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.
Based on kernel density estimation methods, this paper introduces an alternative approach of fire occurrence modeling that addresses the inherent positional inaccuracies of recorded wildland fire ...ignition points. These observations, recorded in longitude and latitude using only degrees and first minutes, contain positional inaccuracies of about ± 700 to ± 925 meters in x and y axes. Kernel density estimation was applied to these historical fire observations recorded between 1985 and 1995 in Halkidiki peninsula, Greece, as well as, to simulated inaccurate points into which positional inaccuracies of the same magnitude were randomly introduced. Substantial differences were observed when a regular grid of quadrants was superimposed over the two point distributions. Although, at higher grid resolution these mismatches were minimized, the problem of generalization appeared. Contrar‐ily, the concept of “moving window” assisted to retain high grid resolution and minimize the effect of inaccurate point observations. In addition, the kernel approach, which considers also the relative position of points within the “moving window,” produced more realistic estimates.
Wildfire potential was introduced as a function in the inventory and evaluation methodology of forest resources management, through a conceptual classification of fire danger and fire resistance. ...Fire occurrence and growth are controlled by complex and interrelated phenomena/factors that according to systems analysis were grouped into: (i) external factors (or fire danger), which formulate the surrounding environment of the forest in a large spatial and temporal scale; and (ii) internal factors (or fire resistance), which characterize the structure of the forest stands affecting flammability. Operations research techniques were employed based on
the direct
method of rating to evaluate the magnitude of each factor, including interactions among factors. This procedure has resulted in the synthesis of a common interval scale of four fire danger and four fire resistance classes. The inventory and evaluation procedure was accomplished in three stages concluding with synthesis of the evaluation results of all information levels (using the quality and weight of each factor). Within this synthesis, it becomes possible to calculate the
function class (fire danger) and/or the
suitability class (fire resistance) of each unit area and map their distribution in the forest. The proposed methodology constitutes a theoretical background on which an analytical and practical inventory procedure for forest fire potential can be developed; it also highlights the direction towards which fire management research should focus on assisting forest management and evaluation of forest functions.
This study aims at quantifying and mapping fire-related characteristics of forest structure through field inventories, statistics, remote sensing, and geographical information systems in the island ...of Lesvos, northeast Aegean Sea, Greece. Simulation of fire behaviour requires forest biomass inputs that describe surface fuel types/models along with canopy fuel properties, such as canopy cover, stand height, crown base height, and crown bulk density, to accurately predict surface and crown fire spread and spotting potential. Forest canopy characteristics and other vegetation attributes were sampled and derived in over 100 field plots, the majority of which were located in coastal pine forest stands. Regression models involving four dependent forest stand variables (stand height, canopy cover, crown base height, and crown bulk density) were developed using generalized additive models. The values of adjusted R² were 0.72 for stand height, 0.68 for canopy cover, 0.51 for crown base height, and 0.33 for crown bulk density. These regression models were used to create forest fuel characteristics layers, which can be used as inputs to fire management applications and state-of-the-art landscape-scale fire behaviour models.
Artificial neural networks (ANNs) show a significant ability to discover patterns in data that are too obscure to go through standard statistical methods. Data of natural phenomena usually exhibit ...significantly unpredictable non-linearity, but the robust behavior of a neural network makes it perfectly adaptable to environmental models such as a wildland fire danger rating system. These systems have been adopted by many developed countries that have invested in wildland fire prevention, and thus civil protection agencies are able to identify areas with high probabilities of fire ignition and resort to necessary actions. Since one of the drawbacks of ANNs is the interpretation of the final model in terms of the importance of variables, this article presents the results of sensitivity analysis performed in a back-propagation neural network (BPN) to distinguish the influence of each variable in a fire ignition risk scheme developed for Lesvos Island in Greece. Four different methods were utilized to evaluate the three fire danger indices developed within the above scheme; three of the methods are based on network's weights after the training procedure (i.e., the percentage of influence--PI, the weight product--WP, and the partial derivatives--PD methods), and one is based on the logistic regression (LR) model between BPN inputs and observed outputs. Results showed that the occurrence of rainfall, the 10-h fuel moisture content, and the month of the year parameter are the most significant variables of the Fire Weather, Fire Hazard, and Fire Risk Indices, respectively. Relative humidity, elevation, and day of the week have a small contribution to fire ignitions in the study area. The PD method showed the best performance in ranking variables' importance, while performance of the rest of the methods was influenced by the number of input parameters and the magnitude of their importance. The results can be used by local forest managers and other decision makers dealing with wildland fires to take the appropriate preventive measures by emphasizing on the important factors of fire occurrence.
We describe a Web-GIS wildfire prevention and management platform (AEGIS) developed as an integrated and easy-to-use decision support tool to manage wildland fire hazards in Greece ...(http://aegis.aegean.gr). The AEGIS platform assists with early fire warning, fire planning, fire control and coordination of firefighting forces by providing online access to information that is essential for wildfire management. The system uses a number of spatial and non-spatial data sources to support key system functionalities. Land use/land cover maps were produced by combining field inventory data with high-resolution multispectral satellite images (RapidEye). These data support wildfire simulation tools that allow the users to examine potential fire behavior and hazard with the Minimum Travel Time fire spread algorithm. End-users provide a minimum number of inputs such as fire duration, ignition point and weather information to conduct a fire simulation. AEGIS offers three types of simulations, i.e., single-fire propagation, point-scale calculation of potential fire behavior, and burn probability analysis, similar to the FlamMap fire behavior modeling software. Artificial neural networks (ANNs) were utilized for wildfire ignition risk assessment based on various parameters, training methods, activation functions, pre-processing methods and network structures. The combination of ANNs and expected burned area maps are used to generate integrated output map of fire hazard prediction. The system also incorporates weather information obtained from remote automatic weather stations and weather forecast maps. The system and associated computation algorithms leverage parallel processing techniques (i.e., High Performance Computing and Cloud Computing) that ensure computational power required for real-time application. All AEGIS functionalities are accessible to authorized end-users through a web-based graphical user interface. An innovative smartphone application, AEGIS App, also provides mobile access to the web-based version of the system.
The Emerge of Semantic Geoportals Nikolaos, Athanasis; Kostas, Kalabokidis; Michail, Vaitis ...
Lecture notes in computer science,
2005
Book Chapter, Conference Proceeding
Recenzirano
Geoportals (geographic portals) are entry points on the Web, where various geographic information resources can be easily discovered. They organize geospatial data and services through catalogs ...containing metadata records, which can be queried in order to give access to related resources. However, the current organization of geographic information in metadata catalogs is unable to capture the semantics of the data being described; therefore users often miss geographical resources of interest when searching for geospatial data in the World Wide Web. In this paper, we present an innovative approach in the development of geoportals, based on the next generation of the Web, called the Semantic Web. This approach relies in the organization of the geo-data at the semantic level through appropriate geographic ontologies, and the exploitation of this organization through the user interface of the geoportal. To the best of our knowledge, this approach is the first that combines the expressiveness of geo-ontologies in the context of geographic portals.
The focus of our study was to create a Main Map of fire occurrence zones from historical wildland fire ignition observations at national level in Greece using a Kernel Density estimation procedure. ...Kernel density estimation, a non parametric statistical method for estimating probability densities, has been widely used for home range estimation in wildlife ecology. It has the advantage of directly producing density estimates that are not influenced by grid size and localization effects. Furthermore, it produces densities of any shape and analyzes any data distributed multi-modally or non-normally. Under this perspective, kernel density surfaces have been created to construct fire occurrence zones. Their observed distribution was statistically significantly different than the expected one that arises under complete spatial randomness. A smoothing effect is certainly observed when increasing the bandwidth size of the kernel density interpolation. Excluding the kernel size of 1000 meters, then the results do not prove any influence of kernel size or control points on the kernel density surfaces.
Effective wildfire management is an essential part of forest firefighting strategies to minimize damage to land resources and loss of human lives. Wildfire management tools often require a large ...number of computing resources at a specific time. Such computing resources are not affordable to local fire agencies because of the extreme upfront costs on hardware and software. The emerging cloud computing technology can be a cost- and result-effective alternative. The purpose of this paper is to present the development and the implementation of a state-of-the-art application running in cloud computing, composed of a wildfire risk and a wildfire spread simulation service. The two above applications are delivered within a web-based interactive platform to the fire management agencies as Software as a Service (SaaS). The wildfire risk service calculates and provides daily to the end-user maps of the hourly forecasted fire risk for the next 112 hours in high spatiotemporal resolution, based on forecasted meteorological data. In addition, actual fire risk is calculated hourly, based on meteorological conditions provided by remote automatic weather stations. Regarding the wildfire behavior simulation service, end users can simulate the fire spread by simply providing the ignition point and the projected duration of the fire, based on the HFire algorithm. The efficiency of the proposed solution is based on the flexibility to scale up or down the number of computing nodes needed for the requested processing. In this context, end users will be charged only for their consumed processing time and only during the actual wildfire confrontation period. The system utilizes both commercial and open source cloud resources. The current prototype is applied in the study area of Lesvos Island, Greece, but its flexibility enables expansion in different geographical areas.
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BFBNIB, DOBA, GIS, IJS, IZUM, KILJ, KISLJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
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