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

July 17, 2026

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.

Publication Year 2026
Title Estimating basal area change by tree size with Sentinel-2 imagery following four fires in California, USA
DOI 10.1071/WF25109
Authors Micah C. Wright, Phillip J. van Mantgem, Christopher Y.S. Wong, Derek J.N. Young, Saba J. Saberi, Andrew M. Latimer, Joseph A.E. Stewart, Adrian J. Das, Calvin A. Farris, Emma J. McClure, Lauren N. Youngblood, Hugh D. Safford, Kristen L. Shive
Publication Type Article
Publication Subtype Journal Article
Series Title International Journal of Wildland Fire
Index ID 70278143
Record Source USGS Publications Warehouse
USGS Organization Western Ecological Research Center
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