scholarly journals ZONEAMENTO DE RISCO DE INCÊNDIOS FLORESTAIS PARA O ESTADO DO PARANÁ

FLORESTA ◽  
2004 ◽  
Vol 34 (2) ◽  
Author(s):  
Danielle Dos Santos De Oliveira ◽  
Antonio Carlos Batista ◽  
Ronaldo Viana Soares ◽  
Leocádio Grodzki ◽  
Jackson Vosgerau
Keyword(s):  
Information System ◽  
High Risk ◽  
Fire Risk ◽  
Geographical Information ◽  
Integrated Analysis ◽  
Fuel Moisture ◽  
Risk Map ◽  
Slope Gradient ◽  
Moderate Risk ◽  
Forest Fire Risk

O objetivo deste trabalho foi obter o Zoneamento de Risco de Incêndio Florestal para o estado do Paraná (ZRIF-PR), considerando o efeito integrado da presença humana, cobertura vegetal, condições meteorológicas e características topográficas. Para esta análise utilizou-se um Sistema de Informações Geográficas. Foram preparados mapas de risco preliminares para cada uma das variáveis em estudo. Estes mapas foram sobrepostos, e o resultado deste cruzamento de informações resultou no ZRIF-PR. De acordo com o ZRIF-PR, 51,87% da área foi classificada como risco moderado e 30,16% como risco alto. Para a validação do ZRIF-PR, o mesmo foi comparado com o mapa de focos de calor e o mapa das ocorrências de incêndio registradas entre 1991 e 2001. O modelo de integração proposto é o mais indicado para gerar o ZRIF-PR porque emprega maior número de variáveis e foi elaborado a partir de condições ambientais similares às do Paraná. Abstract The objective of this research was develop a forest fire risk map through the integrated analysis of human presence, vegetation cover, meteorological variables, fuel moisture, elevation, slope gradient and aspect. For this analysis the Geographical Information System (GIS) was used. The forest risk map (ZRIF-PR) was the result from the superposition of the thematic maps. The fire risk map obtained showed that 51,8% of the area was under moderate risk and 30,16% under high risk.

scholarly journals MODELO DE ZONEAMENTO DE RISCO DE INCÊNDIOS PARA UNIDADES DE CONSERVAÇÃO BRASILEIRAS: O CASO DO PARQUE ESTADUAL DO CERRADO (PR)

FLORESTA ◽  
2011 ◽  
Vol 41 (3) ◽  
Author(s):  
Letícia Koproski ◽  
Matheus Pinheiro Ferreira ◽  
Johann Georg Goldammer ◽  
Antonio Carlos Batista
Keyword(s):  
Protected Areas ◽  
Fire History ◽  
Fire Risk ◽  
Geographical Information ◽  
Slope Aspect ◽  
Integrated Analysis ◽  
Risk Map ◽  
Integration Model ◽  
Forest Protection ◽  
Few Data

Este trabalho teve como objetivo estabelecer um modelo de zoneamento de risco de incêndios pela análise dos fatores físicos, associados às fontes de ignição e aos fatores de propagação dos incêndios, que pudesse ser aplicado à realidade da gestão das áreas protegidas em território brasileiro. Para tanto, o Parque Estadual do Cerrado foi selecionado como área de estudo. Foram produzidos mapas de riscos referentes à cobertura vegetal (V), influências humanas (H), declividade (D), orientação das encostas (E) e altimetria (A). O zoneamento foi gerado pela superposição dos mapas de risco, em função da somatória ponderada dos riscos parciais, representado pela equação: RISCO: 4V +3H + 1D + 1E + 1A. A partir do zoneamento, foi possível identificar duas áreas prioritárias para o manejo do fogo com relação ao risco de incêndios na Unidade. O modelo de integração traduziu adequadamente os níveis de risco e pode ser aplicado em outras unidades de conservação, especialmente em locais onde não existam muitos dados disponíveis sobre o histórico do fogo, ou onde existam poucos dados disponíveis sobre as áreas de estudo. Recomenda-se a utilização do modelo em locais onde não existam diferenças climáticas significativas.Palavras-chave: Mapas de risco; incêndios florestais; SIG; proteção florestal; áreas protegidas. AbstractFire risk mapping for Brazilian protected areas: the case of Cerrado State Park (PR). The aim of this research was to develop a model of forest fire risk map for Brazilian protected areas. The Cerrado State Park, located in Jaguariaíva city, State of Paraná, south of Brazil, was the focused area. The fire risk map was built up through the integrated analysis of vegetation cover (V), slope gradient (G), slope aspect (A), elevation (E), and human activities (H). For this analysis the Geographical Information System (GIS) was used. The fire risk map was the result of the overlay of the preliminary risk maps, by the model represented by the equation: RISK: 4V + 3H + 1G + 1A + 1E. The results presented that the integration model worked successfully for the area, properly managing the variables according to local characteristics and indicated two priority fire management areas in the Park. The model can be applied to protected areas with few data about fire history or few data about the area itself. The model is not recommended to be used in areas with significantly different climates.Keywords: Fire risk map; wildfires; GIS; forest protection.

scholarly journals GIS-Based Forest Fire Risk Model: A Case Study in Laoshan National Forest Park, Nanjing

2021 ◽  
Vol 13 (18) ◽  
pp. 3704
Author(s):  
Pengcheng Zhao ◽  
Fuquan Zhang ◽  
Haifeng Lin ◽  
Shuwen Xu
Keyword(s):  
High Risk ◽  
Forest Fire ◽  
Risk Model ◽  
Fire Risk ◽  
Low Risk ◽  
Assessment Model ◽  
National Forest ◽  
Risk Map ◽  
Forest Park ◽  
Forest Fire Risk

Fire risk prediction is significant for fire prevention and fire resource allocation. Fire risk maps are effective methods for quantifying regional fire risk. Laoshan National Forest Park has many precious natural resources and tourist attractions, but there is no fire risk assessment model. This paper aims to construct the forest fire risk map for Nanjing Laoshan National Forest Park. The forest fire risk model is constructed by factors (altitude, aspect, topographic wetness index, slope, distance to roads and populated areas, normalized difference vegetation index, and temperature) which have a great influence on the probability of inducing fire in Laoshan. Since the importance of factors in different study areas is inconsistent, it is necessary to calculate the significance of each factor of Laoshan. After the significance calculation is completed, the fire risk model of Laoshan can be obtained. Then, the fire risk map can be plotted based on the model. This fire risk map can clarify the fire risk level of each part of the study area, with 16.97% extremely low risk, 48.32% low risk, 17.35% moderate risk, 12.74% high risk and 4.62% extremely high risk, and it is compared with the data of MODIS fire anomaly point. The result shows that the accuracy of the risk map is 76.65%.

scholarly journals Mapping the Forest Fire Risk Zones Using Artificial Intelligence with Risk Factors Data

2022 ◽  
Author(s):  
Volkan Sevinc
Keyword(s):  
Artificial Intelligence ◽  
High Risk ◽  
Forest Fire ◽  
Forest Fires ◽  
Fire Risk ◽  
Geographical Information ◽  
Data Set ◽  
Risk Zone ◽  
Risk Zones ◽  
Forest Fire Risk

Abstract Geographical information system data has been used in forest fire risk zone mapping studies commonly. However, forest fires are caused by many factors, which cannot be explained only by geographical and meteorological reasons. Human-induced factors also play an important role in occurrence of forest fires and these factors depend on various social and economic conditions. This article aims to prepare a fire risk zone map by using a data set consisting of nine human-induced factors, three natural factors, and a temperature factor causing forest fires. Moreover, an artificial intelligence method, k-means, clustering algorithm was employed in preparation of the fire risk zone map. Turkey was selected as the study area as there are social and economic varieties among its zones. Therefore, the forestry zones in Turkey were separated into three groups as low, moderate, and high-risk categories and a map was provided for these risk zones. The map reveals that the forestry zones on the west coast of Turkey are under high risk of forest fire while the moderate risk zones mostly exist in the southeastern zones. The zones located in the interior parts, in the east, and on the north coast of Turkey have comparatively lower forest fire risks.

scholarly journals Zakour Forest fire risk map assessment in the commune of Mamounia (Mascara, Algeria)

2021 ◽  
Vol 63 (1) ◽  
pp. 21-35
Author(s):  
Djamel Anteur ◽  
Abdelkrim Benaradj ◽  
Youcef Fekir ◽  
Djillali Baghdadi
Keyword(s):  
Decision Making ◽  
Forest Fires ◽  
Human Activities ◽  
Forest Ecosystems ◽  
Agricultural Land ◽  
Fire Risk ◽  
Risk Map ◽  
Management Plans ◽  
The City ◽  
Forest Fire Risk

Abstract The great forest of Zakour is located north of the commune of Mamounia (department of Mascara). It is considered the lung of the city of Mascara, covers an area of 126.8 ha. It is a forest that is subject to several natural and human constraints. Among them, the fires are a major danger because of their impacts on forest ecosystems. The purpose of this work is to develop a fire risk map of the Zakour Forest through the contribution of geomatics according to natural and anthropogenic conditions (human activities, agglomeration, agricultural land) while integrating information from ground on the physiognomy of the vegetation. For this, the creation of a clearer fire risk map to delimit the zones potentially sensitive to forest fires in the forest area of Zakour. This then allows good implementation of detection management plans, for better prevention and decision-making assistance in protecting and fighting forest fires.

scholarly journals Application of Geographical Information System Techniques to Determine High Crash-Prone Areas in the Fort Peck Indian Reservation

2020 ◽  
Vol 14 (1) ◽  
pp. 174-185
Author(s):  
Sahima Nazneen ◽  
Mahdi Rezapour ◽  
Khaled Ksaibati
Keyword(s):  
Information System ◽  
High Risk ◽  
Injury Severity ◽  
Statistical Significance ◽  
Temporal Analysis ◽  
The United States ◽  
Geographical Information ◽  
Improvement Program ◽  
Indian Reservation ◽  
Road Segments

Background: Historically, Indian reservations have been struggling with higher crash rates than the rest of the United States. In an effort to improve roadway safety in these areas, different agencies are working to address this disparity. For any safety improvement program, identifying high risk crash locations is the first step to determine contributing factors of crashes and select corresponding countermeasures. Methods: This study proposes an approach to determine crash-prone areas using Geographic Information System (GIS) techniques through creating crash severity maps and Network Kernel Density Estimation (NetKDE). These two maps were assessed to determine the high-risk road segments having a high crash rate, and high injury severity. However, since the statistical significance of the hotspots cannot be evaluated in NetKDE, this study employed Getis-Ord Gi* (d) statistics to ascertain statistically significant crash hotspots. Finally, maps generated through these two methods were assessed to determine statistically significant high-risk road segments. Moreover, temporal analysis of the crash pattern was performed using spider graphs to explore the variance throughout the day. Results: Within the Fort Peck Indian Reservation, some parts of the US highway 13, BIA Route 1, and US highway 2 are among the many segments being identified as high-risk road segments in this analysis. Also, although some residential roads have PDO crashes, they have been detected as high priority areas due to high crash occurrence. The temporal analysis revealed that crash patterns were almost similar on the weekdays reaching the peak at traffic peak hours, but during the weekend, crashes mostly occurred at midnight. Conclusion: The study would provide tribes with the tool to identify locations demanding immediate safety concerns. This study can be used as a template for other tribes to perform spatial and temporal analysis of the crash patterns to identify high risk crash locations on their roadways.

scholarly journals Forest fire risk mapping using analytical hierarchy process (AHP) and earth observation datasets: a case study in the mountainous terrain of Northeast India

2021 ◽  
Vol 3 (4) ◽  
Author(s):  
Ridalin Lamat ◽  
Mukesh Kumar ◽  
Arnab Kundu ◽  
Deepak Lal
Keyword(s):  
High Risk ◽  
Forest Fire ◽  
Analytical Hierarchy Process ◽  
Fire Risk ◽  
Control Measures ◽  
Fire Control ◽  
Risk Zones ◽  
Hierarchy Process ◽  
Forest Fire Risk ◽  
Very High

AbstractThis study presents a geospatial approach in conjunction with a multi-criteria decision-making (MCDM) tool for mapping forest fire risk zones in the district of Ri-Bhoi, Meghalaya, India which is very rich in biodiversity. Analytical hierarchy process (AHP)-based pair-wise comparison matrix was constructed to compare the selected parameters against each other based on their impact/influence (equal, moderate, strong, very strong, and extremely strong) on a forest fire. The final output delineated fire risk zones in the study area in four categories that include very high-risk, high-risk, moderate-risk, and low-risk zones. The delineated fire risk zones were found to be in close agreement with actual fire points obtained from Moderate Resolution Imaging Spectroradiometer (MODIS) fire data for the study area. Results indicated that Ri-Bhoi’s 804.31 sq. km. (32.86%) the area was under ‘very high’ fire susceptibility. This was followed by 583.10 sq. km. (23.82%), 670.47 sq. km. (27.39%), and 390.12 sq. km. (15.93%) the area under high, moderate, and low fire risk categories, respectively. These results can be used effectively to plan fire control measures in advance and the methodology suggested in this study can be adopted in other areas too for delineating potential fire risk zones.

scholarly journals WILD FIRE RISK MAP IN THE EASTERN STEPPE OF MONGOLIA USING SPATIAL MULTI-CRITERIA ANALYSIS

2016 ◽  
Vol XLI-B1 ◽  
pp. 469-473 ◽  
Author(s):  
Elbegjargal Nasanbat ◽  
Ochirkhuyag Lkhamjav
Keyword(s):  
High Risk ◽  
Social Economic ◽  
Risk Index ◽  
Fire Risk ◽  
Low Risk ◽  
Moderate Risk ◽  
Validation Data ◽  
Risk Zone ◽  
Wildfire Risk ◽  
Very High

Grassland fire is a cause of major disturbance to ecosystems and economies throughout the world. This paper investigated to identify risk zone of wildfire distributions on the Eastern Steppe of Mongolia. The study selected variables for wildfire risk assessment using a combination of data collection, including Social Economic, Climate, Geographic Information Systems, Remotely sensed imagery, and statistical yearbook information. Moreover, an evaluation of the result is used field validation data and assessment. The data evaluation resulted divided by main three group factors Environmental, Social Economic factor, Climate factor and Fire information factor into eleven input variables, which were classified into five categories by risk levels important criteria and ranks. All of the explanatory variables were integrated into spatial a model and used to estimate the wildfire risk index. Within the index, five categories were created, based on spatial statistics, to adequately assess respective fire risk: very high risk, high risk, moderate risk, low and very low. Approximately more than half, 68 percent of the study area was predicted accuracy to good within the very high, high risk and moderate risk zones. The percentages of actual fires in each fire risk zone were as follows: very high risk, 42 percent; high risk, 26 percent; moderate risk, 13 percent; low risk, 8 percent; and very low risk, 11 percent. The main overall accuracy to correct prediction from the model was 62 percent. The model and results could be support in spatial decision making support system processes and in preventative wildfire management strategies. Also it could be help to improve ecological and biodiversity conservation management.

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