Natural Resources Institute Finland

About: Natural Resources Institute Finland is a government organization based out in Helsinki, Finland. It is known for research contribution in the topics: Scots pine & Environmental science. The organization has 664 authors who have published 790 publications receiving 13577 citations. The organization is also known as: Luonnonvarakeskus.

Biomass and stem volume equations for tree species in Europe.

Abstract: METLA Project 3306: Forest Carbon Sink and Economical Costs of Kyoto Protocol prduced a database of treewise volume- and biomass equations according to diameter at breast height and/or height includes equations for common European tree species. Equations will be used when estimating biomass expansion factors for major tree species. Database was compiled as a joint project with University of Edinburgh, Finnish Forest Research Institute and partners of COST E21 Action. The attached excel-files files extended and updated versions of those published previously in the printed publication. See the figure about a sample of compiled equations (files: appendix-A.xls and appendix-BC.xls).

Individual Tree Detection and Classification with UAV-Based Photogrammetric Point Clouds and Hyperspectral Imaging

Abstract: Small unmanned aerial vehicle (UAV) based remote sensing is a rapidly evolving technology. Novel sensors and methods are entering the market, offering completely new possibilities to carry out remote sensing tasks. Three-dimensional (3D) hyperspectral remote sensing is a novel and powerful technology that has recently become available to small UAVs. This study investigated the performance of UAV-based photogrammetry and hyperspectral imaging in individual tree detection and tree species classification in boreal forests. Eleven test sites with 4151 reference trees representing various tree species and developmental stages were collected in June 2014 using a UAV remote sensing system equipped with a frame format hyperspectral camera and an RGB camera in highly variable weather conditions. Dense point clouds were measured photogrammetrically by automatic image matching using high resolution RGB images with a 5 cm point interval. Spectral features were obtained from the hyperspectral image blocks, the large radiometric variation of which was compensated for by using a novel approach based on radiometric block adjustment with the support of in-flight irradiance observations. Spectral and 3D point cloud features were used in the classification experiment with various classifiers. The best results were obtained with Random Forest and Multilayer Perceptron (MLP) which both gave 95% overall accuracies and an F-score of 0.93. Accuracy of individual tree identification from the photogrammetric point clouds varied between 40% and 95%, depending on the characteristics of the area. Challenges in reference measurements might also have reduced these numbers. Results were promising, indicating that hyperspectral 3D remote sensing was operational from a UAV platform even in very difficult conditions. These novel methods are expected to provide a powerful tool for automating various environmental close-range remote sensing tasks in the very near future.

Biomass equations for Scots pine and Norway spruce in Finland

Abstract: In this study, biomass equations for the aboveand below-ground tree components of Scots pine (Pinus sylvestris) and Norway spruce (Picea abies [L.] Karst.) were developed. The models were based on 908 pine trees and 613 spruce trees collected in 77 stands located on mineral soil, and represented a wide range of stand and site conditions in Finland. The whole data set consisted of three sub data sets: 33 temporary sample plots, five thinning experiments, and the control plots of 39 fertilization experiments. The biomass equations were estimated for the individual tree components: stem wood, stem bark, living and dead branches, needles, stump, and roots. In the data analysis, a multivariate procedure was applied in order to take into account the statistical dependence among the equations. Three multivariate models for above-ground biomass and one for below-ground biomass were constructed. The multivariate model (1) was mainly based on tree diameter and height, and additional commonly measured tree variables were used in the multivariate models (2) and (3). Despite the unbalanced data in terms of the response variables, the statistical method generated equations that enable more flexible application of the equations, and ensure better biomass additivity compared to the independently estimated equations. The equations provided logical biomass predictions for a number of tree components, and were comparable with other functions used in Finland and Sweden even though the study material was not an objective, representative sample of the tree stands in Finland.

Global distribution of earthworm diversity

Abstract: Soil organisms, including earthworms, are a key component of terrestrial ecosystems. However, little is known about their diversity, their distribution, and the threats affecting them. We compiled a global dataset of sampled earthworm communities from 6928 sites in 57 countries as a basis for predicting patterns in earthworm diversity, abundance, and biomass. We found that local species richness and abundance typically peaked at higher latitudes, displaying patterns opposite to those observed in aboveground organisms. However, high species dissimilarity across tropical locations may cause diversity across the entirety of the tropics to be higher than elsewhere. Climate variables were found to be more important in shaping earthworm communities than soil properties or habitat cover. These findings suggest that climate change may have serious implications for earthworm communities and for the functions they provide.

Biomass equations for birch in Finland

Abstract: Biomass equations were compiled for the aboveand below-ground tree components of birch (Betula pendula Roth and Betula pubescens Ehrh.). The equations were based on 127 sample trees in 24 birch stands located on mineral soil sites. The study material consisted of 20 temporary plots and ten plots from four thinning experiments with different thinning intensities (unthinned, moderately and heavily thinned plots). The equations were estimated for the following individual tree components: stem wood, stem bark, living and dead branches, foliage, stump, and roots. In the data analysis, a multivariate procedure was applied in order to take into account the statistical dependency among the equations. Three multivariate variance component models were constructed for the above-ground biomass components, and one for the below-ground biomass components. The multivariate model (1) was mainly based on tree diameter and height, and in the multivariate models (2) and (3) additional commonly measured tree variables were used. The equations provided logical biomass predictions for a number of tree components, and were comparable with other functions used in Finland and Sweden. The applied statistical method generated equations that gave more reliable biomass estimates than the equations presented earlier. Furthermore, the structure of the multivariate models enables more flexible application of the equations, especially for research purposes.