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406 results for “Forest Cover”
Vegetation Cover in the Clearcut Site at Harvard Forest 2010-2013
We used the line-intercept method to monitor the expansion of vegetation cover at our site, post-clearcut. This dataset was also used to calculate leaf area at the site from 2010 to 2012 and also in upscaling leaf gas exchange measurements collected during the 2010 and 2012 growing seasons. The data was used in two publications listed below (as of June 2014) and numerous poster presentations. Data collection was done during the REU summer programs, with Prof. William’s graduate students and postdocs acting as guides/mentors to the REU students.
Santa Barbara Channel Marine BON: Nearshore kelp forest integrated benthic cover, 1980-ongoing
The Santa Barbara Channel Marine Biodiversity Observation Network (SBCMBON) tracks long-term patterns in species abundance and diversity. This dataset contains cover of kelp forest sessile invertebrates, understory macroalgae, and substrate types by integrating data from four contributing projects working in the kelp forests of the Santa Barbara Channel, USA. Divers collect data on using either uniform point contact (UPC) or random point contact (RPC) methods. The four contributing projects are two research projects: The Santa Barbara Coastal LTER (SBC LTER) and the Partnership for Interdisciplinary Studies of Coastal Oceans (PISCO), the kelp forest monitoring program of the Santa Barbara Channel National Park, and the San Nicolas Island monitoring program supported by USGS. Together, these projects have recorded data for more than 200 species at approximately 100 sites on both the mainland coast and on the Santa Barbara Channel Islands. Sampling began in 1982 and is ongoing. Data were collected by human observation (divers using SCUBA) during regular surveys. Percent cover is recorded for taxa where individuals cannot be counted. Cover can be calculated from the data here as the fraction of total points at which the taxon was present x 100. With UPC and RPC methods, multiple species can be recorded at any given point. The total percent cover of all species combined using this method can exceed 100%; however, the percent cover of any single species cannot exceed 100%. See Methods for information on integration and data processing. MBON is funded by National Aeronautics and Space Administration (NASA), Bureau of Ocean Energy Management (BOEM), and National Oceanic and Atmospheric Administration (NOAA). For users who are interested in using all or part of this integrated datasets, please contact data owners to discuss your research interests, data-related issues or any other questions. A recommended citation for the data package is available from the download page. In
Urban forest canopy cover, vegetation, and site characteristics, Twin Cities Metro Area, 2022 and 2023.
This data was primarily collected to assess forest quality within the Minneapolis-St. Paul (MSP) Metropolitan Area and to link above-ground and below-ground properties as part of the goals of the MSP-LTER Urban Tree Canopy research group. Here, we sampled vegetation on 48 circular plots with a 12.5 m radius distributed across 18 parks, registering the date of sampling, park and management agency names, the plot number, and geolocation (latitude, longitude, and elevation). The plots were randomly selected based on GEDI (Global Ecosystem Dynamics Investigation instrument) 2021 footprints in the MSP Metropolitan Area along accessible forested areas inside public parks, where the management agency allowed sampling. In each plot, we measured forest structure and diversity metrics, species names and abundance, DBH, height, distance from the plot center, the height where each individual canopy starts, and the relative position, exposure, and density of each canopy. We also measured understory plant structure and diversity in 4 subplots per plot, totaling 192 subplots. In these subplots, we surveyed all individual plants with heights over 20 cm, recording species names and abundance, plant basal diameter, plant height, and the total number of branches. Furthermore, we assessed the canopy openness above each subplot by calculating percent DIFN (diffuse non-interceptance) from fish eye pictures of the canopy at 1.3 meters over the subplot.
Percent cover of under- and mid-story vegetation and seedling counts in the Future of Oak Forests project at Black Rock Forest, Cornwall, NY.
Black Rock Forest established a series of 12, 0.56 ha plots in 2005 to assess impacts of the loss of tree in the genus Quercus on the forest ecosystem (entitled the Future of Oak Forests experiment). Three trunk girdling treatments, with control plots were instituted in 2008. Each plot also contained an ~10m by ~15m deer exclosure to assess the impact of herbivory post-disturbance. In 2006 and 2008, before exclosures were erected, pre-treatment surveys were conducted in all unexclosed (n=120) quadrats. Surveys of all 240 understory quadrats were conducted annually in late summer (August to September) from 2009 to 2018 and then again in 2021. At each quadrat, trained observers identified all vascular plants to species and assigned each species a percent cover value. The percent cover of moss was also recorded but moss species were not identified. Counts of tree seedlings and some woody shrubs were also recorded in addition to percent cover values. Seedlings were considered saplings, and therefore not counted, once they reached 1.3 m tall (breast height).
Hubbard Brook Experimental Forest: Mirror Lake Ice Cover 1968 - ongoing
This data set reports ice on and ice off dates for Mirror Lake beginning in 1968 and continuing through the present. Mirror Lake is located within the Hubbard Brook valley in the White Mountains of New Hampshire, and has been the subject of numerous limnological investigations since the early 1960s. These Mirror Lake data are part of the Hubbard Brook Watershed Ecosystem Record (HBWatER), a long-term record of weekly sampling of nine gaged watersheds at Hubbard Brook which includes the stream draining Mirror Lake. The collection and management of the long-term record was initiated in 1963 by Gene E. Likens, F. Herbert Bormann, Robert S. Pierce, and Noye M. Johnson. HBWatER is currently sustained by Tammy Wooster (Cary IES) and Jeff Merriam (USFS) and the dataset is curated and maintained by a team of researchers: Chris Solomon (Cary IES), Emily Bernhardt (Duke), Bill McDowell (UNH), Charley Driscoll (Syracuse U.), Keith Nislow (USFS), and Mark Green (Case Western). Current financial Support for HBWatER is provided by NSF LTREB # 2401760 and the USDA Forest Service Northern Research Station. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station.
SBC LTER: Reef: Kelp Forest Community Dynamics: Cover of bottom substrate and sand depth
These data describe the percent cover of eight bottom substrate types as determined by a uniform point contact method. The type of bottom substrate is recorded at 80 uniformly spaced points along permanent 40m x 2m transects. Percent cover of each substrate type on a transect is estimated as the proportion of the 80 points contacted by that substrate type x 100. In cases where the substrate type is sand, the depth of the sand is measured. These data are part of the SBC LTER’s time series observations of kelp forest dynamics, which began in 2000 and was designed to track long-term patterns in species abundance and diversity of reef-associated organisms in the Santa Barbara Channel, California, USA. The sampling locations in this dataset include nine reef sites along the mainland coast of the Santa Barbara Channel and two on the north side of Santa Cruz Island. These 11 sites were chosen to reflect the wide range of oceanographic conditions present in the Channel and proximity to sources of terrestrial runoff. Data are collected at each site once per year in summer and this dataset is updated annually. The time period of data collection varied among the 11 reef sites. Sampling at BULL, CARP, and NAPL began in 2000, sampling at the other 6 mainland sites (AHND, AQUE, IVEE, GOLB, ABUR, MOHK) began in 2001 (transects 3, 5, 6, 7, 8 at IVEE were added in 2011). Data collection at the two Santa Cruz Island sites (SCTW and SCDI) began in 2004. The two tables in this data package include: 1) The percent cover of eight bottom substrate types; and 2) the sand depth of each sampling point (sand depth = 0 if substrate type ≠sand).
SBC LTER: Reef: Kelp Forest Community Dynamics: Cover of sessile organisms, Uniform Point Contact
These data describe the cover of sessile invertebrates, understory macroalgae, and bottom substrate types as determined by a uniform point contact method. The presence of over 150 taxa of sessile invertebrates and macroalgae are recorded at 80 uniformly spaced points along permanent 40m x 2m transects. Multiple species can be recorded at any given point. Percent cover of a given species on a transect can be estimated from UPC observations as the fraction of total points at which that species was present x 100. The total percent cover of all species combined using this method can exceed 100%; however, the percent cover of any single species cannot exceed 100%. These data are part of SBC LTERs kelp forest monitoring program, which began in 2000 and was designed to track long-term patterns in species abundance and diversity of reef-associated organisms in the Santa Barbara Channel, California, USA. The sampling locations in this dataset include nine reef sites along the mainland coast of the Santa Barbara Channel and at two sites on the north side of Santa Cruz Island. These sites reflect several oceanographic regimes in the channel and vary in distance from sources of terrestrial runoff. Data collection began in 2000 and this dataset is updated annually. The time period of data collection varied among the 11 kelp forest sites. Sampling at BULL, CARP, and NAPL began in 2000, sampling at the other 6 mainland sites (AHND, AQUE, IVEE, GOLB, ABUR, MOHK) began in 2001 (transects 3, 5, 6, 7, 8 at IVEE were added in 2011). Data collection at the two Santa Cruz Island sites (SCTW and SCDI) began in 2004. See Methods for more information. The two tables in this data package include: 1) The percent cover of sessile invertebrate and understory macroalage; and 2) the percent cover of bottom substrate.
SBC LTER: Reef: Seasonal Kelp Forest Community Dynamics: Cover of sessile organisms, Uniform Point Contact
These data describe the percent cover of sessile invertebrates and understory macroalgae within permanent plots of SBCLTER's seasonal kelp forest monitoring program to track long-term patterns in species abundance and diversity. Percent cover was determined using a uniform point contact method that consists of noting the identity and relative vertical position of all organisms under 80 uniformly placed points located within a 1 m wide band centered on permanent 40 m transects in each sampling plot. Each species may only be recorded once per point. Using this method, the percent cover of all species combined may exceed 100%, however, the maximum percent cover possible for any single species cannot exceed 100%. The experiment was initiated in 2008 at five reef sites along the mainland coast of the Santa Barbara Channel.
SBC LTER: Reef: Seasonal Kelp Forest Community Dynamics: Cover of bottom substrate and sand depth
These data describe the percent cover of eight bottom substrate types within permanent plots of SBCLTER's seasonal kelp forest monitoring program to track long-term patterns in species abundance and diversity. The type of bottom substrate was recorded at 80 uniformly spaced points along permanent 40m x 2m transects. Percent cover of each substrate type on transect was estimated as the proportion of the 80 points contacted by the substrate type x 100. In cases where the substrate type was sand, the depth of the sand was measured. The experiment was initiated in 2008 at five reef sites along the mainland coast of the Santa Barbara Channel. The two tables in this data package include: 1) The percent cover of eight bottom substrate types; and 2) the sand depth of each sampling point (sand depth = 0 if substrate type is not sand)
Forest condition anomaly index values covering Germany for 2016-2023
<p><strong>General description:</strong><br>In <a href="https://doi.org/10.1016/j.rse.2024.114323" target="_blank" rel="noopener">Lange et. al (2024)</a> we utilised <em>Sentinel-2</em> tree species-specific reflectance time series for extracting forest condition across Germany from 2016 to 2022. These time series' seasonal evolution - computed separately for seven natural regions - serves as reference when calculating a similarity metric – further called <em>forest condition anomaly index</em> (FCA). The FCA is computed between each single reflectance observation and the respective date within the reference time series, also considering the natural temporal deviations caused by phenology. FCA temporal aggregation allowed generating spatially comprehensive forest condition anomaly maps. FCA patterns in space and time are in line with dominant drivers like fires, storms and insect infestations and in agreement with state-of-the-art forest disturbance products using a threshold of FCA = −0.15 for forest loss. More information can be found in the <a href="https://doi.org/10.1016/j.rse.2024.114323" target="_blank" rel="noopener">related publication</a> and in the <a title="UFZ Forest condition monitor" href="https://web.app.ufz.de/forestconditionmonitor" target="_blank" rel="noopener">UFZ Forest condition monitor web-application</a>.</p> <p><br><strong>Data description:<br></strong>Data is provided in GeoTiff format (projection <a href="https://epsg.io/32632" target="_blank" rel="noopener">EPSG:32632</a>). Forest condition anomaly maps are available in a spatial resolution of 20 <em>m</em> for the years 2016 to 2023 as monthly (May to October), seasonal (spring, summer and fall) and yearly maps. Values are scaled by 10 000 to reduce the file size. Final FCA values are obtained by dividing the raw values by 10 000 and range from -1 to 1. A negative value generally indicates a poorer forest condition, for example, due to negative changes in chlorophyll or water content or due to crown defoliation. Through validation using forest surveys, data from the <em>Copernicus Emergency Management System</em> and other current maps of forest cover loss, it can be relatively accurate determined that a value below -0.15 indicates a heavily damaged or dead forest stand. Stronger damage (such as significant needle/leaf loss or tree mortality) is generally captured more precise than light damage (such as slight needle/leaf loss). Moderate forest condition values correspondingly show no anomaly and represent the expected normal condition for the respective tree species at the given time within the year. Positive forest condition values indicate a positive deviation from the expected state, which might stem from from positive chlorophyll or water content changes or from denser foliage or needle cover.</p> <p> </p> <p><strong>File descriptions</strong>: <br>Data is provided in zip archives containing maps in GeoTiff format (projection <a href="https://epsg.io/32632" target="_blank" rel="noopener">EPSG:32632</a>). 4 zip files are provided:</p> <ul> <li><em>FCA_v0007-0005_Germany_2016-2023_yearly_R20m.zip</em> contains 8 yearly FCA maps </li> <li><em>FCA_v0007-0005_Germany_2016-2023_seasonal_R20m.zip </em>contains 24 seasonal FCA maps (spring, summer and fall for 2016 to 2023)</li> <li><em>FCA_v0007-0005_Germany_2016-2019_monthly_R20m.zip</em> contains 24 monthly maps (May to October for 2016 to 2019)</li> <li><em>FCA_v0007-0005_Germany_2020-2023_monthly_R20m.zip</em> contains 24 monthly maps (May to October for 2020 to 2023)</li> </ul> <p> </p> <p><strong>Please note:</strong><br>Forest pixels were selected according to the tree species map from <a href="https://doi.org/10.1016/j.rse.2024.114069" target="_blank" rel="noopener">Blickensdörfer et al. (2024)</a>. </p>
Tree-covered and intact forest landscapes BC1000, 1995, 2000, 2005, 2010, 2013, 2016 at 250 m
<p>Based on the <a href="http://www.unep-wcmc.org/resources-and-data/generalised-original-and-current-forest">UNEP historic forest cover map</a>, ESA land cover time series and <a href="http://www.intactforests.org/data.ifl.html">intact forest landscape (IFL 2000, 2013 and 2016) data</a>. Processing steps are described in detail <strong><a href="https://gitlab.com/openlandmap/global-layers/tree/master/soil/LDN">here</a></strong>. Antartica is not included.</p> <p>To access and visualize maps use: <a href="http://www.openlandmap.org/">OpenLandMap.org</a></p> <p>If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels:</p> <ul> <li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/issues">https://gitlab.com/openlandmap/global-layers/issues</a> </li> <li>General questions and comments: <a href="https://disqus.com/home/forums/landgis/">https://disqus.com/home/forums/landgis/</a></li> </ul> <p>All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention:</p> <ul> <li>ldg = theme: land degradation,</li> <li>forest.cover = variable: forest / tree cover,</li> <li>esacci.ifl = determination method: combination of ESA land cover and IFL maps,</li> <li>c = factor,</li> <li>250m = spatial resolution / block support: 250 m,</li> <li>s0..0cm = vertical reference: land surface,</li> <li>1995 = time reference: year 1995,</li> <li>v0.1 = version number: 0.1,</li> </ul>
Tree species, diameter, regeneration, and herbaceous cover from 218 plots in 1985 in Black Rock Forest, NY.
A stand inventory was completed in 1985 in Black Rock Forest, Cornwall, NY across 3112 acres. Trees greater than 2" in diameter at breast height (DBH) were tallied using a 10 basal area factor prism in 218 plots across 71 stands. For each tree, species, DBH, number of eight foot pieces, overall form, crown class, and any special notes were recorded. Regeneration was measured at each location by tallying all trees less than 2" DBH in a 2-m radius plot. Shrub and herbaceous cover at each location were also tallied in a 2-m radius plot.
Understory percent cover, plant traits, canopy LAI, PAR, temperature, and soil moisture data at multiple time points for sites in the burn chronosequence and Indian Point forest at the University of Michigan Biological Station, Pellston, MI (2022-2023)
Community ecology has sought to understand the mechanisms by which plant communities are assembled through time and space. One prominent way to address how communities are assembled is by quantifying functional traits. While there is a tremendous body of literature on functional traits, debate persists about how to account for variation in measured traits. For example, intraspecific trait variation (ITV) can be equal to or greater than interspecific trait variation and ITV has also been found to vary greatly across years. Therefore, there is a need to account for variability in functional trait measures among and within species and through time to improve our understanding of community assembly. Chronosequences are a powerful tool to address temporal changes in community dynamics, however, the inclusion of understory plants in forest chronosequence studies is still relatively uncommon. Previous chronosequence studies have been primarily performed in grasslands or in a limited subset of forest types, so further work is needed in understory plant traits across other ecosystems and climates to improve trait-based understanding of understory plant communities through time. Additionally, because plant traits change as ecosystems age, community interactions are likely to change with ecosystem age. Interactions of particular interest are herbivory, arthropod predation, and the influence of plant traits on arthropod diversity.
Santa Barbara Channel Marine BON Darwin Core Archive: Nearshore kelp forest integrated benthic cover, 1980-ongoing
The Santa Barbara Channel Marine Biodiversity Observation Network (SBCMBON) tracks long-term patterns in species abundance and diversity. This dataset contains cover of kelp forest sessile invertebrates, understory macroalgae, and substrate types by integrating data from four contributing projects working in the kelp forests of the Santa Barbara Channel, USA. Divers collect data on using either uniform point contact (UPC) or random point contact (RPC) methods. The four contributing projects are two research projects: The Santa Barbara Coastal LTER (SBC LTER) and the Partnership for Interdisciplinary Studies of Coastal Oceans (PISCO), the kelp forest monitoring program of the Santa Barbara Channel National Park, and the San Nicolas Island monitoring program supported by USGS. Together, these projects have recorded data for more than 200 species at approximately 100 sites on both the mainland coast and on the Santa Barbara Channel Islands. Sampling began in 1982 and is ongoing. Data were collected by human observation (divers using SCUBA) during regular surveys. Percent cover is recorded for taxa where individuals cannot be counted. Cover can be calculated from the data here as the fraction of total points at which the taxon was present x 100. With UPC and RPC methods, multiple species can be recorded at any given point. The total percent cover of all species combined using this method can exceed 100%; however, the percent cover of any single species cannot exceed 100%. See Methods for information on integration and data processing. MBON is funded by National Aeronautics and Space Administration (NASA), Bureau of Ocean Energy Management (BOEM), and National Oceanic and Atmospheric Administration (NOAA). This dataset is formatted as a Darwin Core Archive (DwC-A, occurrence core). This is a derived data product and see provenance for the source data. For users who are interested in using all or part of this integrated datasets, please contact data owners to discuss your
Santa Barbara Channel Marine BON Darwin Core Archive: Nearshore kelp forest integrated quad and swath cover, 1980-ongoing
The Santa Barbara Channel Marine Biodiversity Observation Network (SBCMBON) tracks long-term patterns in species abundance and diversity. This dataset contains counts of algae and invertebrates (both sessile and mobile) by integrating data from four contributing projects working in the kelp forests of the Santa Barbara Channel, USA. The four contributing projects are two research projects: The Santa Barbara Coastal LTER (SBC LTER) and the Partnership for Interdisciplinary Studies of Coastal Oceans (PISCO), the kelp forest monitoring program of the Santa Barbara Channel National Park, and the San Nicolas Island monitoring program supported by USGS. Together, these projects have recorded data for more than 200 species at approximately 100 sites on both the mainland coast and on the Santa Barbara Channel Islands. Sampling began in 1982 and is ongoing. Data were collected by human observation (divers using SCUBA) during regular surveys. The data table documents the number of organisms and the area over which that number was counted for calculation of areal abundance. Data were collected by human observation (divers using SCUBA) during regular surveys. The algae and invertebrate counts record the number of taxa found in each plot, including quad (small square plots such as 1 or 2 m2) and swath (large linear plots such as 60 m2). See Method and protocol for information on integration and data processing. MBON is funded by National Aeronautics and Space Administration (NASA), Bureau of Ocean Energy Management (BOEM), and National Oceanic and Atmospheric Administration (NOAA). This dataset is formatted as a Darwin Core Archive (DwC-A, occurrence core). This is a derived data product and see provenance for the source data. For users who are interested in using all or part of this integrated datasets, please contact data owners to discuss your research interests, data-related issues or any other questions. A recommended citation for the data package is available from the down
Land cover, landscape metrics and typology of European cities for Urban Forest Ecosystem Services (UFES) evaluation
<p>The data refers to the paper "<em>Urban Forests as Regulating Ecosystems: Types and Ranking of European Cities</em>"</p> <p>The datasets provide a typology for 689 European urban areas, the land cover metrics and landscape metrics used to create the typology and the Urban Forest Ecosystem Services (UFES) indexes created from them.</p> <p>The typology of Urban Forest Ecosystem Services (UFES) presents 10 clusters of cities aggregated into 4 groups: Forest cities, Anthropogenic cities, Herbaceous cities and Standard European cities. The data can be used to support urban planning policies at local and regional scales; in urban forestry, urban form and ecosystem services work related at different spatial scales. The metrics used capture the spatial integration of different layers of natural, semi-natural and artificial land within functional urban areas.</p> <p> </p> <p>The datasets are a csv file (<code>Metrics.csv</code>) and a shapefile (<code>UFES.shp</code>) of polygons with attributes.</p> <ul> <li> <p><code>UFES.shp</code> attributes' are the following: FUA codes, country name, main city name, clusters and groups of FUAs resulting from the hierarchical cluster analysis (HCA), the R color codes used in the article, the five UFES budget indexes as well as an aggregated global UFES index for each FUA.</p> </li> <li> <p><code>Metrics.csv</code> contains the FUA codes, the land cover and landscape metrics used in the HCA.</p> </li> </ul> <p> </p>
Dataset from paper "Canopy palm cover across the Brazilian Amazon forests mapped with airborne LiDAR data and deep learning"
<p><strong>Data and code from the paper:</strong></p> <p>Dalagnol, R., Wagner, F. H., Emilio, T., Streher, A. S., Galvão, L. S., Ometto, J. P. H. B., & Aragão, L. E. O. C. (2022). Canopy palm cover across the Brazilian Amazon forests mapped with airborne LiDAR data and deep learning. Remote Sensing in Ecology and Conservation, 1–14. https://doi.org/10.1002/rse2.264</p> <p><strong>Link:</strong> <a href="https://doi.org/10.1002/rse2.264">https://doi.org/10.1002/rse2.264</a></p> <p> </p> <p><strong>This repository contains:</strong></p> <p><strong>1) model_train.R:</strong> This is the code to run the U-Net model in R language.</p> <p><strong>2) input.rar:</strong> Dataset of lidar canopy height model (CHM) images and masks (labels) patches of canopy palms obtained from four sites in the Brazilian Amazon. The images/masks have 128 x 128 pixels, where each pixel represents 0.5 m in the terrain. The dataset contains 2,269 images and masks, with close to 7,000 palms manually labelled.</p> <p><strong>3) unet_weights_best.h5:</strong> These are the best weights for the U-Net architecture achieved in the paper.</p> <p><strong>4) palm_stats.RData:</strong> Data frame with the lat/lon coordinates and palm metrics extracted for the 610 lidar sites in the Brazilian Amazon. (i) n_total is the number of palms, (ii) n_ha is the density of palms per hectare, (iii) crown_ metrics are based on the area of palm segments (in square meters), (iv) cover_total is the total area occupied by palms in the forest canopy (in square meters), (v) cover_rel is the relative cover of palms in the forest canopy (in percentage), (vi) height_ metrics are based on the height of palm segments (in meters), (vii) palm_height_dif_mean is the mean difference between palm height and local canopy height, and (viii) palm_height_dif_pvalue is the p-value assessing the statistical difference between the palm and canopy heights where 0 means no difference and -1/+1 means a negative/positive difference.</p> <p> </p> <p>If you need anything else, please contact the corresponding author: Ricardo Dalagnol (ricds@hotmail.com).</p> <p> </p> <p><strong>If you use these data, please cite the paper:</strong></p> <p>Dalagnol, R., Wagner, F. H., Emilio, T., Streher, A. S., Galvão, L. S., Ometto, J. P. H. B., & Aragão, L. E. O. C. (2022). Canopy palm cover across the Brazilian Amazon forests mapped with airborne LiDAR data and deep learning. Remote Sensing in Ecology and Conservation, 1–14. https://doi.org/10.1002/rse2.264</p>
1-km forest tree height, cover, plant area index, and foliage height diversity for the CONUS
<p>Consistent and spatially explicit periodic monitoring of forest structure is essential for estimating forest-related carbon emissions, analyzing forest degradation, and supporting sustainable forest management policies. To date, few products are available that allow for continental to global operational monitoring of changes in canopy structure. In this study, we explored the synergy between the NASA’s spaceborne Global Ecosystem Dynamics Investigation (GEDI) waveform LiDAR and the Visible Infrared Imaging Radiometer Suite (VIIRS) data to produce spatially explicit and consistent annual maps of canopy height (CH), percent canopy cover (PCC), plant area index (PAI), and foliage height diversity (FHD) across the conterminous United States (CONUS) at 1-km resolution for 2013-2020. The accuracies of the annual maps were assessed using forest structure attribute derived from airborne laser scanning (ALS) data acquired between 2013 and 2020 for the 48 National Ecological Observatory Network (NEON) field sites distributed across the CONUS. The root mean square error (RMSE) values of the annual canopy height maps as compared with the ALS reference data varied from a minimum of 3.31-m for 2020 to a maximum of 4.19-m for 2017. Similarly, the RMSE values for PCC ranged between 8% (2020) and 11% (all other years). Qualitative evaluations of the annual maps using time series of very high-resolution images further suggested that the VIIRS-derived products could capture both large and “more” subtle changes in forest structure associated with partial harvesting, wind damage, wildfires, and other environmental stresses.</p>
Global forest cover loss tipping points leading to changing hydrologic responses
<p>This dataset describes the methods used to develop the results for study entitled: Global forest cover loss tipping points leading to changing hydrologic responses.</p> <p>EVENTS_List_45.docx is a table describing each deforestation event used for the study</p> <p>MATLAB Script 1: Plotting Hydrologic Sensitive Area against Tree cover loss every 10 % tree cover loss for all 45 events and adjusting Richard's curve function to obtain the parameters. This script uses EXCEL SHEET: HSiaresults.xlsx</p> <p>MATLAB Script 2: Computing the critical points of acceleration based on the Richards curve parameters. This script uses the parameters or results obtained in Script one.</p> <p>MATLAB Script 3: Plotting the climate and water yield direction against tree cover loss. This script used EXCEL SHEET: direction.xlsx</p>
Supporting data for "Forest carbon uptake as influenced by snowpack and length of photosynthesis season in seasonally snow-covered forests of North America"
<p>This is a supporting dataset for the paper :</p> <div> <div>Yang, J. C., Bowling, D. R., Smith, K. R., Kunik, L., Raczka, B., Anderegg, W. R. L., Bahn, M., Blanken, P. D., Richardson, A. D., Burns, S. P., Bohrer, G., Desai, A. R., Arain, M. A., Staebler, R. M., Ouimette, A. P., Munger, J. W., and Litvak, M. E.: Forest carbon uptake as influenced by snowpack and length of photosynthesis season in seasonally snow-covered forests of North America, Agricultural and Forest Meteorology, 353, 110054, <a href="https://doi.org/10.1016/j.agrformet.2024.110054">https://doi.org/10.1016/j.agrformet.2024.110054</a>, 2024.</div> </div> <p>Descriptions and units for each column can be found in a dedicated page within the data file. Methods are decribed in the paper.</p>
ScienceDex guides
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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.