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Areas affected by forest fires are increasing worldwide, making salvage logging (i.e., harvesting fire-affected trees) an increasingly used practice to reduce the economic impacts of fire on forestry. However, salvage logging can have strong ecological impacts, notably on post-fire forest regeneration and biodiversity. Burn severity (i.e., the degree to which fires impact the vegetation and soil) is also a central element that interacts with pre-fire forest characteristics and salvages logging to control post-fire forest dynamics and biodiversity. In an ecosystem-based forest management context, spatial patterns of burn severity should thus be considered when planning salvage logging operations. This study presents a simple and innovative method to generate burn severity maps with Landsat and Sentinel-2 multispectral imageries to support salvage logging operations rapidly after a fire event. We assembled a unique dataset involving 330 plots from 10 burns (from 2010 to 2020) in which burn severity has been estimated on the field using the composite burn index (CBI) approach in eastern North America. CBI values were modelled as a univariate function of changes in spectral indices using the first cloud-free post-fire satellite images taken after the burns. Our results demonstrate that using free satellite images with straightforward methods can produce reliable and ecologically meaningful burn severity maps within the few weeks following a fire event. The method was then applied to a case study with salvage logging that illustrates how our burn severity maps could be a useful tool for guiding post-fire forestry operations in an ecosystem-based management context. We combined burn severity maps with pre-fire forest composition and age maps to assess immediate post-fire forest. We discuss how such an approach helps to guide salvage planning and maintain residual forests that are representative of the initial post-fire spatial variability in burn severity and pre-fire veg

Dans un contexte de changements globaux, les évènements naturels extrêmes, tels que les feux de forêt, s’intensifieront (en fréquence et en sévérité), modifiant ainsi la composition et la structure de la forêt boréale canadienne. Ces changements écologiques majeurs risquent d’avoir des impacts majeurs sur la productivité de la forêt boréale. Le projet présenté fait partie d’un effort national pour évaluer et caractériser la sévérité des feux dans un contexte de modélisation de la régénération de l’épinette noire. Cette étude a pour but premier de comparer des approches mono-dates et multi-dates visant à caractériser la sévérité des feux de forêt à trois échelles spatiales différentes : in situ, par images satellitaires à fine résolution spatiale (PlanetScope), et par images satellitaires à moyenne résolution spatiale (Sentinel-2). Dans cette étude, la sévérité de deux feux survenus en juin 2017 et juillet 2018 dans l’ouest du Québec a été étudiée in situ à partir du Composite Burn Index (CBI). Le CBI quantifie l'impact de la sévérité du feu dans chaque étage de l'écosystème forestier, du sol à la canopée. Une méthodologie utilisant la classification orientée-objet mono-date post-feu a été développée à l’échelle fine pour caractériser la sévérité à partir d'images PlanetScope. Avec les images satellitaires de Sentinel-2, les indices spectraux tels que le differenced Normalized Burn Ratio (dNBR), le Relativized differenced Normalized Burn Ratio (RdNBR) et le Relativized Burn Ratio (RBR) ont été évalués pour la caractérisation de la sévérité à échelle moyenne. Les résultats de l'étude démontrent que, dans le contexte boréal de la pessière à mousse, le CBI est une métrique robuste pour la caractérisation de la sévérité in situ. Les résultats de la classification orientée-objet de la sévérité du feu indiquent que l'utilisation des images satellitaires mono-date provenant de PlanetScope peuvent être une bonne solution mitoyenne entre les produits à très fine résol

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.