Biodiversiteetin mittaaminen ja uudet menetelmät

Our planet is undergoing massive global change. We are increasingly aware of the biodiversity crisis, which raises concerns about the future of nature and humankind. Targets and goals set at several multilateral environmental agreements to stop the crisis have been agreed upon, but their effective follow-up and implementation require relevant and timely biodiversity data. For this purpose, a set of policy-relevant Essential Biodiversity Variables (EBVs), describing the biological state and capturing the major dimensions of biodiversity change, have been proposed. Generating EBVs requires integration of in situ and Earth observation data. The former is collected in the field by experts, citizens, or automatic sensor networks, assisted by new technologies such as eDNA and machine learning, while the latter is measured from space or air, enabled by analysis-ready multi-sensor data and cloud computing services. As a case example for better biodiversity monitoring, the Finnish Ecosystem Observatory (FEO) is proposed. FEO will combine and standardize environmental information from different data sources, making the data, metadata and models openly available and easily accessible to users and policy makers

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

Naisten laparoskooppisen ja hysteroskooppisen sterilisaation kustannusvaikutukset

JOHDANTO: Tavoitteena oli selvittää päiväkirurgisesti munatorveen asetettujen puristimien avulla tehdyn sterilisaation ja polikliinisesti munatorven aukkoihin asetetuilla mikroimplanteilla tehdyn sterilisaation kustannuksia sekä kliinisiä tuloksia. AINEISTO JA MENETELMÄT: Kaikki Hyvinkään sairaalassa vuosina 2006 - 2007 tehdyt munatorvien puristin- ja mikroimplanttisterilisaatiot analysoitiin. Tutkimusasetelmana oli takautuva lähtöryhmien mukainen analyysi. Sterilisaatiot hinnoiteltiin ottamalla mukaan suorat ja epäsuorat kustannukset. Lisäksi selvitettiin toimenpiteen onnistuminen, komplikaatiot ja uusintatoimenpiteet. TULOKSET: Mikroimplanttisterilisaation kokonaiskustannuksiksi saatiin 1 146 euroa ja puristinsterilisaation 1 712 euroa potilasta kohden. Mikroimplanttiryhmässä leikkauksenjälkeinen kipu oli merkittävästi vähäisempää, ja potilaan maksettavaksi jäävät kustannukset sekä tulonmenetykset olivat pienemmät. Puristinryhmässä toimenpiteeseen liittyviä haittavaikutuksia esiintyi enemmän. Elämänlaatu tai tyytyväisyys toimenpiteeseen eivät eronneet ryhmissä. Raskauksia ei todettu. PÄÄTELMÄT: Polikliininen mikroimplanttisterilisaatio on kustannusvaikuttavampi kuin laparoskooppinen puristinsterilisaatio. Polikliininen sterilisaatio on kokonaiskustannuksiltaan hieman halvempi, ja siihen liittyy vähemmän komplikaatioita.MATERIAL AND METHODS: A retrospective analysis was carried out on sterilizations conducted at the Hyvinkää hospital in 2006 to 2007 by tubal ligation with clips and by microimplants. RESULTS: Total costs obtained for microimplant sterilization per patient were 1 146 € and for clip sterilization 1 712 €. Postoperative pain was significantly less in the microimplant group, and adverse effects associated with the procedure were more common in the clip sterilization group. CONCLUSIONS: Microimplant sterilization performed on an outpatient basis is more cost-effective than laparoscopic clip sterilization

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

Agricultural Expansion and Its Consequences in the Taita Hills, Kenya

The indigenous cloud forests in the Taita Hills have suffered substantial degradation for several centuries due to agricultural expansion. Additionally, climate change imposes an imminent threat for local economy and environmental sustainability. In such circumstances, elaborating tools to conciliate socioeconomic growth and natural resources conservation is an enormous challenge. This chapter describes applications of remote sensing and geographic information systems for assessing land-cover changes in the Taita Hills and its surrounding lowlands. Furthermore, it provides an overall assessment on the consequences of land-cover changes to water resources, biodiversity and livelihoods. The analyses presented in this study were undertaken at multiple spatial scales, using field data, airborne digital images and satellite imagery. Furthermore, a modelling framework was designed to delineate agricultural expansion projections and evaluate the future impacts of agriculture on soil erosion and irrigation water demand.Peer reviewe

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