Fire Severity Assessment Research Articles

Next-gen regional fire risk mapping: Integrating hyperspectral imagery and National Forest Inventory data to identify hot-spot wildland-urban interfaces

The increasing threat of high-severity wildfires in Mediterranean Wildland-Urban Interface (WUI) areas demands to develop effective fire risk assessment and management strategies. Simultaneously, the newfound accessibility of spaceborne hyperspectral data represents a significant potential for generating fire severity assessments, whereas National Forest Inventories (NFI) offer a vast dataset related to vegetation and fuel loads, which is essential for shaping the planning and strategies of forest services. This research work aims to advance the state-of-the-art in WUI fire risk mapping in the western Mediterranean Basin by combining PRISMA spaceborne hyperspectral data and Spanish NFI data. The proposed methodology had three main stages: (i) fire severity assessment at local scale (a wildfire) by using PRISMA hyperspectral data and Multi-Endmember Spectral Mixture Analysis (MESMA) leveraging field-based measurements of the Composite Burn Index (70 plots); (ii) development of a high fire severity probability map at regional scale from the extrapolation of a Random Forest predictive model calibrated from fire severity estimates, NFI data and topo-climatic variables at local scale (overall accuracy = 92 %; Kappa = 0.8); and (iii) identification and characterization of zones that concentrate WUIs with high probability of high fire severity if a fire event occurs (hot-spot WUIs) by crossing the information from the previous regional high fire severity probability map and a WUI cartography developed at regional scale. Study area was Castilla y León Autonomous Region (larger Spanish region, 94,226 km2), where the second-largest extreme Spanish wildfire event (28,000 ha) occurred. We identified hot-spot WUIs so that stakeholders and decision-makers could (i) prioritize resources and interventions for effective fire management and mitigation, (ii) allocate resources for prevention, and (iii) plan evacuation measures to safeguard lives and property. This study contributes to the development of next-generation fire risk assessment methods that combine remote sensing technologies with comprehensive ground-level datasets.

Methodology for burned areas delimitation and fire severity assessment using Sentinel-2 data. A case study of forest fires occurred in Spain between 2018 and 2023

The objective of this article was to develop a methodology for the burned areas delimitation and fire severity assessment in forest fires occurred in Spain between 2018 and 2022. As input data, this study was based on the use of Sentinel-2 spectral indices, which are characterized by having spectral bands in the near-infrared (NIR) and short-wave infrared (SWIR) spectral regions, allowing a high distinction between burned and unburned areas, and between different fire severity degrees too. All possible combinations between Sentinel-2 bands applied to a spectral normalized difference index (SP) were analyzed, along with the most commonly used burn spectral indices in remote sensing as the Burned Area Index (BAI), the Burned Area Index for Sentinel-2 (BAIS2), the Mid-Infrared Burn Index (MIRBI), the Normalized Burn Ratio (NBR), the Relativized Burn Ratio (RBR) and the relative differential Normalized Burn Ratio (RdNBR). In addition, in order to delete confusions between burned area and the presence of other land cover areas, the Sentinel-2 Global Land Cover (S2GLC 2017) and the temporal differences between pre-fire and post-fire dates were obtained for each spectral index (dSP). The results were compared by: in the case of burned areas, the Emergency Mapping Service (EMS) and the Galicia forest service; in the case of fire severity, using field plots classified as in Ruiz-Gallardo et al. (2004) study (null, low, moderate and high severity). The final statistic results obtained showed that the dNBR2 spectral index (using B11 and B12 Sentinel-2 spectral bands) provided the highest results of burned area delimitation (7% of commission error and 3% omission error, respectively) whereas, the combination of the BAIS2, the NBR and the modified Normalized Burn Ratio (NBR2, using B7 and B12 Sentinel-2 spectral bands), used in areas with low, mix and full vegetation respectively, provided the highest results in fire severity assessment (kappa statistic, F1-score and Balanced Accuracy equal to 0.87, 0.86 and 0.92, respectively). The methodology developed in this work allows obtaining accurate maps of burned area and fire severity in Spain, contributing to the reinforcement of national forest fires statistics.

Assessment of forest fire severity and land surface temperature using Google Earth Engine: a case study of Gujarat State, India

Forest fires are a recurring issue in many parts of the world, including India. These fires can have various causes, including human activities (such as agricultural burning, campfires, or discarded cigarettes) and natural factors (such as lightning). The present study presents a comprehensive and advanced methodology for assessing wildfire susceptibility by integrating diverse environmental variables and leveraging cutting-edge machine learning techniques across Gujarat State, India. The primary goal of the study is to utilize Google Earth Engine to compare locations in Gujarat, India, before and after forest fires. High-resolution satellite data were used to assess the amount and types of changes caused by forest fires. The present study meticulously analyzes various environmental variables, i.e., slope orientation, elevation, normalized difference vegetation index (NDVI), drainage density, precipitation, and temperature to understand landscape characteristics and assess wildfire susceptibility. In addition, a sophisticated random forest regression model is used to predict land surface temperature based on a set of environmental parameters. The maps that result depict the geographical distribution of normalized burn ratio and difference normalized burn ratio and land surface temperature forecasts, providing valuable insights into spatial patterns and trends. The findings of this work show that an automated temporal analysis utilizing Google Earth Engine may be used successfully over a wide range of land cover types, providing critical data for future monitoring of such threats. The impact of forest fires can be severe, leading to the loss of biodiversity, damage to ecosystems, and threats to human settlements.

MAPATON 2023: implementing a tool for the analysis of forest fire zones in Arequipa

The "Mapaton 2023," a collaborative initiative by the Peruvian Space Agency, Paraguayan Space Agency, and AMERIGEOS, introduces an innovative approach to analyze fire-prone areas in Arequipa, a pivotal endeavor for disaster prevention. Focused on the provinces of Arequipa and Caylloma, recent studies identified over 1,500 affected hectares by forest fires, emphasizing the urgency of employing satellite images and geospatial analysis techniques. Leveraging Landsat 8 satellite imagery, the research calculated indices, including the normalized difference vegetation index (NDVI) for vegetation analysis and the differenced normalized burn ratio (dNBR) for fire severity assessment. Results revealed varying impacts, with some areas exhibiting increased vegetation and others displaying significant damage. The use of ArcGIS online facilitated the presentation of geospatial data, emphasizing the utility of remote sensing in comprehending and addressing forest fires. Drawing insights from analogous studies in Mexico and the Amazon, this research underscores the importance of remote sensing and geospatial analysis in informing preventive measures against wildfires. The findings, crucial for environmental management, are recommended for sharing with relevant authorities, and the continued use of diverse satellite imagery sources is encouraged to enhance accuracy in monitoring and mitigating forest fires.

Assessment of the Analytic Burned Area Index for Forest Fire Severity Detection Using Sentinel and Landsat Data

The quantitative assessment of forest fire severity is significant for understanding the changes in ecological processes caused by fire disturbances. As a novel spectral index derived from the multi-objective optimization algorithm, the Analytic Burned Area Index (ABAI) was originally designed for mapping burned areas. However, the performance of the ABAI in detecting forest fire severity has not been addressed. To fill this gap, this study utilizes a ground-based dataset of fire severity (the composite burn index, CBI) to validate the effectiveness of the ABAI in detecting fire severity. First, the effectiveness of the ABAI regarding forest fire severity was validated using uni-temporal images from Sentinel-2 and Landsat 8 OLI. Second, fire severity accuracy derived from the ABAI with bi-temporal images from both sensors was evaluated. Finally, the performance of the ABAI was tested with different sensors and compared with representative spectral indices. The results show that (1) the ABAI demonstrates significant advantages in terms of accuracy and stability in assessing fire severity, particularly in areas with large numbers of terrain shadows and severe burn regions; (2) the ABAI also shows great advantages in assessing regional forest fire severity when using only uni-temporal remotely sensed data, and it performed almost as well as the dNBR in bi-temporal images. (3) The ABAI outperforms commonly used indices with both Sentinel-2 and Landsat 8 data, indicating that the ABAI is normally more generalizable and powerful and provides an optional spectral index for fire severity evaluation.

First evaluation of fire severity retrieval from PRISMA hyperspectral data

The unprecedented availability of spaceborne hyperspectral data has great potential to provide fire severity estimates that align with post-fire management needs, overcoming complex logistics and data acquisition costs of airborne hyperspectral sensors, and the suboptimal sensitivity of broadband data to several post-fire ground components. We analyzed the feasibility of the PRISMA mission -one of the first spaceborne spectrometers operationally active- to assess fire severity by leveraging hyperspectral data dimensionality through the retrieval of sub-pixel components directly related to fire severity in the field. Multispectral data provided by Sentinel-2, commonly used in fire severity quantitative assessments, were used as a benchmark method. Multiple endmember spectral mixture analysis (MESMA) was used to retrieve fractional cover of char, photosynthetic vegetation (PV), as well as non-photosynthetic vegetation and soil (NPVS) from post-fire PRISMA Level 2D and Sentinel-2 Level 2A scenes in one of the largest wildfires ever recorded in the western Mediterranean Basin. Ground-truth data were obtained using the Composite Burn Index (CBI) to procure three field-measured severity metrics: vegetation, soil and site. The relationship between the CBI data on a continuum scale and the cover of char, PV and NPVS image fractions retrieved from PRISMA and Sentinel-2 was assessed through Random Forest regression (RFR). Likewise, Ordinal Forests (OF) algorithm was used to classify categorized CBI data (low, moderate and high fire severity). PRISMA-based RFR fire severity estimates at vegetation, soil and site levels (R2 = 0.64–0.79 and RMSE = 0.33–0.41) outperformed those of Sentinel-2 (R2 = 0.27–0.53 and RMSE = 0.54–0.60), and were in line with previous studies using airborne hyperspectral sensors at higher spatial resolution. Fire severity underestimation for high field CBI values was almost unnoticeable in the PRISMA estimates. Categorical fire severity, not currently estimated using hyperspectral data but with high interest in post-fire management, were accurately predicted by PRISMA-based OF classification, with consistent user's and producer's accuracy for each fire severity category. The high confusion between moderate and low/high fire severity categories, typical when unmixing broadband multispectral data, was overcome by the PRISMA-based classification scheme. Our results suggest that new spaceborne spectrometer missions can support reliable fire severity assessments equivalent to airborne spectrometers, but readily applicable to large-scale assessments of extreme wildfire events.

Assessing fire severity in Turkey’s forest ecosystems using spectral indices from satellite images

Fire severity classifications determine fire damage and regeneration potential in post-fire areas for effective implementation of restoration applications. Since fire damage varies according to vegetation and fire characteristics, regional assessment of fire severity is crucial. The objectives of this study were: (1) to test the performance of different satellite imagery and spectral indices, and two field—measured severity indices, CBI (Composite Burn Index) and GeoCBI (Geometrically structured Composite Burn Index) to assess fire severity; (2) to calculate classification thresholds for spectral indices that performed best in the study areas; and (3) to generate fire severity maps that could be used to determine the ecological impact of forest fires. Five large fires in Pinus brutia (Turkish pine) and Pinus nigra subsp. pallasiana var. pallasiana (Anatolian black pine)—dominated forests during 2020 and 2021 were selected as study sites. The results show that GeoCBI provided more reliable estimates of field—measured fire severity than CBI. While Sentinel-2 and Landsat-8/OLI images performed similarly well, MODIS performed poorly. Fire severity classification thresholds were determined for Sentinel-2 based RdNBR, dNBR, dSAVI, dNDVI, and dNDMI and Landsat-8/OLI based dNBR, dNDVI, and dSAVI. Among several spectral indices, the highest accuracy for fire severity classification was found for Sentinel-2 based RdNBR (72.1%) and Landsat-8/OLI based dNBR (69.2%). The results can be used to assess and map fire severity in forest ecosystems similar to those in this study.

Fire severity assessment on peatland vegetation diversity

Forest fires have a significant impact on peatland ecosystem, including its vegetation diversity. Several approaches applied for forest fire severity assessment are available in the literature. This study aimed to implement field-based and spatial-aware fire severity assessments and to compare both approaches. Peat swamp forests of Ogan Komering Ilir, South Sumatera Province, for field assessment and Siak, Riau Province, for spatial assessment were selected in this research. Field-based fire severity assessment used indicators of vegetation analyses (individual tree damages, burned vegetation severity, and diversity index), which were measured and analyzed from sample plots in both burned and unburned areas. Spatial analyses of fires severity assessment employed Sentinel-2 satellite imagery with Normalized Burn Ratio (NBR) approach. The research revealed a significant change in tree species diversity, indicating a lower diversity in burned areas than the ones in pristine fields. Magnitude of changes in diversity was influenced by fire severity levels in burned areas and pre-fire condition of burned areas. Spatial analyses indicated low to high fire severity with burned area estimation for each fire severity class. Both approaches have their own benefits and challenges. Field-based severity assessment is beneficial for a detailed assessment of fire damages and losses, whereas spatial analyses would be considered for a broader estimation of fire severity. Both assessments are mutually implemented. Field-based assessment can serve as a verification tool for spatial assessment.

EVALUATION OF FIELD-BASED BURN INDICES FOR ASSESSING FOREST FIRE SEVERITY IN LUHANSK REGION, UKRAINE

Evaluation of forest fire severity is a basis of post-fire forest management. Remote sensing-based methods enable reliable delineation of fire perimeters, however, assessments of the degree of forest damage need to be verified and adjusted through field sampling. The forest damage assessment conducted in this study is useful for practitioners to understand and justify the design of clear cuts for restoration purposes. Thus, the aim of the study is to verify the different approaches to field assessment of forest fire severity. In this paper, the authors present a site-specific assessment of large wildfires in Luhansk oblast, Ukraine occurred in 2020 using field-based burn severity indices. The Composite Burn Index (CBI) and the Geometrically Structured Composite Burn Index (GeoCBI) were used to estimate the extent of forest damage. The Burned Area Emergency Response (BAER) methodology was also tested to assess the extent of soil damage. The authors used PlanetScope images to delineate perimeters of burned areas. These perimeters were overlaid over a forest inventory database to extract forest attributes and site characteristics for all forested and unforested areas affected by fires. Within the fire perimeters, the burned area was stratified into six strata to independently account for forest damage in diverse types of land cover. In total 73 test plots were proportionally distributed among different classes of land cover to assess fire severity using CBI, GeoCBI, and BAER approaches. It was found that the fire’s footprints covered 39,782 hectares. Among that area, 21.2% were forested lands. About 78% of burned forests were pine plantations. The highest fire intensity levels were estimated within pure pine plantations that were grown in very dry sites, while the lowest ones were associated with hardwoods forests in moisture site conditions. The average estimates of fire severity using the field-based indices varied within strata (CBI>GeoCBI) which could be an issue for assessing burn severity using remote sensing-based approaches. The authors also concluded that the BAER methodology contributed less to assessing the fire intensity because soil burn severity is not directly related to vegetation damage. This work creates a foundation for further assessment of fire severity using satellite imagery. As a result of this study, a spatial data set of sample plots was proposed that can facilitate calibrating approaches used to map fire severity in the region

Forest and Peatland Fire Severity Assessment at Siak Regency, Riau Province using Sentinel-2 Imagery

Siak Regency, Riau Province is one of the most forest and land fire-prone regencies in Indonesia. Most of the fires occur in peatland areas which contributes to the transboundary haze pollution in the region. Despite limited studies, fire severity assessment is an essential step in post-fire activities to estimate ecological impacts and economic impacts and law enforcement. This study aims to estimate fire severity using Sentinel-2 imagery at Siak Regency, Riau Province. The methods applied Normalized Burn Ratio on Sentinel-2 Imagery as an identification model based on reflectance value for 2019 imagery. The study revealed that burned areas in Siak Regency could be classified into four fire severity classes: low fire severity, moderate-low fire severity, moderate-high fire severity, and high fire severity. High fire severity was found mainly at Sungai Apit and Mempura Districts.

Soil Chemical Properties and Fire Severity Assessment Using VNIR Proximal Spectroscopy in Fire-Affected Abandoned Orchard of Mediterranean Croatia

This paper aims to evaluate the ability of VNIR proximal soil spectroscopy to determine post-fire soil chemical properties and discriminate fire severity based on soil spectra. A total of 120 topsoil samples (0–3 cm) were taken from 6 ha of unburned (control (CON)) and burned areas (moderate fire severity (MS) and high fire severity (HS)) in Mediterranean Croatia within one year after the wildfire. Partial least squares regression (PLSR) and an artificial neural network (ANN) were used to build calibration models of soil pH, electrical conductivity (EC), CaCO3, plant-available phosphorus (P2O5) and potassium (K2O), soil organic carbon (SOC), exchangeable calcium (exCa), magnesium (exMg), potassium (exK), sodium (exNa), and cation exchange capacity (CEC), based on soil reflectance data. In terms of fire severity, CON samples exhibited higher average reflectance than MS and HS samples due to their lower SOC content. The PCA results pointed to the significance of the NIR part of the spectrum for extracting the variance in reflectance data and differentiation between the CON and burned area (MS and HS). DA generated 74.2% correctly classified soil spectral samples according to the fire severity. Both PLSR and ANN calibration techniques showed sensitivity to extract information from soil features based on hyperspectral reflectance, most successfully for the prediction of SOC, P2O5, exCa, exK, and CEC. This study confirms the usefulness of soil spectroscopy for fast screening and a better understanding of soil chemical properties in post-fire periods.

Challenges on fire severity assessment in Indonesia: A vegetation diversity changes perspective

Fire severity assessment plays a significant role in post-fire management, which relates to burned area recovery, economic valuation, and law enforcement. Although various fire severity assessments have been conducted in several burned areas in Indonesia, the standardized method seems limited. This study aims to define criteria and indicators for forest and land fire severity assessment based on systematic reviews using a text mining approach. Systematic reviews on peer-reviewed journal articles related to forest fire severity published in 2010-2021 from Google Scholar source was conducted using Publish or Perish application using keywords of forest fire, severity, criteria, indicator, vegetation, soil, wildlife, living trees, mortality, survival, a burned area, abundance with no citations nor patents included. The linkage among forest fire severity related terms was analyzed using the VOSviewer application, which is based on a text mining approach to identify the most common criteria and indicators for fire severity assessment. The study revealed that there are 991 articles published fits to the keywords, of which 269 articles close related to forest fire severity from 75 peer-reviewed journals. Text mining analyses resulted in 4 clusters of forest fire severity related terms. The criteria commonly used for forest fire severity assessment are vegetation and soil, while the options for indicators including diversity, abundance, forest structure, tree mortality, and burn depth. These criteria and indicators are recommended for formulating forest fire severity assessment standard methods in Indonesia.

Evaluating the Differenced Normalized Burn Ratio for Assessing Fire Severity Using Sentinel-2 Imagery in Northeast Siberian Larch Forests

Fire severity is a key fire regime characteristic with high ecological and carbon cycle relevance. Prior studies on boreal forest fires primarily focused on mapping severity in North American boreal forests. However, the dominant tree species and their impacts on fire regimes are different between North American and Siberian boreal forests. Here, we used Sentinel-2 satellite imagery to test the potential for using the most common spectral index for assessing fire severity, the differenced Normalized Burn Ratio (dNBR), over two fire scars and 37 field plots in Northeast Siberian larch-dominated (Larix cajanderi) forests. These field plots were sampled into two different forest types: (1) dense young stands and (2) open mature stands. For this evaluation, the dNBR was compared to field measurements of the Geometrically structured Composite Burn Index (GeoCBI) and burn depth. We found a linear relationship between dNBR and GeoCBI using data from all forest types (R2 = 0.42, p < 0.001). The dNBR performed better to predict GeoCBI in open mature larch plots (R2 = 0.56, p < 0.001). The GeoCBI provides a holistic field assessment of fire severity yet is dominated by the effect of fire on vegetation. No significant relationships were found between GeoCBI components (overall and substrate stratum) and burn depth within our fires (p > 0.05 in all cases). However, the dNBR showed some potential as a predictor for burn depth, especially in the dense larch forests (R2 = 0.63, p < 0.001). In line with previous studies in boreal North America, the dNBR correlated reasonably well with field data of aboveground fire severity and showed some skills as a predictor of burn depth. More research is needed to refine spaceborne fire severity assessments in the larch forests of Northeast Siberia, including assessments of additional fire scars and integration of dNBR with other geospatial proxies of fire severity.

Spalling Mechanism: Key for Structural Fire Resistance of Tunnels

The need to consider tunnel safety, especially with respect to fire, has been highlighted in the past by various severe tunnel fires that have occurred throughout Europe. This article provides a general overview of the current rules and regulations governing the fire resistance design of tunnel structures. Subsequently, the engineering perspective is presented, focusing on the assessment of fire severity and the resulting thermal loading on the tunnel structure, which may result in spalling of concrete. Thereafter, a scientific perspective is provided, reflecting recent advances made in numerical calculations with respect to the spalling mechanism. From the numerical results and comparison with observations from full-scale tests, it is concluded that thermal restraint is dominant for spalling. Spalling is triggered by the buckling of concrete layers loaded with strains imposed by temperature, which is a new perspective for future scientific research and innovative engineering solutions.

Bark charcoal reflectance may have the potential to estimate the heat delivered to tree boles by wildland fires

Currently, our ability to link wildland fire behaviour to fire effects is through the lens of fire severity assessments, because there are no ground-based post-fire metrics that are able to quantitively capture aspects of heat transfer to plants. This presents a particular challenge when considering tree mortality linked to cambial damage, which can occur in both low-intensity surface fires through to high-intensity crown fires. Recent research suggests that measuring the amount of light reflected from charcoals produced by wildland fires will provide information about the energy flux that created the char. We created an experimental forest fire in which we had instrumented individual trees to record the energy delivered to the bark close to the base of the trees. We then assessed the bark charcoal reflectance of the same trees. We found that bark charcoal reflectance showed a strong positive correlation (r2 > 0.86, P = 0.0031) with increasing duration of heating and the total energy delivered to the bark. We suggest that this may provide useful quantitative data that can be included in models or post-fire surveys to estimate tree mortality due to cambial kill.

Potential Hazard of Open Space Fire in Black Pine Stands (Pinus nigra J.F. Arnold) in Regard to Fire Severity

Black pine (Pinus nigra J.F. Arnold) is one of the most important reforestation species for dry and rocky terrain in the sub-Mediterranean zone. Fire is an important factor in black pine stands that largely defines the distribution range of black pine and the floristic composition of its stands. Fire causes less damage during autumn or early spring when tree cones contain ripe seeds that can partially reforest burnt surfaces. Every fire is a potentially significant threat to forest habitats. There are many damages, from direct damages to wood mass and reforestation costs, to indirect ecological, edaphic, protective damages and the loss of biological diversity. The aim of prevention works in forestry is to reduce the number of fires and reduce burnt surface area per fire. Research was based on vegetation analysis performed on each experimental plot in a black pine stand. The assessment of fire severity was performed using the Canadian Forest Fire Weather Index (FWI). The assessment of the severity of fire indicates the suitability of conditions for the occurrence of forest fires, and enables sound and effective protective methods beginning with silvicultural works. The results indicate better quality stands in the higher parts of the sub-Mediterranean area. Taking into account the climatic parameter, the areas on the border with the continental climate have better habitat conditions for black pine. Also, attention should be focused on 2012 and 2015. High values (Figure 13 – 16.22, Figure 11 – 6.99, Figure 8 – 8.38, Figure 7 – 12.02 and 12.25, Figure 5 – 8.32) indicate the overlapping of extremely dry periods and extremely high summer temperatures, and probably a strong wind influence that further increases the index. Fire severity assessments indicate the suitability of conditions for the onset of fire.

Unitemporal approach to fire severity mapping using multispectral synthetic databases and Random Forests

Fire severity assessment is crucial for predicting ecosystem response and prioritizing post-fire forest management strategies. Although a variety of remote sensing approaches have been developed, more research is still needed to improve the accuracy and effectiveness of fire severity mapping. This study proposes a unitemporal simulation approach based on the generation of synthetic spectral databases from linear spectral mixing. To fully exploit the potential of these training databases, the Random Forest (RF) machine learning algorithm was applied to build a classifier and regression model. The predictive models parameterized with the synthetic datasets were applied in a case study, the Sierra de Luna wildfire in Spain. Single date Landsat-8 and Sentinel-2A imagery of the immediate post-fire environment were used to develop the validation spectral datasets and a Pléiades orthoimage, providing the ground truth data. The four defined severity categories – unburned (UB), partial canopy unburned (PCU), canopy scorched (CS), and canopy consumed (CC) – demonstrated high accuracy in the bootstrapped (about 95%) and real validation sets (about 90%), with a slightly better performance observed when the Sentinel-2A dataset was used. Abundance of four ground covers (green vegetation, non-photosynthetic vegetation, soil, and ash) was also quantified with moderate (~45% for NPV) or high accuracy (higher than 75% for the remaining covers). No specific pattern in the comparison of sensors was observed. Variable importance analysis highlighted the complementary behavior of the spectral bands, although the contrast between the near and shortwave infrared regions stood out above the rest. Comparison of procedures reinforced the usefulness of the approach, as RF image-derived models and the multiple endmember spectral unmixing technique (MESMA) showed lower accuracy. The capabilities for detailed mapping are reflected in the development of different types of cartography (classification maps and fraction cover maps). The approach holds great potential for fire severity assessment, and future research needs to extend the predictive modeling to other burned areas – also in different ecosystems – and analyze its competence and the possible adaptations needed.

Evaluating the severity of building fires with the analytical hierarchy process, big data analysis, and remote sensing

This study assessed the severity of building fires in 17 villages that comprise Taishan District, New Taipei City, Taiwan. A literature review was performed to discuss the impact of fire severity assessment criteria in order to develop items and factors for the analytic hierarchy process (AHP). We identified six items for the building fire severity assessment: rescue response time, narrow road density, water-sacristy area density, building risk, hazardous materials’ place density, and fire safety inspection-regulated premises density. Big data analysis and remote sensing were employed to facilitate devising the AHP structure with items and factors. We also compared the annual average burned area from 2005 to 2015 through the building fire severity assessment to validate assessment accuracy. The actual yearly average burned area in each village of Taishan District was used to verify the building fire severity assessment, and the compliance rate of the rating (i.e., high, moderate, and low) was, respectively, 60%, 67%, and 67%. The proposed assessment is evidently feasible and can act as a reference for quantitative analyses for assessments of building fire severity.

Assessing the effect of fire severity on sediment connectivity in central Chile

Chilean territory is recurrently affected by severe wildfires, which drastically reduce the forest cover and promote runoff, soil erosion and slope instabilities. To understand how the geomorphic system responds to wildfires in terms of sediment dynamics, the assessment of sediment connectivity, i.e. the property describing the relationships between compartments of a geomorphic system, is crucial. This study aims to quantify the spatial linkages between fire severity and sediment connectivity to identify common patterns and driving factors. The compound use of field data and open-source satellite imagery helped to apply the Relative differenced Normalized Burn Ratio (RdNBR) and the Index of Connectivity (IC) in the context of two consecutive wildfires, occurred in 2002 and 2015, in the Rio Toro catchment (Chile). The fire severity assessment showed that the 2002 event affected 90% of the catchment, with high severity areas representing around 70%. The 2015 wildfire instead, affected 76% of the catchment with moderate severity around 42%. Accordingly, as result of the sudden reduction in forest cover in severely affected areas, the IC changed after both wildfires with an overall increase of 1.07 and 0.54, respectively. However, only for the second disturbance, it was possible to observe a clear relationship between the RdNBR and the IC variations. The different degree of vegetation cover heterogeneity between the two pre-wildfire scenarios contributed to different fire severity and IC variability between the two disturbances. The use of open-source satellite data and the development of a weighting factor (W), to be used in IC and able to capture the land cover change driven by the wildfires, could make the application of this approach straightforward, promoting its reproducibility in other catchments for land management and risk mitigation purposes.

Non-Destructive Assessment of Heated Rc Columns Cooled by Two Cooling Methods

Fire constitutes a major hazard to the buildings. The structural members exposed to high temperatures during fire accidents. The phenomenon observed was the structural members undergo dehydration of concrete, thermal distress in the form of cracking and spalling due to excessive heating. This leads to cause degradation of concrete strength. After tackle the fire hazard, the next step is to find the suitability of the building for repair or restoration, which involves assessment of fire severity and fire damage. Nondestructive testing (NDT) is the most practical and widely used methods to obtain in place strength or even for new structure strength. It is an effective tool for damage assessment and quality assurance, as well. In this paper, reinforced concrete (RC) columns made with OPC mix and HVFA mix, exposed to high temperatures ranging from 100 to 800°C. After heating, the specimens were brought to room temperature by air cooling and water quenching methods. The specimens were tested for residual compressive strengths and material integrity of concrete columns, by non-destructive testing methods, to understand how parameters like cooling condition and exposure duration influence the mechanical properties of conventional concrete mix and fly ash concrete mix. OPC concrete mix columns performed better strengths over HVFA mix columns when they cooled by air. Whereas water quenched HVFA mix columns upkeep the concrete quality grading on comparing the OPC mix specimens.