Facilitating Public Involvement in Strategic Planning for Wildland Fire Management∗

Wildland fire creates a complex relationship between federal agencies and public stakeholders. Questions surrounding the public's role in federal wildland fire management point to broader questions about the relationships among scientists, policymakers, federal agencies, and the public. In this article we report on a project to provide information about the key components of federal policies that govern wildland fire management. The goal is to enhance the decision support capabilities of a geographic information system (GIS)-based fire risk model and the capacity of users to interpret the outputs of the model from a fire policy perspective. We conclude by making recommendations for how this and other such tools might be refined to improve and expand policy interactions among government land and resource management agencies, scientists, and the public.     

Modeling the spatial patterns of human wildfire ignition in Yunnan province,China

Despite wildfire being an important regulator of dryland ecosystems, uncontrolled wildfire can be harmful to both forest ecosystems and human society, and wildfire prevention and control continue to raise worldwide concern. Wildfire management depends on knowledge of wildfire ignitions, both for cause and location. The regimes and factors influencing wildfire ignition have been studied at length. Humans have a profound effect on fire regimes and human activity is responsible for igniting the largest number of fires in our study area. Understanding the spatial patterns of ignitions is foremost to achieving efficiency in wildfire prevention. Previous studies mainly concentrate on overall wildfire risk integrating numerous factors simultaneously, yet the importance of human factors on ignition has not received much attention. In this study, we mapped human accessibility to explore the influence of human activity on wildfire ignition in a simple and straightforward way. A Bayesian weights-of-evidence (WofE) method was developed based on fire hotspots in China's Yunnan province extracted from satellite images and verified as known wildfires for the period 2007–2013. We considered a set of factors that impact fire ignition as associated with human accessibility: the locations of settlements, roads, water and farmland susceptible to human wildfire ignition. Known points of likely wildfire ignition were selected as training samples and all suspected thematic maps of the factors were taken as explanatory layers. Next, the weights of each layer in terms of its explanatory power were computed and used to generate evidence based on a threshold to pass a statistical test. The conditional independence (CI) of each layer was checked with the Agterberg-Cheng test. Finally, the posterior probability was calculated and its precision validated using samples of both presence and absence by withheld validation data. A comparison of WofE models was made to test the predictability. Results show proximity to villages, roads and farmland are strongly associated with human wildfire ignition and that wildfire more often occurs at an intermediate distance from high-density human activity. The WofE method proved more powerful than logistic regression, improving predictive accuracy by 10% and was more straightforward in presenting the association of dependence and independence. In addition, WofE with 1000 m buffer bands is more robust in predicting human wildfire ignition risk than binary or 100 m buffers for the ecoregion studied. Our results are significant for advising practical wildfire management and resource allocation, evaluation of human ignition control and also provides a foundation for future efforts toward integrated wildfire prediction.     

Human-ignited wildfire patterns and responses to policy shifts

Development of efficient forest wildfire policies requires an understanding of the underlying reasons behind forest fire occurrences. Globally, there is a close relationship between forest wildfires and human activities; most wildfires are human events due to negligence (e.g., agricultural burning escapes) and deliberate actions (e.g., vandalism, pyromania, revenge, land use change attempts). We model the risk of wildfire as a function of the spatial pattern of urban development and the abandonment/intensity of agricultural and forestry activities, while controlling for biophysical and climatic factors. We use a count data approach to model deliberately set fires in Galicia, N.W. Spain, where wildfire is a significant threat to forest ecosystems, with nearly 100,000 wildfires recorded during a thirteen-year period (1999–2011). The spatial units of analysis are more than 3600 parishes. Data for the human influences are derived from fine-resolution maps of wildland–urban interface (WUI), housing spatial arrangements, road density, forest ownership, and vegetation type. We found wildfire risk to be higher where there are human populations and development/urbanisation pressure, as well as in unattended forest areas due to both rural exodus and a fragmented forest ownership structure that complicates the profitability of forestry practices. To better help direct management efforts, parameter estimates from our model were used to predict wildfire counts under alternative scenarios that account for variation across space on future land-use conditions. Policies that incentivize cooperative forest management and that constrain urban development in wildlands at hotspot fire locations are shown to reduce wildfire risk. Our results highlight the need for spatially targeted fire management strategies.     

The Retention,Revival, and Subjugation of Indigenous Fire Knowledge through Agency Fire Fighting in Eastern Australia and California

This article explores the potential impact of training and employment with wildfire management agencies on the retention of Indigenous fire knowledge. It focuses on the comparative knowledge and experiences of Indigenous Elders, cultural practitioners, and land stewards in connection with “modern” political constructs of fire in New South Wales and Queensland, Australia, and California in the United States of America. This article emphasises the close link between cross-cultural acceptance, integration of Indigenous and agency fire cultures, and the ways in which knowledge types are shared or withheld. While agency fire fighting provides an opportunity for Indigenous people to connect and care for country, it simultaneously allows for the breaking of traditional rules surrounding what knowledge is shared with whom in the context of Indigenous cultural burning. By highlighting how privilege intersects with ethnicity, class, gender and age, this article demonstrates how greater cross-cultural acceptance could aid ongoing debates on how to coexist with wildfire today.     

Social,Institutional, and Psychological Factors Affecting Wildfire Incident Decision Making

Managing wildland fire incidents can be fraught with complexity and uncertainty. Myriad human factors can exert significant influence on incident decision making, and can contribute additional uncertainty regarding programmatic evaluations of wildfire management and attainment of policy goals. This article develops a framework within which human sources of uncertainty in wildfire management can be classified and managed, specifically identifying social, institutional, and psychological factors that can affect wildland fire incident decision making. These factors are reviewed in the context of wildland fire incident management and the literature regarding fire manager decision making. I then provide specific recommendations for addressing these issues, with a focus on improving incident decision processes. Extending this framework to consider a broader set of human factors and to consider how human factors affect the broader wildfire management spectrum could lead to improved fire management outcomes.     

Integrating Fire Behavior and Pedestrian Mobility Models to Assess Potential Risk to Humans from Wildfires Within the U.S.–Mexico Border Zone∗

Wildfires create a risk to pedestrians traveling through rural areas, because they might not be aware of the presence of a wildfire or its direction and rate of spread until is too late to successfully evacuate. In wildland areas of southern San Diego County, immigrants crossing the U.S.–Mexico border and border security agents are particularly at risk to wildfires. The objective of this study is to develop a framework of analysis and associated tools for examining the combined behavior of wildfires and pedestrian mobility to assess the potential threat of fire to pedestrians in wildland areas. Outputs from a geographic information system (GIS) overlay model for determining potentially dangerous fire zones, the Wildland–Urban Interface Evacuation (WUIVAC) model, and a model of pedestrian mobility in wildland areas were combined to generate wildfire risk to pedestrian maps. The key technical contributions of the study are the development and testing of the pedestrian mobility model and the framework and logic for integrating the results of three GIS-based models. The applied geography contribution is the testing of two scenarios of high risk from wildfires to pedestrians within the U.S.–Mexico border zone of San Diego County, California.

The study results show that the travel times calculated by the pedestrian mobility model appear to be realistic and are affected by the terrain and vegetation characteristics of a study site, whereas the evacuation trigger buffers (ETBs) from WUIVAC are mostly influenced by the wind speed and direction parameters of the FlamMap fire spread model. A moderate fire danger to pedestrians in the most remote wildland locations of the study area is determined. The scenario test results suggest that if a wildfire occurs within 2 km (extreme southwesterly winds) or 6 km (extreme northeasterly wind) of pedestrians in the worst case location within the San Diego border region they would likely not have a sufficient amount of time to reach a nearby safety zone.     

Factors Influencing the Development of Rangeland Fire Protection Associations: Exploring Fire Mitigation Programs for Rural,Resource-Based Communities

Increases in wildfire frequency and extent in rangelands pose a growing threat to private property and ecosystem health. The state of Idaho, USA, recently promoted rangeland fire protection associations (RFPAs)—nonprofit organizations of local citizens who contribute to firefighting efforts on public rangelands—as one way to mitigate rangeland wildfire risk. This study used in-depth interviews with RFPA members and land management professionals to explore the local circumstances that influenced the establishment and functioning of one RFPA. Results indicate that intergenerational ties to “working the land,” existing reciprocity among neighbors, a culture of self-reliance, and informal social networks all contributed to RFPA formation and functioning. Interaction between RFPA members and professionals improved their relationships and promoted shared understandings about wildfire response. We conclude by discussing conditions that might enable or inhibit RFPAs in other areas and how our findings advance research on adapting mitigation programs to local context.     

Resilience: Analyzing the Crisis Management of Ski Resorts in China

Resilience has become an important concept for the ski resorts of China, which have suffered heavy losses due to COVID-19. In order to help China’s ski resort service industry successfully adapt to the crisis and achieve sustainable development, the goal of this paper is to develop the definition of resilience of the ski resort service industry through interviews based on the concept and general analysis framework of resilience. The ski resort resilience theory analysis framework is then constructed from the three basic elements (market, skiing, and stakeholders) and four system features (flexibility, adaptability, and collaborative learning ability). The results indicate several measures that can be taken to spread risk: enrich the product supply; eliminate risks and build a multi-agent networked industrial governance system; and establish a risk prevention and management mechanism based on a multi-organization alternative learning mechanism to overcome the difficulties encountered in the development of ski resorts.     

A STATISTICAL RELATIONSHIP BETWEEN EL NIÑO-SOUTHERN OSCILLATION AND FLORIDA WILDFIRE OCCURRENCE

Wildfires have long been an important environmental concern in Florida. Recent wildfires along the urban-rural interface of large cities in Florida have pointed to the need for an increased understanding of the physical and biological mechanisms that govern wildfire occurrence. Increased awareness of the relationships between global climate changes occurring in the tropics and their teleconnections outside the tropics dictate a reevaluation of the role of phenomena such as El Niño-Southern Oscillation on the risk of wildfire. One simple way of illustrating the relationship between ENSO and wildfire occurrence is the use of an Analysis of Variance (ANOVA) on wildfire data that has been categorized according to ENSO status (El Niño, La Niña, and neutral years). This research shows that a statistically significant relationship exists between ENSO conditions and annual wildfire occurrence in Florida when ENSO conditions are treated as a potential precursor to wildfire conditions. In particular, a statistically significant relationship exists between both acreage burned and average fire size, when the data are separated into El Niño and La Niña categories according to the previous year's ENSO status. This supports the idea that the climate from previous years has a measurable effect upon fire statistics in the years following the climate measurements, and that it may be possible to create a regional fire prediction model that incorporates ENSO information. [Key words: wildfire, natural hazards, ANOVA, Florida, ENSO.]     

Predicting the visualization intensity for interactive spatio-temporal visual analytics: a data-driven view-dependent approach

The continually increasing size of geospatial data sets poses a computational challenge when conducting interactive visual analytics using conventional desktop-based visualization tools. In recent decades, improvements in parallel visualization using state-of-the-art computing techniques have significantly enhanced our capacity to analyse massive geospatial data sets. However, only a few strategies have been developed to maximize the utilization of parallel computing resources to support interactive visualization. In particular, an efficient visualization intensity prediction component is lacking from most existing parallel visualization frameworks. In this study, we propose a data-driven view-dependent visualization intensity prediction method, which can dynamically predict the visualization intensity based on the distribution patterns of spatio-temporal data. The predicted results are used to schedule the allocation of visualization tasks. We integrated this strategy with a parallel visualization system deployed in a compute unified device architecture (CUDA)-enabled graphical processing units (GPUs) cloud. To evaluate the flexibility of this strategy, we performed experiments using dust storm data sets produced from a regional climate model. The results of the experiments showed that the proposed method yields stable and accurate prediction results with acceptable computational overheads under different types of interactive visualization operations. The results also showed that our strategy improves the overall visualization efficiency by incorporating intensity-based scheduling.     

沙坡头站荒漠生态环境长期定位监测数据信息管理系统的建设与发展

积累、管理和共享科学数据是沙坡头沙漠研究试验站科研、监测的日常工作之一。基于“沙坡头站动态监测数据库管理系统”逐步建立和完善起来的“沙坡头站荒漠生态环境长期定位监测数据信息管理系统”,在系统结构、后台管理、数据共享服务方式及Web页面与用户的交互性等方面均有优化提高,用户将更加快捷、直观地了解沙坡头站监测数据的管理与集成,有效使用和共享数据。该信息系统将为生态环境保护、资源可持续利用搭建长期、综合的数据与信息交流平台。     

Multivariate analysis of landscape wildfire dynamics in a Mediterranean ecosystem of Greece

This paper focuses on spatial distribution of long-term fire patterns versus physical and anthropogenic elements of the environment that determine wildfire dynamics in Greece. Logistic regression and correspondence analysis were applied in a spatial database that had been developed and managed within a Geographic Information System. Cartographic fire data were statistically correlated with basic physical and human geography factors (geomorphology, climate, land use and human activities) to estimate the degree of their influence at landscape scale. Land cover types of natural and agricultural vegetation were the most influential factors for explaining landscape wildfire dynamics in conjunction with topography and grazing.     

分布式水文模型软件系统研究综述

分布式水文模型软件系统作为分布式水文模型的技术外壳,是模型应用的重要技术保障。当前分布式水文模型应用呈现出多过程综合模拟、用户群范围广和计算量大的特点,对分布式水文模型软件系统的灵活性、易用性和高效性提出了更高的要求。本文首先分析了分布式水文模型应用的主要流程,之后从应用视角对现有分布式水文模型软件系统的特点进行了归纳,主要结论为:①软件系统按照模型结构灵活性的高低分为以下3种类型:不支持子过程选择和算法设置,不支持子过程选择、但支持算法设置,同时支持子过程选择和算法设置;②根据用户操作数据预处理软件方式的不同,参数提取方式分为菜单/命令行式和向导式;③按照模型的程序实现方法分为串行和并行方式,按照模型运行环境分为本地和网络模式。现有软件系统在灵活性、易用性和高效性方面存在如下问题:一是尚未解决模型结构灵活性和对用户知识依赖性之间的矛盾;二是现有菜单/命令行式和向导式的参数提取方式步骤繁琐,难以实现参数的自动提取;三是模型大多为串行方式和本地模式,容易遇到计算瓶颈问题。最后从模块化、智能化、网络化及移动化、并行化和虚拟仿真等方面探讨了分布式水文模型软件系统的发展趋势和研究方向。     

土地规划的自然过程基础与大数据时代的方法探索

随着城市化与工业化程度不断加深,产业空间结构不断重组,导致土地供需矛盾日益突出,国土资源无序开发日益严重,因而优化国土资源空间格局,成为生态文明建设的首要任务。同时,科学合理配置土地资源对土地规划提出了更高要求,传统土地规划方法有待改革。在基于尊重自然、顺应自然的开发理念下,总结土地自然过程基础对土地规划的影响,实现“山水路林田生命共同体”的协调规划发展。其次,“大数据”时代的来临,云计算、空间数据整合、云分析等技术对土地规划方法提供新的技术支撑;最后,针对土地规划数据特殊性、移动用户终端的广泛性,提出应创建土地规划云服务平台,使土地规划实现数据集成管理和更新,从而提高土地规划质量。     

Mapping the Globalization of the Temporary Staffing Industry*

Very little is currently known about the globalization of the temporary staffing industry, a strategically significant sector given its role in promulgating wider labor market flexibility. This article starts to rectify this research lacuna in four ways: by conceptualizing the international expansion of temporary staffing and comparing it to other business service sectors, by identifying and mapping the top twenty transnational staffing agencies, by offering a typology of the leading transnational agencies based on their functional and geographic characteristics, and by charting a research agenda for future work on this sector.     

Reply to Comment on ‘Marine GIS: Identification of mesoscale oceanic thermal fronts’

Geographic information service (GIService) has become popular in the last decade to develop applications for addressing global challenges. Performance is one of the most important criteria to help users select distributed online GIService for developing geospatial applications including natural hazards and emergency responses. However, performance accuracy is limited by the single-location-based evaluation mechanism while service performance is dynamic in space and time between end-users and services. We propose a spatiotemporal performance evaluation mechanism to improve the accuracy. Specially, a cloud and volunteer computing mechanism is proposed to collect performance information of globally distributed GIServices. A global spatiotemporal performance model is designed to integrate spatiotemporal dynamics for better performance evaluation for users from different regions at different times. This model is tested to support GIService selection in global spatial data infrastructures (SDIs). The experiment confirms that the proposed model provides more accurate evaluations for global users and better supports geospatial resource utilizations in SDIs than previous mechanisms. The methodology can be adopted to improve the services of other regional and global distributed operational systems.     

Enabling point pattern analysis on spatial big data using cloud computing: optimizing and accelerating Ripley’s K function

Performing point pattern analysis using Ripley’s K function on point events of large size is computationally intensive as it involves massive point-wise comparisons, time-consuming edge effect correction weights calculation, and a large number of simulations. This article presented two strategies to optimize the algorithm for point pattern analysis using Ripley’s K function and utilized cloud computing to further accelerate the optimized algorithm. The first optimization sorted the points on their x and y coordinates and thus narrowed the scope of searching for neighboring points down to a rectangular area around each point in estimating K function. Using the actual study area in computing edge effect correction weights is essential to estimate an unbiased K function, but is very computationally intensive if the study area is of complex shape. The second optimization reused the previously computed weights to avoid repeating expensive weights calculation. The optimized algorithm was then parallelized using Open Multi-Processing (OpenMP) and hybrid Message Passing Interface (MPI)/OpenMP on the cloud computing platform. Performance testing showed that the optimizations effectively accelerated point pattern analysis using K function by a factor of 8 using both the sequential version and the OpenMP-parallel version of the optimized algorithm. While the OpenMP-based parallelization achieved good scalability with respect to the number of CPU cores utilized and the problem size, the hybrid MPI/OpenMP-based parallelization significantly shortened the time for estimating K function and performing simulations by utilizing computing resources on multiple computing nodes. Computational challenge imposed by point pattern analysis tasks on point events of large size involving a large number of simulations can be addressed by utilizing elastic, distributed cloud resources.     

市场化、社会化、网络化的河北省科技中介服务体系构建研究

科技中介服务机构作为国家科技创新服务体系的重要组成部分,在实现科技向生产力转化、促进经济和科技有效结合方面起着重要作用。河北省科技中介服务机构的发展还面临规模较小、内容单一、从业人员素质较低、政策法规体系不完善、投(融)资服务需求难以满足等问题。该文从管理体制与运行机制、人力资本管理、法律法规体系、信息平台建设等方面探讨了构建市场化、社会化、网络化的河北省科技中介服务体系的对策。     

Forest fire risk maps: a GIS open source application – a case study in Norwest of Portugal

Forest fires are widely recognized as one of the most critical events in global change. Successful fire management depends on effective fire prevention, detection, and presuppression, having an adequate fire suppression capability, and consideration of fire ecology relationships. Geographical information systems (GIS) provide tools to create, transform, and combine georeferenced variables. In Portugal, as in many other countries, it is mandatory that all the municipalities produce forest fire risk maps on an annual basis, following the rules of the Portuguese Forest Authority, a governmental association. This article presents the results of a research project aimed at producing forest fire risk maps in a GIS open source environment in Portugal. The requirements of an open source application are better quality, higher reliability, more flexibility, lower cost, and an end to predatory vendor lock-in. Three different open source desktop GIS software projects were evaluated: Quantum GIS (QGIS), generalitat valenciana, Sistema d'Informacio Geografica, and Kosmo. Taking into account the skills and experience of the authors, the main advantage of QGIS relies on the easiness and quickness in developing new plug-ins, using Python language. Therefore, this project was developed in QGIS platform and the interface was created in Python. This application incorporates seven procedures under a single toolbar. The production of the forest fire risk map comprises several steps and the production of several maps: probability, susceptibility, hazard, vulnerability, economic value, potential loss, and finally the forest fire risk map. The forest fire risk map comprises five classes: very low risk (dark green), low risk (green), medium risk (yellow), high risk (orange), and very high risk (red). This application was tested in three different municipal governments of the Norwest zone of Portugal. This application has the advantages of grouping in a unique toolbar all the procedures needed to produce forest fire risk maps and is free for the institution/user. Beyond being an open source application, this application may be faster and easier when compared with the GIS proprietary solutions that usually comprise several steps and the use of different software extensions. This work presents several contributions for the area of the GIS open source applications to forest fire risk management.