A keystone species, European aspen (Populus tremula L.), in boreal forests: Ecological role, knowledge needs and mapping using remote sensing

Kivinen, Sonja; Koivisto, Elina; Keski‐Saari, Sarita; Poikolainen, Laura; Tanhuanpää, Topi; Kuzmin, Anton; Viinikka, Arto; Heikkinen, Risto K.; Pykälä, Juha; Virkkala, Raimo; Vihervaara, Petteri; Kumpula, Timo

doi:10.1016/j.foreco.2020.118008

Cited by 48 publications

(29 citation statements)

References 164 publications

(208 reference statements)

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“…In previous studies, the accuracy for European aspen detection with various remote sensing methods has been rather low comparing to other dominant species. In the latest aspen review, Kivinen et al [6] reported a user's accuracy ranging from 56% to 86% and producer's accuracy from 24% to 71% for aspen. In this sense, our results for aspen discrimination were relatively good.…”

Section: Impact Of Spectral Features For Aspen Discriminationmentioning

confidence: 99%

“…European aspen (Populus tremula L.) (hereafter, 'aspen') is a keystone species in boreal forests that has a sparse and scattered occurrence in northern Europe. Both living and dead aspen trees contribute significantly to the biological diversity of various taxa, such as epiphytic bryophytes and lichens, fungi, invertebrates, and mammals [5,6]. The ecological importance of aspen is illustrated by the fact that numerous aspen-associated species are red-listed [7,8].…”

Section: Introductionmentioning

confidence: 99%

“…From the perspective of long-term occurrence of aspen-associated biodiversity, the maintenance of a spatial and temporal continuum of aspen trees is a crucial task [5,11,12]. However, the explicit spatial data of aspen occurrence in the boreal landscapes remain scarce and incomprehensive, which hampers biodiversity management and conservation efforts [6].…”

Section: Introductionmentioning

confidence: 99%

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Detecting European Aspen (Populus tremula L.) in Boreal Forests Using Airborne Hyperspectral and Airborne Laser Scanning Data

et al. 2020

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Sustainable forest management increasingly highlights the maintenance of biological diversity and requires up-to-date information on the occurrence and distribution of key ecological features in forest environments. European aspen (Populus tremula L.) is one key feature in boreal forests contributing significantly to the biological diversity of boreal forest landscapes. However, due to their sparse and scattered occurrence in northern Europe, the explicit spatial data on aspen remain scarce and incomprehensive, which hampers biodiversity management and conservation efforts. Our objective was to study tree-level discrimination of aspen from other common species in northern boreal forests using airborne high-resolution hyperspectral and airborne laser scanning (ALS) data. The study contained multiple spatial analyses: First, we assessed the role of different spectral wavelengths (455–2500 nm), principal component analysis, and vegetation indices (VI) in tree species classification using two machine learning classifiers—support vector machine (SVM) and random forest (RF). Second, we tested the effect of feature selection for best classification accuracy achievable and third, we identified the most important spectral features to discriminate aspen from the other common tree species. SVM outperformed the RF model, resulting in the highest overall accuracy (OA) of 84% and Kappa value (0.74). The used feature set affected SVM performance little, but for RF, principal component analysis was the best. The most important common VI for deciduous trees contained Conifer Index (CI), Cellulose Absorption Index (CAI), Plant Stress Index 3 (PSI3), and Vogelmann Index 1 (VOG1), whereas Green Ratio (GR), Red Edge Inflection Point (REIP), and Red Well Position (RWP) were specific for aspen. Normalized Difference Red Edge Index (NDRE) and Modified Normalized Difference Index (MND705) were important for coniferous trees. The most important wavelengths for discriminating aspen from other species included reflectance bands of red edge range (724–727 nm) and shortwave infrared (1520–1564 nm and 1684–1706 nm). The highest classification accuracy of 92% (F1-score) for aspen was achieved using the SVM model with mean reflectance values combined with VI, which provides a possibility to produce a spatially explicit map of aspen occurrence that can contribute to biodiversity management and conservation efforts in boreal forests.

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Section: Impact Of Spectral Features For Aspen Discriminationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Detecting European Aspen (Populus tremula L.) in Boreal Forests Using Airborne Hyperspectral and Airborne Laser Scanning Data

et al. 2020

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“…On this date, aspen was in the late leaf flush stage. The accuracy that was achieved is the highest overall accuracy (when aspen is considered as a separate species) recorded in literature thus far (see Kivinen et al 2020;Viinikka et al 2020).…”

Section: Remote Sensing For Aspen Detection Shows Promising Results mentioning

confidence: 82%

“…Fassnacht et al 2016), this is still a developing field that struggles under an increasing number of species and spectral/structural similarities between species. For aspen specifically, few attempts have been made to distinguish it from other species and when this has been attempted, it has generally led to poor outcomes as a result of spectral and structural similarities with common European boreal tree species (reviewed by Kivinen et al 2020). In previous studies involving aspen, however, seasonal variation in canopy phenology has been ignored, which could improve classification accuracy.…”

Section: Remote Sensing For Aspen Detection Shows Promising Results mentioning

confidence: 99%

Dynamics of biodiversity-rich deciduous trees and microhabitats in boreal forests

Hardenbol¹

2020

Diss. For.

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Establishing protected areas and maintaining biodiversity in managed forests are the main methods to conserve forest habitats and their biodiversity. The habitat characteristics that affect forest biodiversity in both protected and managed forests occur on different spatial scales, with the smallest scale consisting of structures at the level of single trees and smaller. Under the influence of dynamic processes (ecological succession, natural and anthropogenic disturbances) small-scale structures may be strongly affected, altering their contribution to local biodiversity. In this thesis, I studied two small-scale structures in northern boreal forests: deciduous trees (mainly European aspen (Populus tremula)) in conifer-dominated forests and woodpecker-made breeding cavities. These structures provide resources for multiple species. However, their abundance could be affected by dynamic processes, also in protected areas. I examined the temporal dynamics of these structures through long-term (16-30 years) monitoring. Specifically, I studied the recruitment of deciduous trees in managed forests, aspen demography in old-growth forests, the effect of tree-and stand-level factors on cavity persistence, and the potential to detect aspen for conservation purposes via remote sensing. My main findings were: 1) Recruitment of deciduous trees is enhanced by prescribed burning. Mammalian herbivory impacts recruitment significantly, although its effect depends on forest age. 2) Living aspens declined by 37% in protected old-growth forests over an 18-year period and recruitment rate was low despite an abundance of saplings. Recruitment primarily occurred on forest edges. 3) Tree species, size, and condition affect the persistence of woodpecker-made cavities, with cavities lasting longer in coniferous, healthy, and larger trees. For aspen, cavities in small trees also persisted a long time. 4) Multispectral drone images can be used to detect scattered mature aspen trees in oldgrowth forests, most ideally in late spring. My results emphasize that dynamic processes in forests can alter forest characteristics that are important for biodiversity quickly. Small-scale structures, that are essential to maintain local biodiversity, underwent strong changes in just a few decades. My thesis highlights the importance of continuous monitoring of biodiversity-rich habitat structures to uncover major changes in the ability of protected areas to sustain biodiversity. Remote sensing is potentially a valuable monitoring tool, also for tree-level small-scale forest structures.

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Living Trees and Biodiversity

Hämäläinen

Runnel

Mikusiński

et al. 2023

Advances in Global Change Research

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Living trees are fundamental for boreal forest biodiversity. They contribute to stand structural diversity, which determines the range of habitat niches available for forest-dwelling species. Specific characteristics of living trees, such as species, age, and presence of microhabitats, determine how species utilize trees for food, as nesting places, or as growing substrates. This chapter explores the associations between living trees and aboveground biodiversity, reviews the factors such as soil productivity, hydrological regime, stand successional stage, and forestry activities that influence the characteristics of living trees and stand structural diversity, and presents the consequences of current and future climate change on boreal biodiversity.

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A keystone species, European aspen (Populus tremula L.), in boreal forests: Ecological role, knowledge needs and mapping using remote sensing

Cited by 48 publications

References 164 publications

Detecting European Aspen (Populus tremula L.) in Boreal Forests Using Airborne Hyperspectral and Airborne Laser Scanning Data

Detecting European Aspen (Populus tremula L.) in Boreal Forests Using Airborne Hyperspectral and Airborne Laser Scanning Data

Dynamics of biodiversity-rich deciduous trees and microhabitats in boreal forests

Living Trees and Biodiversity

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