scholarly journals Innovative Action for Forest Fire Prevention in Kythira Island, Greece, through Mobilization and Cooperation of the Population: Methodology and Challenges

2022 ◽  
Vol 14 (2) ◽  
pp. 594
Author(s):  
Gavriil Xanthopoulos ◽  
Miltiadis Athanasiou ◽  
Alexia Nikiforaki ◽  
Konstantinos Kaoukis ◽  
Georgios Mantakas ◽  
...  

The island of Kythira in Greece suffered a major forest fire in 2017 that burned 8.91% of its total area and revealed many challenges regarding fire management. Following that, the Hellenic Society for the Protection of Nature joined forces with the Institute of Mediterranean and Forest Ecosystems in a project aiming to improve fire prevention there through mobilization and cooperation of the population. This paper describes the methodology and the results. The latter include an in-depth analysis of fire statistics for the island, development of a forest fuels map, and prevention planning for selected settlements based on fire modeling and on an assessment of the vulnerability of 610 structures, carried out with the contribution of groups of volunteers. Emphasis was placed on informing locals, including students, through talks and workshops, on how to prevent forest fires and prepare their homes and themselves for such an event, and on mobilizing them to carry out fuel management and forest rehabilitation work. In the final section of the paper, the challenges that the two partners faced and the project achievements and shortcomings are presented and discussed, leading to conclusions that can be useful for similar efforts in other places in Greece and elsewhere.

2021 ◽  
Author(s):  
yudong Li ◽  
Zhongke Feng ◽  
Ziyu Zhao ◽  
Wenyuan Ma ◽  
Shilin Chen ◽  
...  

Abstract Forest fires can cause serious harm. Scientifically predicting forest fires is an important basis for preventing them. Currently, there is little research on the prediction of long time-series forest fires in China. Choosing a suitable forest fire prediction model and predicting the probability of Chinese forest fire occurrence are of great importance to China’s forest fire prevention and control work. Based on fire hotspot, meteorological, terrain, vegetation, infrastructure, and socioeconomic data collected from 2003 to 2016, we used a random forest model as a feature-selection method to identify 13 major drivers of forest fires in China. The forest fire prediction models developed in this study are based on four machine-learning algorithms: an artificial neural network, a radial basis function network, a support-vector machine, and a random forest. The models were evaluated using the five performance indicators of accuracy, precision, recall, f1 value, and area under the curve. We used the optimal model to obtain the probability of forest fire occurrence in various provinces in China and created a spatial distribution map of the areas with high incidences of forest fires. The results showed that the prediction accuracy of the four forest fire prediction models was between 75.8% and 89.2%, and the area under the curve value was between 0.840 and 0.960. The random forest model had the highest accuracy (89.2%) and area under the curve value (0.96); thus, it was used as the optimal model to predict the probability of forest fire occurrence in China. The prediction results indicate that the areas with high incidences of forest fires are mainly concentrated in north-eastern China (Heilongjiang Province and northern Inner Mongolia Autonomous Region) and south-eastern China (including Fujian Province and Jiangxi Province). In areas at high risk of forest fire, management departments can improve forest fire prevention and control by establishing watch towers and using other monitoring equipment. This study helps in understanding the main drivers of forest fires in China, provides a reference for the selection of high-precision forest fire prediction models, and provides a scientific basis for China’s forest fire prevention and control work.


Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-17 ◽  
Author(s):  
Josué Toledo-Castro ◽  
Pino Caballero-Gil ◽  
Nayra Rodríguez-Pérez ◽  
Iván Santos-González ◽  
Candelaria Hernández-Goya ◽  
...  

Huge losses and serious threats to ecosystems are common consequences of forest fires. This work describes a forest fire controller based on fuzzy logic and decision-making methods aiming at enhancing forest fire prevention, detection, and fighting systems. In the proposal, the environmental monitoring of several dynamic risk factors is performed with wireless sensor networks and analysed with the proposed fuzzy-based controller. With respect to this, meteorological variables, polluting gases and the oxygen level are measured in real time to estimate the existence of forest fire risks in the short-term and to detect the recent occurrence of fire outbreaks over different forest areas. Besides, the Analytic Hierarchy Process method is used to determine the level of fire spread, and, when necessary, environmental alerts are sent by a Web service and received by a mobile application. For this purpose, integrity, confidentiality, and authenticity of environmental information and alerts are protected with implementations of Lamport’s authentication scheme, Diffie-Lamport signature, and AES-CBC block cipher.


2019 ◽  
Vol 26 (1) ◽  
pp. 147-159
Author(s):  
Raffles Brotestes Panjaitan ◽  
Sumartono Sumartono ◽  
Sarwono Sarwono ◽  
Choirul Saleh

Purpose The purpose of this paper is to investigate forest fires and their relationship to prevention and mitigation strategies based on the empirical problems raised by this study. Public policy implementation (in this case, the policy of forest fire management) is influenced by the role played by government and by the participation of the public and stakeholders (in this case, companies), as well as the effects of good governance. Thus, from the empirical problems associated with theoretical problems and normative problems, this study raises the influence of the role of central and local government on the implementation of forest fire prevention policy in Indonesia, which is moderated by the good governance variable. Design/methodology/approach This study used a quantitative approach by adopting survey methodology. The study has aimed to assess both large and small population groups, by selecting and reviewing carefully chosen samples of the population to find the incidence, distribution and relative interrelation of the variables considered (Kerlinger and Lee, 2000). The survey was undertaken in areas of Indonesia that have a high level of vulnerability to forest fires. There are currently six provinces – Riau, Jambi, South Sumatra, West Kalimantan, East Kalimantan and South Kalimantan – that have the highest intensity of forest fires. The study population was taken from 105 villages in those six major provinces experiencing forest fires. Sample size precision was determined by using Slovin’s formula with a precision of 10 percent and, thus, a sample size of 52 was obtained. Findings The central government’s activities have no significant effect on regional forest fire prevention. However, the results found that there is a significant effect caused by the interaction between the central and local governments and their governance of forest fire prevention. Even though the direct effect is not significant, the interaction effect significantly influences the forest fire prevention governance variable, which is a pure moderator. This study found that the role of central government has no effect on forest fire prevention. If the role of the central government is high, it will not impact the effectiveness of forest fire prevention, which is reflected in the aspects of prevention and early warning, reward and punishment, the improvement and management of ecosystems by reviewing courts, law enforcement and national and regional synergy. Originality/value This is one of the few public administration science studies to have investigated the relationship between good governance and forest fire policy in Indonesia, particularly the combined roles played by central and local governments.


2010 ◽  
Vol 161 (11) ◽  
pp. 460-464
Author(s):  
Andrea Kaltenbrunner

Thanks to fast alarm systems and modern fire-fighting equipment most forest fires can be extinguished while still very small. Nevertheless, the fire brigade and forest organisations in the Grisons are recurringly confronted with larger fires. Over the past twenty years the Grisons Forestry Service and the fire section of the cantonal Building Insurance Company have invested in fire prevention and improved fire-fighting techniques. To monitor and assess the risk of forest fires, the computer-aided forest fire forecasting system “Incendi” was developed. On its basis, regional forest fire risk maps are drawn up and bans on the lighting of fires are imposed. For use in case of fire, the Forestry Service has drawn up maps of the whole Canton Grisons showing water supply points in and near the forest. Where there are gaps in the water supply, artificial water sources are being created. Fifteen years ago a concept of forest fire-fighting bases was elaborated. The most important elements of this concept are the 18 regional depots of mobile fire-fighting material, which in case of emergency can be transported where needed. The present-day administrative structures and the precautionary measures taken in the Grisons fulfil the conditions for efficient forest fire prevention and control.


2020 ◽  
Author(s):  
yudong Li ◽  
Zhongke Feng ◽  
Ziyu Zhao ◽  
Shilin Chen ◽  
Hanyue Zhang

Abstract Forest fires can cause serious harm in many ways. Studying the scientific prediction of forest fires is an important basis for preventing such fires. At present, there is little research on the prediction of long time series forest fires in China. Choosing a suitable forest fire prediction model is of great importance to China’s forest fire prevention and control work. Based on data on fire hotspots, meteorology, terrain, vegetation, infrastructure, and socio-economics collected from 2003 to 2016, we used a random forest model as a feature-selection method to determine 13 major drivers of forest fires in China (such as temperature, terrain etc.). The forest fire prediction models developed in this study are based on four machine-learning algorithms: an artificial neural network, a radial basis function network, a support-vector machine, and a random forest. The models were evaluated using the five performance indicators of accuracy, precision, recall, f1 value, and area-under-the-curve value. We used the optimal model to obtain the probability of forest fire occurrence in various provinces in China and create a spatial distribution map of the areas with high incidences of forest fires. The results show that the prediction accuracy of the four forest fire prediction models is between 75.8% and 89.2%, and the area-under-the-curve value is between 0.840 and 0.960. The random forest model has the highest accuracy (89.2%) and area-under-the-curve value (0.96). It is used as the optimal model to predict the probability of forest fire occurrence in China. The prediction results indicate that the areas with high incidences of forest fires are mainly concentrated in northeastern China (Heilongjiang Province and northern Inner Mongolia Autonomous Region), southeastern China (including Fujian Province and Jiangxi Province) etc. In those areas at high risk of forest fires, the management departments can improve the forest fire prevention and control by establishing watch towers and using other monitoring equipment. This study not only helps in understanding the main drivers of forest fires in China, but it also provides a reference for the selection of high-precision forest fire prediction models and provides a scientific basis for China’s forest fire prevention and control work.


Author(s):  
Pham Xuan Canh

Son La is a mountainous province in the Northwest of Vietnam with many ethnic groups, and has an area of ​​14,125 km², accounting for 4.27% of the total area of ​​Vietnam. The ​​forest land accounts for 73% of the total natural area of ​​the province with 357,000 ha of forest. Among this having 4 areas of special use and the natural reserve forest. Every year, hundreds of forest fires cause huge natural, economic and ecological damages to the province. Due to the climate change, forest fires tend to increase in recent years. In order to prevent the fires, warning maps of the forest fire risk are needed. The research has analyzed mechanism and causes of the forest fires, and built a forest fire-related database with multi-layers of natural, social and economic information, in these, some layers were extracted from the Landsat 7 images. The expert method was applied for assessement and Saaty's Hierarchical Analysis (AHP) methods were applied to determine the weight for separated parameters related to forest fires. The research applied the MCA method to build a multi-indicator function with 9 parameters for establishing the forest fire risk map at the scale of 1:100,000 for provincial levels. In verifying the results by regression correlation analysis, the R2 value reached 0.71.These maps have been used for the purpose of forest fire prevention planning for Son La province.


2000 ◽  
Vol 151 (9) ◽  
pp. 325-335 ◽  
Author(s):  
Giovanni Bovio

Important forest fire prevention developments of the Lombardy, Piedmont and Aosta Valley regions are highlighted in this study and a certain number of activities considered able to improve the situation are proposed.


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