Geology topics

Emma J. McClure

Publications and source records attributed to Emma J. McClure.

4 recordsLinked to original sources

USGS2026

Estimating basal area change by tree size with Sentinel-2 imagery following four fires in California, USA

Background Failure to account for tree size when estimating burn severity may not accurately capture post-fire tree mortality and post-fire forest structure. Aims We explored whether basal area mortality by tree size class could be determined from remotely-sensed burn severity indices based solely on Sentinel-2 satellite imagery. Methods We used data collected in four large California wildfires to model the relationship between proportional basal area mortality and burn severity indices derived from Sentinel-2 imagery for three tree diameter class thresholds: small (15 to 30 cm), medium (30 to 50 cm) and large (>50 cm). Key results Our models showed that for a given burn severity index value, the proportion of mortality was greater overall in smaller trees, and that the proportion of mortality in large trees changed more slowly than that of smaller trees with changing burn severity index values. Conclusions We found that models that accounted for tree size can more precisely estimate changes in forest size structure than a similar model that did not account for tree size. Implications Explicitly accounting for tree size can improve estimates of post-fire forest structure, including for large trees which make up the bulk of stand biomass and post-fire seed sources.

California↗

USGS2026

Recovery trajectories of surface fuels and forest trees following prescribed fire in low elevation conifer forests of California and southern Oregon

Background Prescribed fire is commonly used to manage surface fuels and stand structure in dry coniferous forests in the western United States. While the effectiveness of prescribed fire to manage fuel loads and live forest structure has been well documented, less is known about the shape of recovery trajectories more than a decade following treatment. We used up to 30 years of longitudinal observations from > 180 monitoring plots treated with a single prescribed fire-only treatment across six national parks in California and Oregon to compare competing recovery trajectory models: generalized linear models (GLM) that presumed exponential trajectories over time, generalized additive models (GAM) that had maximum flexibility to fit responses, and generalized Michaelis–Menten models that describe an asymptotic recovery trajectory. Results The Michaelis–Menten model had the lowest median RMSE of the fuel recovery models, with predicted recovery times and maximal fuel loads tending to increase with increasing fuel particle size. However, predictive performance was similar between model forms. GAM best predicted forest structure, showing that stem density of live overstory trees (> 15 cm stem diameter) declined immediately following prescribed fire without appreciable postfire accumulation. Results for live stem biomass were similar, but with less pronounced reductions. Conclusions We found that reductions in surface fuel biomass from a single prescribed fire-only treatment can last for more than a decade for some fuel types. Our analysis also demonstrates that asymptotic models reasonably describe surface fuel recovery trajectories. However, we lack the evidence to definitively conclude that asymptotic models best describe postfire recovery, even with repeated observations of up to three decades postfire. We also found that stem density and biomass of live trees with stem diameter > 15 cm both declined in the first few years immediately following the fire, after which they remained relatively unchanged for the remainder of the observation period.

California, Oregon↗

USGS2026

Effects of repeat prescribed burning in dry coniferous forests in national parks of California

Background Prescribed fire is a common approach to reduce fuels and mitigate fire hazards. The accumulation of live and dead fuels following initial treatment means that repeated application of prescribed fire could be used to maintain this benefit. However, the effect of repeated prescribed fires is not well documented in many dry coniferous forests in the western United States. Here, we present observations of changes in live trees and surface fuels following two prescribed fires in dry coniferous forests in national parks of California. Results Changes in forest structure and accumulation of surface fuels were similar over time following initial-entry and second-entry fires. An exception was that repeated fires were associated with substantial reductions in stem density. There were smaller changes in live tree basal area and stem biomass. Conclusions Our results indicate that following initial-entry fires, subsequent burning maintained reductions in surface fuel loads without major inadvertent losses of live tree basal area and stem biomass, implying the survival of large trees.

California↗

USGS2026

Basal area loss from fire using field-calibrated remote sensing refines western US fire severity measurements

The spatial patterns of fire effects and tree mortality have profound consequences for forest resilience. Cost-effective, medium-resolution, and spatiotemporally extensive fire severity measurements are essential for informing post-fire restoration and improving our understanding of wildfires—from forest stands to continents and from days to decades. Remote sensing advancements have improved burn severity mapping, but methods vary in interpretability, scalability, generalizability, and alignment with field measurements. One meaningful metric of fire effects on forests is proportion basal area loss, but existing methods are limited by a lack of region-specific field reference data and a scalable mapping framework. To address these issues, we compiled 3280 field reference plots from 123 fires in forests across the Western US to calculate the proportion of fire-induced basal area loss. We then used spatially cross-validated machine learning models with concurrent hyperparameter tuning to select a skillful, parsimonious model from a large candidate set of remotely-sensed, climatic, and topographic predictors. Spectral-only measures of severity over- or underestimated basal area loss in dry versus wet years and across aspects, demonstrating the value of incorporating climatic and topographic context. We also tested model performance on a separate holdout dataset in the Southwest US as a demonstration of reproducibility and transparency. We provide a Google Earth Engine tool for estimating proportional basal area loss for any fire perimeter in the Western US, enabling rapid map creation for land management and ecological modeling. All code, model parameters, and training data are released to support reproducibility, community adoption, regional refinement, and adaptation to new regions.

western United States↗