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

In recent years, there have been rapid improvements in both remote sensing methods and satellite image availability that have the potential to massively improve burn severity assessments of the Alaskan boreal forest. In this study, we utilized recent pre- and post-fire Sentinel-2 satellite imagery of the 2019 Nugget Creek and Shovel Creek burn scars located in Interior Alaska to both assess burn severity across the burn scars and test the effectiveness of several remote sensing methods for generating accurate map products: Normalized Difference Vegetation Index (NDVI), Normalized Burn Ratio (NBR), and Random Forest (RF) and Support Vector Machine (SVM) supervised classification. We used 52 Composite Burn Index (CBI) plots from the Shovel Creek burn scar and 28 from the Nugget Creek burn scar for training classifiers and product validation. For the Shovel Creek burn scar, the RF and SVM machine learning (ML) classification methods outperformed the traditional spectral indices that use linear regression to separate burn severity classes (RF and SVM accuracy, 83.33%, versus NBR accuracy, 73.08%). However, for the Nugget Creek burn scar, the NDVI product (accuracy: 96%) outperformed the other indices and ML classifiers. In this study, we demonstrated that when sufficient ground truth data is available, the ML classifiers can be very effective for reliable mapping of burn severity in the Alaskan boreal forest. Since the performance of ML classifiers are dependent on the quantity of ground truth data, when sufficient ground truth data is available, the ML classification methods would be better at assessing burn severity, whereas with limited ground truth data the traditional spectral indices would be better suited. We also looked at the relationship between burn severity, fuel type, and topography (aspect and slope) and found that the relationship is site-dependent.

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Urban Fire Severity and Vegetation Dynamics in Southern California

Remote Sensing ◽

10.3390/rs13010019 ◽

2020 ◽

Vol 13 (1) ◽

pp. 19

Author(s):

Lauren E. H. Mathews ◽

Alicia M. Kinoshita

Keyword(s):

Vegetation Cover ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Current Knowledge ◽

Fire Severity ◽

Satellite Image ◽

Burn Severity ◽

Native Vegetation ◽

Riparian Areas ◽

The Impact

A combination of satellite image indices and in-field observations was used to investigate the impact of fuel conditions, fire behavior, and vegetation regrowth patterns, altered by invasive riparian vegetation. Satellite image metrics, differenced normalized burn severity (dNBR) and differenced normalized difference vegetation index (dNDVI), were approximated for non-native, riparian, or upland vegetation for traditional timeframes (0-, 1-, and 3-years) after eleven urban fires across a spectrum of invasive vegetation cover. Larger burn severity and loss of green canopy (NDVI) was detected for riparian areas compared to the uplands. The presence of invasive vegetation affected the distribution of burn severity and canopy loss detected within each fire. Fires with native vegetation cover had a higher severity and resulted in larger immediate loss of canopy than fires with substantial amounts of non-native vegetation. The lower burn severity observed 1–3 years after the fires with non-native vegetation suggests a rapid regrowth of non-native grasses, resulting in a smaller measured canopy loss relative to native vegetation immediately after fire. This observed fire pattern favors the life cycle and perpetuation of many opportunistic grasses within urban riparian areas. This research builds upon our current knowledge of wildfire recovery processes and highlights the unique challenges of remotely assessing vegetation biophysical status within urban Mediterranean riverine systems.

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Effects of Soil Abiotic and Biotic Factors on Tree Seedling Regeneration Following a Boreal Forest Wildfire

Ecosystems ◽

10.1007/s10021-021-00666-0 ◽

2021 ◽

Author(s):

Theresa S. Ibáñez ◽

David A. Wardle ◽

Michael J. Gundale ◽

Marie-Charlotte Nilsson

Keyword(s):

Boreal Forest ◽

Betula Pendula ◽

Fire Severity ◽

Tree Regeneration ◽

Soil Biota ◽

Fire Disturbance ◽

Burn Severity ◽

Soil Conditions ◽

Tree Seedling ◽

Seedling Regeneration

AbstractWildfire disturbance is important for tree regeneration in boreal ecosystems. A considerable amount of literature has been published on how wildfires affect boreal forest regeneration. However, we lack understanding about how soil-mediated effects of fire disturbance on seedlings occur via soil abiotic properties versus soil biota. We collected soil from stands with three different severities of burning (high, low and unburned) and conducted two greenhouse experiments to explore how seedlings of tree species (Betula pendula, Pinus sylvestris and Picea abies) performed in live soils and in sterilized soil inoculated by live soil from each of the three burning severities. Seedlings grown in live soil grew best in unburned soil. When sterilized soils were reinoculated with live soil, seedlings of P. abies and P. sylvestris grew better in soil from low burn severity stands than soil from either high severity or unburned stands, demonstrating that fire disturbance may favor post-fire regeneration of conifers in part due to the presence of soil biota that persists when fire severity is low or recovers quickly post-fire. Betula pendula did not respond to soil biota and was instead driven by changes in abiotic soil properties following fire. Our study provides strong evidence that high fire severity creates soil conditions that are adverse for seedling regeneration, but that low burn severity promotes soil biota that stimulates growth and potential regeneration of conifers. It also shows that species-specific responses to abiotic and biotic soil characteristics are altered by variation in fire severity. This has important implications for tree regeneration because it points to the role of plant–soil–microbial feedbacks in promoting successful establishment, and potentially successional trajectories and species dominance in boreal forests in the future as fire regimes become increasingly severe through climate change.

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Determination of burn severity models ranging from regional to national scales for the conterminous United States

Remote Sensing of Environment ◽

10.1016/j.rse.2021.112569 ◽

2021 ◽

Vol 263 ◽

pp. 112569

Author(s):

Joshua J. Picotte ◽

C. Alina Cansler ◽

Crystal A. Kolden ◽

James A. Lutz ◽

Carl Key ◽

...

Keyword(s):

United States ◽

Burn Severity

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Predicting wildfire impacts on the prehistoric archaeological record of the Jemez Mountains, New Mexico, USA

Fire Ecology ◽

10.1186/s42408-021-00103-6 ◽

2021 ◽

Vol 17 (1) ◽

Author(s):

Megan M. Friggens ◽

Rachel A. Loehman ◽

Connie I. Constan ◽

Rebekah R. Kneifel

Keyword(s):

New Mexico ◽

Fire Severity ◽

Fire Effects ◽

Burn Severity ◽

Fire Season ◽

Archaeological Sites ◽

Archaeological Record ◽

Human Environment ◽

Jemez Mountains ◽

Archaeological Materials

Abstract Background Wildfires of uncharacteristic severity, a consequence of climate changes and accumulated fuels, can cause amplified or novel impacts to archaeological resources. The archaeological record includes physical features associated with human activity; these exist within ecological landscapes and provide a unique long-term perspective on human–environment interactions. The potential for fire-caused damage to archaeological materials is of major concern because these resources are irreplaceable and non-renewable, have social or religious significance for living peoples, and are protected by an extensive body of legislation. Although previous studies have modeled ecological burn severity as a function of environmental setting and climate, the fidelity of these variables as predictors of archaeological fire effects has not been evaluated. This study, focused on prehistoric archaeological sites in a fire-prone and archaeologically rich landscape in the Jemez Mountains of New Mexico, USA, identified the environmental and climate variables that best predict observed fire severity and fire effects to archaeological features and artifacts. Results Machine learning models (Random Forest) indicate that topography and variables related to pre-fire weather and fuel condition are important predictors of fire effects and severity at archaeological sites. Fire effects were more likely to be present when fire-season weather was warmer and drier than average and within sites located in sloped, treed settings. Topographic predictors were highly important for distinguishing unburned, moderate, and high site burn severity as classified in post-fire archaeological assessments. High-severity impacts were more likely at archaeological sites with southern orientation or on warmer, steeper, slopes with less accumulated surface moisture, likely associated with lower fuel moistures and high potential for spreading fire. Conclusions Models for predicting where and when fires may negatively affect the archaeological record can be used to prioritize fuel treatments, inform fire management plans, and guide post-fire rehabilitation efforts, thus aiding in cultural resource preservation.

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