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430 results for “Forest Structure”
Spatial distribution of Forest Structure and Carbon Stock in Japan
<p>This repository contains the results for the paper entitled:High-Resolution Mapping of Forest Structure and Carbon Stock using Multi-source Remote Sensing Data in Japan.</p> <p><a href="https://doi.org/10.1016/j.rse.2024.114322">https://doi.org/10.1016/j.rse.2024.114322</a></p> <p>The dataset includes forest height, volume, AGB and carbon density across Japan with a spatial resolution at 10 m.</p> <div> <p>The data is retained in integer format (8 or 16-bit unsigned). For higher precision data, please contact the corresponding author.</p> <p>-------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p>Projection: EPSG:3857 WGS 84 / Pseudo-Mercator</p> <p>Spatial resolution: 10 m</p> <p>Forest mask: JAXA HRLULC 21.03</p> <p>Traning data: Airborne LiDAR derived forest structures squared plots.</p> <p>Predictors variables: PALSAR-2,Sentinel-2,SRTM</p> <p>-------------------------------------------------------------------------------------------------------------------------------------------------------</p> </div>
Supplementary Data for article: Robust characterization of forest structure from airborne laser scanning – a systematic assessment and sample workflow for ecologists
<p>This is a collection of scripts and research data to assess the robustness of forest structure characterization from airborne laser scanning (ALS). It replicates the main analysis in the article <em>Robust characterization of forest structure from airborne laser scanning – a systematic assessment and sample workflow for ecologists</em> and accompanies the main research data set (<a href="https://doi.org/10.5281/zenodo.10878070" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.10878070</a>).</p> <p>In this replication study, we assess the derivation of canopy height models (CHMs) from point cloud data, how sensitive CHM algorithms are to pulse density variation and how uncertainties and biases propagate to commonly used forest structure metrics.</p> <p>The main data source for this study are ALS point clouds from nine U.S. sites, acquired by the National Ecological Observatory Network (NEON, 6 sites, 3 km x 3 km) and by the United States Geological Survey's 3DEP program (3 sites, also 3 km x 3 km). The underlying data can be found here: https://data.neonscience.org/data-products/DP3.30024.001 (NEON) and here: https://apps.nationalmap.gov/lidar-explorer (3DEP)</p> <p>The different data layers are:</p> <p><strong>replicate.US.R</strong> </p> <p> is a single R script that contains all the code necessary to reproduce the analyses, including point cloud manipulations and derivation of CHMs from the raw data as well as the overall robustness analysis. To replicate the processing of the raw point clouds step by step, this script should be located in a folder called "rscripts".</p> <p><strong>pointclouds_original.zip</strong></p> <p> is the set of original point clouds (3 km x 3 km in extent) used for the replication test, separated into 9 subfolders/sites. Can be used to reproduce the original workflow by placing them in a folder called "data/original". The script will then automatically produce derived point clouds at pulse densities of 2 and 16 per squaremetre and process them into digital terrain models (DTMs), digital surface models (DSMs) and CHMs. Note that, for convienence, these derived products are also included in a separate .zip file (cf. below).</p> <p><strong>processed_foranalysis.zip</strong></p> <p> is the set of derived products (DTMs, DSMs, CHMs), separated into 9 subfolders/sites, i.e. the result of processing the original point clouds. To use these layers directly with the provided script, they should be put into a folder called "processed_foranalysis".</p> <p><strong>summaries.zip</strong></p> <p><strong> </strong>is a set of summary statistics (as .csv files) that were used to generate the main analysis tables in the replication study. To use these summary statistics directly with the provided script, they should be put into a folder called "summaries".</p> <p><strong>figures.zip</strong></p> <p> is a set of figures displayed in the Supplementary Material of the paper <em>Robust characterization of forest structure from airborne laser scanning – a systematic assessment and sample workflow for ecologists</em>.</p>
Figure C2 from: Riley Peterson KN, Browne RA, Erwin TL (2021) Carabid beetle (Coleoptera, Carabidae) richness, diversity, and community structure in the understory of temporarily flooded and non-flooded Amazonian forests of Ecuador. In: Spence J, Casale A, Assmann T, Liebherr JК, Penev L (Eds) Systematic Zoology and Biodiversity Science: A tribute to Terry Erwin (1940-2020). ZooKeys 1044: 831-876. https://doi.org/10.3897/zookeys.1044.62340
Figure C2 Number of rare morphospecies for the FP and TF forest (P = 0.04).
Figure 7 from: Riley Peterson KN, Browne RA, Erwin TL (2021) Carabid beetle (Coleoptera, Carabidae) richness, diversity, and community structure in the understory of temporarily flooded and non-flooded Amazonian forests of Ecuador. In: Spence J, Casale A, Assmann T, Liebherr JК, Penev L (Eds) Systematic Zoology and Biodiversity Science: A tribute to Terry Erwin (1940-2020). ZooKeys 1044: 831-876. https://doi.org/10.3897/zookeys.1044.62340
Figure 7 Rank abundance distribution curves for FP (blue squares) and TF (green circles) forests.
Figure 1 from: Riley K, Browne R (2011) Changes in ground beetle diversity and community composition in age structured forests (Coleoptera, Carabidae). ZooKeys 147: 601-621. https://doi.org/10.3897/zookeys.147.2102
Figure 1 - Map of the study area in Piedmont, North Carolina, with 33 sample sites indicated.
Data from: Identification of any structure-specific hepatotoxic potential of different pyrrolizidine alkaloids using Random Forest and artificial Neural Network
Open the record for dataset details and reuse information.
Microbial community structure and functions are resilient to metal pollution along two forest soil gradients
GEO Series GSE59620. uncultured bacterium; Bacteria. 12 samples. Type: Genome variation profiling by array.
Data from "Lithological substrates influence tropical dry forest structure, diversity, and composition, but not its dynamics"
<p>Datasets and script of the manuscript “Lithological substrates influence tropical dry forest structure, diversity, and composition, but not its dynamics” authored by R. Muñoz*, M. Enríquez, F. Bongers, R.D. López-Mendoza, C. Miguel-Talonia & J.A. Meave*, published in Frontiers in Forests and Global Change (2023).</p> <p>* Correspondence: R. Muñoz (rod.munozaviles@gmail.com) & J.A. Meave (jorge.meave@ciencias.unam.mx)</p> <p>The original publication can be found in https://doi.org/10.3389/ffgc.2023.1082207</p> <p> </p> <p><strong>TERMS OF USE FOR THE CURRENT DATASETS AND SCRIPTS</strong></p> <p>All data and scripts associated with the current publication are intended ONLY for the reproduction and validation of the analyses conducted in the manuscript cited above. Use of this data for other purposes (for example, other publications or meta-analyses) is strictly forbidden without prior consent from the corresponding authors (R. Muñoz and/or J.A. Meave, contact details above).</p> <p> </p> <p><strong>FOLDER STRUCTURE</strong></p> <p>The ZIP folder is structured in the following manner:</p> <p>– Munoz et al 2023 Frontiers.zip</p> <p> – READ ME.txt</p> <p> – Script Munoz et al 2023 Frontiers.R</p> <p> – Data source</p> <p> – Dataset Munoz et al 2023 Frontiers stand data.csv</p> <p> – Dataset Munoz et al 2023 Frontiers species matrix.csv</p> <p> – Dataset Munoz et al 2023 Frontiers ONI.csv</p> <p> – Dataset Munoz et al 2023 Frontiers ENSO events.csv</p> <p> </p> <p><strong>DESCRIPTION OF SCRIPT</strong></p> <p>The script provided in the root of the ZIP folder (Script Munoz et al 2023 Frontiers.R) allows to reproduce the analyses, figures and tables supporting the original publication in Frontiers. When executed in full, the script generates a new folder named “Figures” where all figures are stored in their raw, unedited version. The figures for publication were later edited in Adobe Illustrator to enhance their visual appearance.</p> <p> </p> <p><strong>DESCRIPTION OF DATASETS</strong></p> <p>Four datasets are provided in this ZIP file (“Data source” folder):</p> <p>1. Dataset Munoz et al 2023 Frontiers stand data.csv (<em>Stand data</em>)</p> <p>2. Dataset Munoz et al 2023 Frontiers species matrix.csv (<em>Species matrix</em>)</p> <p>3. Dataset Munoz et al 2023 Frontiers ONI.csv (<em>ONI</em>)</p> <p>4. Dataset Munoz et al 2023 Frontiers ENSO events.csv (<em>ENSO events</em>)</p> <p> </p> <p><em>STAND DATA </em>contains information about the seven forest attributes included in the study, per substrate and year. It contains the following variables:</p> <ol> <li>Year: Year of measurement</li> <li>Plot: Plot code</li> <li>Set: Can only be “MatCan” (Mature Canopy)</li> <li>Subset: Either “Lim” (limestone) or “Phy" (phyllite)</li> <li>Dynamics: Whether there is a previous measurement allowing the estimation of dynamic rates (e.g., net change; FALSE/TRUE) </li> <li>Basal: Basal area expressed in m2/ha</li> <li>DeltaBasal: Annual net change in basal area</li> <li>R.basal: Annual change in basal area due to recruitment</li> <li>G.basal: Annual change in basal area due to growth</li> <li>M.basal: Annual change in basal area due to mortality</li> <li>AGB: Aboveground biomass expressed in Mg/ha, estimated from the allometric equation of Chave et al. 2014 (including DBH, height and WD)</li> <li>DeltaAGB: Annual net change in AGB</li> <li>R.agb: Annual change in AGB due to recruitment</li> <li>G.agb: Annual change in AGB due to growth</li> <li>M.agb: Annual change in AGB due to mortality</li> <li>Dens: Tree density expressed in individuals/ha</li> <li>DeltaDens: Annual net change in tree density</li> <li>R.dens: Annual change in tree density due to recruitment</li> <li>G.dens: Annual change in tree density due to “growth”. Here, “growth” is a term introduced to account for small differences in tree densities between years due to changes in the extrapolation factor of a tree. Due to the nested sampling design of the vegetation survey, sometimes trees change their extrapolation factor as they grow larger. Thus, is a tree changes extrapolation factor, those differences (that are neither recruitment or mortality) are added up here.</li> <li>M.dens: Annual change in tree density due to mortality</li> <li>Species: Species richness expressed in spp/plot. Redundant with “q0” column.</li> <li>DeltaSpecies: Annual net change in species richness</li> <li>R.species: Annual change in species richness due to recruitment</li> <li>M.species: Annual change in species richness due to mortality</li> <li>Height: Average plot canopy height expressed in m</li> <li>q0: Hill number of order 0 expressed in species effective number (species richness)</li> <li>q1: Hill number of order 1 expressed in species effective number (typical species)</li> <li>q2: Hill number of order 2 expressed in species effective number (dominant species)</li> </ol> <p> </p> <p><em>SPECIES MATRIX</em> contains an abundance matrix per species, plot and year. It contains the following variables:</p> <ol> <li>PlotYear: This column actually does not have a name to it in the file, but is the first column in the dataset, It contains the three-character identifier for the plot and the four numbers of the year of measurement. For instance, “BER2008” would represent the observations made for the plot BER in 2008.</li> <li>treat: This indicates whether the plot is located on limestone (1) or phyllite (2) substrate</li> <li>sp001-sp127: indicates the abundance (in number of individuals per plot) of a given species. Species numbers were assigned randomly, thus they do not match the order of the table provided in Supplementary Material 3 of the publication in Frontiers.</li> </ol> <p> </p> <p><em>ONI</em> contains the Oceanic El Niño Index values per month and year. It is a “year by month” contingency matrix, where years are presented in the rows name, and months are presented in the columns name. ONI values are given in Celsius degrees, and they represent the 3-month rolling average of the temperature anomaly in the Nino3.4 region. The data source and details of this dataset can be found at the NOAA webpage (https://origin.cpc.ncep.noaa.gov/products/analysis_monitoring/ensostuff/ONI_v5.php).</p> <p> </p> <p><em>ENSO EVENTS</em> contains the occurrence of events of El Niño (warm and dry episodes) and La Niña (cold and wet episodes). It contains the following variables:</p> <ol> <li>Year: Year</li> <li>Month: Month</li> <li>ONI: Oceanic El Niño Index (see ONI dataset description above)</li> <li>Year.cont: Time as a continuous variable (instead of having years and months separately, for plotting)</li> <li>Nino: El Niño (warm and dry) episode occurrence (“1” indicates occurrence)</li> <li>Nina: La Niña (cold and wet) episode occurrence (“1” indicates occurrence)</li> </ol>
Ecophysiological and molecular basis of drought responses in forest trees: the modulating role of canopy structure and light environment
GEO Series GSE208073. Abies pinsapo. 12 samples. Type: Expression profiling by high throughput sequencing.
Effect of sampling approaches on quantifying urban forest structure
<p>R code and data used in the paper.</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.