A four‐dimensional agent‐based model: A case study of forest‐fire smoke propagation

Dynamic geospatial complex systems are inherently four‐dimensional (4D) processes and there is a need for spatio‐temporal models that are capable of realistic representation for improved understanding and analysis. Such systems include changes of geological structures, dune formation, landslides, pollutant propagation, forest fires, and urban densification. However, these phenomena are frequently analyzed and represented with modeling approaches that consider only two spatial dimensions and time. Consequently, the main objectives of this study are to design and develop a modeling framework for 4D agent‐based modeling, and to implement the approach to the 4D case study for forest‐fire smoke propagation. The study area is central and southern British Columbia and the western parts of Alberta, Canada for forest fires that occurred in the summer season of 2017. The simulation results produced realistic spatial patterns of the smoke propagation dynamics.     

时空拓扑关系定义及时态拓扑关系描述

目的，国际上对于作为时空数据模型概念和形式化基础的时空拓扑关系定义和描述还没有一个明确的阐述。本文在分析ＧＩＳ的时空特性后，给出了一个语义层时空拓扑关系定义。同时，提出并证明了完备、唯一的描述时态拓扑关系的４Ｉ框架，列出了时态拓扑关系描述的谓词集，区别了时态拓扑关系和时间方向关系的内涵和谓词描述形式。     

Forest fire susceptibility modeling using hybrid approaches

Forest fires are considered one of the most highly damaging and devastating of natural disasters, causing considerable casualties and financial losses every year. Hence, it is important to produce susceptibility maps for the management of forest fires so as to reduce their harmful effects. The purpose of this study is to map the susceptibility to forest fires over Nowshahr County in Iran, using an integrated approach of index of entropy (IOE) with fuzzy membership value (FMV), frequency ratio (FR), and information value (IV) with a comparison of their precision. The spatial database incorporated the inventory of forest fire and conditioning factors. As a whole, 41 forest fire locations were identified. Out of these, 29 locations (≈70%) were randomly chosen for the forest fire susceptibility modeling (FFSM), and the remaining 12 locations (≈30%) were utilized for the validation of the models. Subsequently, utilizing FMV‐IOE, FR‐IOE, and IV‐IOE models, forest fire susceptibility maps were acquired. Finally, the modeling ability of the models for FFSM was assessed using an area under the receiver operating characteristic (AUROC) curve. The results manifested that the prediction accuracy of the FMV‐IOE model is slightly higher than that of the FR‐IOE and IV‐IOE models. The incorporation of IOE with FMV, FR, and IV models had AUROC values of 0.890, 0.887, and 0.878, respectively. The resulting FFSM can be effective in fire repression resource planning, sustainable development, and primary warning in regions with similar conditions.     

Harnessing spatio‐temporal patterns in data for nominal attribute imputation

Missing data in Volunteered Geographic Information (VGI) are an unavoidable consequence of data collection by non‐experts, guided by only vague and informal mapping guidelines. While various Missing Value Imputation (MVI) techniques have been proposed as data cleansing strategies, they have primarily targeted numerical data attributes in non‐spatial databases. There remains a significant gap in methods for imputing nominal attribute values (e.g., Street Name) in map databases. Here, we present an imputation algorithm called the Membership Imputation Algorithm (MIA), targeting spatial databases and enabling imputation of nominal values in spatially referenced records. By targeting membership classes of spatial objects, MIA harnesses spatio‐temporal characteristics of data and proposes efficient heuristics to impute the class name (i.e., a membership). Experimental results show that the proposed algorithm is able to impute the membership with high levels of accuracy (over 94%) when assigning Street Name(s), across highly diverse regional contexts. MIA is effective in challenging spatial contexts such as street intersections. Our research serves as a first step in highlighting the effectiveness of spatio‐temporal measures as a key driver for nominal imputation techniques.     

Spatio‐temporal prediction of land surface temperature using semantic kriging

Spatio‐temporal prediction and forecasting of land surface temperature (LST) are relevant. However, several factors limit their usage, such as missing pixels, line drops, and cloud cover in satellite images. Being measured close to the Earth's surface, LST is mainly influenced by the land use/land cover (LULC) distribution of the terrain. This article presents a spatio‐temporal interpolation method which semantically models LULC information for the analysis of LST. The proposed spatio‐temporal semantic kriging (ST‐SemK) approach is presented in two variants: non‐separable ST‐SemK (ST‐SemK_NSep) and separable ST‐SemK (ST‐SemK_Sep). Empirical studies have been carried out with derived Landsat 7 ETM+ satellite images of LST for two spatial regions: Kolkata, India and Dallas, Texas, U.S. It has been observed that semantically enhanced spatio‐temporal modeling by ST‐SemK yields more accurate prediction results than spatio‐temporal ordinary kriging and other existing methods.     

An Experimental Performance Evaluation of Spatio-Temporal Join Strategies

Many applications capture, or make use of, spatial data that changes over time. This requirement for effective and efficient spatio‐temporal data management has given rise to a range of research activities relating to spatio‐temporal data management. Such work has sought to understand, for example, the requirements of different categories of application, and the modelling facilities that are most effective for these applications. However, at present, there are few systems with fully integrated support for spatio‐temporal data, and thus developers must often construct custom solutions for their applications. Developers of both bespoke solutions and of generic spatio‐temporal platforms will often need to support the fusion of large spatio‐temporal data sets. Supporting such requests in a database setting involves the use of join operations with both spatial and temporal conditions – spatio‐temporal joins. However, there has been little work to date on spatio‐temporal join algorithms or their evaluation. This paper presents an evaluation of several approaches to the implementation of spatio‐temporal joins that build upon widely available indexing techniques. The evaluation explores how several algorithms perform for databases with different spatial and temporal characteristics, with a view to helping developers of generic infrastructures or custom solutions in the selection and development of appropriate spatio‐temporal join strategies.     

GIS中面向对象时空数据模型

由于当前的地理信息系统软件难以处理时态现象，时态数据模型已忧为ＧＩＳ领域的一个研究热点。许多学者提出了多种时态数据模型。本文作者在提出了矢量栅格一体化的面向对象数据模型之后，再一次对时态问题进行了分析研究，净面向对象的数据模型扩充到时间维。有三种方法表达空间对象的历史变化。第一种是将版本信息记录在关系表上；第二种是将版本信息标记在记录上；第三种是将版本信息标记在属性上。本文采用面向对象的方法，将版     

近实时中高空间分辨率森林火灾监测系统展望

森林火灾是最为常见的灾害之一,严重危及人类生命安全。及时准确监测森林火灾的发生及火场状况,对应对火灾及减少损失至关重要。当前,森林火灾卫星遥感监测主要以低空间分辨率的卫星遥感为主,空间分辨率过低导致无法探测规模较小火灾及掌握详细火场态势。针对这一问题,结合近些年中高空间分辨率卫星观测、共享及处理能力的发展,本文从森林火灾卫星遥感监测的基本原理、当前可用中高空间分辨率卫星数据及其特点、中高分辨率森林着火区监测算法,以及数据共享与云端存储与计算等4个技术环节,对森林火灾中高分辨率卫星遥感监测当前研究现状与存在问题进行了总结,阐述了近实时中高空间分辨率森林火灾监测系统的可行性。近实时中高空间分辨率森林火灾监测系统可对已有低空间分辨率森林火灾监测体系形成重要补充,依托其空间分辨率的优势有助于及早、准确发现小规模火情,进而为森林火灾的防治与管理提供更好支撑。     

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 Hybrid Geocoding Methodology for Spatio‐Temporal Data

As tools for collecting data continue to evolve and improve, the information available for research is expanding rapidly. Increasingly, this information is of a spatio‐temporal nature, which enables tracking of phenomena through both space and time. Despite the increasing availability of spatio‐temporal data, however, the methods for processing and analyzing these data are lacking. Existing geocoding techniques are no exception. Geocoding enables the geographic location of people and events to be known and tracked. However, geocoded information is highly generalized and subject to various interpolation errors. In addition, geocoding for spatio‐temporal data is especially challenging because of the inherent dynamism of associated data. This article presents a methodology for geocoding spatio‐temporal data in ArcGIS that utilizes several additional supporting procedures to enhance spatial accuracy, including the use of supplementary land use information, aerial photographs and local knowledge. This hybrid methodology allows for the tracking of phenomenon through space and over time. It is also able to account for reporting inconsistencies, which is a common feature of spatio‐temporal data. The utility of this methodology is demonstrated using an application to spatio‐temporal address records for a highly mobile group of convicted felons in Hamilton County, Ohio.     

FY-3D/MERSI-II全球火点监测产品及其应用

FY-3D/MERSI-II全球火点监测产品主要包括全球范围内的火点位置、亚像元火点面积和火点强度等信息,可用于实时监测全球范围的森林草原火灾、秸秆焚烧等生物质燃烧状况。火点判识算法主要根据中红外通道对高温热源的敏感特性,即含有火点的中红外通道像元辐亮度和亮温较远红外通道的辐亮度和亮温偏高,同时较周边非火点的中红外像元偏高,建立合适的阈值可探测含有火点的像元。亚像元火点面积估算主要使用中红外单通道估算,根据亚像元火点面积估算结果对火点强度进行分级,不同的级别表示不同程度的火点辐射强度。基于全球火点自动判识结果,每日生成0.01°分辨率的卫星遥感日全球火点产品,每月生产0.25°×0.25°格点的全球月火点密度图。在利用FY-3D/MERSI-II火点产品开展的全球火点监测应用中,对多起全球重大野火事件进行了监测,为防灾减灾、全球气候变化研究、生态环境保护等方面提供卫星遥感信息支持。     

Temporal data mining using genetic algorithm and neural network —A case study of air pollutant forecasts

This paper integrates genetic algorithm and neural network techniques to build new temporal predicting analysis tools for geographic information system (GIS). These new GIS tools can be readily applied in a practical and appropriate manner in spatial and temporal research to patch the gaps in GIS data mining and knowledge discovery functions. The specific achievement here is the integration of related artificial intelligent technologies into GIS software to establish a conceptual spatial and temporal analysis framework. And, by using this framework to develop an artificial intelligent spatial and temporal information analyst (ASIA) system which then is fully utilized in the existing GIS package. This study of air pollutants forecasting provides a geographical practical case to prove the rationalization and justness of the conceptual temporal analysis framework.     

利用动态阈值的森林草原火灾火点检测算法

针对传统固定阈值的森林和草原火灾火点遥感检测算法因时空环境变化而导致的火点漏检和误检问题,提出了一种综合季节、地理位置和土地覆盖类型等影响因素的时空动态阈值火点检测算法。实验结果表明,时空动态阈值火点检测算法能有效改善火点检测结果,具有时空自适应性。     

Temporal Data Mining Using Genetic Algorithm and Neural Network--A Case Study of Air Pollutant Forecasts

This paper integrates genetic algorithm and neural network techniques to build new temporal predicting analysis tools for geographic information system (GIS). These new GIS tools can be readily applied in a practical and appropriate manner in spatial and temporal research to patch the gaps in GIS data mining and knowledge discovery functions. The specific achievement here is the integration of related artificial intelligent technologies into GIS software to establish a conceptual spatial and temporal analysis framework. And, by using this framework to develop an artificial intelligent spatial and temporal information analyst (ASIA) system which then is fully utilized in the existing GIS package. This study of air pollutants forecasting provides a geographical practical case to prove the rationalization and justness of the conceptual temporal analysis framework.     

土地利用动态监测中的时空数据模型研究

针对传统GIS数据模型存在的问题,开展了基于特征的时空数据模型研究.结合我国土地利用动态监测,提出了一个新的时空数据模型-基于变化特征状态的时空数据模型(SCFSTDM),该模型保持了地理现象的完整性,地理信息的完备性以及时空专题信息的有机集成,模型有利于面向目标定向分析方法的应用和时空分析与推理的实现,有利于地理数据的共享,设计和开发了基于SCFSTDM的时态土地利用信息系统,实现了基于特征实例的时空复合查询,时空推理以及动态播放等功能.     

面向对象的规划道路中线时空数据模型

回顾了两种类型的时空数据模型，发展了面向对象的规划道路中线时空数据模型，进一步实现了对规划道路中线对象的查询及更新维护。该模型具有充分利用GIS已有的空间分析和方便实现时态分析的功能，同时也具有数据维护和历史数据恢复方便简单，空间信息没有冗余，时态信息冗余小，且能表达变化原因等特点。     

Using GIS to Model Incidence, Prevalence and Spread of Non-legal Drug Use

Current methods for estimating the incidence and prevalence of non‐legal drug use tend to be retrospective and are not capable of forecasting spatial characteristics. This paper details the development of a GIS model for forecasting and displaying spatio‐temporal trends in non‐legal drug use. It builds upon a current model that estimates levels of drug use, using GIS to develop and visualise the spatial dimension and to predict levels of use for unknown locations using radial basis functions with a thin spline interpolator. The model is calibrated against known data for the onset and spread of heroin use. Results of validation and cross validation of the interpolated surfaces give some confidence to the accuracy of the method. It provides a first stage in a process to develop more complex models that might be used to examine the introduction of new drug use practices, socio‐economic characteristics of different drug use habits, harm reduction measures or to further examine the role of space in the spread of drug use.     

Himawari-8静止气象卫星时序法火点探测

随着新一代静止气象卫星的发射,高频次和高时效的观测特性对于火点探测具有独特优势。本文基于Himawari-8新一代静止气象卫星高频次观测特点,提出有利于火情初期火点判识的时序探测方法。与传统的极轨气象卫星遥感火情监测采用的上下文法不同,时序探测法判识火点的方法依据为探测像元亮温在观测时间上的差异。研究结果显示,在无云及无异常热源条件下,相邻时次中红外亮温差异较小,当前后时次亮温差达到3K时,可判识出火点,而上下文法的阈值均在6 K以上,时序法的火点判识阈值较上下文法明显降低,探测相应的亚像元火点面积减小一倍以上,从而提高了火情判识的灵敏度,实现火点早期发现。本文介绍了时序法火点判识方法,并以黑龙江桦川县的星地同步观测实验进行验证,研究表明,时序法较上下文法在初发火点探测灵敏度方面有明显优势,时序法和上下文法的结合可提高气象卫星对火情发展过程的监测能力。