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Landsat-based fire severity datasets are an invaluable resource for monitoring and research purposes. These gridded fire severity datasets are generally produced with pre- and post-fire imagery to estimate the degree of fire-induced ecological change. Here, we introduce methods to produce three Landsat-based fire severity metrics using the Google Earth Engine (GEE) platform: The delta normalized burn ratio (dNBR), the relativized delta normalized burn ratio (RdNBR), and the relativized burn ratio (RBR). Our methods do not rely on time-consuming a priori scene selection but instead use a mean compositing approach in which all valid pixels (e.g., cloud-free) over a pre-specified date range (pre- and post-fire) are stacked and the mean value for each pixel over each stack is used to produce the resulting fire severity datasets. This approach demonstrates that fire severity datasets can be produced with relative ease and speed compared to the standard approach in which one pre-fire and one post-fire scene are judiciously identified and used to produce fire severity datasets. We also validate the GEE-derived fire severity metrics using field-based fire severity plots for 18 fires in the western United States. These validations are compared to Landsat-based fire severity datasets produced using only one pre- and post-fire scene, which has been the standard approach in producing such datasets since their inception. Results indicate that the GEE-derived fire severity datasets generally show improved validation statistics compared to parallel versions in which only one pre-fire and one post-fire scene are used, though some of the improvements in some validations are more or less negligible. We provide code and a sample geospatial fire history layer to produce dNBR, RdNBR, and RBR for the 18 fires we evaluated. Although our approach requires that a geospatial fire history layer (i.e., fire perimeters) be produced independently and prior to applying our methods, we suggest that our GEE methodology can reasonably be implemented on hundreds to thousands of fires, thereby increasing opportunities for fire severity monitoring and research across the globe.

Background. Phenological correction of pre- and post-fire imagery is used to improve remotely sensed burn severity evaluations. Unburned offset values standardize greenness between image pairs, however, efficacy across diverse scenarios remains underexplored. Aims. We evaluated the impact of phenological offset correction methods to support analyst decision-making across fire-prone environments. Methods. We generated burn severity spectral index values for a dataset of Composite Burn Index (CBI) field plots across the conterminous U.S. The effectiveness of offset corrections was tested across image selection techniques, spectral indices, offset generation methods, and burn perimeter sources. We assessed the influence of offset corrections on the modeled relationship with CBI, agreement between burn severity thresholds, and potential bias. Key Results. Applying offset corrections consistently improved the modeled relationship with CBI by addressing extreme outlier severity values. However, automated offset corrections had the potential to introduce bias, systematically lowering severity values and reducing correspondence to observed burn severity categories. Conclusions. Offset corrections offer benefits but also pose tradeoffs to accurately representing remotely sensed burn severity. Implications. The utility of offset corrections depends on the environment, methods, and scale of analysis. We propose a decision-tree framework for analysts to consider when employing offset corrections given their study scope.

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.

The Composite Burn Index (CBI) is commonly linked to remotely sensed data to understand spatial and temporal patterns of burn severity. However, a comprehensive understanding of the tradeoffs between different methods used to model CBI with remotely sensed data is lacking. To help understand the current state of the science, provide a blueprint towards conducting broad-scale meta-analyses, and identify key decision points and potential rationale, we conducted a review of studies that linked remotely sensed data to continuous estimates of burn severity measured with the CBI and related methods. We provide a roadmap of the different methodologies applied and examine potential rationales used to justify them. Our findings largely reflect methods applied in North America – particularly in the western USA – due to the high number of studies in that region. We find the use of different methods across studies introduces variations that make it difficult to compare outcomes. Additionally, the existing suite of comparative studies focuses on one or few of many possible sources of uncertainty. Thus, compounding error and propagation throughout the many decisions made during analysis is not well understood. Finally, we suggest a broad set of methodological information and key rationales for decision-making that could facilitate future reviews.

Satellite-derived spectral indices such as the relativized burn ratio (RBR) allow fire severity maps to be produced in a relatively straightforward manner across multiple fires and broad spatial extents. These indices often have strong relationships with field-based measurements of fire severity, thereby justifying their widespread use in management and science. However, satellite-derived spectral indices have been criticized because their non-standardized units render them difficult to interpret relative to on-the-ground fire effects. In this study, we built a Random Forest model describing a field-based measure of fire severity, the composite burn index (CBI), as a function of multiple spectral indices, a variable representing spatial variability in climate, and latitude. CBI data primarily representing forested vegetation from 263 fires (8075 plots) across the United States and Canada were used to build the model. Overall, the model performed well, with a cross-validated R2 of 0.72, though there was spatial variability in model performance. The model we produced allows for the direct mapping of CBI, which is more interpretable compared to spectral indices. Moreover, because the model and all spectral explanatory variables were produced in Google Earth Engine, predicting and mapping of CBI can realistically be undertaken on hundreds to thousands of fires. We provide all necessary code to execute the model and produce maps of CBI in Earth Engine. This study and its products will be extremely useful to managers and scientists in North America who wish to map fire effects over large landscapes or regions.

BackgroundThe southeastern United States (‘Southeast’) experiences high levels of fire activity, but the preponderance of small and prescribed fires means that existing burn severity products are incomplete across the region.AimsWe developed and applied a burn severity model across the Southeast to enhance our understanding of regional burn severity patterns.MethodsWe used Composite Burn Index (CBI) plot data from across the conterminous US (CONUS) to train a gradient-boosted decision tree model. The model was optimised for the Southeast and applied to the annual Landsat Burned Area product for 2000–2022 across the region.Key resultsThe burn severity model had a root mean square error (RMSE) of 0.48 (R2 = 0.70) and 0.50 (R2 = 0.37) for the CONUS and Southeast, respectively. The Southeast, relative to CONUS, had lower mean absolute residuals in low and moderate burn severity categories. Burn severity was consistently lower in areas affected by prescribed burns relative to wildfires.ConclusionsAlthough regional performance was limited by a lack of high burn severity CBI plots, the burn severity dataset demonstrated patterns consistent with low-severity, frequent fire regimes characteristic of Southeastern ecosystems.ImplicationsMore complete data on burn severity will enhance regional management of fire-dependent ecosystems and improve estimates of fuels and fire emissions.

Wildfires are a dominant disturbance to boreal forests, and in North America, they typically cause widespread tree mortality. Forest fire burn severity is often measured at a plot scale using the Composite Burn Index (CBI), which was originally developed as a means of assigning severity levels to the Normalized Burn Ratio (NBR) computed from Landsat satellite imagery. Our study investigated the potential to map biophysical indicators of burn severity (residual green vegetation and charred organic surface) at very high (3 cm) resolution, using color orthomosaics and vegetation height models derived from UAV-based photographic surveys and Structure from Motion methods. These indicators were scaled to 30 m resolution Landsat pixel footprints and compared to the post-burn NBR (post-NBR) and differenced NBR (dNBR) ratios computed from pre- and post-fire Landsat imagery. The post-NBR showed the strongest relationship to both the fraction of charred surface (exponential R2 = 0.79) and the fraction of green crown vegetation above 5 m (exponential R2 = 0.81), while the dNBR was more closely related to the total green vegetation fraction (exponential R2 = 0.69). Additionally, the UAV green fraction and Landsat indices could individually explain more than 50% of the variance in the overall CBI measured in 39 plots. These results provide a proof-of-concept for using low-cost UAV photogrammetric mapping to quantify key measures of boreal burn severity at landscape scales, which could be used to calibrate and assign a biophysical meaning to Landsat spectral indices for mapping severity at regional scales.

Accurate assessment of burn severity is a critical need for an improved understanding of fire behavior and ecology and effective post-fire management. Although NASA Landsat satellites have a long history of use for remotely sensed mapping of burn severity, the recently launched (2015 and 2017) European Space Agency Sentinel-2 satellite constellation offers increased temporal and spatial resolution with global coverage, combined with free data access. Evaluations of burn severity derived from Landsat and Sentinel generally show comparable results, but these studies only assessed a small number of fires with limited field data. We used 912 ground calibration plots from 26 fires that burned between 2016 and 2019 in western North America to compare Sentinel- and Landsat-derived burn severity estimates with the field-based composite burn index. We mapped burn severity using two methods; the well-established paired scene approach, in which a single pre- and post-fire scene are selected for each fire, and also a mean image compositing approach that automatically integrates multiple scenes using the cloud-based remote sensing platform Google Earth Engine. We found that Sentinel generally performed as well or better than Landsat for four spectral indices of burn severity, particularly when using atmospherically corrected Sentinel imagery. Additionally, we tested the effects of mapping burn severity at Sentinel’s finer spatial resolution (10 m) on estimates of the spatial complexity of stand-replacing fire, resulting in a 5% average reduction per-fire in area mapped as high-severity patch interiors (24,273 ha total) compared to mapping at the resolution of Landsat (30 m). These findings suggest Sentinel may improve ecological discrimination of fine-scale fire effects, but also warrant caution when comparing estimates of burn severity spatial patterns derived at different resolutions. Overall, these results indicate that burn severity mapping will benefit substantially from the integration of Sentinel imagery through increased imagery availability, and that Sentinel’s higher spatial resolution improves opportunities for examining finer-scale fire effects across ecosystems.

Uncontrolled, large fires are a major threat to the biodiversity of protected heath landscapes. The severity of the fire is an important factor influencing vegetation recovery. We used airborne imaging spectroscopy data from the Airborne Prism Experiment (APEX) sensor to: (1) investigate which spectral regions and spectral indices perform best in discriminating burned from unburned areas; and (2) assess the burn severity of a recent fire in the Kalmthoutse Heide, a heathland area in Belgium. A separability index was used to estimate the effectiveness of individual bands and spectral indices to discriminate between burned and unburned land. For the burn severity analysis, a modified version of the Geometrically structured Composite Burn Index (GeoCBI) was developed for the field data collection. The field data were collected in four different vegetation types: Calluna vulgaris-dominated heath (dry heath), Erica tetralix-dominated heath (wet heath), Molinia caerulea (grass-encroached heath), and coniferous woodland. Discrimination between burned and unburned areas differed among vegetation types. For the pooled dataset, bands in the near infrared (NIR) spectral region demonstrated the highest discriminatory power, followed by short wave infrared (SWIR) bands. Visible wavelengths performed considerably poorer. The Normalized Burn Ratio (NBR) outperformed the other spectral indices and the individual spectral bands in discriminating between burned and unburned areas. For the burn severity assessment, all spectral bands and indices showed low correlations with the field data GeoCBI, when data of all pre-fire vegetation types were pooled (R2 maximum 0.41). Analysis per vegetation type, however, revealed considerably higher correlations (R2 up to 0.78). The Mid Infrared Burn Index (MIRBI) had the highest correlations for Molinia and Erica (R2 = 0.78 and 0.42, respectively). In Calluna stands, the Char Soil Index (CSI) achieved the highest correlations, with R2 = 0.65. In Pinus stands, the Normalized Difference Vegetation Index (NDVI) and the red wavelength both had correlations of R2 = 0.64. The results of this study highlight the superior performance of the NBR to discriminate between burned and unburned areas, and the disparate performance of spectral indices to assess burn severity among vegetation types. Consequently, in heathlands, one must consider a stratification per vegetation type to produce more reliable burn severity maps.

The future status of peatlands as carbon stores/sinks is uncertain given current and predicted environmental change. Several factors can affect the magnitude of the peatland carbon sink including disturbances such as wildfire. There is at present little evidence of how wildfire affects the emission of carbon dioxide (CO 2 ) and methane (CH 4 ) via perturbation to aerobic and anaerobic respiration. The greatest effects, which are likely to vary according to wildfire severity, would be expected in the immediate post-fire stages when little recovery has taken place. Here, we investigate five UK peatland wildfires (2011–2012) in the immediate post-wildfire period measuring CO 2 and CH 4 fluxes using static chambers. Fire severity was described using a modified form of the composite burn index. A hierarchical partitioning approach indicated time since fire was the most strongly associated variable that fluxes of both CO 2 , and CH 4 followed by soil temperature for CO 2 and fire severity for CH 4 . Using a liner mixed modelling approach to account for repeated measures; fire severity was a significant term for CH 4 and borderline significant for CO 2 . Mean fluxes of CH 4 were consistently lower on burnt sites. In contrast, data from a fire in the north of Scotland appeared to show the opposite relationship for CH 4 with higher fluxes on the burnt sites. These results suggest that wildfire can affect gaseous carbon fluxes, but the responses can be variable in both space and time and that disruption to anaerobic processes may be site and/or fire dependent.