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375 results for “Boreal forests”
Detection of standing retention trees in boreal forests with airborne laser scanning point clouds and multispectral imagery
<p>1. In a landscape consisting primarily of intensive forestry interspersed with some protected areas, multifunctional forestry with retention trees can play a crucial role in nature conservation. Accurate mapping of retention trees is important for guiding landscape-level conservation and forest management and improving landscape connectivity. Sizeable dead and living retention trees play a particularly important ecological role but even their large-scale inventory is often intensive through field work and/or inaccurate. We aimed to detect and classify retention trees using the novel nationwide Finnish airborne laser scanning (ALS) data (~ 5 pulses/m<sup>2</sup>) in conjunction with unrectified color-infrared (CIR) aerial imagery. 2. Applying photogrammetric principles, we added spectral information from the CIR imagery to the ALS-derived point cloud. For a training dataset of 160 retention trees from 19 stands and a geographically separate validation dataset of 79 trees from 8 stands, we segmented trees via individual tree detection (ITD), removed most trees belonging to the regenerating vegetation layer, and classified trees into living conifers, living broadleaves, and dead trees by linear discriminant analysis. 3. The detection rate via ITD differed considerably for dead and living trees, with 41.7% of all dead and 83.8% of all living trees being detected with relatively low commission error rates. Dead trees with smaller diameters and heights were more likely missed, while grouping caused living tree omission. For classification into living conifers, living broadleaves, and dead trees, an overall accuracy of 67.3% was achieved in training and 71.2% in validation data only ALS-derived metrics. When adding spectral metrics, the overall accuracies were 79.6% and 61.0% for training and validation, respectively. 4. Our findings imply that wall-to-wall large-scale high density ALS data can be used to detect retention trees rather accurately – even larger dead trees – and that metrics derived solely from ALS data can accurately classify detected retention trees into living conifers, living broadleaves, and dead trees. Considering the ecological value of retention trees, our results are promising and indicate that ALS data of the studied pulse density are a cost-effective option for large area mapping of retention trees in countries with such data available.</p>
Text-fig. 1. Modern vegetation proxies as delivered by the Drudge 1 and 2 tools for Parschlug. Left column results from KovarEder et al. (2021) based on the floristic spectrum published by Kovar-Eder et al. (2004). The other three columns result from three variants using the enlarged floristic spectrum herein. Differences between variants 1–3 from this study are caused by differences in assignment of some taxa and morphotypes (see Appendix 1). European vegetation formations: Formation C – Subarctic, boreal and nemoral-montane open woodlands as well as subalpine and oro-Mediterranean vegetation; Formation D – Mesophytic and hygromesophytic coniferous and mixed broad-leaved-coniferous forests; Formation F – Mesophytic broadleaved deciduous and mixed broadleaved/conifer forests; Formation G – Thermophilous mixed deciduous broadleaved forests; Formation J – Mediterranean sclerophyllous forests and scrub; Formation K – Xerophytic coniferous forests, coniferous woodland and scrub. East Asian vegetation types: MCF China, Japan – Montane Coniferous Forests China, Honshu, Yakushima; BLDF N and NE Provinces, China – Broad-leaved Deciduous Forests of the Northern and Northeastern Provinces (China); BLDF Upper Yangtze, Honshu – Broad-leaved Deciduous Forest, Upper Yangtze Provinces, Mt. Emei, and Honshu; MMF China – Mixed Mesophytic Forest, Lower Yangtze Provinces; BLEF China, Japan – Broad-leaved Evergreen Forests, China, Japan; Meili Snow Mt. high altitude SCL and BLF, China – Meili Snow Mt., Sclerophyllous and broad-leaved forest zone (2,580-3,650 m alt.). (Designations of European vegetation formations follow Bohn et al. (2004) and Asian ones follow Kovar-Eder et al. (2021). in Floristic, Vegetation And Climate Assessment Of The Early/Middle Miocene Parschlug Flora Indicates A Distinctly Seasonal Climate
Text-fig. 1. Modern vegetation proxies as delivered by the Drudge 1 and 2 tools for Parschlug. Left column results from KovarEder et al. (2021) based on the floristic spectrum published by Kovar-Eder et al. (2004). The other three columns result from three variants using the enlarged floristic spectrum herein. Differences between variants 1–3 from this study are caused by differences in assignment of some taxa and morphotypes (see Appendix 1). European vegetation formations: Formation C – Subarctic, boreal and nemoral-montane open woodlands as well as subalpine and oro-Mediterranean vegetation; Formation D – Mesophytic and hygromesophytic coniferous and mixed broad-leaved-coniferous forests; Formation F – Mesophytic broadleaved deciduous and mixed broadleaved/conifer forests; Formation G – Thermophilous mixed deciduous broadleaved forests; Formation J – Mediterranean sclerophyllous forests and scrub; Formation K – Xerophytic coniferous forests, coniferous woodland and scrub. East Asian vegetation types: MCF China, Japan – Montane Coniferous Forests China, Honshu, Yakushima; BLDF N and NE Provinces, China – Broad-leaved Deciduous Forests of the Northern and Northeastern Provinces (China); BLDF Upper Yangtze, Honshu – Broad-leaved Deciduous Forest, Upper Yangtze Provinces, Mt. Emei, and Honshu; MMF China – Mixed Mesophytic Forest, Lower Yangtze Provinces; BLEF China, Japan – Broad-leaved Evergreen Forests, China, Japan; Meili Snow Mt. high altitude SCL and BLF, China – Meili Snow Mt., Sclerophyllous and broad-leaved forest zone (2,580-3,650 m alt.). (Designations of European vegetation formations follow Bohn et al. (2004) and Asian ones follow Kovar-Eder et al. (2021).
FCH and FS Datasets for the paper "Integrating Multi-Source Remote Sensing Data for Mapping Boreal Forest Canopy Height and Species in interior Alaska in Support of Radar Modeling"
<p>This dataset provides forest canopy height and forest species in Delta Junction, interior Alaska in 2017. This dataset was produced based on the multi-source remote sensing datasets (AirMOSS, UAVSAR, Sentinel-1, Sentinel-2, topography), using a XGBoost approach.</p>
Data for "The role of H2SO4-NH3 anion clusters in ion-induced aerosol nucleation mechanisms in the boreal forest"
<p>This is the dataset that has been analyzed for "The role of H2SO4-NH3 anion clusters in ion-induced aerosol nucleation mechanisms in the boreal forest". Please contact the author (chao.yan@helsinki.fi) for more details. </p>
Data for "Vertical characterization of highly oxygenated molecules (*HOMs) below and above a boreal forest canopy"
<p>This excel file consists of the data been analyzed in the manuscript "Vertical characterization of highly oxygenated molecules (*HOMs) below and above a boreal forest canopy". For more details, please contact the author (qiaozhi.zha@helsinki.fi). </p>
Dataset to: Vertical distribution of ice nucleating particles over the boreal forest of Hyytiälä, Finland
<p>This repository contains the datasets used in the study 'Vertical distribution of ice nucleating particles over the boreal forest of Hyytiälä, Finland'. Detailed information and technical aspects of the data can be found in the publication.</p>
Data set: Forest management to increase carbon sequestration in boreal Pinus sylvestris forests
<p>Data supporting the results and analyses published in Plant and Soil, "Forest management to increase carbon sequestration in boreal <em>Pinus sylvestris </em>forests".</p> <p>Data from a long-term fertilization (N and N+P) and thinning experiment in <em>Pinus sylvestris </em>stands across Sweden (56–67°N). Carbon stocks in soil and trees, tree growth, soil respiration and soil available nitrogen (ammonium, nitrate) are included.</p> <p>Data (data file + meta data file) include:</p> <p>jorgensen_etal_plantsoil_treesoil_data.csv (site data, carbon stocks: trees and their separate parts and soil, soil available nitrogen)</p> <p>jorgensen_etal_plantsoil_treesoil_data_METADATA.csv</p> <p>jorgensen_etal_plantsoil_resp_data.csv (site data, soil respiration, temperature, moisture)</p> <p>jorgensen_etal_plantsoil_resp_data_METADATA.csv</p> <p>R-script:</p> <p>Jorgensen_etal_plantsoil.R</p>
Dataset for "Climatic Variation Drives Loss and Restructuring of Carbon and Nitrogen in Boreal Forest Wildfire"
<p>This dataset is uploaded to support the report 'Climatic Variation Drives Loss and Restructuring of Carbon and<br> Nitrogen in Boreal Forest Wildfire' published in the journal Biogeosciences. See README file for more details.</p>
A boreal forest model benchmarking dataset for North America: a case study with the Canadian Land Surface Scheme including Biogeochemical Cycles (CLASSIC)
<p>A boreal forest model benchmarking dataset for North America by harmonizing eddy covariance and supporting measurements from black spruce (Picea mariana)-dominated mature forest stands.</p> <p>Dataset glossary and users’ instructions are documented in ‘README.md’. </p>
Modeling the recent drought and thinning impacts on energy, water and carbon fluxes in a boreal forest
<p>This dataset includes the data used for model calibration and validation, as well as the simulation files with accepted runs, which are available for the readers to re-generate the results of this work. The *.bin files are the data for driving the model and for calibration and validation. They are specifically in the format for the CoupModel. Therefore, to check the data the CoupModel software needs to be installed. </p> <p>Additionally, we provide the software for CoupModel, which the readers could install on local computers to check the simulations. For detailed instructions on how to run CoupModel, please visit the CoupModel website www.coupmodel.com.</p>
Boreal forest tower-based remote sensing data (solar-induced fluorescence and reflectance-based vegetation indices)
<p>Data includes remote sensing products from PhotoSpec (a scanning spectrometer) from August 2019-December 2021 at the Southern Old Black Spruce site in Saskatchewan Canada and the National Ecological Observatory Network (NEON) Delta Junction. We provide half-hourly averaged vegetation indices (NIRv, NDVI, PRI, CCI) and Solar-Induced Fluorescence (SIF) and for stand-representative targets. Additionally, we provide half-hourly Photosynthetically Active Radiation (PAR), a fraction of direct vs. diffuse radiation (Df), Air Temperature (Tair) and Gross Primary Productivity (GPP). </p>
MODIS tree cover change of North American boreal forests 2000-2019
<p>The published files are two maps of North American boreal forest tree cover trends between 2000 and 2019. Pixel values are annual trends in tree cover expressed as % change per year. The trends are based on annual tree cover estimates from the MODIS Vegetation Continuous Field version 6 product. We quantified tree cover trends per pixel through Theil-Sen's slope estimation using the 'zyp' package in R. We followed the Yue-Pilon pre-whitening method to account for temporal autocorrelation. We created a trend map for all data points within the boreal biome boundary following Gauthier et al. 2015, Science (<a href="https://doi.org/10.1126/science.aaa9092">DOI: 10.1126/science.aaa9092</a>) and added a 120km buffer around it (tcchange_all_points_clipped.tif). We also produced a map where we masked out non-significant trends based on a Mann-Kendall-test (tcchange_significant_points.tif). Both maps have a spatial resolution of around 1,000m.</p> <p>The map forms the key results in our manuscript: Rotbarth et al. 2023. 'North American boreal forests: Northern expansion is not compensating for southern declines'. Nature Communications</p>
Fine-root production in boreal peatland forests: effects of stand and environmental factors
<p>Fine-root production (FRP) data along with climatic variables (annual precipitation, temperature sum, and latitude) and stand variables (tree stand stem volume; tree stand basal area; stand basal area of tree species including Scots pine, Norway spruce and deciduous trees; site type; peat type; peat depth; C:N ratio of topmost 20 cm peat layer; grouping of sites to nutrient rich and nutrient poo; average soil water-table level) from 28 forestry-drained peatland forest sites in Finland,</p> <p>FRP and its depth distribution were estimated using ingrowth cores. The ingrowth cores were installed between October 15th and November 27th, 2013, and recovered after two years in late November 2015.</p>
Microbial community composition of earthworm-invaded and earthworm-free soils of the Canadian boreal forest
<p>Earthworm invasion in North American forests has the potential to greatly impact soil microbiomes by altering soil physicochemical properties. We characterized and compared microbial communities of earthworm-invaded and non-invaded soils in previously described sites across three major soil types found in the Canadian boreal forest using phospholipid fatty acid (PLFA) analysis and metabarcoding of the 16S rRNA gene (bacteria and archaea) and ITS2 region (fungi).</p>
Approaching a thermal tipping point in the Eurasian boreal forest at its southern margin
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Identifying functional impacts of heat-resistant fungi on boreal forest recovery after wildfire
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Rebuilding green infrastructure in boreal production forest given future global wood demand
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Detection of standing retention trees in boreal forests with airborne laser scanning point clouds and multispectral imagery
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Data from: Historical reindeer corrals in northern boreal forests reveal divergent post-disturbance reorganization by forest type
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Microbial community composition of earthworm-invaded and earthworm-free soils of the Canadian boreal forest
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.