Risk assessment for wildfire occurrence in high-voltage power line corridors by using remote-sensing techniques: a case study in Hubei Province,China

Wildfires are a significant problem for power line maintenance that can lead to widespread power outages and economic loss in China. Remote sensing (RS) and geographic information system (GIS) technologies and their derived applications are effective tools for power line surveillance and disaster prevention. Using multi-source data and remote-sensing techniques, a risk assessment of wildfire occurrence in high-voltage power line corridors in Hubei Province, China, is presented in this study. Both natural and human causes are considered in the assessment, including a traditional Chinese spring custom. Historical ignition points along power line corridors in Hubei Province that occurred from 2009 to 2014 were collected as training data to create a quantitative analysis. Next, a logistic regression model was applied, and ignition probability maps were produced for power lines. Several wildfire accidents that occurred in 2015 were used as validation data to evaluate the performance of the proposed method. The experimental results indicated that the drought conditions significantly influenced wildfires as natural cause, and human activities play important roles in causing wildfires during specific time periods. The effectiveness and robustness of our method is thereby demonstrated, and our method can be used to provide valuable suggestions for wildfire management for the electrical department in Hubei Province.     

Extraction of landslide-related factors from ASTER imagery and its application to landslide susceptibility mapping

The aim of this study is to extract landslide-related factors from remote-sensing data, such as Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite imagery, and to examine their applicability to landslide susceptibility near Boun, Korea, using a geographic information system (GIS). Landslide was mapped from interpretation of aerial photographs and field surveying. Factors that influence landslide occurrence were extracted from ASTER imagery. The slope, aspect and curvature were calculated from the digital elevation model (DEM) with 25.77 m root mean square error (RMSE), which was derived from ASTER imagery. Lineaments, land-cover and normalized difference vegetation index (NDVI) layers were also estimated from ASTER imagery. Landslide-susceptible areas were analysed and mapped using the occurrence factors by a frequency ratio and logistic regression model. Validation results were 84.78% in frequency ratio and 84.20% in logistic regression prediction accuracy for the susceptibility map with respect to ground-truth data.     

Forecasting and visualization of wildfires in a 3D geographical information system

This paper describes a wildfire forecasting application based on a 3D virtual environment and a fire simulation engine. A novel open-source framework is presented for the development of 3D graphics applications over large geographic areas, offering high performance 3D visualization and powerful interaction tools for the Geographic Information Systems (GIS) community. The application includes a remote module that allows simultaneous connections of several users for monitoring a real wildfire event. The system is able to make a realistic composition of what is really happening in the area of the wildfire with dynamic 3D objects and location of human and material resources in real time, providing a new perspective to analyze the wildfire information. The user is enabled to simulate and visualize the propagation of a fire on the terrain integrating at the same time spatial information on topography and vegetation types with weather and wind data. The application communicates with a remote web service that is in charge of the simulation task. The user may specify several parameters through a friendly interface before the application sends the information to the remote server responsible of carrying out the wildfire forecasting using the FARSITE simulation model. During the process, the server connects to different external resources to obtain up-to-date meteorological data. The client application implements a realistic 3D visualization of the fire evolution on the landscape. A Level Of Detail (LOD) strategy contributes to improve the performance of the visualization system.     

A method of sensor fault detection and identification

A method of Bayesian belief network (BBN)-based sensor fault detection and identification is presented. It is applicable to processes operating in transient or at steady-state. A single-sensor BBN model with adaptable nodes is used to handle cases in which process is in transient. The single-sensor BBN model is used as a building block to develop a multi-stage BBN model for all sensors in the process under consideration. In the context of BBN, conditional probability data represents correlation between process measurable variables. For a multi-stage BBN model, the conditional probability data should be available at each time instant during transient periods. This requires generating and processing a massive data bank that reduces computational efficiency. This paper presents a method that reduces the size of the required conditional probability data to one set. The method improves the computational efficiency without sacrificing detection and identification effectiveness. It is applicable to model- and data-driven techniques of generating conditional probability data. Therefore, there is no limitation on the source of process information. Through real-time operation and simulation of two processes, the application and performance of the proposed BBN method are shown. Detection and identification of different sensor fault types (bias, drift and noise) are presented. For one process, a first-principles model is used to generate the conditional probability data, while for the other, real-time process data (measurements) are used.     

Application of logistic regression model and its validation for landslide susceptibility mapping using GIS and remote sensing data

The aim of this study is to evaluate the hazard of landslides at Penang, Malaysia, using a Geographical Information System (GIS) and remote sensing. Landslide locations were identified in the study area from interpretation of aerial photographs and from field surveys. Topographical and geological data and satellite images were collected, processed and constructed into a spatial database using GIS and image processing. The factors chosen that influence landslide occurrence were: topographic slope, topographic aspect, topographic curvature and distance from drainage, all from the topographic database; lithology and distance from lineament, taken from the geologic database; land use from Thematic Mapper (TM) satellite images; and the vegetation index value from Système Probatoire de l'Observation de la Terre (SPOT) satellite images. Landslide hazardous areas were analysed and mapped using the landslide‐occurrence factors by logistic regression model. The results of the analysis were verified using the landslide location data and compared with probabilistic model. The validation results showed that the logistic regression model is better in prediction than probabilistic model.     

A national-scale GIS-based system for modelling impacts of land use on water quality

Management of freshwater quality requires modelling tools for rapid evaluation of land use and management scenarios. This paper presents the CLUES (Catchment Land Use and Environmental Sustainability) model to address this need. CLUES provides steady state, spatially distributed, integrated catchment models tightly coupled to GIS software to predict mean annual loads of total nitrogen, total phosphorus, sediments and E. coli, and concentration of nutrients throughout New Zealand (268,000 km²) with a subcatchment resolution of 0.5 km². CLUES also estimates potential nutrient concentrations for estuaries and provides key farm socio-economic indicators. The model includes a user interface for study area selection, scenario creation, data geo-visualisation, and export of results. It is pre-populated with spatial data and parameter values for New Zealand. Evaluation of the model and a summary of applications demonstrate the tractability and utility of national-scale rapid scenario assessment tools within a GIS framework.     

Evaluation of wildfire propagation susceptibility in grasslands using burned areas and multivariate logistic regression

This research simulated wildfire propagation susceptibility based on multivariate logistic regression. Moderate Resolution Imaging Spectrometer (MODIS)-derived fuel indicators and topographic factors were the independent variables, and burnt areas served as the dependent variable. MODIS data were collected daily during the wildfire seasons of April to May and September to October from 2001 to 2007 to acquire information about live and dead fuel in the Mongolia–China grasslands. The inputs for the independent parameters for wildfire propagation susceptibility modelling were the normalized difference vegetation index (NDVI), optimized soil-adjusted vegetation index (OSAVI), moisture stress index (MSI), global vegetation moisture index (GVMI), dead fuel INDEX (DFI), elevation, slope, and aspect. Multivariate logistic regression ranking indicates that DFI, MSI, DEM, and OSAVI are the top four factors, with an overall accuracy of 80%. ‘Leave one out’ cross-validation demonstrated that the overall accuracy of the propagation susceptibility modelling ranged from 65% to 87%. Finally, the model was used to produce 10 day average wildfire propagation susceptibility maps during the wildfire seasons of 2001–2007 and to predict the location of burned areas. This research will be useful for understanding the propagation susceptibility of wildfires in grassland areas and for creating policies for preventing wildfire spread.     

A Bayesian belief network modelling of household factors influencing the risk of malaria: A study of parasitaemia in children under five years of age in sub-Saharan Africa

Studies that focus on integrated modelling of household factors and the risk for malaria parasitaemia among children in sub-Saharan Africa (SSA) are scarce. By using Malaria Indicator Survey, Demographic Health Survey, AIDS Indicator Survey datasets, expert knowledge and existing literature on malaria, a Bayesian belief network (BBN) model was developed to bridge this gap. Results of sensitivity analysis indicate that drinking water sources, household wealth, nature of toilet facilities, mother's educational attainment, types of main wall, and roofing materials, were significant factors causing the largest entropy reduction in malaria parasitaemia. Cattle rearing and residence type had less influence. Model accuracy was 86.39% with an area under the receiver-operating characteristic curve of 0.82. The model's spherical payoff was 0.80 with the logarithmic and quadratic losses of 0.53 and 0.35 respectively indicating a strong predictive power. The study demonstrated how BBN modelling can be used in determining key interventions for malaria control.     

Adaptive modelling for adaptive water quality management in the Great Barrier Reef region,Australia

The development and use of a Bayesian Belief Network (BBN) model, within an adaptive management process for the management of water quality in the Mackay Whitsunday region of Queensland, Australia is described. The management goal is firstly to set achievable targets for water quality entering the Great Barrier Reef lagoon from the Mackay Whitsunday natural resource management region and then secondly to define and implement a strategy to achieve these targets. The BBN serves as an adaptive framework that managers and scientists may use to articulate what they know about the managed system. It then provides a tool to guide where, when and what interventions (including research) are most likely to achieve management outcomes. Importantly the BBN provides a platform for collective learning.BBN estimates of total suspended sediment (TSS) loads and event mean concentrations (EMCs) were compared to observed data and results from current best practice models. The BBN estimates were reasonable relative to empirical observations. Example results from the BBN are thereafter used to illustrate the use of the model in estimating the likelihood of exceeding water quality targets with and without proposed actions to improve water quality. Example results are also used to illustrate what spatial or land use elements might contribute most to exceeding water quality targets. Finally key limitations of the tool are discussed and important learnings from the process are highlighted.     

一种时空四维数据模型

现有的地理信息系统软件一般没有考虑时间因素,难以处理时态现象。本文在对多种时态数据模型进行分析,对一些时间概念进行讨论之后,提出了一种面向对象的时空数据模型并对它的数据结构进行了描述。     

新的空间数据模型选取方法与实现

空间数据模型是地理信息系统开发应用的核心和基础,选择合适的空间数据模型,直接关系到整个系统的算法实现和开发效率.结合软件工程学的思想,提出一种新的空间数据模型选取方法,并以城镇土地定级估价系统的构建为例,详细介绍该方法的实现过程.实践表明,该方法具有抗风险、敏捷等特点,在实际工程运用中具有较好的可行性和优越性.     

Green urban garden landscape simulation platform based on high-resolution image recognition technology and GIS

Green urban garden landscape architects, primarily for large-scale applications, such as planning and managing the local environment, ecology and natural resources, have been using a Geographic Information System (GIS). GIS applications explore an urban landscape of the block with the local community analysis is very difficult. Geographic Information System (GIS) provides a means to collect and use geographic data to support the development of agricultural technology. The proposed Support Geographic Regression (SGR) algorithm is responsible for assessing the conditions and global growth estimate crop area, yield and production of crops, cereals, oilseeds and cotton. Digital map is usually higher than printed on paper. The digital version can be used for the analysis presented in combination with other information data sources in much the same map graphics. GIS software, in combination with the management of another information layer to be synthesized in large quantities a variety of data, can get the data more effectively. GIS, to serve them better farmers and breeding community, provides a powerful means of agriculture. The proposed SGR and GIS determine the production, estimate the yield and area extraction procedure, several different satellite data sources, and use the climate data crop model and data.     

Use of Bayesian Belief Networks when combining disparate sources of information in the safety assessment of software-based systems

The paper discusses how disparate sources of information can be combined in the safety assessment of software-based systems. The emphasis is put on an emerging methodology, relevant for intelligent product-support systems, to combine information about disparate evidences systematically based on Bayesian Belief Networks. The objective is to show the link between basic information and the confidence one can have in a system. How one combines the Bayesian Belief Net (BBN) method with a software safety standard (RTCA/DO-178B) for safety assessment of software-based systems is also discussed. Finally, the applicability of the BBN methodology and experiences from cooperative research work together with Kongsberg Defence & Aerospace and Det Norske Veritas, and ongoing research with VTT Automation are presented.     

基于INTERNET的分布式组件GIS集成的实现方法

信息共享是提高地理信息系统应用深度和广度、走向社会化的必由之路。基于ＩＮＴＥＲＮＥＴ的分布式、组件化开发为ＧＩＳ信息资源的共享开辟了广阔的应用领域。简要介绍和比较了几种软件组件模型在ＧＩＳ系统开发中的应用,并提出了一个经改进的基于ＩＮＴＥＲＮＥＴ的分布式组件ＧＩＳ应用模型     

Mining and visualising ordinal data with non-parametric continuous BBNs

Data mining is the process of extracting and analysing information from large databases. Graphical models are a suitable framework for probabilistic modelling. A Bayesian Belief Net (BBN) is a probabilistic graphical model, which represents joint distributions in an intuitive and efficient way. It encodes the probability density (or mass) function of a set of variables by specifying a number of conditional independence statements in the form of a directed acyclic graph. Specifying the structure of the model is one of the most important design choices in graphical modelling. Notwithstanding their potential, there are only a limited number of applications of graphical models on very complex and large databases. A method for mining ordinal multivariate data using non-parametric BBNs is presented. The main advantage of this method is that it can handle a large number of continuous variables, without making any assumptions about their marginal distributions, in a very fast manner. Once the BBN is learned from data, it can be further used for prediction. This approach allows for rapid conditionalisation, which is a very important feature of a BBN from a user’s standpoint.     

THE USE OF BAYESIAN BELIEF NETS IN SAFETY ASSESSMENT OF SOFTWARE BASED SYSTEMS

The paper describes an investigation of methods to perform a reliability and safety assessment of the software in programmable safety relevant systems. It emphasises in particular how disparate information sources and different quantitative and qualitative methods should be combined in such an assessment. It starts with a general discussion of rule based, probabilistic and expert judgement methods and their applicability on software reliability. Then a method for combining different evidences in a reliability and safety assessment is pinpointed, viz. the Bayesian Belief Net (BBN) methodology. It is also illustrated how this method may be applied for safety assessment of software.     

Effects of topography and human activity on the net primary productivity (NPP) of alpine grassland in northern Tibet from 1981 to 2004

Using the Carnegie–Ames–Stanford Approach (CASA) model on remote-sensing (RS), climatic, and other related data from 1981 to 2004, the researchers estimated the net primary productivity (NPP) of alpine grassland in northern Tibet. Geographical information system (GIS) techniques were used to analyse the spatial pattern of change in the NPP of alpine grassland and its response to the intensity of human activity. The researchers found that the mean values of NPP on flat (slope gradients <1°) and sunny slopes were relatively lower. Between 1981 and 2004, the NPP of alpine grassland in northern Tibet tended to decrease, but with relatively large annual fluctuations. In northern Tibet, the alpine grassland NPP for high-elevation regions has a greater proportion of area (over 26%) showing a decreasing trend. The change is more significant in areas where the slope is 15–30° and aspect has little influence on the extent of the change. The negative effects of local residential areas on the rate of change of alpine grassland NPP are smaller than those of roads.     

A dynamic data model for mobile GIS

A fast response time is a major objective for Mobile Geographic Information System (GIS) applications. This study provides a solution for improving the performance of response time by a dynamic data model. A conceptual dynamic data model is proposed, which covers (a) “position” information of selected geographic objects relevant to the GIS user's interest within his current location, (b) the selected attribute information in which the mobile GIS user is interested. In this approach, first, the attribute information is selected through a validating process making use of the temporal and attribute filters. Second, a specially designed dynamic database is employed to enable the implementation of the conceptual dynamic data model. This dynamic database is continually updated in accordance with the spatial, temporal and attribute constraints specified for the conceptual model. This design of a dynamic data model increases the availability of spatial data to mobile GIS users by providing up to date accurate information relevant to the area of interest, in a limited communication bandwidth. Third, an experimental study has been conducted and the results demonstrate that by using a dynamic database the response time can be reduced to one-third of that of a conventional database. The response time performance can be further improved as the size of the database is increased.     

GeoVR: a web-based tool for virtual reality presentation from 2D GIS data

A tool called GeoVR has been designed and developed under a client/server architecture to enable the interactive creation of a 3D scene and virtual reality modeling language (VRML) model from 2D spatial data by integrating Internet geographical information system (GIS) and HTML programming. The client front-end of this tool provides an HTML form to set properties for building 3D scenes, while the server back-end supported by off-the-shelf software: ArcView Internet Map Server and ArcView 3D Analyst through Avenue programming, processes the parameters and generates a 3D scene. This 3D scene is then transformed into a VRML model, which, together with its legend, is sent back to the VRML-enabled WWW browser for display and navigation. It is demonstrated that this tool, not only automates the conversion of the conventional 2D GIS data into VRML, but also adapts the current GIS 3D capabilities to the increasingly popular Web environment. The development of GeoVR offers new opportunities for geoscientists to build applications that benefit from virtual reality presentation based upon the existing GIS spatial databases.     

基于实体数据模型的空间邻接定量分析探讨*

空间邻接是一种基本的空间拓扑关系, 它对研究地理现象的分布规律和演化过程有着重要的意义。而目前许多GIS 软件所采用的空间实体数据模型并不包含拓扑信息, 故难以定量地计算、分析地理对象间的空间邻接关系。针对上述缺陷提出了基于实体数据模型的空间邻接定量分析算法, 并以MapInfo 为例予以实现, 最后以实例分析验证算法的有效性。