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  • Saarinen, Ninni; Vastaranta, Mikko; Nasi, Roope; Rosnell, Tomi; Hakala, Teemu; Honkavaara, Eija; Wulder, Michael A.; Luoma, Ville; Tommaselli, Antonio M. G.; Imai, Nilton N.; Ribeiro, Eduardo A. W.; Guimaraes, Raul B.; Holopainen, Markus; Hyyppa, Juha (2018)
    Forests are the most diverse terrestrial ecosystems and their biological diversity includes trees, but also other plants, animals, and micro-organisms. One-third of the forested land is in boreal zone; therefore, changes in biological diversity in boreal forests can shape biodiversity, even at global scale. Several forest attributes, including size variability, amount of dead wood, and tree species richness, can be applied in assessing biodiversity of a forest ecosystem. Remote sensing offers complimentary tool for traditional field measurements in mapping and monitoring forest biodiversity. Recent development of small unmanned aerial vehicles (UAVs) enable the detailed characterization of forest ecosystems through providing data with high spatial but also temporal resolution at reasonable costs. The objective here is to deepen the knowledge about assessment of plot-level biodiversity indicators in boreal forests with hyperspectral imagery and photogrammetric point clouds from a UAV. We applied individual tree crown approach (ITC) and semi-individual tree crown approach (semi-ITC) in estimating plot-level biodiversity indicators. Structural metrics from the photogrammetric point clouds were used together with either spectral features or vegetation indices derived from hyperspectral imagery. Biodiversity indicators like the amount of dead wood and species richness were mainly underestimated with UAV-based hyperspectral imagery and photogrammetric point clouds. Indicators of structural variability (i.e., standard deviation in diameter-at-breast height and tree height) were the most accurately estimated biodiversity indicators with relative RMSE between 24.4% and 29.3% with semi-ITC. The largest relative errors occurred for predicting deciduous trees (especially aspen and alder), partly due to their small amount within the study area. Thus, especially the structural diversity was reliably predicted by integrating the three-dimensional and spectral datasets of UAV-based point clouds and hyperspectral imaging, and can therefore be further utilized in ecological studies, such as biodiversity monitoring.
  • Rissanen, Kaisa; Martin-Guay, Marc-Olivier; Riopel-Bouvier, Anne-Sophie; Paquette, Alain (2019)
    Biodiversity affects ecosystem functioning in forests by, for example, enhancing growth and altering the forest structure towards greater complexity with cascading effects on other processes and trophic levels. Complexity in forest canopy could enhance light interception and form a link between diversity and productivity in polyculture forests, but the effect of canopy structure on light interception is rarely directly measured. We modelled the canopy surface structure of a tree diversity experiment by photographing it using unmanned aerial vehicle (UAV) and combining the photos into a digital elevation model with photogrammetry tools. We analysed the effects of tree diversity and functional diversity on canopy structural complexity and light interception with a structural equation model. Our results show that: a) increased structural complexity of the canopy reduces light interception, whereas b) tree diversity increases the structural complexity of the canopy, and has a dual impact on light interception. Tree diversity decreased light interception through the structural complexity of the canopy but increased it probably through canopy packing and crown complementarity. However, the effects of both tree diversity and structural complexity of canopy were smaller than the effect of the functional identities of the tree species, especially the differences between deciduous and evergreen trees. We conclude that more complexity in canopy structure can be gained through increased tree diversity, but complex canopy structure does not increase light interception in young forests.
  • Uusitalo, Ruut Jaael; Siljander, Mika; Culverwell, Christine Lorna; Mutai, Noah; Forbes, Kristian Michael; Vapalahti, Olli; Pellikka, Petri Kauko Emil (2019)
    Mosquitoes are vectors for numerous pathogens, which are collectively responsible for millions of human deaths each year. As such, it is vital to be able to accurately predict their distributions, particularly in areas where species composition is unknown. Species distribution modeling was used to determine the relationship between environmental, anthropogenic and distance factors on the occurrence of two mosquito genera, Culex Linnaeus and Stegomyia Theobald (syn. Aedes), in the Taita Hills, southeastern Kenya. This study aims to test whether any of the statistical prediction models produced by the Biomod2 package in R can reliably estimate the distributions of mosquitoes in these genera in the Taita Hills; and to examine which factors best explain their presence. Mosquito collections were acquired from 122 locations between January–March 2016 along transects throughout the Taita Hills. Environmental-, anthropogenic- and distance-based geospatial data were acquired from the Taita Hills geo-database, satellite- and aerial imagery and processed in GIS software. The Biomod2 package in R, intended for ensemble forecasting of species distributions, was used to generate predictive models. Slope, human population density, normalized difference vegetation index, distance to roads and elevation best estimated Culex distributions by a generalized additive model with an area under the curve (AUC) value of 0.791. Mean radiation, human population density, normalized difference vegetation index, distance to roads and mean temperature resulted in the highest AUC (0.708) value in a random forest model for Stegomyia distributions. We conclude that in the process towards more detailed species-level maps, with our study results, general assumptions can be made about the distribution areas of Culex and Stegomyia mosquitoes in the Taita Hills and the factors which influence their distribution.