Search Results (26)

Eaton Canyon in California serves as the focal point for a comprehensive post-wildfire ecological impact assessment. This study employs an approach integrating satellite imagery from the European Space Agency’s Sentinel constellation to study an area of 271.49 km². The data encompasses both radar and multispectral data, offering a multi-dimensional view of the affected landscape. The analysis leverages the power of the random forest algorithm. Firstly, three widely used indices—the difference normalized burn ratio (dNBR), relative burn ratio (RBR), and relative difference normalized burn ratio (RdNBR)—were calculated and compared based on their accuracy and Kappa index. Secondly, we developed a fusion approach by using all the fire indices to obtain a precise severity map by classifying the affected area into distinct severity classes. Thirdly, a separate fusion approach was developed utilizing the normalized difference vegetation index (NDVI), radar vegetation index (RVI), and modified normalized difference vegetation index (MNDVI) to analyze the distribution of vegetation before and after the wildfire. The merger proposals were developed using a combination of index values to obtain better information on the fire severity map and post-fire vegetation distribution. The results indicated an accuracy of 78% when employing the dNBR index. A higher accuracy of 81% was observed with the RBR index, while the RdNBR demonstrated an accuracy of 95%. Our approach, which combines all fire indicators, offers optimal accuracy of 99%. A percentage of 56.76% did not burn due to the topography of the canyon creating natural firebreaks. Areas classified as low severity (7.83%) showed minimal damage with minimal tree mortality. Moderate- to low-severity areas (5.83%) represented regions with partial crown burns and some tree mortality. Moderate- to high-severity areas (7.22%) showed significant tree mortality. Finally, high-severity areas (22.36%), characterized by complete tree mortality and significant loss of vegetation cover, were largely concentrated in specific sections of the canyon, likely influenced by factors such as slope and fuel type. These findings provide valuable information for post-fire ecological recovery efforts and future land management strategies in Eaton Canyon and similar fire-prone landscapes. Full article

We present a site-agnostic workflow to identify Fireline Tactical Support Points (FTSPs) and corridors following wildfire where spectral-change proxies (dNBR, RdNBR, and dNDVI) are paired pre/post-fire and co-registered on a 20 m grid together with a 72 h rainfall accumulation layer, which is treated as an operational feasibility and safety overlay, complementing access and terrain. Applied to the Vesuvius National Park (Italy) wildfire episode of August 2025, the pipeline yields suitability/susceptibility surfaces, ranked factors, and corridor candidates, with estimated successes including coherent prioritization within high-severity mosaics, improved continuity toward existing access routes, and reduced overlap with mapped sensitive areas at like-for-like suitability. Low-carbon staging is retained as a design safeguard, while detailed greenhouse-gas accounting is intentionally deferred to future, fleet-resolved multi-criteria analyses. The approach enables rapid, repeatable decision support and is relevant to SDG 11 (Sustainable Cities and Communities), SDG 13 (Climate Action), and SDG 15 (Life on Land). Full article

Chyulu Hills, Kenya, serves as one of the region’s water towers by supplying groundwater to surrounding streams and springs in southern Kenya. In a semiarid region, this water is crucial to the survival of local people, farms, and wildlife. The Chyulu Hills is also very prone to fires, and large areas of the range burn each year during the dry season. Currently, there are no detailed fire records or burn scar maps to track the burn history. Mapping burn scars using remote sensing is a cost-effective approach to monitor fire activity over time. However, it is not clear whether spectral burn indices developed elsewhere can be directly applied here when Chyulu Hills contains mostly grassland and bushland vegetation. Additionally, burn scars are usually no longer detectable after an intervening rainy season. In this study, we calculated the Differenced Normalized Burn Ratio (dNBR) and two versions of the Relative Differenced Normalized Burn Ratio (RdNBR) using Landsat Operational Land Imager (OLI) and Sentinel-2 MultiSpectral Instrument (MSI) data to determine which index, threshold values, instrument, and Sentinel near-infrared (NIR) band work best to map burn scars in Chyulu Hills, Kenya. The results indicate that the Relative Differenced Normalized Burn Ratio from Landsat OLI had the highest accuracy for mapping burn scars while also minimizing false positives (commission error). While mapping burn scars, it became clear that adjusting the threshold value for an index resulted in tradeoffs between false positives and false negatives. While none were perfect, this is an important consideration going forward. Given the length of the Landsat archive, there is potential to expand this work to additional years. Full article

Pre-fire environmental conditions play a critical role in wildfire severity. This study investigated the impact of pre-fire forest conditions on burn severity as a result of the 2020 Bighorn Fire in the Santa Catalina Mountains in Arizona. Using a stepwise regression model and remotely sensed data from Landsat 8 and LiDAR, we analyzed the effects of structural and functional vegetation traits and environmental factors on burn severity. This analysis revealed that the difference normalized burn ratio (dNBR) was a more reliable indicator of burn severity compared to the relative dNBR (RdNBR). Stepwise regression identified pre-fire normalized difference vegetation index (NDVI), canopy cover, and tree density as significant variables across all land cover types that explained burn severity, suggesting that denser areas with higher vegetation greenness experienced more severe burns. Interestingly, residuals between the actual and estimated dNBR were lower in herbaceous zones compared to denser forested areas at similar elevations, suggesting potentially more predictable burn severity in open areas. Spatial analysis using Geary’s C statistics further revealed a strong negative autocorrelation: areas with high burn severity tended to be clustered, with lower severity areas interspersed. Overall, this study demonstrates the potential of readily available remote sensing data to predict potential burn severity values before a fire event, providing valuable information for forest managers to develop strategies for mitigating future wildfire damage. Full article

Construction projects demand a higher amount of energy predominantly for heating, ventilation, and illumination purposes. Modular construction has come into the limelight in recent years as a construction method that uses sustainable building materials and optimizes energy efficiency. Ventilation openings in buildings are designed to facilitate air circulation by naturally driven ventilation and could aid in reducing energy consumption in construction projects. However, a knowledge gap makes it difficult to propose the best dimensions of ventilation openings in buildings. Hence, the aim of this work is to empower the decision-making process in terms of proposing the best ventilation opening dimensions toward sustainable energy use and management in buildings. A novel framework is presented herein to evaluate the impact and propose the best dimensions of ventilation openings for metal frame modular construction in Brazil, using building information modeling. The ventilation openings were constructed and their dimensions evaluated in eight Brazilian cities, based on the bioclimatic zone (BioZ) classification indicated in ABNT NBR 15220: Curitiba (1st BioZ); Rio Negro (2nd BioZ); São Paulo (3rd BioZ); Brasília (4th BioZ); Campos (5th BioZ); Paranaíbe (6th BioZ); Goiás (7th BioZ); and Rio de Janeiro (8th BioZ). The study results show that the energy consumption of the same building model would vary based on the dimensions of ventilation openings for each BioZ in Brazil. For instance, modeling the same modular construction unit in the city of Rio Negro could consume around 50% of the energy compared to the same unit constructed in the city of Rio de Janeiro, using the small opening sizes based on the smallest dimensions of the ventilation openings. Similarly, modeling the construction unit in Curitiba, São Paulo, Brasília, Campos, Paranaíba, and Goiás could reduce energy consumption by around 40%, 34%, 36%, 18%, 20%, and 16%, respectively, compared to constructing the same building in the city of Rio de Janeiro, using the small opening sizes based on the smallest dimensions of the ventilation openings. This work could help practitioners and professionals in modular construction projects to design the best dimensions of the ventilation openings based on each BioZ towards increasing energy efficiency and sustainability. Full article

In many regions, the frequency and extent of wildfires has increased in recent years, a trend which is expected to continue. Hence, there is a need for effective fire management strategies. Such strategies need to be based on accurate and complete data on vegetation condition and post-fire effects, collected in the field as well as by remote sensing approaches. Unfortunately, wildfire databases are often incomplete in terms of their spatial and temporal coverage, as well as the documentation of fire outcomes. The aim of this study was to devise a methodology to construct a comprehensive national wildfire database. We developed and tested this methodology for Israel, as a case study. The methodology combines data on wildfires in Israel from two sources: remote sensing and field data collected by governmental agencies, representing the period between 2015 and 2022. The resulting database contains 2276 wildfires larger than 10 ha with information (where and when available) on their occurrence date, fire duration, ignition cause, and more. The methodology presented here provides a blueprint for developing large-scale and comprehensive fire databases elsewhere, and facilitates future studies on wildfire risk by providing a robust, unified database of the fire history of Israel from 2015 onwards. Full article

Fire severity mapping can capture heterogeneous fire severity patterns over large spatial extents. Although numerous remote sensing approaches have been established, regional-scale fire severity mapping at fine spatial scales (<5 m) from high-resolution satellite images is challenging. The fire severity of a vast forest fire that occurred in Southwest China was mapped at 2 m spatial resolution by random forest models using Sentinel 2 and GF series remote sensing images. This study demonstrated that using the combination of Sentinel 2 and GF series satellite images showed some improvement (from 85% to 91%) in global classification accuracy compared to using only Sentinel 2 images. The classification accuracy of unburnt, moderate, and high severity classes was significantly higher (>85%) than the accuracy of low severity classes in both cases. Adding high-resolution GF series images to the training dataset reduced the probability of low severity being under-predicted and improved the accuracy of the low severity class from 54.55% to 72.73%. RdNBR was the most important feature, and the red edge bands of Sentinel 2 images had relatively high importance. Additional studies are needed to explore the sensitivity of different spatial scales satellite images for mapping fire severity at fine spatial scales across various ecosystems. Full article

Fire severity, which quantifies the degree of organic matter consumption, is an important component of the fire regime. High-severity fires have major ecological implications, affecting carbon uptake, storage and emissions, soil nutrients, and plant regeneration, among other ecosystem services. Accordingly, spatially explicit maps of the fire severity are required to develop improved tools to manage and restore the most damaged areas. The aim of this study is to develop spatially explicit maps of the field-based fire severity (composite burn index—CBI) from different spectral indices derived from Sentinel 2A images and using several regression models. The study areas are two recent large fires that occurred in Tunisia in the summer of 2021. We employed different spectral severity indices derived from the normalized burn ratio (NBR): differenced NBR (dNBR), relative differenced NBR (RdNBR), and relativized burn Ratio (RBR). In addition, we calculated the burned area index for Sentinel 2 (BAIS2) and the thermal anomaly index (TAI). Different tree decision models (i.e., the recursive partitioning regression method [RPART], bagging regression trees [Bagging], and boosted regression trees [BRT]), as well as a generalized additive model [GAM]), were applied to predict the CBI. The main results indicated that RBR, followed by dNBR, were the most important spectral severity indices for predicting the field-based CBI. Moreover, BRT was the best regression model, explaining 92% of the CBI variance using the training set of points and 88% when using the validation set. These results suggested the adequacy of RBR index derived from Sentinel 2A for assessing and mapping forest fire severity in Mediterranean forests. These spatially explicit maps of field-based CBI could help improve post-fire recovery and restoration efforts. Full article

After more than a century of low fire activity in the western United States, wildfires are now becoming more common. Reburns, which are areas burned in two or more fires, are also increasing. How fires interact over time is of interest ecologically as well as for management. Wildfires may act as fuel treatments, reducing subsequent fire severity, or they may increase subsequent fire severity by leaving high fuel loads behind. Our goal was to assess whether previous wildfire severity influenced subsequent fire severity across vegetation types and over time in the Southwest U.S. using remotely sensed fire severity data in 2275 fires that burned between 1984 and 2019. Points that reburned tended to be those that burned with lower severity initially. Shrublands burned predominantly at moderate to high severity in initial fires and in reburns. Pinyon-juniper-oak systems burned with mixed severity, and fire severity was consistent from fire to fire. In ponderosa pine and aspen-mixed conifer, fire severity tended to decrease with each fire. Initial and subsequent fire severity was lower in points that reburned after a short interval. These remotely sensed observations of reburn severity need verification through field work to understand specific effects caused by reburns in different ecosystems. However, in ponderosa pine and aspen-mixed conifer forests, it may be beneficial to consider wildfires as fuel treatments and work to maintain the fuel reduction effects they have on forested ecosystems. Full article

The increase of wildfire incidence in highly populated areas significantly enhances the risk for ecosystems and human lives, activities and infrastructures. In central and southern Italy, recent decades’ fire records indicate that 2007 and 2017 were extreme years in terms of the number of fires and total burned area. Among them, we selected large fire events and explored their features and drivers of burn severity. We used a standardized extraction procedure to identify large wildfires (>100 ha) from the MODIS burned areas database and Landsat multi-spectral images. We mapped burn severity with the Relative Difference Normalized Burn Ratio index and explored the main drivers of severity using topographic, land-cover and anthropogenic predictors. We selected 113 wildfires for a collective total burned area of over 100,000 ha. Large fires were more frequent in the southern than in the central and northern regions, especially in July and August. The average fire size was about 900 ha and occurred mainly in shrublands (30.4%) and broadleaf forests (19.5%). With a random forest model, we observed that the highest severity occurred in conifer plantations and shrublands, in highly populated areas and at lower elevations. Burn severity models, at the landscape or regional scales, can be very useful tools for pre- and post-fire forest management planning. Full article

Despite being a natural ecological process, wildfires are dramatic events that, accelerated by global change, could negatively affect ecosystem services depending on their severity level. However, because of data processing constraints, fire severity has been mostly neglected in risk analysis (especially at regional levels). Indeed, previous studies addressing fire severity focused mainly on analyzing single fire events, preventing the projection of the results over large areas. Although, building and projecting robust models of fire severity to integrate into risk analysis is of main importance to best anticipate decisions. Here, taking advantage of free data-processing platforms, such as Google Earth Engine, we use more than 1000 fire records from Western Italy and Southern France in the years 2004–2017, to assess the performance of random forest models predicting the relativized delta normalized burn ratio (rdNBR) used as proxy of fire severity. Furthermore, we explore the explanatory capacity and meaning of several variables related to topography, vegetation, and burning conditions. To show the potentialities of this approach for operational purposes, we projected the model for one of the regions (Sardinia) within the study area. Results showed that machine learning algorithms explain up to 75% of the variability in rdNBR, with variables related to vegetation amount and topography being the most important. These results highlight the potential usefulness of these tools for mapping fire severity in risk assessments. Full article

Estimates of burn severity and forest change following wildfire are used to determine changes in forest cover, fuels, carbon stocks, soils, wildlife habitat, and to evaluate fuel and fire management strategies and effectiveness. However, current remote-sensing models for assessing burn severity and forest change in the U.S. are generally based on data collected from California, USA, forests and may not be suitable in other forested ecoregions. To address this problem, we collected field data from 21 wildfires in the American Southwest and developed region-specific models for assessing post-wildfire burn severity and forest change from remotely sensed imagery. We created indices (delta normalized burn ratio (dNBR), relative delta normalized burn ratio (RdNBR), and the relative burn ratio (RBR)) from Landsat and Sentinel-2 satellite imagery using pre- and post-fire image pairs. Burn severity models built from southwest U.S. data had clear advantages compared to the current California-based models. Canopy cover and basal area change models built from southwest U.S. data performed better as continuous predictors but not as categorical predictors. Full article

Fire is a concern for the sustainability of dry forests such as those of the Mediterranean region, especially under warming climate and high human use. We used data derived from Landsat and MODIS sensors to assess forest changes in the Talassemtane National Park (TNP) in North Africa from 2003–2018. The Talassemtane National Park is a protected area in northern Morocco, a biodiverse, mountainous region with endemic species of concern such as the Moroccan fir (Abies marocana) and Barbary macaque (Macaca sylvanus). To help the managers of the TNP better understand how the forest has been impacted by fire vs. other disturbances, we combined information from remotely derived datasets. The Hansen Global Forest Change (GFC) data are a global resource providing annual forest change, but without specifying the causes of change. We compared the GFC data to MODIS wildfire data from Andela’s Global Fire Atlas (GFA), a new global tool to identify fire locations and progression. We also analyzed surface reflectance-corrected Landsat imagery to calculate fire severity and vegetation death using Relative Differenced Normalized Burn Ratio analysis (RdNBR). In the park, GFC data showed a net loss of 1695 ha over 16 years, corresponding to an approximately 0.3% annual loss of forest. The GFA identified nine large fires that covered 4440 ha in the study period, coinciding with 833 ha of forest loss in the same period. Within these fires, detailed image analysis showed that GFA fire boundaries were approximately correct, providing the first quantitative test of GFA accuracy outside North America. High-severity fire, as determined by RdNBR analysis, made up about 32% of burned area. Overall, the GFA was validated as a useful management tool with only one non-detected wildfire in the study period; wildfires were linked to approximately 49% of the forest loss. This information helps managers develop conservation strategies based on reliable data about forest threats. Full article

The extent and severity of bushfires in a landscape are largely governed by meteorological conditions. An accurate understanding of the interactions of meteorological variables and fire behaviour in the landscape is very complex, yet possible. In exploring such understanding, we used 2693 high-confidence active fire points recorded by a Moderate Resolution Imaging Spectroradiometer (MODIS) sensor for nine different bushfires that occurred in Victoria between 1 January 2009 and 31 March 2009. These fires include the Black Saturday Bushfires of 7 February 2009, one of the worst bushfires in Australian history. For each fire point, 62 different meteorological parameters of bushfire time were extracted from Bureau of Meteorology Atmospheric high-resolution Regional Reanalysis for Australia (BARRA) data. These remote sensing and meteorological datasets were fused and further processed in assessing their relative importance using four different tree-based ensemble machine learning models, namely, Random Forest (RF), Fuzzy Forest (FF), Boosted Regression Tree (BRT), and Extreme Gradient Boosting (XGBoost). Google Earth Engine (GEE) and Landsat images were used in deriving the response variable–Relative Difference Normalised Burn Ratio (RdNBR), which was selected by comparing its performance against Difference Normalised Burn Ratio (dNBR). Our findings demonstrate that the FF algorithm utilising the Weighted Gene Coexpression Network Analysis (WGCNA) method has the best predictive performance of 96.50%, assessed against 10-fold cross-validation. The result shows that the relative influence of the variables on bushfire severity is in the following order: (1) soil moisture, (2) soil temperature, (3) air pressure, (4) air temperature, (5) vertical wind, and (6) relative humidity. This highlights the importance of soil meteorology in bushfire severity analysis, often excluded in bushfire severity research. Further, this study provides a scientific basis for choosing a subset of meteorological variables for bushfire severity prediction depending on their relative importance. The optimal subset of high-ranked variables is extremely useful in constructing simplified and computationally efficient surrogate models, which can be particularly useful for the rapid assessment of bushfire severity for operational bushfire management and effective mitigation efforts. Full article

The increasing number of wildfires in southern Europe is making our ecosystem more vulnerable to water erosion; i.e., the loss of vegetation and subsequent runoff increase cause a shift in large quantities of sediment. Fire severity has been recognized as one of the most important parameters controlling the magnitude of post-fire soil erosion. In this paper, we adopted a combination of methods to easily assess post-fire erosion and prevent potential risk in subsequent rain events. The model presented is structured into three modules that were implemented in a GIS environment. The first module estimates fire severity with the Monitoring Trends in Burn Severity (MTBS) method; the second estimates runoff with rainfall depth–duration curves and the Soil Conservation Service Curve Number (SCS-CN) method; and the third estimates pre- and post-fire soil erosion. In addition, two post-fire scenarios were analyzed to assess the influence of fire severity on soil erosion: the former based on the Normalized Difference Vegetation Index (NDVI) and the latter on the Relative differenced Normalized Burn Index (RdNBR). The results obtained in both scenarios are quite similar and demonstrate that transitional areas, such as rangelands and rangelands with bush, are the most vulnerable because they show a significant increase in erosion following a fire event. The study findings are of secondary importance to the combined approach devised because the focal point of the study is to create the basis for a future tool to facilitate decision making in landscape management. Full article