Developing fine-grained nationwide predictions of valuable forests using biodiversity indicator bird species

Publisher Copyright: © 2021 The Authors. Ecological Applications published by Wiley Periodicals LLC on behalf of The Ecological Society of America.The use of indicator species in forest conservation and management planning can facilitate enhanced preservation of biodiversity from the negative effects of forestry and other uses of land. However, this requires detailed and spatially comprehensive knowledge of the habitat preferences and distributions of selected focal indicator species. Unfortunately, due to limited resources for field surveys, only a small proportion of the occurrences of focal species is usually known. This shortcoming can be circumvented by using modelling techniques to predict the spatial distribution of suitable sites for the target species. Airborne laser scanning (ALS) and other remote sensing (RS) techniques have the potential to provide useful environmental data covering systematically large areas for these purposes. Here, we focused on six bird of prey and woodpecker species known to be good indicators of boreal forest biodiversity values. We used known nest sites of the six indicator species based on nestling ringing records. Thus, the most suitable nesting sites of these species provide important information for biodiversity-friendly forest management and conservation planning. We developed fine-grained, i.e., 96 x 96 m grid cell resolution, predictive maps across the whole of Finland of the suitable nesting habitats based on ALS and other RS data and spatial information on the distribution of important forest stands for the six studied biodiversity indicator bird species based on nesting habitat suitability modelling, i.e., the MaxEnt model. Habitat preferences of the study species, as determined by MaxEnt, were in line with the previous knowledge of species-habitat relations. The proportion of suitable habitats of these species in protected areas was considerable, but our analysis also revealed many potentially high-quality forest stands outside protected areas. However, many of these sites are increasingly threatened by logging due to increased pressures for using forests for bioeconomy and forest industry based on National Forest Strategy. Predicting habitat suitability based on information on the nest sites of indicator species provides a new tool for systematic conservation planning over large areas in boreal forests in Europe, and corresponding approach would also be feasible and recommendable elsewhere where similar data are available.The use of indicator species in forest conservation and management planning can facilitate enhanced preservation of biodiversity from the negative effects of forestry and other uses of land. However, this requires detailed and spatially comprehensive knowledge of the habitat preferences and distributions of selected focal indicator species. Unfortunately, due to limited resources for field surveys, only a small proportion of the occurrences of focal species is usually known. This shortcoming can be circumvented by using modeling techniques to predict the spatial distribution of suitable sites for the target species. Airborne laser scanning (ALS) and other remote sensing (RS) techniques have the potential to provide useful environmental data covering systematically large areas for these purposes. Here, we focused on six bird of prey and woodpecker species known to be good indicators of boreal forest biodiversity values. We used known nest sites of the six indicator species based on nestling ringing records. Thus, the most suitable nesting sites of these species provide important information for biodiversity-friendly forest management and conservation planning. We developed fine-grained, that is, 96 x 96 m grid cell resolution, predictive maps across the whole of Finland of the suitable nesting habitats based on ALS and other RS data and spatial information on the distribution of important forest stands for the six studied biodiversity indicator bird species based on nesting-habitat suitability modeling, that is, the MaxEnt model. Habitat preferences of the study species, as determined by MaxEnt, were in line with the previous knowledge of species-habitat relations. The proportion of suitable habitats of these species in protected areas (PAs) was considerable, but our analysis also revealed many potentially high-quality forest stands outside PAs. However, many of these sites are increasingly threatened by logging because of increased pressures for using forests for bioeconomy and forest industry based on National Forest Strategy. Predicting habitat suitability based on information on the nest sites of indicator species provides a new tool for systematic conservation planning over large areas in boreal forests in Europe, and a corresponding approach would also be feasible and recommendable elsewhere where similar data are available.Peer reviewe

Detection of European Aspen (Populus tremula L.) Based on an Unmanned Aerial Vehicle Approach in Boreal Forests

European aspen (Populus tremula L.) is a keystone species for biodiversity of boreal forests.Large-diameter aspens maintain the diversity of hundreds of species, many of which are threatened in Fennoscandia. Due to a low economic value and relatively sparse and scattered occurrence of aspen in boreal forests, there is a lack of information of the spatial and temporal distribution of aspen, which hampers efficient planning and implementation of sustainable forest management practices and conservation efforts. Our objective was to assess identification of European aspen at the individual tree level in a southern boreal forest using high-resolution photogrammetric point cloud (PPC) and multispectral (MSP) orthomosaics acquired with an unmanned aerial vehicle (UAV). The structure-from-motion approach was applied to generate RGB imagery-based PPC to be used for individual tree-crown delineation. Multispectral data were collected using two UAV cameras:Parrot Sequoia and MicaSense RedEdge-M. Tree-crown outlines were obtained from watershed segmentation of PPC data and intersected with multispectral mosaics to extract and calculate spectral metrics for individual trees. We assessed the role of spectral data features extracted from PPC and multispectral mosaics and a combination of it, using a machine learning classifier—Support Vector Machine (SVM) to perform two different classifications: discrimination of aspen from the other species combined into one class and classification of all four species (aspen, birch, pine, spruce) simultaneously. In the first scenario, the highest classification accuracy of 84% (F1-score) for aspen and overall accuracy of 90.1% was achieved using only RGB features from PPC, whereas in the second scenario, the highest classification accuracy of 86 % (F1-score) for aspen and overall accuracy of 83.3% was achieved using the combination of RGB and MSP features. The proposed method provides a new possibility for the rapid assessment of aspen occurrence to enable more efficient forest management as well as contribute to biodiversity monitoring and conservation efforts in boreal forests.peerReviewe

Tree species classification from airborne hyperspectral and LiDAR data using 3D convolutional neural networks

During the last two decades, forest monitoring and inventory systems have moved from field surveys to remote sensing-based methods. These methods tend to focus on economically significant components of forests, thus leaving out many factors vital for forest biodiversity, such as the occurrence of species with low economical but high ecological values. Airborne hyperspectral imagery has shown significant potential for tree species classification, but the most common analysis methods, such as random forest and support vector machines, require manual feature engineering in order to utilize both spatial and spectral features, whereas deep learning methods are able to extract these features from the raw data. Our research focused on the classification of the major tree species Scots pine, Norway spruce and birch, together with an ecologically valuable keystone species, European aspen, which has a sparse and scattered occurrence in boreal forests. We compared the performance of three-dimensional convolutional neural networks (3D-CNNs) with the support vector machine, random forest, gradient boosting machine and artificial neural network in individual tree species classification from hyperspectral data with high spatial and spectral resolution. We collected hyperspectral and LiDAR data along with extensive ground reference data measurements of tree species from the 83 km2 study area located in the southern boreal zone in Finland. A LiDAR-derived canopy height model was used to match ground reference data to aerial imagery. The best performing 3D-CNN, utilizing 4 m image patches, was able to achieve an F1-score of 0.91 for aspen, an overall F1-score of 0.86 and an overall accuracy of 87%, while the lowest performing 3D-CNN utilizing 10 m image patches achieved an F1-score of 0.83 and an accuracy of 85%. In comparison, the support-vector machine achieved an F1-score of 0.82 and an accuracy of 82.4% and the artificial neural network achieved an F1-score of 0.82 and an accuracy of 81.7%. Compared to the reference models, 3D-CNNs were more efficient in distinguishing coniferous species from each other, with a concurrent high accuracy for aspen classification. Deep neural networks, being black box models, hide the information about how they reach their decision. We used both occlusion and saliency maps to interpret our models. Finally, we used the best performing 3D-CNN to produce a wall-to-wall tree species map for the full study area that can later be used as a reference prediction in, for instance, tree species mapping from multispectral satellite images. The improved tree species classification demonstrated by our study can benefit both sustainable forestry and biodiversity conservation.peerReviewe

Temporal cycles and spatial asynchrony in the reproduction and growth of a rare nectarless orchid, Cypripedium calceolus

The timing and intensity of plant reproduction vary due to internal and external factors. Although this variation has been widely studied in species exhibiting masting (intermittent synchronous reproduction), it has attracted less attention in nonmasting species. Here, we studied intra-individual variation in the flowering intensity and plant size of a nonmasting, rare terrestrial orchid, Cypripedium calceolus, using long-term monitoring data from three populations in Finland and two populations in Estonia. Flowering intensity and plant size showed 2-year cycles, indicating that reproduction and growth were regulated by past costs of reproduction and extensive clonal growth. In addition, flowering intensity and plant size were positively correlated with size from the previous year and were also affected by the weather conditions of spring and of the previous growing season. However, there was little synchrony among plants, suggesting that the climatic control of reproduction and growth is sufficiently low as to be masked by high annual variation in these two vital rates. Together, these results indicate that the reproduction and growth of C. calceolus depend on individual demographic history and past weather conditions and that intrinsic factors can also lead to cyclic fluctuation in reproduction in nonmasting species

Detecting european aspen (Populus tremula L.) in boreal forests using airborne hyperspectral and airborne laser scanning data

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

Developing a spatially explicit modelling and evaluation framework for integrated carbon sequestration and biodiversity conservation: application in southern Finland

The challenges posed by climate change and biodiversity loss are deeply interconnected. Successful co-managing of these tangled drivers requires innovative methods that can prioritize and target management actions against multiple criteria, while also enabling cost-effective land use planning and impact scenario assessment. This paper synthesises the development and application of an integrated multidisciplinary modelling and evaluation framework for carbon and biodiversity in forest systems. By analysing and spatio-temporally modelling carbon processes and biodiversity elements, we determine an optimal solution for their co-management in the study landscape. We also describe how advanced Earth Observation measurements can be used to enhance mapping and monitoring of biodiversity and ecosystem processes. The scenarios used for the dynamic models were based on official Finnish policy goals for forest management and climate change mitigation. The development and testing of the system were executed in a large region in southern Finland (Kokemäenjoki basin, 27 024 km2) containing highly instrumented LTER (Long-Term Ecosystem Research) stations; these LTER data sources were complemented by fieldwork, remote sensing and national data bases. In the study area, estimated total net emissions were currently 4.2 TgCO2eq a-1, but modelling of forestry measures and anthropogenic emission reductions demonstrated that it would be possible to achieve the stated policy goal of carbon neutrality by low forest harvest intensity. We show how this policy-relevant information can be further utilised for optimal allocation of set-aside forest areas for nature conservation, which would significantly contribute to preserving both biodiversity and carbon values in the region. Biodiversity gain in the area could be increased without a loss of carbon-related benefits.The challenges posed by climate change and biodiversity loss are deeply interconnected. Successful co-managing of these tangled drivers requires innovative methods that can prioritize and target management actions against multiple criteria, while also enabling cost-effective land use planning and impact scenario assessment. This paper synthesises the development and application of an integrated multidisciplinary modelling and evaluation framework for carbon and biodiversity in forest systems. By analysing and spatio-temporally modelling carbon processes and biodiversity elements, we determine an optimal solution for their co-management in the study landscape. We also describe how advanced Earth Observation measurements can be used to enhance mapping and monitoring of biodiversity and ecosystem processes. The scenarios used for the dynamic models were based on official Finnish policy goals for forest management and climate change mitigation. The development and testing of the system were executed in a large region in southern Finland (Kokemäenjoki basin, 27,024 km2) containing highly instrumented LTER (Long-Term Ecosystem Research) stations; these LTER data sources were complemented by fieldwork, remote sensing and national data bases. In the study area, estimated total net emissions were currently 4.2 TgCO2eq a−1, but modelling of forestry measures and anthropogenic emission reductions demonstrated that it would be possible to achieve the stated policy goal of carbon neutrality by low forest harvest intensity. We show how this policy-relevant information can be further utilized for optimal allocation of set-aside forest areas for nature conservation, which would significantly contribute to preserving both biodiversity and carbon values in the region. Biodiversity gain in the area could be increased without a loss of carbon-related benefits.Peer reviewe

The roles of individual demographic history and environmental conditions in the performance and conservation of northern orchids

Abstract A population growth rate is the sum of all individuals’ reproduction and survival, which in turn depend on many external and internal factors, e.g. weather and individual reproductive history. In plants, for example, previous reproduction can deplete an individual’s resources, resulting in trade-offs between demographic functions. To understand these demographic processes, it is necessary to follow populations for many years. Such long-term studies are especially crucial for endangered species, as they can reveal the causes of population declines and provide information that is directly applicable for the management. In my thesis, I applied this approach to the study of rare orchids. Specifically, I analyzed long-term orchid monitoring data from two countries, Finland and Estonia, to assess the external and internal factors that affect the performance of these long-lived plants, which reproduce both sexually (via seeds) and vegetatively (via new ramets). My research reveals that plant performance depends on both the demographic history and the environment of a plant. For example, although Finnish and Estonian populations of the lady’s slipper orchid, Cypripedium calceolus, differed in direction and statistical significance of their responses to environmental factors, the two most-influential weather variables in both cases were spring snow depth and the temperature of the previous summer. However, the influence of weather on both flowering and vegetative growth was dwarfed by the effect of plants’ own demographic histories: there was a trade-off between current and future reproduction which created asynchronous two-year cycles in reproduction and growth. Furthermore, in all three studied orchid species — the lady’s slipper orchid (C. calceolus), the fairy’s slipper orchid (Calypso bulbosa), and the dark-red helleborine (Epipactis atrorubens) — the probability of dormancy (a state in which the plant spends a year or more underground) and the demographic costs this state incurred with respect to size or future reproduction depended on a plant’s size and whether it flowered prior to dormancy. In other words, dormancy had both absolute and relative costs in large, but not in small, individuals. Finally, I show here that environmental alteration via selective tree removal can be used as a management method to increase orchid reproduction via both seeds and ramets.Tiivistelmä Populaation kasvunopeus riippuu siitä, kuinka monta yksilöä populaatioon syntyy ja kuinka monta yksilöä kuolee. Yksilöiden lisääntyvyyteen ja elossa säilyvyyteen puolestaan vaikuttavat monet ulkoiset ja sisäiset tekijät, kuten sää ja yksilön oma lisääntymishistoria. Kasvilla on rajallinen määrä resursseja, joten sen pitää tehdä kompromisseja eri elintoimintojen, esimerkiksi kasvun ja lisääntymisen, välillä. Klonaaliset kasvit voivat myös lisääntyä usealla tavalla: joko suvullisesti siemenistä tai kasvullisesti tuottamalla uusia versoja. Demografisten prosessien tutkimisessa pitkäaikaiset seuranta-aineistot ovat välttämättömiä. Pitkäaikaisseurannat voivat myös paljastaa uhanalaisen lajin populaation taantumisen syyt ja näistä seurannoista saatua tietoa voidaan soveltaa harvinaisten lajien, esimerkiksi kämmeköiden, suojelutoimien suunnittelussa. Tässä väitöskirjassa analysoin aineistoa kämmeköiden pitkäaikaisseurannoista Suomesta ja Virosta. Tavoitteenani oli arvioida ulkoisten ja sisäisten tekijöiden merkitystä pitkäikäisten kasvien menestykselle. Tulokset osoittavat, että kasvin menestys riippuu sekä yksilön omasta demografisesta historiasta että sen ympäristöstä. Eri säätekijöiden vaikutus tikankontin (Cypripedium calceolus) kasvuun ja kukkimiseen vaihteli Suomen ja Viron välillä, mutta lumen syvyys ja edellisen kasvukauden lämpötila nousivat merkittävimmiksi tekijöiksi molemmissa maissa. Tikankontin kasvu ja kukinta riippuivat kuitenkin säätä enemmän kasvin omasta demografisesta historiasta. Runsas lisääntyminen edeltävällä kasvukaudella vähensi lisääntymistä tulevalla kasvukaudella, mikä johti kaksivuotiseen jaksottaisuuteen tikankontin lisääntymisessä ja kasvussa. Tutkiessani dormanssia (lepotila, jossa kasvi ei tuota maanpäällistä versoa) kolmella kämmekkälajilla, tikankontilla, neidonkengällä (Calypso bulbosa) ja tummaneidonvaipalla (Epipactis atrorubens), havaitsin lisäksi, että todennäköisyys siirtyä dormanssiin riippui kasvin koosta. Myöskin tämän lepotilan aiheuttamat kustannukset olivat riippuvaisia kasvin aikaisemmasta tilasta. Isoilla kasveilla dormanssilla oli sekä suoria kustannuksia että kustannuksia suhteessa versomiseen. Pienillä kasveilla näitä kustannuksia ei ollut. Osoitan väitöskirjassani myös, että maltillisella puunpoistolla voidaan lisätä tikankonttipopulaatioiden siementuottoa ja versotiheyttä

Tree removal as a management strategy for the lady’s slipper orchid, a flagship species for herb-rich forest conservation

Abstract In boreal herb-rich forests, the dominance of Norway spruce (Picea abies) decreases light availability for understory species, many of which depend on canopy gaps for reproduction. Here, we explored the response of a rare clonal understory herb, the lady’s slipper orchid (Cypripedium calceolus), to tree removal. We used demographic data spanning 16 years from ten unharvested control sites and ten harvest sites which were divided into three treatments with differing harvest intensity: (1) dense spruce forests, where half of the total tree basal area (TBA) was cut, (2) sparse spruce forests, where one-fourth of the spruce TBA was cut and (3) sparse broadleaf forests, where one-fourth of the total TBA was cut. The effects of harvesting on different demographic rates (ramet density, reproduction, survival, and dormancy) were studied with generalized linear mixed models with harvest intensity, time since harvest and the starting level of the response variable as explanatory variables. Tree removal sites had 2.2 times higher orchid ramet density, 2.4 times higher odds of survival, and 2.1–3.1 times higher odds of flowering and fruiting than the control sites, but these effects were not seen at all treatment levels at all times. Tree removal had no effect on dormancy or seedling or flower density. Orchid flowering and fruiting probabilities increased only at the most intensively harvested sites (both spruce forest sites, and dense spruce forest with 50% TBA removal, respectively), while survival and ramet density increased at the moderately harvested broadleaf forest sites. The effects on flowering and fruiting probabilities and survival disappeared quickly (after three years) when the canopy gaps closed, whereas ramet density responded only with a lag of over three years and was maintained to the end of the study. Our results thus demonstrate that for the lady’s slipper orchid, selective tree harvest might be a suitable management method that increases population size at the ramet level

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

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