%0 Research article
%T Optimising rare tree species detection for large-area mapping: a case study on European aspen (<i>Populus tremula</i>)
%A Hardenbol, Alwin A.
%A Ørka, Hans Ole
%A Gobakken, Terje
%A Kostensalo, Joel
%D 2026
%J Silva Fennica
%V 60
%N 3
%R doi:10.14214/sf.26012
%U https://silvafennica.fi/article/26012
%X Some locally rare tree species – like European aspen (<i>Populus tremula</i> L.) – contribute disproportionately to forest biodiversity, making their accurate monitoring important for conservation. However, most remote sensing studies of rare tree species ignore their low prevalence/base rate, necessary for model transferability. We aimed to assess European aspen classification under realistic landscape prevalence and find methods to improve classification performance. Across 253 plots from field data in Våler, Norway we detected and segmented 5455 living trees, including 55 European aspens (ca. 1% prevalence), from high-density airborne laser scanning (ALS) data merged with multispectral aerial imagery. Individual segments were then classified as European aspen or non-aspen using random forest and synthetic minority oversampling (SMOTE). We evaluated classification based on tree counts and summed tree volumes. To underpin the importance of prevalence consideration and compare our results, we also re-analysed published Fennoscandian European aspen classification studies. Contrary to a perfect balance, we found an optimal SMOTE-ratio at ca. 0.07 (European aspen–non-aspen ratio). Our count-based classification achieved 32% (16–52) false discovery rate (FDR) and 73% (60–83) false negative rate (FNR), whereas volume-based classification achieved 31% (11–51) FDR and 54% (38–71) FNR. Re-analysed studies averaged 66% FDR and 33% FNR at a 1% prevalence. Our results demonstrate that rare tree species classification benefits from optimising majority–minority ratios and, given the ecological value of larger trees, volume-based approaches are warranted. Finally, we urge reporting landscape prevalence and adjust classification metrics to reflect these to enable transferability to real-world applications in rare tree species classification.