Every year, hundreds of fires occur in the forests and rangelands across the world and damage thousands hectare of trees, shrubs, and plants which cause environmental and economic damages. This study aims to establish a real time forest fire alert system for better forest management and monitoring in Golestan Province. In this study, in order to prepare fire hazard maps, the required layers were produced based on fire data in Golestan forests and MODIS sensor data. At first, the natural fire data was divided into two categories of training and test samples randomly. Then, the vegetation moisture stresses and greenness were considered using six indexes of NDVI, MSI, WDVI, OSAVI, GVMI and NDWI in natural fire area of training category on the day before fire occurrence and a long period of 15 years, and the risk threshold of the parameters was considered in addition to selecting the best spectral index of vegetation. Finally, the model output was validated for fire occurrences of the test category. The results showed the possibility of prediction of fire site before occurrence of fire with more than 80 percent accuracy.

The main long-term goal of international communities is to achieve sustainable development. This issue is currently highly topical in most European Union (EU) countries due to the ongoing energy crisis. Building Integrated Photovoltaics (BIPV), which can be integrated into the building surface (roof or facade), thereby replacing conventional building materials, contributes significantly to achieving zero net energy buildings. However, fire safety is important when using BIPV as a structural system in buildings, and it is essential that the application of BIPV as building facades and roofs does not adversely affect the safety of the buildings, their occupants, or the responding firefighters. As multifunctional products, BIPV modules must meet fire safety requirements in the field of electrical engineering as well as in the construction industry. In terms of building regulations, the fire safety requirements of the BIPV must comply with national building regulations. Within this article, aspects and fire hazards associated with BIPV system installations will be defined, including proposals for installation and material requirements that can help meet fire safety.

Fire hazard is often mapped as a static conditional probability of fire characteristics’ occurrence. We developed a dynamic product for operational risk management to forecast the probability of occurrence of fire radiative power in the locally possible near-maximum fire intensity range. We applied standard machine learning techniques to remotely sensed data. We used a block maxima approach to sample the most extreme fire radiative power (FRP) MODIS retrievals in free-burning fuels for each fire season between 2001 and 2020 and associated weather, fuel, and topography features in northwestern south America. We used the random forest algorithm for both classification and regression, implementing the backward stepwise repression procedure. We solved the classification problem predicting the probability of occurrence of near-maximum wildfire intensity with 75% recall out-of-sample in ten annual test sets running time series cross validation, and 77% recall and 85% ROC-AUC out-of-sample in a twenty-fold cross-validation to gauge a realistic expectation of model performance in production. We solved the regression problem predicting FRP with 86% r² in-sample, but out-of-sample performance was unsatisfactory. Our model predicts well fatal and near-fatal incidents reported in Peru and Colombia out-of-sample in mountainous areas and unimodal fire regimes, the signal decays in bimodal fire regimes.

Forest fire, as a discontinuous ecological factor of forest, causes the changes of carbon storage and carbon distribution in forest ecosystem, and affects the process of forest succession and national carbon capacity. Taking the burned land with different forest fire interference intensity as the research object, using the comparison method of adjacent sample plots, and taking the combination of field investigation sampling and indoor test analysis as the main means, this paper studies the influence of different forest fire interference intensity on the carbon pool of forest ecosystem and the change and spatial distribution pattern of ecosystem carbon density, and discusses the influence mechanism of forest fire interference on ecosystem carbon density and distribution pattern. The results showed that forest fire disturbance reduced the carbon density of vegetation (P < 0.05). The carbon density of vegetation in the light, moderate and high forest fire disturbance sample plots were 67.88, 35.68 and 15.50 t∙hm^-2, which decreased by 15.86%, 55.78% and 80.79% respectively compared with the control group. In the light, moderate and high forest fire disturbance sample plots, the carbon density of litter was 1.43, 0.94 and 0.81 t∙hm^-2, which decreased by 28.14%, 52.76% and 59.30% respectively compared with the control group. The soil organic carbon density of the sample plots with different forest fire disturbance intensity is lower than that of the control group, and the reduction degree gradually decreases with the increase of soil profile depth. The soil organic carbon density of the sample plots with light, moderate and high forest fire disturbance is 103.30, 84.33 and 70.04 t∙hm^-2 respectively, which is 11.670%, 27.899% and 40.11% lower than that of the control group respectively; the carbon density of forest ecosystem was 172.61, 120.95 and 86.35 t∙hm^-2 after light, moderate and high forest fire disturbance, which decreased by 13.53%, 39.41% and 56.74% respectively compared with the control group; forest fire disturbance reduced the carbon density of eucalyptus forest, which showed a law of carbon density decreasing with the increase of forest fire disturbance intensity. Compared with the control group, the effect of light forest fire disturbance intensity on the carbon density of eucalyptus forest was not significant (P > 0.05), while the effect of moderate and high forest fire disturbance intensity on the carbon density of eucalyptus forest was significant (P < 0.05).

Fire, a phenomenon occurs in most parts of the world and causes severe financial losses, even, irreparable damages. Many parameters are involved in the occurrence of a fire; some of which are constant over time (at least in a fire cycle), but the others are dynamic and vary over time. Unlike the earthquake, the disturbance of fire depends on a set of physical, chemical, and biological relations. Monitoring the changes to predict the occurrence of fire is efficient in forest management. Method: In this research, the Persian and English databases were structurally searched using the keywords of fire risk modeling, fire risk, fire risk prediction, remote sensing and the reviewed papers that predicted the fire risk in the field of remote sensing and geographic information system were retrieved. Then, the modeling and zoning data of fire risk prediction were extracted and analyzed in a descriptive manner. Accordingly, the study was conducted in 1995-2017. Findings: Fuzzy analytic hierarchy process (AHP) zoning method was more practical among the applied methods and the plant moisture stress measurement was the most efficient among the remote sensing indices. Discussion and Conclusion: The findings indicate that RS and GIS are effective tools in the study of fire risk prediction.

The Sipongi System is essential in dealing with forest and land fires because this system provides real-time data that empowers stakeholders and communities to proactively overcome fire dangers. Its advantages are seen in its ability to provide detailed information regarding weather conditions, wind patterns, water levels in peatlands, air quality, and responsible work units. This data facilitates efficient decision-making and resource allocation for fire prevention and control. As an embodiment of Collaborative Governance, the Sipongi System actively involves various stakeholders, including government institutions, local communities, environmental organizations and the private sector. This cooperative approach fosters collective responsibility and accountability, improving fire management efforts. The Sipongi approach is critical in reducing forest and land fires in Indonesia by providing real-time data and a collaborative governance model. This results in faster response times, more effective fire prevention and better resource allocation. Although initially designed for Indonesia, the adaptable nature of the system makes it a blueprint for addressing similar challenges in other countries and regions, tailored to specific needs and environmental conditions. Qualitative research methods underlie this study, including interviews with key stakeholders and analysis of credible sources. Government officials, community leaders, environmental experts and organizational representatives were interviewed to comprehensively examine the mechanisms of the Sipongi System and its impact on forest and land fire management in Indonesia. Future research should explore the application of Sipongi Systems and collaborative governance in various contexts by conducting comparative studies across countries and ecosystems. Additionally, assessing the long-term impact and sustainability of the Sipongi System is critical to evaluating its effectiveness over time.