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161 results for “Forest Map”
Tree Ring Data from the Lyford Mapped Tree Plot at Harvard Forest 1861-2014
Is it possible to reconstruct aboveground carbon/biomass from tree rings? If so, how far back in time can researchers go when using tree-ring data in the reconstruction of past biomass? Answers to these questions will have a significant impact on our understanding of dynamics in the terrestrial carbon sink. Long tree-ring records of biomass can reveal intra-annual to annual to multidecadal variations that cannot be resolved by forest census data that is not conducted annually. Additionally, while these dynamics might be resolved using remote sensing, most remotely-sensed products are only two decades or less in length. By having long records of carbon biomass, we can then identify not only the dominant drivers of biomass, but how the importance of these drivers might change during different eras as environmental factors change (e.g., climate, air pollution, disturbance). To test these and other questions, we collected tree-ring records from three 20m radius plots set within the Lyford Plot at the Harvard Forest. The Lyford Plot has been remeasured, on average, decadally since 1969. We can convert these data to biomass using allometric equations and compare tree-ring inferred aboveground biomass to the census data going back in time. Dye et al. (2016) have shown that these data fall within the range of uncertainty of census data sampled in similar plots going back to 1969. Dye, A., Barker Plotkin, A., Bishop, D., Pederson, N., Poulter, B. and Hessl, A., 2016. Comparing tree‐ring and permanent plot estimates of aboveground net primary production in three eastern US forests. Ecosphere, 7(9).
Mapped Trees in CRUI Land Use Project at Harvard Forest 1996-2006
Numerous variables related to land use disturbance and recovery processes influence forest composition, structure, and growth. We measured forest communities in six sites that were formerly plowed, pastured, or continuously forested woodlots in Prospect Hill to test predictions about species composition, stand structure, and productivity in response to agricultural land use legacies. A permanent 30 x 50 m plot gridded in 5 x 5 m sub-plots was established in each of the 6 sites. All trees at least 2.5 cm DBH (diameter at breast height, 1.3 m) were mapped visually in the field, marked with an aluminum tag, and their DBH recorded in summer 1996. Individual boles of multi-stemmed trees were measured separately and a composite single DBH was calculated. Standing dead trees were also mapped and their DBH recorded as well. The 1996 data were used to determine above ground woody biomass using allometric equations for individual trees. The permanent plots were re-surveyed ten years later in fall 2006. The status of each tree mapped in 1996 was recorded (alive, dead standing, dead fallen, forked) and DBH’s were re-measured. Additional trees that grew across the 2.5 cm DBH threshold during the ten-year period were mapped and their DBH’s measured. Composite diameters and above ground woody biomass were determined for forked trees as in 1996. The 2006 re-measurements were used to analyze changes in species composition, stand structure (density, diameter distribution), and mortality patterns among species and size classes, and to calculate net changes in above ground woody biomass across the ten-year period.
Hemlock Mapped Tree Plot at Harvard Forest since 1990
Most of the central New England landscape was cleared for agriculture in the mid-19th century and then naturally reforested into "secondary forests" with the abandonment of agricultural land. Some sites, often poorly drained, remained forested, but were usually subjected to intensive fuelwood cutting or logging and are termed "primary forests." The Hemlock Woodlot was never cleared for agriculture, but has a history of cutting and natural disturbance. The hemlock woodlot is located in the center of Harvard Forest's Prospect Hill Tract, adjacent to a spruce-blackgum swamp. Soils are moist and rocky, with a thick organic layer. Hemlock dominates tree species composition (62% by basal area), with hardwoods and scattered large white pine comprising the remainder. Most of the trees are 100-150 years old, with a few hemlock trees up to 230 years old. While the site was never cleared for agriculture, it was logged several times and chestnut blight removed a chestnut-dominated overstory in the 1910s. The 0.72 ha stem-mapped plot is at the center of a 4-ha hemlock-dominated forest. This plot serves as a major reference site and is part of a network of hemlock forests that are being intensively sampled as the hemlock woolly adelgid arrives.
Lyford Mapped Tree Plot at Harvard Forest since 1969
Permanent forest plots provide an empirical understanding of forest change over time, and are an invaluable part of forestry and ecological research. Walter Lyford began measurements of a 2.88 ha red oak-red maple forest on the Prospect Hill Tract of Harvard Forest in 1969. All trees over 2 inches (5 cm) were mapped on very large-scale (1 inch = 5 feet) hand-drawn maps, and included live and dead trees, stumps, windthrows and other features such as stone walls, boulders, soil moisture and a damage boundary from the 1938 hurricane. All living and dead trees have been re-located and measured (diameter at breast height, canopy class for live trees; condition, decay class, diameter, bole length and stem orientation for fallen dead trees) in 1969, 1975, 1987-1992, 2001, and 2011. In 2001, the original, hand-drawn maps were digitized using ArcView GIS. From 1969 to 2011, red oak (Quercus rubra) increased its dominance of the stand’s total basal area from 52% to 60%; however, red maple (Acer rubrum) has become relatively less abundant, decreasing from 30% to 23%. While red oak and red maple continue to account for the majority of the basal area in the stand, the secondary species experienced a dramatic increase in relative abundance of individuals in the stand; yellow birch (Betula alleghaniensis), black birch (Betula lenta), American chestnut (Castanea dentata), American beech (Fagus grandifolia), witch hazel (Hamamelis virginiana), eastern white pine (Pinus strobus), and eastern hemlock (Tsuga canadensis) have increased from comprising 25% of the individuals in the stand in 1969 to comprising 52% in 2011. The total biomass of living individuals is increasing linearly (R2=0.99, p=0.0002), which implies that the stand has not yet experienced an age-induced decrease in biomass accumulation.
Overstory Mapped Tree Plots at Harvard Forest since 1990
These plots were established and mapped in 1990 for an experiment designed to study the effects of selective overstory tree mortality. The planned manipulation was to kill and leave standing one species in each of four plots, to simulate mortality by a species-specific pathogen. This manipulation was never done, for logistical reasons and because the appearance of the hemlock woolly adelgid provided a more pressing "natural" experiment to study; however, the plots are maintained and have been used in other studies. There are four 50m x 50m plots, located in a mixed hardwood forest (red oak and maple species are major components), north of the experimental hurricane. Tree data from the control plot of the experimental hurricane study could be added to this set for some analyses, since all the plots are in the same general area and forest type, and similar types of measurements were made on all of these trees. Tree diameter and condition were re-surveyed in these plots in Autumns 2003, 2013 and 2023, and saplings growing into the "tree" size class of greater than or equal to 5cm diameter were measured, tagged and mapped.
Annual Maps of Forest Harvest Events in Maine from LANDSAT Imagery 1986-2019
We used Landsat satellite imagery and forest inventory plot measurements to develop a time series of annual maps representing potential forest harvest events for the state of Maine in the Northeastern US for the years 1986 to 2019. We first generated a set of LandTrendr temporal segmentation results for three different spectral indices. Change results were filtered to remove events greater than two years in duration, then results were combined using a seven-parameter degenerate decision trees model that determined a set of thresholds on disturbance patch size, magnitude of spectral change, and change “votes” across indices. We found that we were able to detect harvest events that removed at least 30% of total basal area with a mean F1 score of 0.72 (σ = 0.02) with a mean false negative error rate (omission) of 0.32 (σ = 0.02) and mean false positive error rate (commission) of 0.23 (σ = 0.03), and these scores further improve when maps are masked to remove human land use (built and agriculture) and water based on National Land Cover Dataset and JRC Global Surface Water classifications (mean F1 = 0.73, σ = 0.02). Comparisons with an out-of-sample reference dataset and an existing national forest disturbance dataset indicate our forest harvest maps are a locally accurate source of information for characterizing spatial and temporal variability in long-term harvest patterns across the industrial forests of northern Maine. Here, we provide annual ensemble-based maps of potential harvest events; cross-validated results, which give an indication of detection agreement across subsets of our forest inventory reference datasets; and ancillary datasets that can be used to mask false detections in urban and agricultural land uses and water.
Invasive Species Mapping at Harvard Forest 2005
We are monitoring vegetation at the Harvard Forest for invasive plant populations with respect to land use history and other factors. Using our historical database for the Harvard Forest Prospect Hill tract, we have begun mapping the current distribution of non-native plants as a function of past land use. The 320 ha tract of Prospect Hill is mapped by parcels with known land-use history, soils, vegetation composition, and long-term vegetation dynamics. We plan to conduct annual vegetation surveys at Harvard Forest and the adjacent Quabbin Reservation to document six key non-native species currently present in the area: A. petiolata, the non-native shrubs B. thunbergii, Rhamnus cathartica, R. frangula, Lonicera spp. and the climbing vine, Celastrus orbiculatus. We will map the GPS coordinates and record cover estimates of each species and conduct spatial analyses on these data using our extensive records and GIS maps of land use histories at these locations. Detailed site histories will be determined certain species of interest, using field and archival records. Using similar techniques, we also plan to monitor invasive plant populations at key experimental plots (including the hemlock removal experiment at Simes Tract, and the "recovery phase" of the Chronic N addition plots). Together, these landscape-level studies will provide a novel historical context for understanding biological invasions in a historical context.
Forest disturbances in Europe mapped at high spatial detail and in near-real-time: Logging in protected Estonian forests
<p><strong>Data description</strong></p> <p>These datasets were generated for the Geostory "Forest disturbances in Europe mapped at high spatial detail and in near-real-time: Logging in protected Estonian forests" in the context of the Open Earth Monitor Cyberinfrastructure project.</p> <p>We used open source high-resolution Sentinel-1 satellite data to develop a wall-to-wall map of forest disturbances in the four-year period between the start of 2020 and end of 2023 in Estonia. First results are presented. The methodology is based on RADD-alerts developed for the pan-tropics (Reiche et al. 2021). Three years (2017-2019) of imagery was used as a historical period, and detections were generated for ~4 years (2020-2023). Winter images from November through March were not included as frozen conditions can introduce false detections. This will be addressed in the next version. Disclaimer: Disturbance maps have not been validated.</p> <p>Two additional layers are provided for visualization: a forest baseline layer (<em>forestcover</em>), masking out non-forest disturbance detections, was derived from Copernicus 10m 2018 forest cover density and GLAD 30m 2019 tree removal datasets, and a protected areas layer (<em>natura</em>), which displays the extent of Natura 2000 coverage in Estonia.</p> <p>'.SLD' files are provided for visualization (note: the <em>disturbance</em> .SLC file must be adjusted to contain appropriate time reference fields).</p> <p><strong>Naming Convention</strong></p> <p>To ensure consistency and ease of use across and within the projects, we follow the standard Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describes important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. For instance:</p> <ul> <li>disturbance_radd_c_10m_s_20200101_20200131_eu_epsg.3035_v20240222.tif</li> </ul> <p>with the following fields:</p> <ul> <li>Generic variable name: <strong>disturbance</strong></li> <li>Variable procedure combination i.e. method standard: <strong>radd</strong></li> <li>Position in the probability distribution / variable type: <strong>c</strong></li> <li>Spatial support: <strong>10m</strong></li> <li>Depth reference or depth interval e.g. below ("b"), above ("a") ground or at surface ("s"): <strong>s</strong></li> <li>Time reference begin time (YYYYMMDD): <strong>20200101</strong></li> <li>Time reference end time: <strong>20200131</strong></li> <li>Bounding box (2 letters max): <strong>eu </strong></li> <li>EPSG code: <strong>epsg.3035</strong></li> <li>Version code i.e. creation date: <strong>v20240222</strong></li> </ul> <p><strong>Source Data</strong></p> <p>Disturbance maps:</p> <p>Contains modified Copernicus Sentinel data [2017-2023] and Generated using European Union's EEA-10 Copernicus DEM; https://doi.org/10.5270/ESA-c5d3d65</p> <p>Forest baseline:</p> <p>Generated using European Union's Copernicus Land Monitoring Service information; https://doi.org/10.2909/486f77da-d605-423e-93a9-680760ab6791 and GLAD tree removal; https://doi.org/10.1016/j.rse.2023.113797</p> <p>Natura 2000: </p> <p>Generated using European Environmental Agency's Natura 2000 layers; https://sdi.eea.europa.eu/data/dae737fd-7ee1-4b0a-9eb7-1954eec00c65</p>
Imputed Forest Composition Map for New England Screened by Species Range Boundaries 2001-2006
Initializing forest landscape models (FLMs) to simulate changes in tree species composition requires accurate fine-scale forest attribute information mapped contiguously over large areas. Nearest-neighbor imputation maps have high potential for use as the initial condition within FLMs, but the tendency for field plots to be imputed over large geographical distances results in species frequently mapped outside of their home ranges, which is problematic. We developed an approach for evaluating and selecting field plots for imputation based on their similarity in feature-space, their species composition, and their geographical distance between source and imputation to produce a map that is appropriate for initializing an FLM. We applied this approach to map 13m ha of forest throughout the six New England states (Rhode Island, Connecticut, Massachusetts, New Hampshire, Vermont, and Maine). The map itself is a .img raster file of FIA plot CN numbers. To access FIA data from this map, one has to link the mapcodes in this map to FIA data supplied by USDA FIA database (https://apps.fs.usda.gov/fia/datamart/datamart.html). Due to plot confidentiality and integrity concerns, pixels containing FIA plots were always assigned to some other plot than the actual one found there.
Harvard Forest CTFS-ForestGEO Mapped Forest Plot since 2014
To investigate the forest dynamics across a larger range of scales in which many processes operate, Harvard Forest (HF) researchers, with assistance from the Center for Tropical Forest Science (CTFS) and the Smithsonian Institute’s Forest Global Earth Observatory (ForestGEO), completed an initial census of all woody stems within a 35 ha plot located at the Harvard Forest in 2014. The HF MegaPlot is part of a global array of large-scale plots established by CTFS whose goals are to increase sampling efforts into temperate forests to explore ecosystem processes beyond population dynamics and biodiversity. The geography and size of the HF MegaPlot (500 m x 700 m) is designed to include a continuous, expansive, and varied natural forest landscape. The strategic plot location will yield opportunities for the study of forest dynamics and demography while capturing a large amount of existing NSF funded LTER (Long Term Ecological Research) science infrastructure (e.g. eddy flux towers, gauged sections of a small watershed, existing smaller permanent plots) and a century of observations and studies. The HF MegaPlot will enable an integrated study of ecosystem processes (e.g., biogeochemistry, hydrology, carbon dynamics) and forest dynamics by melding the past with current and future forest research. The second census was conducted during the summers of 2018 and 2019 but did not contain the central swamp portion of the plot.
Forest Type Maps for New England from Historical Studies 1912-1956
This data package contains 3 GIS layers showing generalized forest types across New England as delineated in older forestry publications. These were digitized so that they can be used to illustrate broad vegetation patterns across the region in modern publications. These GIS layers include maps drawn by Hawley and Hawes (1912), RT Fisher (1933), and Westveld and the Committee on Silviculture, New England Section, Society of American Foresters (1956).
Forest Map of New Caledonia
<h1>Description</h1> <p>This dataset contains the shapefile of the Caledonian forest produced by digitization at a scale of 1:3,000 from a mosaic of satellite images (Sentinel 2, Quickbird, Pléiades) and aerial photographs provided by the department of infrastructure, topography and land transport (DITTT) of the government of New Caledonia and available on the georep map server (<a href="https://georep.nc/" target="_blank" rel="noopener">georep.nc</a>). Satellite images and aerial photographs were taken between 2009 and 2021 with a maximum spatial resolution of 0.5 m. We classified the vegetation as a forest when the plants (which must be over 5 meters tall) formed a continuous canopy, obscuring the ground surface over an area of at least 0.5 hectares (FAO 2020). Finally, we consider a polygon as an isolated forest fragment if it is more than 10 meters away from another polygon. When the distance is less than 10 meters, the polygons are merged.</p> <p>Each of the 12,058 digitized polygons was extensively cross-checked with the Tropical Rainforest Cover Change (TMF) dataset using 41 years of Landsat time series (<a href="https://www.biorxiv.org/content/10.1101/2020.09.17.295774v1" target="_blank" rel="noopener">Vancutsem et al., 2021</a>) and the ETH model providing 10 m resolution of global canopy height (<a href="https://www.nature.com/articles/s41559-023-02206-6" target="_blank" rel="noopener">Lang et al., 2023</a>). This consistency analysis between photo-interpretation and radiometric detection was conducted on a 2 km² grid, with verification of each polygon when the divergence exceeded 15%. In total, 1564 polygons, either barely discernible in the images or exhibiting a homogeneous canopy texture, were visually verified by helicopter overflight. At least, forest polygons were verified through databases of plant occurrences collected in the forest, in particular the <a title="NOU Herbarium" href="http://publish.plantnet-project.org/project/nou" target="_blank" rel="noopener">Herbarium of New Caledonia database</a> (NOU) and the <a title="NC-PIPPN" href="https://amap.cirad.fr/reseauxparcelles/npippn.php" target="_blank" rel="noopener">New Caledonian Plant Inventories and Permanent Plots Network </a>(NC-PIPPN).</p> <h1>Content</h1> <p>This dataset was generated, analyzed, and validated using a suite of open-source software tools, including QGIS, PostgreSQL, PostGIS, Python, and the GDAL library, operating on a Linux platform. The compressed file includes six essential files formatted for an ESRI GIS system, utilizing the WGS84 international coordinate system. It is compatible for upload into spatial databases such as PostgreSQL/PostGIS..</p> <p>Each entry in the attribute table represents a forest fragment (a polygon) with associated fields (restricted to 10 characters) :</p> <table> <tbody> <tr> <td><strong>Field</strong></td> <td><strong>Type</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td><strong>id</strong></td> <td>INTEGER</td> <td>Unique identifier</td> </tr> <tr> <td><strong>pn</strong></td> <td>BOOLEAN</td> <td>True or False, indicating if the polygon overlaps with the Northern province</td> </tr> <tr> <td><strong>ps</strong></td> <td>BOOLEAN</td> <td>True or False, indicating if the polygon overlaps with the Southern province</td> </tr> <tr> <td><strong>pil</strong></td> <td>BOOLEAN</td> <td>True or False, indicating if the polygon overlaps with the Islands province (Loyalty Islands)</td> </tr> <tr> <td><strong>area_ha</strong></td> <td>NUMERIC (2 DECIMALS)</td> <td>Area of the polygon in hectares</td> </tr> <tr> <td><strong>dry_ha</strong></td> <td>NUMERIC (2 DECIMALS)</td> <td>Area of the polygon in hectares within the dry life zone according to Holdridge</td> </tr> <tr> <td><strong>moist_ha</strong></td> <td>NUMERIC (2 DECIMALS)</td> <td>Area of the polygon in hectares within the moist life zone according to Holdridge</td> </tr> <tr> <td><strong>rain_ha</strong></td> <td>NUMERIC (2 DECIMALS)</td> <td>Area of the polygon in hectares within the rain life zone according to Holdridge</td> </tr> <tr> <td><strong>created_by</strong></td> <td>TEXT</td> <td>Creator of the polygon</td> </tr> <tr> <td><strong>date_creat</strong></td> <td>DATE</td> <td>Date when the polygon was first created</td> </tr> <tr> <td><strong>date_updat</strong></td> <td>DATE</td> <td>Date when the polygon was updated</td> </tr> </tbody> </table> <h1>Limitations</h1> <p>Currently, only the North and South provinces are available, an area of 16,395 km² out of a total of 18,345 km². Digitization of the Loyaulties Islands will be available soon. This dataset is periodically updated with corrections made by field observations and the addition of new expert interpretations, especially on smaller islands that have not been digitized before. We plan to produce the whole map step by step.</p>
May to July 2018 regions of interest (ROIs) of tidal marsh and tidal forest plant species to be used as ground reference data in habitat mapping
We collected field data from sites distributed in habitats along the salinity axis of the Altamaha River estuary and the Duplin River to be used as ground reference data for habitat mapping. Regions of interest (ROIs) for tidal marsh (salt, brackish, tidal fresh) and tidal fresh forest vegetation species were generated near ground control points (GCP) by digitizing vegetation areas in ArcGIS 10.4 based on field maps.These observations will be used to create habitat maps from aerial photographs of the Altamaha River estuary, GA taken following Hurricane Irma to better understand how the storm surge affected tidal vegetation and to examine any shifts in vegetation type.
Map of Swedish Forest Fires >10ha from 2016-2023
<p><span>A vector map of perimeters of forest fires in Sweden above 10ha from 2016 to 2023 and the Sala Megafire from 2014. Fire locations were identified from multiple sources: the <em>Skogsstyrelsen Skogsbränder Map 2020</em>, <em>EFFIS burned area map</em> and the<em> NASA FIRMS MODIS or VIIRS Fire/Hotspot Data</em> thermal anomalies products. <br>For fires from 2018 to 2020, fire perimeters were sourced directly from <em>Skogsbränder Map 2020</em> or the <em>EFFIS burned area map</em>. The <em>EFFIS burned area map</em> provided fire occurrence dates. For fires lacking occurrence dates (from <em>Skogsbränder Map 2020</em>), we cross-referenced locations and assigned the dates from either the <em>EFFIS burned area map</em> or the <em>MSB incident reports database</em>, using ArcGIS.</span></p> <p><span>Fire locations (points) from 2016, 2017, 2021, 2022 and 2023 were identified from <em>NASA FIRMS MODIS or VIIRS Fire/Hotspot Data</em> thermal anomalies products, the fire date was set as the earliest detection date. We then digitized these fire perimeters using post-fire images from Sentinel-2 (10m x10m resolution). First fire locations were manually examined </span><span>using Sentinel Hub EO Browser as false colour composites (band combination Near Infrared – Red – Green) and image chips downloaded for fires over 10ha. We then manually delineated fire perimeters in ArcGIS; unburned areas located within the fires were excluded from fire perimeters. Fire perimeters include areas burned that were not forest land cover; however, fires that occurred on entirely non-forested land were excluded.</span></p> <p><span> </span></p> <p><span>The dataset is in vector (.shp) format with single part polygons representing burned areas; each associated with an occurrence date. This means that fire events with non-continuous burned areas are represented by several single-part polygons, which share the same fire ID number and date.</span></p> <p><span>Data References</span></p> <p><span>European Forest Fire Information System. 2020. EFFIS Burned Area Product. European</span></p> <p><span>Forest Fire Information System -Copernicus. (Available here </span><span><a href="https://forest-fire.emergency.copernicus.eu/applications/data-and-services"><span>https://forest-fire.emergency.copernicus.eu/applications/data-and-services</span></a></span><span>)</span></p> <p><span> </span></p> <p><span>NASA FIRMS MODIS or VIIRS Fire/Hotspot Data,</span> <span>Country Yearly Summary. 2024. (Available here </span><span><a href="https://firms.modaps.eosdis.nasa.gov/country/"><span>https://firms.modaps.eosdis.nasa.gov/country/</span></a></span><span>)</span></p> <p><span><br></span><span>Myndigheten för samhällsskydd och Beredskap. </span><span>2020. Incident reports from municipal</span></p> <p><span>fire brigades. (Available through direct contact with MSB)</span></p> <p><span> </span></p> <p><span>Skogsstyrelsen. 2020. Skogsbränder. (Accessed via Skogsstyrelsen FTP service, under https://www.skogsstyrelsen.se/sjalvservice/karttjanster/geodatatjanster/ftp/)</span></p>
Live Fuel Moisture Content Mapping in the Mediterranean Basin Using Random Forests and Combining MODIS Spectral and Thermal Data
<p>Live fuel moisture content (LFMC), defined as the mass of water in the foliage and small twigs relative to its total dry mass, is a key factor affecting fire potential and determining wildfire danger and activity. Fuel moisture is directly related to the amount of energy needed to evaporate water before ignition. Consequently, high moisture values reduce, or even inhibit, ignitability and subsequent fire spread.</p> <p>To cover the absence of a specific model to estimate LFMC for the Mediterranean Basin at the sub-continental scale, we built an empirical model based on Random Forests (LFMC<sub>RF</sub>) and combining MODIS spectral bands, vegetation indices, land surface temperature, and the day of year as predictors. The details on the modeling and validation methods, and the accuracy of the estimates are in the related publication <strong><a href="https://doi.org/10.3390/rs14133162">Cunill Camprubí et al., 2022</a></strong>.</p> <p>This dataset contains a collection of weekly LFMC maps from February 2000 to December 2021. The maps cover the Mediterranean and part of the Temperate biomes of the Mediterranean Basin. File <em>mapping_area_LFMC-RF_W-1.0.png</em> shows the target mapping areas.</p> <p>Metadata:</p> <ul> <li>Spectral Information: MODIS MCD43A4 C.6</li> <li>Land Surface Temperature: MODIS MOD11A2 C.6</li> <li>Land Cover Mask: MODIS MCD12Q1 C.6</li> <li>Coordinate Reference System: Native MODIS Sinusoidal</li> <li>Temporal Resolution: Weekly (W)</li> <li>Spatial Resolution: ~500 m</li> <li>File Format: NetCDF v.4</li> <li>Scale Factor: 0.01</li> </ul> <p>Fundings:</p> <p>The study was funded by the MICINN (RTI2018-094691-B-C31), European Union’s Horizon 2020-Research and Innovation Framework Programme under grant agreement no. 101003890 project FirEUrisk, the National Natural Science Foundation of China (U20A20179, 31850410483), and the talent proposals in Sichuan Province (2020JDRC0065) from Southwest University of Science and Technology (18ZX7131).</p>
Indicative distribution map for Ecosystem Functional Group T2.5 Temperate pyric humid forests
<p>This archive contains indicative distribution maps and profiles for <strong>T2.5 Temperate pyric humid forests</strong>, a ecosystem functional group (EFG, level 3) of the <a href="https://global-ecosystems.org/">IUCN Global Ecosystem Typology</a> (v2.0). Please refer to Keith <em>et al.</em> (2020) for details.</p> <p>The descriptive profiles provide brief summaries of key ecological traits and processes, maps are indicative of global distribution patterns, and are not intended to represent fine-scale patterns. The maps show areas of the world containing major (value of 1, coloured red) or minor occurrences (value of 2, coloured yellow) of each ecosystem functional group. Minor occurrences are areas where an ecosystem functional group is scattered in patches within matrices of other ecosystem functional groups or where they occur in substantial areas, but only within a segment of a larger region. Given bounds of resolution and accuracy of source data, the maps should be used to query which EFG are likely to occur within areas, rather than which occur at particular point locations. Detailed methods and references for the maps are included in the profile (xml format).</p>
Indicative distribution map for Ecosystem Functional Group T2.6 Temperate pyric sclerophyll forests and woodlands
<p>This archive contains indicative distribution maps and profiles for <strong>T2.6 Temperate pyric sclerophyll forests and woodlands</strong>, a ecosystem functional group (EFG, level 3) of the <a href="https://global-ecosystems.org/">IUCN Global Ecosystem Typology</a> (v2.0). Please refer to Keith <em>et al.</em> (2020) for details.</p> <p>The descriptive profiles provide brief summaries of key ecological traits and processes, maps are indicative of global distribution patterns, and are not intended to represent fine-scale patterns. The maps show areas of the world containing major (value of 1, coloured red) or minor occurrences (value of 2, coloured yellow) of each ecosystem functional group. Minor occurrences are areas where an ecosystem functional group is scattered in patches within matrices of other ecosystem functional groups or where they occur in substantial areas, but only within a segment of a larger region. Given bounds of resolution and accuracy of source data, the maps should be used to query which EFG are likely to occur within areas, rather than which occur at particular point locations. Detailed methods and references for the maps are included in the profile (xml format).</p>
Indicative distribution map for Ecosystem Functional Group T2.1 Boreal and temperate high montane forests and woodlands
<p>This archive contains indicative distribution maps and profiles for <strong>T2.1 Boreal and temperate high montane forests and woodlands</strong>, a ecosystem functional group (EFG, level 3) of the <a href="https://global-ecosystems.org/">IUCN Global Ecosystem Typology</a> (v2.0). Please refer to Keith <em>et al.</em> (2020) for details.</p> <p>The descriptive profiles provide brief summaries of key ecological traits and processes, maps are indicative of global distribution patterns, and are not intended to represent fine-scale patterns. The maps show areas of the world containing major (value of 1, coloured red) or minor occurrences (value of 2, coloured yellow) of each ecosystem functional group. Minor occurrences are areas where an ecosystem functional group is scattered in patches within matrices of other ecosystem functional groups or where they occur in substantial areas, but only within a segment of a larger region. Given bounds of resolution and accuracy of source data, the maps should be used to query which EFG are likely to occur within areas, rather than which occur at particular point locations. Detailed methods and references for the maps are included in the profile (xml format).</p>
Indicative distribution map for Ecosystem Functional Group M1.2 Kelp forests
<p>This archive contains indicative distribution maps and profiles for <strong>M1.2 Kelp forests</strong>, a ecosystem functional group (EFG, level 3) of the <a href="https://global-ecosystems.org/">IUCN Global Ecosystem Typology</a> (v2.0). Please refer to Keith <em>et al.</em> (2020) for details.</p> <p>The descriptive profiles provide brief summaries of key ecological traits and processes, maps are indicative of global distribution patterns, and are not intended to represent fine-scale patterns. The maps show areas of the world containing major (value of 1, coloured red) or minor occurrences (value of 2, coloured yellow) of each ecosystem functional group. Minor occurrences are areas where an ecosystem functional group is scattered in patches within matrices of other ecosystem functional groups or where they occur in substantial areas, but only within a segment of a larger region. Given bounds of resolution and accuracy of source data, the maps should be used to query which EFG are likely to occur within areas, rather than which occur at particular point locations. Detailed methods and references for the maps are included in the profile (xml format).</p>
Indicative distribution map for Ecosystem Functional Group M1.5 Photo-limited marine animal forests
<p>This archive contains indicative distribution maps and profiles for <strong>M1.5 Photo-limited marine animal forests</strong>, a ecosystem functional group (EFG, level 3) of the <a href="https://global-ecosystems.org/">IUCN Global Ecosystem Typology</a> (v2.0). Please refer to Keith <em>et al.</em> (2020) for details.</p> <p>The descriptive profiles provide brief summaries of key ecological traits and processes, maps are indicative of global distribution patterns, and are not intended to represent fine-scale patterns. The maps show areas of the world containing major (value of 1, coloured red) or minor occurrences (value of 2, coloured yellow) of each ecosystem functional group. Minor occurrences are areas where an ecosystem functional group is scattered in patches within matrices of other ecosystem functional groups or where they occur in substantial areas, but only within a segment of a larger region. Given bounds of resolution and accuracy of source data, the maps should be used to query which EFG are likely to occur within areas, rather than which occur at particular point locations. Detailed methods and references for the maps are included in the profile (xml format).</p>
ScienceDex guides
Understand access before you commit
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.