A new wildland fire danger index for a Mediterranean region and some validation aspects

Wildland fires are the main cause of tree mortality in Mediterranean Europe and a major threat to Spanish forests. This paper focuses on the design and validation of a new wildland fire index especially adapted to a Mediterranean Spanish region. The index considers ignition and spread danger components. Indicators of natural and human ignition agents, historical occurrence, fuel conditions and fire spread make up the hierarchical structure of the index. Multi-criteria methods were used to incorporate experts¿ opinion in the process of weighting the indicators and to carry out the aggregation of components into the final index, which is used to map the probability of daily fire occurrence on a 0.5-km grid. Generalised estimating equation models, which account for possible correlated responses, were used to validate the index, accommodating its values onto a larger scale because historical records of daily fire occurrence, which constitute the dependent variable, are referred to cells on a 10-km grid. Validation results showed good index performance, good fit of the logistic model and acceptable discrimination power. Therefore, the index will improve the ability of fire prevention services in daily allocation of resources.The authors acknowledge the support received from the Ministry of Science and Innovation through the research project Modelling and Optimisation Techniques for a Sustainable Development, Ref. EC02008-05895-C02-01/ECON.Vicente López, FJD.; Crespo Abril, F. (2012). A new wildland fire danger index for a Mediterranean region and some validation aspects. 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Satellite-Based Assessment of Grassland Conversion and Related Fire Disturbance in the Kenai Peninsula, Alaska

Spruce beetle-induced (Dendroctonus rufipennis (Kirby)) mortality on the Kenai Peninsula has been hypothesized by local ecologists to result in the conversion of forest to grassland and subsequent increased fire danger. This hypothesis stands in contrast to empirical studies in the continental US which suggested that beetle mortality has only a negligible effect on fire danger. In response, we conducted a study using Landsat data and modeling techniques to map land cover change in the Kenai Peninsula and to integrate change maps with other geospatial data to predictively map fire danger for the same region. We collected Landsat imagery to map land cover change at roughly five-year intervals following a severe, mid-1990s beetle infestation to the present. Land cover classification was performed at each time step and used to quantify grassland encroachment patterns over time. The maps of land cover change along with digital elevation models (DEMs), temperature, and historical fire data were used to map and assess wildfire danger across the study area. Results indicate the highest wildfire danger tended to occur in herbaceous and black spruce land cover types, suggesting that the relationship between spruce beetle damage and wildfire danger in costal Alaskan forested ecosystems differs from the relationship between the two in the forests of the coterminous United States. These change detection analyses and fire danger predictions provide the Kenai National Wildlife Refuge (KENWR) ecologists and other forest managers a better understanding of the extent and magnitude of grassland conversion and subsequent change in fire danger following the 1990s spruce beetle outbreak

On the use of satellite Sentinel 2 data for automatic mapping of burnt areas and burn severity

In this paper, we present and discuss the preliminary tools we devised for the automatic recognition of burnt areas and burn severity developed in the framework of the EU-funded SERV_FORFIRE project. The project is focused on the set up of operational services for fire monitoring and mitigation specifically devised for decision-makers and planning authorities. The main objectives of SERV_FORFIRE are: (i) to create a bridge between observations, model development, operational products, information translation and user uptake; and (ii) to contribute to creating an international collaborative community made up of researchers and decision-makers and planning authorities. For the purpose of this study, investigations into a fire burnt area were conducted in the south of Italy from a fire that occurred on 10 August 2017, affecting both the protected natural site of Pignola (Potenza, South of Italy) and agricultural lands. Sentinel 2 data were processed to identify and map different burnt areas and burn severity levels. Local Index for Statistical Analyses LISA were used to overcome the limits of fixed threshold values and to devise an automatic approach that is easier to re-apply to diverse ecosystems and geographic regions. The validation was assessed using 15 random plots selected from in situ analyses performed extensively in the investigated burnt area. The field survey showed a success rate of around 95%, whereas the commission and omission errors were around 3% of and 2%, respectively. Overall, our findings indicate that the use of Sentinel 2 data allows the development of standardized burn severity maps to evaluate fire effects and address post-fire management activities that support planning, decision-making, and mitigation strategies.Fil: Lasaponara, Rosa. Consiglio Nazionale delle Ricerche; ItaliaFil: Tucci, Biagio. Consiglio Nazionale delle Ricerche; ItaliaFil: Ghermandi, Luciana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto de Investigaciones en Biodiversidad y Medioambiente. Universidad Nacional del Comahue. Centro Regional Universidad Bariloche. Instituto de Investigaciones en Biodiversidad y Medioambiente; Argentina. Universidad Nacional del Comahue. Centro Regional Universitario Bariloche. Laboratorio de Ecotono; Argentin

Detection, Emission Estimation and Risk Prediction of Forest Fires in China Using Satellite Sensors and Simulation Models in the Past Three Decades—An Overview

Forest fires have major impact on ecosystems and greatly impact the amount of greenhouse gases and aerosols in the atmosphere. This paper presents an overview in the forest fire detection, emission estimation, and fire risk prediction in China using satellite imagery, climate data, and various simulation models over the past three decades. Since the 1980s, remotely-sensed data acquired by many satellites, such as NOAA/AVHRR, FY-series, MODIS, CBERS, and ENVISAT, have been widely utilized for detecting forest fire hot spots and burned areas in China. Some developed algorithms have been utilized for detecting the forest fire hot spots at a sub-pixel level. With respect to modeling the forest burning emission, a remote sensing data-driven Net Primary productivity (NPP) estimation model was developed for estimating forest biomass and fuel. In order to improve the forest fire risk modeling in China, real-time meteorological data, such as surface temperature, relative humidity, wind speed and direction, have been used as the model input for improving prediction of forest fire occurrence and its behavior. Shortwave infrared (SWIR) and near infrared (NIR) channels of satellite sensors have been employed for detecting live fuel moisture content (FMC), and the Normalized Difference Water Index (NDWI) was used for evaluating the forest vegetation condition and its moisture status

Vurderer skogbrannen i Chitwan nasjonal park, Nepal

Although fire has long been an Essent foremost of the forest ecosystem and has a significant impact on the flora and fauna, it is also widely believed to be one of the main causes of biodiversity loss and environmental deterioration. Furthermore, little study has been conducted on the timing and location of wildfires in Nepal. Because of this, Chitwan National Park is highly susceptible to wildfires (DFRS, 2015). For wildfire monitoring, detection, and management, geographic information systems (GIS) and remote sensing (RS) are frequently used. Quick and affordable solutions are produced through RS and GIS. United States Geological Survey Earth Explorer website was used to retrieve the Landsat image and digital elevation module. The ICIMOD website was used to acquire information on the study area's land use, land cover, road network, and population. A difference-normalized burn ratio (dNBR) was calculated using geographic information software to assess the severity of the burns and A multi-criteria weighted-overlay analysis was performed to determine the wildfire risk zone. Throughout the research period, 3617 fire events were reported in CNP, with 3135 of them taking place in the core region and 482 in the buffer zone. The variance in the mean value of fire frequency was examined using one-way ANOVA, and it was found that the number of wildfire occurrences during the summer months was substantially high (p-value less than 0.05 at the 5% level of significance). Since 2021 saw the most fire events in CNP from 2001 and 2021 (384 fire incidents), the severity of the year's burns was calculated. A total of 76558.68 hectares of forest were burned in CNP in 2021, per the burn severity study. The research indicates that there is a high risk of wildfire for 6391.6 hectares in CNP, a moderate risk for 154054.4 hectares, and a low risk for 7754.02 hectares. Most events took place in the core area, which was the consequence of deliberate fire used to manage grasslands and slow down succession. However, prevention is advised since it might impair the species that depend on a specific grassland ecosystem

Annual Brome (Bromus tectorum) Wildfire Fuel Breaks: Web-Enabled GIS Wildfire Model Decision Support System

Annual brome (Bromus tectorum), also known as Cheatgrass, is a non-native invasive plant that has degraded rangeland, and wildlife habitat, and has increased the frequency and severity of wildfires in the Western United States. An ArcGIS Server web-enabled GIS decision support system has been developed to empower landowners and land managers to identify critical areas for placing wildfire suppression annual brome fuel break on their land in order to protect their lives and property. The model identifies the critical predictive annual brome habitat and wildfire threat parameters, and uses web services to input the relevant GIS data that represent the model parameters. The GIS analysis is geoprocessed remotely and the potential fire break locations are distributed as a map web service accessible by a web browser with a graphical user interface, on a thinclient computer, along with technical information about the installation of wildfire fuel breaks

Artificial neural networks to detect forest fire prone areas in the southeast fire district of Mississippi

An analysis of the fire occurrences parameters is essential to save human lives, property, timber resources and conservation of biodiversity. Data conversion formats such as raster to ASCII facilitate the integration of various GIS software’s in the context of RS and GIS modeling. This research explores fire occurrences in relation to human interaction, fuel density interaction, euclidean distance from the perennial streams and slope using artificial neural networks. The human interaction (ignition source) and density of fuels is assessed by Newton’s Gravitational theory. Euclidean distance to perennial streams and slope that do posses a significant role were derived using GIS tools. All the four non linear predictor variables were modeled using the inductive nature of neural networks. The Self organizing feature map (SOM) utilized for fire size risk classification produced an overall classification accuracy of 62% and an overall kappa coefficient of 0.52 that is moderate (fair) for annual fires