Differenced Normalized Burn Ratio Research Articles

Environmental indicators combined with risk analysis to evaluate potential wildfire incidence on the Dadu Plateau in Taiwan

Wildfire is a common disaster in the world, and it has a considerable impact on the safety of residents and ecological disturbance. Periodic wildfires are an urgent problem to be solved. This research uses big data from relevant departments to extract environmental indicators that affect wildfires, including satellite images, meteorological observations, and field surveys and establishes a risk model for the Spatio-temporal distribution of wildfires based on risk analysis. Previous studies using Differenced Normalized Burn Ratio (dNBR) to assess fire severity and distinguish wildfire ruins did not deal with the impact of atmospheric humidity on dNBR values. In this study, an adjustable fire threshold was developed to enable dNBR to improve the accuracy of identifying wildfire locations. Regarding the temporal distribution of wildfire risks, environmental vulnerability cannot specifically reflect the frequency of actual wildfires. If the hazard degree is introduced to calculate the wildfire risk, the coefficient of determination can be increased from 0.49 to 0.79. The verification of the village boundary zone depicts that the risk analysis can effectively show the temporal and spatial distribution of wildfire hotspots. On this basis, a village-level wildfire disaster prevention strategy can be formulated.

Siberian taiga and tundra fire regimes from 2001–2020

Circum-boreal and -tundra systems are crucial carbon pools that are experiencing amplified warming and are at risk of increasing wildfire activity. Changes in wildfire activity have broad implications for vegetation dynamics, underlying permafrost soils, and ultimately, carbon cycling. However, understanding wildfire effects on biophysical processes across eastern Siberian taiga and tundra remains challenging because of the lack of an easily accessible annual fire perimeter database and underestimation of area burned by MODIS satellite imagery. To better understand wildfire dynamics over the last 20 years in this region, we mapped area burned, generated a fire perimeter database, and characterized fire regimes across eight ecozones spanning 7.8 million km2 of eastern Siberian taiga and tundra from ∼61–72.5° N and 100° E–176° W using long-term satellite data from Landsat, processed via Google Earth Engine. We generated composite images for the annual growing season (May–September), which allowed mitigation of missing data from snow-cover, cloud-cover, and the Landsat 7 scan line error. We used annual composites to calculate the difference Normalized Burn Ratio (dNBR) for each year. The annual dNBR images were converted to binary burned or unburned imagery that was used to vectorize fire perimeters. We mapped 22 091 fires burning 152 million hectares (Mha) over 20 years. Although 2003 was the largest fire year on record, 2020 was an exceptional fire year for four of the northeastern ecozones resulting in substantial increases in fire activity above the Arctic Circle. Increases in fire extent, severity, and frequency with continued climate warming will impact vegetation and permafrost dynamics with increased likelihood of irreversible permafrost thaw that leads to increased carbon release and/or conversion of forest to shrublands.

Geospatial assessment of vegetation condition pre-wildfire and post-wildfire on Luštica (Montenegro) using differenced Normalized Burn Ratio (dNBR) index

Wildfire is one of the most dangerous environmental stressors in most vegetation zones worldwide. Determining and monitoring this stressor is important because of the disturbances that occur during the burning of biomass in ecosystems, as well as because of the damage or suffering of organisms. In the last decade, a greater number of wildfires and burnt areas were recorded in Southern Europe and Montenegro. Therefore, it is important to develop optimal methodology and models to help in better management of forest protection against wildfire. The spatial component in firefighting plays a significant role in management. In this context, Remote Sensing and Geographic Information Systems (GIS) come to the fore, which analyze spatial data and turn it into useful information - models applied in practice. The study aims to geospatial assess condition of vegetation pre-wildfire and post-wildfire in study area of the Luštica peninsula in Montenegro during the summer of 2017. Open and publicly available Sentinel 2 satellite was used. The scaled index differenced Normalized Burn Ratio (dNBR) of burned vegetation was applied as an indicator for assessing the state of vegetation after a wildfire in the open source software Quantum GIS (QGIS). The results of the damage assessment of the burned area based on the applied scaled index reveal that the category of low severity occupies an area of 335.86 ha (7%), moderately-low severity 250.13 ha (5%), moderately-high severity 406.22 ha (8%), high severity 238.03 ha (5%). The unburned areas occupy an area of 3624.95 ha (75%). This study contributes to assessing vegetation conditions and other accompanying activities pre-wildfire and post-wildfire using modern open-source geospatial tools.

Multitemporal lidar captures heterogeneity in fuel loads and consumption on the Kaibab Plateau

BackgroundCharacterization of physical fuel distributions across heterogeneous landscapes is needed to understand fire behavior, account for smoke emissions, and manage for ecosystem resilience. Remote sensing measurements at various scales inform fuel maps for improved fire and smoke models. Airborne lidar that directly senses variation in vegetation height and density has proven to be especially useful for landscape-scale fuel load and consumption mapping. Here we predicted field-observed fuel loads from airborne lidar and Landsat-derived fire history metrics with random forest (RF) modeling. RF models were then applied across multiple lidar acquisitions (years 2012, 2019, 2020) to create fuel maps across our study area on the Kaibab Plateau in northern Arizona, USA. We estimated consumption across the 2019 Castle and Ikes Fires by subtracting 2020 fuel load maps from 2019 fuel load maps and examined the relationship between mapped surface fuels and years since fire, as recorded in the Monitoring Trends in Burn Severity (MTBS) database.ResultsR-squared correlations between predicted and ground-observed fuels were 50, 39, 59, and 48% for available canopy fuel, 1- to 1000-h fuels, litter and duff, and total surface fuel (sum of 1- to 1000-h, litter and duff fuels), respectively. Lidar metrics describing overstory distribution and density, understory density, Landsat fire history metrics, and elevation were important predictors. Mapped surface fuel loads were positively and nonlinearly related to time since fire, with asymptotes to stable fuel loads at 10–15 years post fire. Surface fuel consumption averaged 16.1 and 14.0 Mg ha− 1 for the Castle and Ikes Fires, respectively, and was positively correlated with the differenced Normalized Burn Ratio (dNBR). We estimated surface fuel consumption to be 125.3 ± 54.6 Gg for the Castle Fire and 27.6 ± 12.0 Gg for the portion of the Ikes Fire (42%) where pre- and post-fire airborne lidar were available.ConclusionsWe demonstrated and reinforced that canopy and surface fuels can be predicted and mapped with moderate accuracy using airborne lidar data. Landsat-derived fire history helped account for spatial and temporal variation in surface fuel loads and allowed us to describe temporal trends in surface fuel loads. Our fuel load and consumption maps and methods have utility for land managers and researchers who need landscape-wide estimates of fuel loads and emissions. Fuel load maps based on active remote sensing can be used to inform fuel management decisions and assess fuel structure goals, thereby promoting ecosystem resilience. Multitemporal lidar-based consumption estimates can inform emissions estimates and provide independent validation of conventional fire emission inventories. Our methods also provide a remote sensing framework that could be applied in other areas where airborne lidar is available for quantifying relationships between fuels and time since fire across landscapes.

Detection of forest fire burn-area using landsat-8 and sentinel-2: a case study of Nivale (Kolhapur) beat of Chandoli National Park, Maharashtra, India

Forests play an important role in maintaining environmental equilibrium in the ecosystem. Forest fires, which can occur for a variety of reasons, are the greatest threat to forests. In order to control forest fires, it is critical to assess the formation and behavioral characteristics of forest fires. Detecting fire areas and the severity is much easier with satellite images obtained with advancing technologies. The main objective of this study is to extract burn area from Landsat-8 and Sentinel-2 satellite images of 2017 using six vegetation indices. Remote sensing data was used to study a forest fire in the Nivale (Kolhapur) beat of Chandoli National Park of Maharashtra. The Department of Divisional Forest Officer (Wildlife)- Chandoli National Park at Karad (DDFO-CNP) provided reference data indicating that the fire had damaged 194 hectares of Scrub forest. In addition, forest fire areas were determined using an objectbased image classification technique. When the findings of the study are compared to the values obtained by DDFO- CNP, it is found that Sentinel-2's object-based analysis provided the highest accuracy with an overall accuracy of 84 % and 0.795 Kappa statistics. Landsat-8 image has 82 % overall accuracy and 0.765 Kappa value. The findings of Sentinel-2 and Landsat-8 spectral indices showed the Sentinel-2 had better results in all indices. Differenced Normalized Difference Vegetation Index (dNDVI) and Relative Difference Normalized Burn Ratio (RdNBR) performed better than other indexes with a difference of only 18.27 and 30.88 hectares respectively. According to the fire severity analysis, a burn area with high intensity in Sentinel-2 was identified as moderate-high in Landsat-8. As per the research findings, sentinel-2 had a high severity area of 28.72 ha and a low severity area of 37.04 ha. It shows that satellite images of Sentinel-2 are highly suitable than Landsat-8 for estimating scrub forest fire areas. Finally, the findings of this study could be useful to forest managers to monitor burn regions quickly after the fire and in reducing severity and frequency of forest fires.

Impact of Forest Fires on Air Quality in Wolgan Valley, New South Wales, Australia—A Mapping and Monitoring Study Using Google Earth Engine

Forests are an important natural resource and are instrumental in sustaining environmental sustainability. Burning biomass in forests results in greenhouse gas emissions, many of which are long-lived. Precise and consistent broad-scale monitoring of fire intensity is a valuable tool for analyzing climate and ecological changes related to fire. Remote sensing and geographic information systems provide an opportunity to improve current practice’s accuracy and performance. Spectral indices techniques such as normalized burn ratio (NBR) have been used to identify burned areas utilizing satellite data, which aid in distinguishing burnt areas using their standard spectral responses. For this research, we created a split-panel web-based Google Earth Engine app for the geo-visualization of the region severely affected by forest fire using Sentinel 2 weekly composites. Then, we classified the burn severity in areas affected by forest fires in Wolgan Valley, New South Wales, Australia, and the surrounding area through Difference Normalized Burn Ratio (dNBR). The result revealed that the region’s burnt area increased to 6731 sq. km in December. We also assessed the impact of long-term rainfall and land surface temperature (LST) trends over the study region to justify such incidents. We further estimated the effect of such incidents on air quality by analyzing the changes in the column number density of carbon monoxide and nitrogen oxides. The result showed a significant increase of about 272% for Carbon monoxide and 45% for nitrogen oxides. We conclude that, despite fieldwork constraints, the usage of different NBR and web-based application platforms may be highly useful for forest management to consider the propagation of fire regimes.

Biophysical Settings that Influenced Plantation Survival During the 2015 Wildfires in Northern Rocky Mountain Moist Mixed-Conifer Forests

Abstract Fire suppression and the loss of western white pine (WWP) have made northern Rocky Mountain moist mixed-conifer forests less disturbance resilient. Although managers are installing hundreds of plantations, most of these plantations have not experienced wildfire since establishment. In 2015, wildfires burned through 100 WWP plantations in this region, providing an opportunity to evaluate the effects of wildfires on sapling survival. A Weibull distribution approach was used to characterize the variation of fire severity pixels, as indicated by the differenced normalized burn ratio. The distribution parameters provided a method to identify the biophysical setting and plantation characteristics influencing fire severity and sapling survival. Plantations located on lower slope positions were more resistant to wildfires than plantations located midslope or close to the ridges. Snow water equivalent was positively correlated with wildfire resistance and resilience. Results will help focus reforestation efforts and identify locations where future plantations can potentially survive wildfires.

An automatic procedure for generating burn severity maps from the satellite images-derived spectral indices

ABSTRACT Fire, especially wildfire, which can be considered as one of the main threats to vegetation cover and animals' life, has attracted lots of attention from environmental researchers. To better manage the fire crisis and take the necessary measures to compensate for its damages, it is essential to have detailed information about the burn severity levels. Accordingly, satellite images and their spectral indices have been widely considered in the literature as powerful tools in producing burn severity information. Despite the efficiency of the previously proposed methods, the necessity of ground reference data for their thresholding step faces them with serious challenges. To address this problem, in this study, an automatic procedure based on the change-point analysis is presented for thresholding differenced normalized burn ratio (dNBR) and its another version, dNBR2. In this procedure, a mean-shift based change-point analysis is performed on the dNBR and dNBR2 images for classifying them into burn severity levels. Experiments, conducted on some parts of Alaska and California in the United States, illustrated the high efficiency of the proposed method. Moreover, as an applied experiment, the severity of the fires, occurred in 2020 in the Khaeiz protected area in Iran, was estimated and compared with local reports.

The effect of adjacent vegetation on fire severity in Afrotemperate forest along the southern Cape coast of South Africa

Recently two large wildfires (around the towns of Knysna in 2017 and George in 2018) jointly burnt over 80 000 ha in the southern Cape, South Africa. Here, patches of closed-canopy Afrotemperate forest occur within a matrix of fire-prone (native) fynbos shrublands or adjacent to commercial plantations of invasive alien Pinus trees (a transformed state). We compared the distances that fire penetrated into forest margins adjacent to fynbos to those adjacent to plantations. Furthermore, we used a satellite-derived index of fire severity, the differenced Normalised Burn Ratio (dNBR), to compare fire severity in forest margins adjacent to fynbos to those adjacent to plantations. Results were inconsistent between the two fires. In the Knysna fire, no significant difference was noted in fire penetration into forest, while forest margins adjacent to plantations burnt at significantly lower severity than those adjacent to fynbos. In the George fire, penetration into and fire severity in forest margins adjacent to plantations were significantly greater than in those adjacent to fynbos. Not only were fire severity and penetration in forest margins with plantations adjacent dissimilar between the George and Knysna fires, but variability in fire severity was greater in forest margins with plantation adjacent than in those with fynbos adjacent. Our study provides evidence that fire severity in forest margins is associated with the adjacent vegetation type and that plantations potentially expose forests to greater variability in fire severity and extent than the forest may experience under natural conditions. Other important factors that may affect the severity and extent of burn in forest margins are forest patch size and topographical position, the nature of the ecotone between forest and the adjacent vegetation, and the fuel load and structure of the adjacent vegetation which varies considerably in relation to stand age and management practices in plantations.

Analysis of the Relationship between Forest Fire and Land Surface Temperature using Landsat 8 OLI/TIRS Imagery

Forest fires are the main threat to the sustainability of forests around the world destroying the valuable forest biodiversity and nearby infrastructure. Forest covers provide shading, cooling the environment, and lower thermal absorption. However, the changes in forest cover due to forest fires increase the land surface temperature (LST) locally. This study is aimed to analyze the relationship between forest fires and LST using Landsat-8 OLI/TIRS imagery in Kuala Baram, Sarawak. More than 1500ha of forests and plantations have been destroyed after 26 days of fires, deteriorating the air quality to hazardous levels, damaging the nearby house and infrastructure. The differenced normalized burn ratio (dNBR) between pre-fire and post-fire NBR was used to estimate the burn severity categories in Kuala Baram, Sarawak. It was found that the burn severity ranges from moderate-low to moderate-high levels. The correlation between forest fire and LST in Kuala Baram, Sarawak in June 2019 and September 2019 was calculated using Pearson’s correlation coefficients. It indicates the strongest negative relationship between NBR and LST in pre-fires (R2 =-0.985) and post-fires (R2 =-0.996). The findings from this study showed that forest fires also affecting the land surface temperature of Kuala Baram, Sarawak along with the physical damages that have been diminished from the forest fires occurrences.

North American boreal forests are a large carbon source due to wildfires from 1986 to 2016

Wildfires are a major disturbance to forest carbon (C) balance through both immediate combustion emissions and post-fire ecosystem dynamics. Here we used a process-based biogeochemistry model, the Terrestrial Ecosystem Model (TEM), to simulate C budget in Alaska and Canada during 1986–2016, as impacted by fire disturbances. We extracted the data of difference Normalized Burn Ratio (dNBR) for fires from Landsat TM/ETM imagery and estimated the proportion of vegetation and soil C combustion. We observed that the region was a C source of 2.74 Pg C during the 31-year period. The observed C loss, 57.1 Tg C year−1, was attributed to fire emissions, overwhelming the net ecosystem production (1.9 Tg C year−1) in the region. Our simulated direct emissions for Alaska and Canada are within the range of field measurements and other model estimates. As burn severity increased, combustion emission tended to switch from vegetation origin towards soil origin. When dNBR is below 300, fires increase soil temperature and decrease soil moisture and thus, enhance soil respiration. However, the post-fire soil respiration decreases for moderate or high burn severity. The proportion of post-fire soil emission in total emissions increased with burn severity. Net nitrogen mineralization gradually recovered after fire, enhancing net primary production. Net ecosystem production recovered fast under higher burn severities. The impact of fire disturbance on the C balance of northern ecosystems and the associated uncertainties can be better characterized with long-term, prior-, during- and post-disturbance data across the geospatial spectrum. Our findings suggest that the regional source of carbon to the atmosphere will persist if the observed forest wildfire occurrence and severity continues into the future.

Contrasting Geographic Patterns of Ignition Probability and Burn Severity in the Mojave Desert

The extent and frequency of fire has increased in many arid systems over the last century, with a large proportion of area in some regions undergoing transitions to novel conditions. Portions of the Mojave Desert in southwestern North America have undergone such transitions, most often from woody to herbaceous-dominated systems. These transitions have often been attributed to the proliferation of invasive annual grasses that promote more frequent fire, but recent evidence indicates that transitions can also occur independent of fire frequency if burn severity is high. In addition, high probability of ignition (i.e., potentially high fire frequency) and high burn severity may not always be geographically related. Therefore, our goals were to: (1) map potential burn severity, fire frequency, and probability of ignition across the Mojave; and, (2) evaluate spatial association among predicted burn severity, fire frequency and probability of ignition. We first mapped perimeters of 250 wildfires > 405 ha that occurred from 1972 to 2010, then extracted data on fire frequency (number of times burned from 1972 to 2010), burn severity (the difference Normalized Burn Ratio), and 15 predictor variables representing physiography, climate, ignition, and vegetation. Maximum entropy was used to predict probability of ignition and Random Forest models were used to predict dNBR and fire frequency. Areas with high burn severity and high ignition probability had opposite spatial trends; areas with high burn severity were predicted to predominantly be in the northwest part of the region whereas areas with high ignition probability were predicted to be in the northeast. The models indicate the existence of a number of spatially structured but temporally dynamic fire regimes throughout the Mojave Desert. Two prevalent and ecologically significant regimes include one with frequent fires of low to moderate severity and another with infrequent fire of high severity. Areas with high fire frequency are currently limited in extent (<1% total area). However, cover of invasive grasses can remain high decades after a burn of high or moderate severity, so grass-fire cycles could develop in areas where there may be expectations of infrequent fire as well as those with relatively high fire frequency.

Evaluation of geomorphometric characteristics and soil properties after a wildfire using Sentinel-2 MSI imagery for future fire-safe forest

Understanding spatiotemporal geomorphological and pedological changes as a consequence of wildfires can allow stakeholders, land planners, and policymakers to design efficient fire safety-based afforestation and restoration programs of forest lands. The use of remote sensing techniques is a key tool to achieve this goal. The suitable combination of Sentinel-2 MSI data for mapping of different spectral indices related to burn severity and their relationship with other morphometric and soil properties can contribute to a better understanding of the impact of fire, and this is relevant in regions where is still scarce fire-related research such as Turkey. In this investigation, the use of NDVI (Normalized Difference Vegetation Index), dNDVI (Difference Normalized Difference Vegetation Index), NDWI (Normalized Difference Water Index), NBR (Normalized Burn Ratio), dNBR (Difference Normalized Burn Ratio), RBR (Relativized Burn Ratio), SBI (Soil Bare Index), As (Upslope area), CTI (Compound Topographic Index), TCI (Terrain Characterization Index), SPI (Stream Power Index) and Curvature (Standard Curvature) were combined. As a study case, 47.43 ha in a burned area of Çınarpınar forest unit, Andırın, Kahramanmaraş in Turkey was selected. The results showed that dNDVI, dNBR, RBR, SBI contribute to relevant information about the effect of the wildfire. According to the dNBR fire severity classification, 75% of the total area has been exposed to high-severity fire. The relationship of Sentinel MSI satellite images with some soil and morphometric features have been found meaningful to understand the impact of forest fire in Mediterranean ecosystems. The information collected in the Turkish forest areas affected by wildfires should be relevant for planning and represent a key contribution to the selection of restoration programs and afforestation techniques for a future fire-safe forest.

Automated Mapping for Long-Term Analysis of Shifting Cultivation in Northeast India

Assessment of the spatio-temporal dynamics of shifting cultivation is important to understand the opportunities for land restoration. The past studies on shifting cultivation mapping of North-East (NE) India lack systematic assessment techniques. We have developed a decision tree-based multi-step threshold (DTMT) method for consistent and long-term mapping of shifting cultivation using Landsat data from 1975 to 2018. Widely used vegetation indices such as normalized difference vegetation index (NDVI), Normalized Burn Ratio (NBR) and its relative difference NBR (RdNBR) were integrated with the suitable thresholds in the classification, which yielded overall accuracy above 85%. A significant decrease in total shifting cultivation area was observed with an overall reduction of 75% from 1975–1976 to 2017–2018. The methodology presented in this study is reproducible with minimal inputs and can be useful to map similar changes by optimizing the index threshold values to accommodate relative differences for other landscapes. Furthermore, the crop-suitability maps generated by incorporating climate and soil factors prioritizes suitable land use of shifting cultivation plots. The Google Earth Engine (GEE) platform was employed for automatic mapping of the shifting cultivation areas at desired time intervals for facilitating seamless dissemination of the map products. Besides the novel DTMT method, the shifting cultivation and crop-suitability maps generated in this study, can aid in sustainable land management.

Evaluating the Near and Mid Infrared Bi-Spectral Space for Assessing Fire Severity and Comparison with the Differenced Normalized Burn Ratio

Fire severity, defined as the degree of environmental change caused by a fire, is a critical fire regime attribute of interest to fire emissions modelling and post-fire rehabilitation planning. Remotely sensed fire severity is traditionally assessed by the differenced normalized burn ratio (dNBR). This spectral index captures fire-induced reflectance changes in the near infrared (NIR) and short-wave infrared (SWIR) spectral regions. This study evaluates a spectral index based on a band combination including the NIR and mid infrared (MIR) spectral regions, the differenced normalized difference vegetation index with mid infrared (dNDVIMID), to assess fire severity. This evaluation capitalized upon the unique opportunity stemming from the pre- and post-fire airborne acquisitions over the Rim (2013) and King (2014) fires in California with the MODIS/ASTER Airborne Simulator (MASTER) instrument. The field data consist of 85 Geometrically structured Composite Burn Index (GeoCBI) plots. In addition, six different index combinations, respectively three with a NIR–SWIR combination and three with a NIR–MIR combination, were evaluated based on the optimality of fire-induced spectral displacements. The optimality statistic ranges between zero and one, with values of one representing pixel displacements that are unaffected by noise. The results show that the dNBR demonstrated a stronger relationship with GeoCBI field data when field measurements over the two fire scars were combined than the dNDVIMID approaches. The results yielded an R2 of 0.68 based on a saturated growth model for the best performing dNBR index, whereas the performance of the dNDVIMID indices was lower with an R2 = 0.61 for the best performing dNDVIMID index. The dNBR also outperformed the dNDVIMID in terms of spectral optimality across both fires. The best performing dNBR index yielded median optimality statistics of 0.56 over the Rim and 0.60 over the King fire. The best performing dNDVIMID index recorded optimality values of 0.49 over the Rim and 0.46 over the King fire. We also found that the dNBR approach led to considerable differences in the form of the relationship with the GeoCBI between the two fires, whereas the dNDVIMID approach yielded comparable relationships with the GeoCBI over the two fires. This suggests that the dNDVIMID approach, despite its slightly lower performance than the dNBR, may be a more robust method for estimating and comparing fire severity over large regions. This premise needs additional verification when more air- or spaceborne imagery with NIR and MIR bands will become available with a spatial resolution that allows ground truthing of fire severity.

Explaining Sentinel 2-based dNBR and RdNBR variability with reference data from the bird’s eye (UAS) perspective

Characterizing the spatial variability of the severity of wildfires is important to assess ecological and economic consequences and to coordinate mitigation strategies. Vegetation indices such as the differenced Normalized Burn Ratio (dNBR) have become a standard tool to assess burn or fire severity across larger areas and are being used operationally. Despite the frequent application of dNBR-like vegetation indices, it is not yet fully understood which variables exactly drive the variability in dNBR observed by multispectral satellites. One reason for this is the lack of high quality prefire information about vegetation structure and composition. Consequently, the influence of prefire vegetation composition and other potentially influential variables such as cast shadows has hardly been examined. Here, we use very high resolution Unmanned Aerial System (UAS) orthoimages collected briefly before and after the large wildfires in Central Chile in the fire season 2016/2017 to derive variables related to the pre- and postfire landscape composition and structure. The variables are used as predictors in Generalized Additive Models (GAM) to explain the spatial variability in dNBR and RdNBR pixel values as observed by Sentinel-2. Our models explain more than 80% and 75% of the variability in dNBR and RdNBR values, respectively, using a sparse set of five predictors. The results suggest that in our study area the largest fraction of variability in Sentinel-2 based dNBR and RdNBR values can be explained by variables related to the fraction of consumed canopy cover while the vegetation composition before the fire does not have a large influence on dNBR and RdNBR.Our results further suggest that cast-shadows of snags and standing dead trees with remaining crown structure have a notable influence on the dNBR signal which may have been underestimated so far. We conclude that spatially continuous, very high spatial resolution data from UAS can be a valuable data source for an improved understanding of the exact meaning of common vegetation index products, operationally used for monitoring the environment.

Intensified burn severity in California’s northern coastal mountains by drier climatic condition

The severity of wildfire burns in interior lands of western US ecosystems has been increasing. However, less is known about its coastal mountain ecosystems, especially under extreme weather conditions, raising concerns about the vulnerability of these populated areas to catastrophic fires. Here we examine the fine-scale association between burn severity and a suite of environmental drivers including explicit fuel information, weather, climate, and topography, for diverse ecosystems in California’s northern coastal mountains. Burn severity was quantified using Relative difference Normalized Burn Ratio from Landsat multispectral imagery during 1984–2017. We found a significant increasing trend in burned areas and severity. During low-precipitation years, areas that burned had much lower fuel moisture and higher climatic water deficit than in wetter years, and the percentage of high-severity areas doubled, especially during the most recent 2012–2016 drought. The random forest (RF) machine learning model achieved overall accuracy of 79% in classifying categories of burn severity. Aspect, slope, fuel type and availability, and temperature were the most important drivers, based on both classification and regression RF models. We further examined the importance of drivers under four climatic conditions: dry vs. wet years, and during two extended drought periods (the 2012–2016 warmer drought vs. the 1987–1992 drought). During warm and dry years, the spatial variability of burn severity was a mixed effect of slope, long-term minimum temperature, fuel amount, and fuel moisture. In contrast, climatic water deficit and short-term weather became dominant factors for fires during wetter years. These results suggest that relative importance of drivers for burn severity in the broader domain of California’s northern coastal mountains varied with weather scenarios, especially when exacerbated by warm and extended drought. Our findings highlight the importance of targeting areas with high burn severity risk for fire adaptation and mitigation strategies in a changing climate and intensifying extremes.

A flexible approach for predicting and mapping postfire wood borer attacks in black spruce and jack pine forests using the differenced normalized burn ratio (dNBR)

Postfire salvage logging is used to reduce economic losses; however, burned trees are rapidly colonized by wood-boring insects, which reduce the merchantable value of the wood. This study aims to predict wood borer (Monochamus Megerle in Dejean, 1821) attacks after wildfire as a function of rapidly available variables such as tree basal area, stem diameter, and burn severity using the differenced normalized burn ratio (dNBR). In 2011, we sampled 60 black spruce (Picea mariana (Mill.) Britton, Sterns & Poggenb.) or jack pine (Pinus banksiana Lamb.) plots in five burns from 2010 in the Haute-Mauricie region of Quebec, Canada. A 50 cm bole section was debarked on seven trees in each plot to estimate wood borer attack density. Wood borer attacks were more abundant in black spruce than in jack pine. As a continuous variable, dNBR unveiled a quadratic effect of burn severity on attack density in black spruce, which was higher at moderate burn severity. In jack pine, the highest levels of attack density were found at high burn severity. Models produced in this article will help forest managers to better prioritize areas for salvage logging and thus reduce economic losses due to wood borer activity.

Evaluation of fire severity in fire prone-ecosystems of Spain under two different environmental conditions

Severe fires associated to climate change and land cover changes are becoming more frequent in Mediterranean Europe. The influence of environmental drivers on fire severity, especially under different environmental conditions is still not fully understood. In this study we aim to determine the main environmental variables that control fire severity in large fires (>500 ha) occurring in fire-prone ecosystems under two different environmental conditions following a transition (Mediterranean-Oceanic)-Mediterranean climatic gradient within the Iberian Peninsula, and to provide management recommendations to mitigate fire damage. We estimated fire severity as the differenced Normalized Burn Ratio, through images obtained from Landsat 8 OLI. We also examined the relative influence of pre-fire vegetation structure (vegetation composition and configuration), pre-fire weather conditions, fire history and topography on fire severity using Random Forest machine learning algorithms. The results indicated that the severity of fires occurring along the transition (Mediterranean-Oceanic)-Mediterranean climatic gradient was primarily controlled by pre-fire vegetation composition. Nevertheless, the effect of vegetation composition was strongly dependent on interactions with fire recurrence and pre-fire vegetation structural configuration. The relationship between fire severity, weather and topographic predictors was not consistent among fires occurring in the Mediterranean-Oceanic transition and Mediterranean sites. In the Mediterranean-Oceanic transition site, fire severity was determined by weather conditions (i.e., summer cumulative rainfall), rather than being associated to topography, suggesting that the control exerted by topography may be overwhelmed by weather controls. Conversely, results showed that topography only had a major effect on fire severity in the Mediterranean site. The results of this study highlight the need to prioritise fuel treatments aiming at breaking fuel continuity and reducing fuel loads as an effective management strategy to mitigate fire damage in areas of high fire recurrence.

Is the RdNBR a better estimator of wildfire burn severity than the dNBR? A discussion and case study in southeast China

A growing number of wildfires around the world have been resulting in severe post-fire effects. Understanding the accurate distribution of the wildfire burn severity is particularly important for post-fire management. The differenced Normalized Burn Ratio (dNBR) and its relative dNBR (RdNBR) are the two main indices for burn severity assessment. In China, little information is available on the capabilities of the dNBR and RdNBR for the burn severity assessment. Moreover, differing results of the detection capabilities of the dNBR and RdNBR for the burn severity have been reported worldwide. This study aimed to assess the potential of the dNBR and RdNBR for the wildfire burn severity estimation in southeast China to influence the post-fire management. Landsat satellite imageries on 15 August 2017 (pre-fire) and 15 August 2019 (post-fire) and field-based burn severity surveys were used to develop effective burn severity models using regression methods. The results showed that the dNBR is a better and more reliable index for burn severity mapping than the RdNBR. Using the RdNBR to estimate burn severity likely leads to an underestimation. RdNBR was designed to normalize for variations in pre-fire vegetation and therefore should most useful in heterogenous landscapes, such as the field area, which has areas of grassland and areas of forest. However, we found the opposite pattern: for predicting burn severity, the dNBR resulted in higher R2 and smaller RMSE values than the RdNBR for forested areas alone, and for combined forested and grassland areas (i.e. the entire field area). For grassland areas alone, the dNBR had similar R2 and RMSE to that of the RdNBR. In brief, the inconsistent results of the detection capabilities of the dNBR and RdNBR for burn severity around the world are closely related to diverse local conditions. The RdNBR must be used with caution in estimating the burn severity at a specific site. Our study enriches the knowledge of the wildfire burn severity assessment in the southeast coast of China.