Alwin A. Hardenbol, Hans Ole Ørka, Terje Gobakken, Joel Kostensalo. (2026). Optimising rare tree species detection for large-area mapping: a case study on European aspen (Populus tremula). Silva Fennica vol. 60 no. 3 article id 26012. https://doi.org/10.14214/sf.26012

Keywords: Bayesian statistics; biodiversity conservation; base rate problem; broadleaves; individual tree map; model transferability

Highlights: Balancing false discovery and false negative rates improves rare tree species detection; Preferring volume- over count-based approaches makes ecological sense for rare tree species; We detected 70% of aspen volume and classified this volume with 31% false discovery rate and 54% false negative rate; Base rates of rare tree species have been overlooked in remote sensing literature.

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Some locally rare tree species – like European aspen (Populus tremula 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.

Hardenbol, Natural Resources Institute Finland (Luke), Natural Resources Unit, Yliopistokatu 6B, FI-80100 Joensuu, Finland https://orcid.org/0000-0002-0615-505X E-mail: alwin.hardenbol@luke.fi
Ørka, Norwegian University of Life Sciences (NMBU), Faculty of Environmental Sciences and Natural Resource Management, P.O. Box 5003, NO-1432 Ås, Norway https://orcid.org/0000-0002-7492-8608 E-mail: hans-ole.orka@nmbu.no
Gobakken, Norwegian University of Life Sciences (NMBU), Faculty of Environmental Sciences and Natural Resource Management, P.O. Box 5003, NO-1432 Ås, Norway https://orcid.org/0000-0001-5534-049X E-mail: terje.gobakken@nmbu.no
Kostensalo, Natural Resources Institute Finland (Luke), Natural Resources Unit, Yliopistokatu 6B, FI-80100 Joensuu, Finland https://orcid.org/0000-0001-9883-1256 E-mail: joel.kostensalo@luke.fi

article id 10272, category Research article

Ana de Lera Garrido, Terje Gobakken, Hans Ole Ørka, Erik Næsset, Ole M. Bollandsås. (2020). Reuse of field data in ALS-assisted forest inventory. Silva Fennica vol. 54 no. 5 article id 10272. https://doi.org/10.14214/sf.10272

Keywords: airborne laser scanning; data reuse; temporal model transferability

Highlights: Six biophysical forest attributes were estimated for small stands without using up-to-date field data; The approaches included reused model relationships and forecasted field data; The accuracy of height estimates was comparable with the accuracy of an ordinary forest inventory with up-to-date field- and ALS data; Both approaches tended to produce estimates systematically different from the ground reference.

Abstract | Full text in HTML | Full text in PDF | Author Info

Forest inventories assisted by wall-to-wall airborne laser scanning (ALS), have become common practice in many countries. One major cost component in these inventories is the measurement of field sample plots used for constructing models relating biophysical forest attributes to metrics derived from ALS data. In areas where ALS-assisted forest inventories are planned, and in which the previous inventories were performed with the same method, reusing previously acquired field data can potentially reduce costs, either by (1) temporally transferring previously constructed models or (2) projecting field reference data using growth models that can serve as field reference data for model construction with up-to-date ALS data. In this study, we analyzed these two approaches of reusing field data acquired 15 years prior to the current ALS acquisition to estimate six up-to-date forest attributes (dominant tree height, mean tree height, stem number, stand basal area, volume, and aboveground biomass). Both approaches were evaluated within small stands with sizes of approximately 0.37 ha, assessing differences between estimates and ground reference values. The estimates were also compared to results from an up-to-date forest inventory relying on concurrent field- and ALS data. The results showed that even though the reuse of historical information has some potential and could be beneficial for forest inventories, systematic errors may appear prominent and need to be overcome to use it operationally. Our study showed systematic trends towards the overestimation of lower-range ground references and underestimation of the upper-range ground references.

de Lera Garrido, Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, P.O. Box 5003, NO-1432 Ås, Norway E-mail: ana.maria.lera.garrido@nmbu.no
Gobakken, Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, P.O. Box 5003, NO-1432 Ås, Norway E-mail: terje.gobakken@nmbu.no
Ørka, Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, P.O. Box 5003, NO-1432 Ås, Norway E-mail: hans-ole.orka@nmbu.no
Næsset, Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, P.O. Box 5003, NO-1432 Ås, Norway E-mail: erik.naesset@nmbu.no
Bollandsås, Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, P.O. Box 5003, NO-1432 Ås, Norway E-mail: ole.martin.bollandsas@nmbu.no

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