented and are similar to patterns reported in the available recent literature.     

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.     

空间染色镶嵌模型理论与应用

地理空间数据模型及其对地理对象-空间关系的描述是地理信息科学（GIS）理论与应用研究的核心内容。介绍和讨论了一种新的GIS空间数据模型—空间染色模型（spatial chromatic model,SCM）的结构、操作、应用及其在地理学上的含义。SCM的特点是其每个基础数据单元（细胞）具有一个唯一的编码,这些编码提供了面向各种GIS空间分析和运算的丰富信息。SCM的本质是一种基于实体第一性的相对性地理空间,它有助于从一个新的视角更加深入地理解地理实体-空间关系。     

基于网络Voronoi图启发式和群智能的最大覆盖空间优化

提出一种基于网络Voronoi面域图的最大覆盖选址模型及相应的粒子群优化方法,并应用于城市响应时间敏感型公共服务设施的空间优化。本文考虑设施功能沿交通网络辐射以及需求非均匀分布情形,对设施在网络连续空间上进行布局优化,选址模型采用网络Voronoi面域图划分布局设施的功能辐射域,以启发空间优化最小化重叠覆盖。模型同时顾及了设施利用率的最大化,规定设施对给定距离以内的需求实行的完全服务覆盖和对给定距离以外的需求实行随距离衰减的部分服务覆盖。本研究提出基于遗传机制和广义Voronoi图改进的粒子群算法以提高其空间优化性能,通过对南京市消防站最大覆盖空间优化实验表明,该研究取得了较为理想的结果,可应用于城市化区域应急设施最大覆盖空间优化。     

Integrating GIS‐Based Geo‐Atom Theory and Voxel Automata to Simulate the Dispersal of Airborne Pollutants

Environmental processes are usually conceptualized as complex systems whose dynamics are best understood by examining the relationships and interactions of their constituent parts. The cellular automata paradigm, as a bottom‐up modeling approach, has been widely used to study the macroscopic behavior of these complex natural processes. However, the cellular automata models are largely restricted to the two‐dimensional spatial perspective even though the process dynamics they represent evolve in the three spatial dimensions. The objective of this study is to develop a voxel‐based automata approach for modeling the propagation of airborne pollutants in three‐dimensional space over time. The GIS‐based geo‐atom theory was used to manage the data within the automaton. The simulation results indicate the model has the capability to generate effective four‐dimensional (4D) simulations from simple transition rules that describe the processes of particle advection and diffusion. The application of voxel‐based automata and the geo‐atom concepts allows for a detailed 4D analysis and tracking of the changes in the voxel space at every time‐step. The proposed modeling approach provides new means to examine the relationships between pattern and process in 4D.     

A stochastic model for active fire detection using the thermal bands of MODIS data

Active fire detection using satellite thermal sensors usually involves thresholding the detected brightness temperature in several bands. Most frequently used features for fire detection are the brightness temperature in the 4-/spl mu/m wavelength band (T/sub 4/) and the brightness temperature difference between 4- and 11-/spl mu/m bands (/spl Delta/T=T/sub 4/-T/sub 11/). In this letter, the task of active fire detection is examined in the context of a stochastic model for target detection. The proposed fire detection method consists of applying a decorrelation transform in the (T/sub 4/,/spl Delta/T) space. Probability density functions for the fire and background pixels are then computed in the transformed variable space using simulated Moderate Resolution Imaging Spectroradiometer (MODIS) thermal data under different atmospheric humidity conditions and for cases of flaming and smoldering fires. The Pareto curve for each detection case is constructed. Optimal thresholds are derived by minimizing a cost function, which is a weighted sum of the omission and commission errors. The method has also been tested on a MODIS reference dataset validated using high-resolution SPOT images. The results show that the detection errors are comparable with the expected values, and the proposed method performs slightly better than the standard MODIS absolute detection method in terms of the lower cost function.