Find research datasets worth reusing

opencc-by-4.0Sep 2022View details →

Gridded fossil CO2 emissions and related O2 combustion consistent with national inventories

Data Access Notice Please note that, at present, the data for a sample of years are provided in this data record due to Zenodo's 50GB data limit. Data for all years 1959-2023 can be accessed via the following link: <a href="http://opendap.uea.ac.uk/opendap/hyrax/greenocean/GridFED/GridFEDv2024.0/contents.html">http://opendap.uea.ac.uk/opendap/hyrax/greenocean/GridFED/GridFEDv2024.0/contents.html</a> Product Description See Jones et al. (2021) for a detailed description of this dataset and the core methods used to produce it. Key details are provided below. GCP-GridFED (version 2024.0) is a gridded fossil emissions dataset that is consistent with the national CO2 emissions reported by the Global Carbon Project (GCP; <a href="https://www.globalcarbonproject.org/">https://www.globalcarbonproject.org/</a>) in the annual editions of its Global Carbon Budget (Friedlingstein et al., 2023). GCP-GridFEDv2024.0 provides monthly fossil CO2 emissions for the period 1959-2023 at a spatial resolution of 0.1° × 0.1°. The gridded emissions estimates are provided separately for fossil CO2 emitted by the oxidation of oil, coal and natural gas, international bunkers, and the calcination of limestone during cement production. The dataset also includes the cement carbonation sink of CO2. Note that positive values in GridFED signify a surface-to-atmosphere CO2 flux (emissions). Negative values signify an atmosphere-to-surface flux and apply only to the cement carbonation sink. GCP-GridFED also includes gridded uncertainties in CO2 emission, incorporating differences in uncertainty across emissions sectors and countries, and gridded estimates of corresponding O2 uptake based on oxidative ratios for oil, coal and natural gas (see Jones et al., 2021). Core Methodology in Brief GCP-GridFEDv2024.0 was produced by scaling monthly gridded emissions for the year 2010, from the Emissions Database for Global Atmospheric Research (EDGAR v4.3.2; Janssens-Maenhout et al., 2019), to the national annual emissions estimates compiled as part of the 2024 global carbon budget (GCP-NAE) for the years 1959-2023 (Friedlingstein et al., 2024).  GCP-GridFEDv2024.0 uses a preliminary release of GCP-NAE covering the years 1959-2023 (timestamp 1st August 2024; an update from Andrew and Peters [2023]). The GCP-NAE estimates for year 2023 are based on data available at the timestamp and the estimates are thus expected to differ somewhat from those that will be presented by Friedlingstein et al. (2024), which will adopt updates to GCP-NAE since the timestamp. For full details of the core methodology, see Jones et al. (2021). Changes to the Seasonality of Emissions in GCP-GridFEDv2022.2 onwards The seasonality of emissions (monthly distribution of annual emissions) for the following countries/sources is now based on the seasonality observed in the Carbon Monitor dataset (Liu et al., 2020; Dou et al., 2022):  <ul> <li>Austria, Belgium, Brazil, Bulgaria, China, Croatia, Cyprus, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, India, Ireland, Italy, Japan, Latvia, Lithuania, Luxembourg, Malta, Netherlands, Poland, Portugal, Romania, Russia, Slovakia, Slovenia, Spain, Sweden, United Kingdom, United States.</li> <li>State or province-level data is used for Brazil, China, Russia, and the United States.</li> <li>This also applies for the Bunker Aviation and Bunker Shipping sectors.</li> </ul> Seasonality is determined in the following ways for those countries/sources: <ul> <li>The seasonality of emissions in 2019-2023 is taken from Carbon Monitor.</li> <li>The seasonality of emissions in all years prior to 2019 is assigned as the average of the seasonality from Carbon Monitor in all years excluding 2020 (due to the impact of COVID-19 on the seasonality of emissions in 2020).</li> </ul> For all countries not listed above and all years 1959-2023, GCP-GridFED adopts the seasonality from EDGAR v4.3.2 (year 2010; Janssens-Maenhout et al., 2019) and applies a small correction based on heating/cooling degree days to account for inter-annual climate variability which effects emissions in some sectors (see Jones et al., 2021). Other New Features of GCP-GridFEDv2024.0 <ul> <li>There have been no changes to the functionality of the GridFED code in this update versus the previous update (v2023.1).</li> </ul>

opencc-by-4.0Aug 2021View details →

iLAUT – iPad laser scanner data from Austrian forest Inventory plots

The estimation of stand- and individual tree information is one of the major goals of forest inventory. Conventionally, field data in forest inventory are collected at tree level on sample plots by means of manual measurements (e.g., caliper, tape). In recent years, modern laser-supported sensors and automatic routines for feature extraction were increasingly used instead of the traditional forest inventory methods. In 2020, Apple (Apple Inc. Cupertino, California, USA) implemented a LiDAR (Light Detection and Ranging) sensor into the new 4th Generation of Apple iPad Pro. Consequently, LiDAR-generated 3D point clouds can nowadays be recorded with consumer-level devices for the first time. Novel methodology is able to provide estimates of the terrain height, tree positions, stem diameters, and tree heights from these 3D point clouds. This dataset was made publicly accessible to show recent iPad 3D point clouds of forest inventory sample plots and to test new software routines for the automatic measurement of trees. Benchmark studies with performance tests of different algorithms are welcome. The dataset contains co-registered raw 3D point-cloud data collected on 21 forest inventory sample plots in Austria. The data was collected by two different laser scanning systems: (i) the iPad pro (Apple Inc. Cupertino, California, USA), and (ii) a mobile personal laser scanner (PLS) (ZEB Horizon, GeoSLAM Ltd., Nottingham, UK). The data also contains application videos of the iPad, digital terrain models (DTM), field measurements as reference data (“ground-truth”), and the output of recent software routines for the automatic tree detection and the automatic stem diameter measurement.

opencc-by-4.0Nov 2022View details →

IPCC Climate Zones (from the 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories)

Description These data (re)create spatial data for the 2019 IPCC Climate Zones, shown in Figure 3A.5.1 of <a href="https://www.ipcc-nggip.iges.or.jp/public/2019rf/pdf/4_Volume4/19R_V4_Ch03_Land%20Representation.pdf">Chapter 3: Consistent Representation of Lands</a> in <a href="https://www.ipcc-nggip.iges.or.jp/public/2019rf/vol4.html">Volume 4: Agriculture, Forestry and Other Land Use</a> of the <a href="https://www.ipcc-nggip.iges.or.jp/public/2019rf/index.html">2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories</a>. I recreated these data because I could not readily identify the data in a spatial format online, a problem which has previously been noted by ESDAC, who produced a <a href="https://esdac.jrc.ec.europa.eu/content/support-renewable-energy-directive#tabs-0-description=1">spatial version of Figure 3A.5.1 from the original 2006 guidelines</a>. Resolution: 0.5 arc degree CRS: lon/lat WGS 84 If you use these data please ensure you also cite the IPCC - Calvo Buendia, E et al. (2019). 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories. IPCC, Switzerland.   Methods The data were derived using the classification scheme shown in Figure 3A.5.2 based on the gridded Climate Research Unit (CRU) Time Series (TS) monthly climate data (<a href="https://rmets.onlinelibrary.wiley.com/doi/10.1002/joc.3711">Harris et al., 2014</a>) for the period from 1985 to 2015 following the methods described in Annex 3A.5 Default climate and soil classifications of the above Chapter. All data were processed in R version 4.2.1, with the packages <a href="https://cran.r-project.org/web/packages/elevatr/index.html">elevatr</a> (v0.4.2), <a href="https://cran.r-project.org/web/packages/lubridate/index.html">lubridate</a> (v1.8.0), <a href="https://cran.r-project.org/web/packages/magrittr/index.html">magrittr</a> (v2.0.3), and <a href="https://cran.r-project.org/web/packages/terra/index.html">terra</a> (v1.6-7) attached. The full session info is included as a .txt file. As these methods are not exhaustively described in the Annex, the following assumptions were made: <ul> <li><a href="http://http://dx.doi.org/10.5285/c311c7948e8a47b299f8f9c7ae6cb9af">CRU TS3.25</a> was used as the most recently published data (published on 2017-09-22) that could have been incorporated into the Refinement. Other possibilities include CRU TS3.24 (which are the first data to include 2015), or CRU TS4.00 or CRU TS4.01 (both of which were published in parallel to 3.24 and 3.25). These data were all investigated, and CRU TS3.25 produced results that were the most visually similar to the published Figure 3A.5.1 (though non-identical).</li> <li>As the methods did not mention a preferred elevation data source, the <a href="https://cran.r-project.org/web/packages/elevatr/index.html">elevatr</a> R package was used to obtain data at zoom level 2 (approx resolution of 0.15 arc degree), that was then resampled to match the 0.5-degree resolution of the CRU data. These data originally come from the <a href="https://www.ngdc.noaa.gov/mgg/global/global.html">ETOPO1 global relief model</a>.</li> </ul>   Known discrepancies <ul> <li>The distribution of Tropical Wet and Tropical Moist in South America does not exactly match the original data.</li> <li>There are small discrepancies in Tropical Montane classifications (likely arising from the use of a different elevation layer). These are most noticeable in, but not restricted to, Africa.</li> <li>The classification of Boreal Dry, Polar Dry, and Polar Moist in northern Russia and (to a lesser extent) in northern Canada does not exactly match the original data.</li> <li>There are a small number of Cool Temperate Dry pixels in the UK, and Warm Temperate Dry pixels around Brittany which do not occur in the original data.</li> </ul>   Disclaimer I am not affiliated with the IPCC in any way, I just needed spatial data of the Climate Zones, and could not readily identify any online. This is a problem which has previously been noted by ESDAC, who produced a <a href="https://esdac.jrc.ec.europa.eu/content/support-renewable-energy-directive#tabs-0-description=1">spatial version of Figure 3A.5.1 from the original 2006 guidelines</a>.   File description <ul> <li>README.html - ~this description file.</li> <li>IPCC_Climate_Zones_ts_3.25.tif - the output Climate Zones map at 0.5-arc degree resolution based on the CRU TS3.25 data.</li> <li>IPCC_Climate_Zones_colour_map.clr - a colour map file to render the output map with the same colours as in the IPCC 2019 Refinement figure.</li> <li>IPCC_Climate_Zones_ts_3.25.png - an image file of the output Climate Zones map.</li> <li>ipcc_climate_zones_2019.R - the script used to produce these data.</li> <li>session_info.txt - the R session info.</li> </ul>

opencc-by-4.0Dec 2021View details →

Datasets for greenhouse gasses emissions and removals from inventories and global models over Africa

This file includes the data from Mostefaoui et al. (ESSD, under submission), for  54 countries African countries  The data includes:  (1) CO2 fluxes from global models - satellite inversions and Dynamic Global Vegetation Models (DGVM) -, and from a collection of national inventories for LULUCF, GFEDv4 and FAO data.  DGVM values are the median of 14 models, consistent with the Global Carbon Budget 2020 (https://essd.copernicus.org/articles/12/3269/2020/) LULUCF UNFCCC  corrected values are from Grassi  <a href="https://priv-bx-myremote.tech.ec.europa.eu/preprints/essd-2022-104/,DanaInfo=.aetugDhuwm0xto76O48y,SSL+">https://essd.copernicus.org/preprints/essd-2022-104/</a>   (2) CH4 fluxes from global models consistent with the Global Methane Budget 2020 (https://essd.copernicus.org/articles/12/1561/2020/) (3 N2O fluxes from global models (three inversions) For further methodological details, see Mostefaoui et al. (ESSD, under submission): Mounia Mostefaoui, Philippe Ciais,  Matthew J. McGrath, Philippe Peylin, Prabir Patra. Greenhouse gasses emissions and their trends over the last three decades across Africa, ESSD (under submission)

opencc-by-4.0Feb 2023View details →

The homogenized glacier inventories for South Tirol 1997-2005-2017

Data set of three homogenized glacier inventories for the Autonomous Province of Bolzano - South Tyrol for the reference years 1997, 2005 and 2017 in ESRI-shapefile format. The shapefiles are supplemented by a MS-excel file containing analyses of glacier changes between the single inventories and a detailed technical report describing the inventories and the underlying data and methods. The inventories were compiled based on orthophoto and airborne laser-scanning data using a homogenized methodology to enable for direct comparisons between the single inventories. Besides the vectorized glacier outlines and several identifiers (glacier names and WGMS/CGI codes), the inventories provide information on typical topographic parameters such as glacier area, min max and median altitude, main aspect, slope and geographic location.

opencc-by-4.0Feb 2023View details →

Migration Drivers Data Inventory Records

This inventory includes metadata on various quantitative sources of information on migration drivers that can be used for modelling purposes. Additionally, the inventory includes information on articles that used those quantitative sources such as the statistical effect found in their analysis.

opencc-by-4.0Oct 2023View details →

GIS Dataset of Colour and Materials at Het Loo Palace in the Apartments of William III of Orange-Nassau (1713 Inventory)

Abstract The presented datasets concerning the colour choices documented in the 1713 inventory within the two apartments of William III of Orange-Nassau (1652-1702) at Het Loo palace (Apeldoorn) for historical mapping correspond to the article: Bernert, Sara. 'Interior Colour Practices in the Apartments of William III of Orange-Nassau at Het Loo Palace. A New Methodology for Digital Reconstruction through the Use of GIS'. In New Digital Approaches, edited by Krista De Jonge and Sanne Maekelberg, 71–88. PALATIUM, 2023. This methodology deals with the captivating world of interior colour practices in stately apartments within courtly contexts, using the power of digital humanities. The aim is to bridge historical sources, such as palace inventories, with their corresponding spaces with a Geographic Information System (GIS). Please see the PDF for further description.   Dataset Contents The separate CSV sheets and the equivalent Excel workbook<a href="#_ftn2">[2]</a> include: <ul> <li>The datasets of colour carriers, their quantities and described colours by room as noted in the inventory: <ul> <li>Overall colour distribution across both apartments: <ul> <li>In short form for the macro layer of the palace for an over-regional comparison.<a href="#_ftn3">[3]</a></li> <li>And a detailed version of the microlayer of the palace.<a href="#_ftn4">[4]</a></li> </ul> </li> </ul> </li> <li>A separate dataset of the first apartment of William III as it was around 1686, still in his function as Prince of Orange and Stadtholder of Holland, Zeeland, Utrecht, Guelders, and Overijssel.<a href="#_ftn5">[5]</a></li> <li>A separate dataset of the second apartment of William III in the state around 1694, as he had already become King of England.<a href="#_ftn6">[6]</a></li> <li>Precise data on the polygons of the ground plan from 1695.<a href="#_ftn7">[7]</a></li> <li>Furthermore, the key to colour factoring by the space the colour carriers take up within the room, as described in the associated article pp.73-74.<a href="#_ftn8">[8]</a></li> </ul> _______________________________________________________________________________ <a href="#_ftnref1">[1]</a> The inventory was amongst other inventories of the Orange-Nassau dynasty published in the 1970s and is accessible online: Sophie Wilhelmina Albertine Drossaers and Theodoor Herman Lunsingh Scheurleer, eds., ‘Inventaris van de Inboedel van Het Huis Het Loo, Het Oude Loo En Het Huis Merwel 1713’, in Inventarissen van de Inboedels in de Verblijven van de Oranjes En Daarmee Gelijk Te Stellen Stukken 1567-1795, vol. 1, 3 vols, Rijks Geschiedkundige Publicatiën, GS 147 (Den Haag: Rijks Geschiedkundige Publicatiën, 1974), 647–94, https://resources.huygens.knaw.nl/retroboeken/inboedelsoranje/#source=1&page=686&accessor=toc&view=imagePane&size=877. <a href="#_ftnref2">[2]</a> Cf. 2023_HetLoo_1713_WilliamIII_Data_Color_Material_Apartments©Bernert2023 <a href="#_ftnref3">[3]</a> Cf. 2023_HetLoo_1713_WilliamIII_ColourShort_Both_Apartments_©Bernert2023 <a href="#_ftnref4">[4]</a> Cf. 2023_HetLoo_1713_WilliamIII_Inventory_Long_Both_Apartments_©Bernert2023 <a href="#_ftnref5">[5]</a> Cf. 2023_HetLoo_1713_WilliamIII_First_Apartment_©Bernert2023 <a href="#_ftnref6">[6]</a> Cf. 2023_HetLoo_1713_WilliamIII_Second_Apartment_©Bernert2023 <a href="#_ftnref7">[7]</a> Cf. 2023_HetLoo_1695_GIS_PolygonData_Spaces_©Bernert2023. The map referred to: Anonymous, Het Loo Palace. Goundplan of the First Floor, around 1695, RL-7596, Het Loo Palace Collection. <a href="#_ftnref8">[8]</a> Cf. 2023_GIS_Colour_Counting_Sheet_©Bernert2023 and Sara Bernert, ‘Interior Colour Practices in the  Apartments of William III of Orange-Nassau at Het Loo Palace. A New Methodology for Digital Reconstruction through the Use of GIS’, in New Digital Approaches, ed. Krista De Jonge and Sanne Maekelberg, PALATIUM, 2023, 73–74.

openCustomNov 2016View details →

Fine woody debris inventory data from reference stands and inventory plots in the Pacific Northwest, 1992 to 2000

These data provide an inventory of the mass of downed fine woody debris stored within various forest types. This data is used to determine total organic matter, carbon, and nutrient stores in forests.

openOpenNov 2022View details →

Tree inventory for adjacent stands of Picea mariana and Betula neoalaskana located in the 1958 Murphy Dome fire scar - 2012

This dataset contains the diameter of all trees measured as part of a project examining tree species affects on forest stand characteristics and plant-soil-microbial feedbacks. Study was conducted across 3 spatially explicit blocks containing adjacent stands of Picea mariana and Betula neoalaskana in forest that established following the 1958 Murphy Dome fire. All included tree diameter measurements were collected in Summer 2012.

Fire Self-Limitation (FiSL) Experiment: Quantifying Wildfire Carbon Combustion Losses in boreal Deciduous and Mixed Forests in Interior Alaska and the Boreal Cordillera II: Tree Inventory Data 2022

This dataset contains tree combustion measurements collected in the field for plots in 8 fire scars in Interior Alaska and the Yukon. Data was collected in the summer of 2022. Fire scars sampled included Shovel Creek (2019), Aggie Creek (2015), Hess Creek (2019), Baker (2015), Munson Creek (2021), Isom Creek (2020), 2019MA014 (2019), and 2019BC005 (2019). Tree species, diameters (DBH where possible, otherwise BD), condition (living/dead, standing/fallen, etc), and component combustion are recorded for every tree in each 10 m * 2 m plot.

Fire Self-Limitation (FiSL) Experiment: Quantifying Wildfire Carbon Combustion Losses in boreal Deciduous and Mixed Forests in Interior Alaska and the Boreal Cordillera III: Shrub Inventory Data

This dataset contains shrub combustion measurements collected in the field for plots in 8 fire scars in Interior Alaska and the Yukon. Data was collected in the summer of 2022. Fire scars sampled included Shovel Creek (2019), Aggie Creek (2015), Hess Creek (2019), Baker (2015), Munson Creek (2021), Isom Creek (2020), 2019MA014 (2019), and 2019BC005 (2019). Shrub species, stem diameters (BD), and component combustion were recorded for every shrub in each 10 m * 2 m plot.

Fire Self-Limitation (FiSL) Experiment: Quantifying Wildfire Carbon Combustion Losses in boreal Deciduous and Mixed Forests in Interior Alaska and the Boreal Cordillera VII: Coarse and Fine Woody Debris Inventory 2022

This dataset contains characteristics of coarse woody debris and snags collected in the field for plots in 8 fire scars in Interior Alaska and the Yukon. Data was collected in the summer of 2022. Fire scars sampled included Shovel Creek (2019), Aggie Creek (2015), Hess Creek (2019), Baker (2015), Munson Creek (2021), Isom Creek (2020), 2019MA014 (2019), and 2019BC005 (2019).

Fire Self-Limitation (FiSL) Experiment: Quantifying Wildfire Carbon Combustion Losses in boreal Deciduous and Mixed Forests in Interior Alaska and the Boreal Cordillera VIII: Seedling Inventory 2022

This dataset contains characteristics of post-fire seedlings and resprouts collected in the field for plots in 8 fire scars in Interior Alaska and the Yukon. Data was collected in the summer of 2022. Fire scars sampled included Shovel Creek (2019), Aggie Creek (2015), Hess Creek (2019), Baker (2015), Munson Creek (2021), Isom Creek (2020), 2019MA014 (2019), and 2019BC005 (2019).

openCC0Apr 2024View details →

Inventory of Eddy Covariance Tower Data in AmeriFlux from Everglades Towers, Florida: 2004-ongoing

There are six eddy covariance towers that make continuous measurements of regional hydrology and carbon balance in the region. This long-term eddy covariance tower network includes 2008-ongoing data collection in a freshwater marl prairie (TS/Ph-1; US-Esm) and a freshwater marsh (SRS-2; US-Elm), 2016-ongoing data collection from a mangrove scrub (TS/Ph-7; US-TaS), 2004-ongoing data collection from a tall mangrove forest (SRS-6; US-Skr), 2020-ongoing data collection from a tower at the ecotone of marl prairie and mangrove scrub (SE1; US-EvM), and 2018-ongoing data collection from an aquatic Tower in Florida Bay (Bob Allen Key; US-FBE). Everglades ecosystems occur in predictable zonal patterns, and current Florida Coastal Everglades Long Term Ecological Research (FCE-LTER) sites are arranged to capture the variation in hydrology, community composition, and productivity. The hydrology and disturbance regime in the Everglades region developed a rich diversity of communities that have variable capacities to capture and sequester carbon. At each site, open-path infrared gas analyzers (IRGA, LI-7500 and Li-7700, Li-COR Inc., Lincoln, NE) are used to measure CO2 (mg mol-1), water vapor molar density (mg mol-1), and CH4. A paired sonic anemometer (CSAT3, Campbell Scientific Inc., Logan, UT) is employed to measure sonic temperature (Ts; K) and 3-dimensional wind speed (u, v and w, respectively; m s-1). Additional meteorological data is measured at the site to monitor conditions. This data package is an inventory of eddy covariance tower data and metadata available through the AmeriFlux repository.

openCC (other)Feb 2024View details →

Inventory of High-resolution phylogenetic profiles of the planktonic microbial communities (via 16S and 18S rRNA gene amplicons) from Shark River Slough and Taylor Slough, Everglades National Park (FCE LTER), Florida, USA, 2017 - ongoing

Planktonic microbial communities mediate many vital biogeochemical processes in wetland ecosystems, yet compared to other aquatic ecosystems, like oceans, lakes, rivers, or estuaries, they remain relatively underexplored. Our study site, the Florida Everglades (USA)—a vast iconic wetland consisting of a slow-moving system of shallow rivers connecting freshwater marshes with coastal mangrove forests and seagrass meadows—is a highly threatened model ecosystem for studying salinity and nutrient gradients, as well as the effects of sea level rise and saltwater intrusion. This dataset provides the first high-resolution phylogenetic profiles of planktonic bacterial and eukaryotic microbial communities (using 16S and 18S rRNA gene amplicons) from these environments. The dataset contains 16S and 18S rRNA data from 2017, and contains 16S rRNA data for monthly (2019) and quarterly water samples (2020-ongoing). The 2017 data are published in Laas et al. 2022. A detailed list of sequence data and their accession numbers in GenBank is provided and will be updated as more data are published. This data package is an inventory of sequence read archive (SRA) entries available through GenBank BioProject PRJNA525456 (at https://www.ncbi.nlm.nih.gov/bioproject/PRJNA525456) and BioProject PRJNA1018945 (at https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1018945). This data package is associated with the following publication: Laas, P., Ugarelli, K., Travieso, R., Stumpf, S., Gaiser, E. E., Kominoski, J. S., & Stingl, U. (2022). Water column microbial communities vary along salinity gradients in the Florida Coastal Everglades wetlands. Microorganisms, 10(2), 215. https://doi.org/10.3390/microorganisms10020215 Instead of citing this package, which is an inventory, please cite the original GenBank data or journal article, as appropriate. Citation guidance for the journal article is available on the respective publisher's website.

openCC (other)Mar 2022View details →

Hubbard Brook Experimental Forest: Valleywide Plot Tree and Sapling Inventory – 1995, 2005, 2015

The valley-wide plots are a grid of 431 sites along fifteen N–S transects established at 500-m intervals spanning the entire Hubbard Brook Valley. The plot network was designed by Paul Schwarz for spatial analysis of tree species distribution patterns within the valley. Multiple above- and below-ground attributes have been measured on these plots. This dataset includes forest inventory data at 10 year intervals, for 1995, 2005, and 2015. The full survey takes three seasons to complete, with the datatable listing the exact measurement interval for each tree. Data are included for both trees and saplings on 371 core plots (all surveys) and 60 densified plots (1998, 2008). Locations of plots in this study can be found in the following dataset: Hubbard Brook Experimental Forest Valleywide Plots: GIS Shapefile (2022.) https://doi.org/10.6073/pasta/440b176372e0cdeb341731aea816b67c 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. These data have been used in a number of publications including: Schwarz, P.A., Fahey, T.J., Martin, C.W., Siccama, T.G., and Bailey, A. 2001. Structure and composition of three northern hardwood–conifer forests with differing disturbance histories. For. Ecol. Manage. 144(1–3): 201–212. doi:10.1016/S0378-1127(00)00371-6. Schwarz, P.A., Fahey, T.J., and McCulloch, C.E. 2003. Factors controlling spatial variation of tree species abundance in a forested landscape. Ecology, 84(7): 1862–1878. doi:10.1890/0012-9658(2003)084[1862:FCSVOT]2.0.CO;2. van Doorn, N.S., Battles, J.J., Fahey, T.J., Siccama, T.G., and Schwarz, P.A. 2011. Links between biomass and tree demography in a northern hardwood forest: a decade of stability and change in Hubbard Brook Valley, New Hampshire. Can. J. For. Res. <emphasis role="strong">41</emphasis>(7): 1369–1379. doi:10.1139/X11-063. C

openCC (other)Mar 2022View details →

Hubbard Brook Experimental Forest: Watershed 4 Vegetation Inventory

Twenty-five meter strips were cut progressively on a 2 year cycle on Watershed 4 at HBEF from 1970-1974 for moderate impact on the terrain. 25 x 25 meter vegetation plots were established post cutting and have been measured regularly for estimates and timing of herbaceous plant and tree succession. Ten years into regrowth there was no noticeable difference between the age of the strips and vegetation cover. By year 40 successional pin cherry died off and northern hardwoods returned as the dominant trees with yellow birch playing a particularly prominent role due to the abundance of seeds provided by remaining adults along cut strips. This dataset contains a table of tree measurements and a table of sapling measurements. These vegetation measurements continue at approximately 10 year intervals. Details about this study and data collection can be found in: Martin, C. Wayne; Hornbeck James W. 1989. Revegetation after strip cutting and block clearcutting in northern hardwoods: a 10-year history. Res. Pap. NE-625. Broomall, PA: US. Department of Agriculture, Forest Service, Northeastern Forest Experiment Station. 17 p. https://doi.org/10.2737/NE-RP-625 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.

openCC (other)Apr 2024View details →

Forest Inventory of a Northern Hardwood Forest: Bird Area at the Hubbard Brook Experimental Forest, 1981

The forest inventory surveys in the bird area were initiated in 1981 and transects were made permanent in 1991. The inventory is representative of approximately 2.5 km-squared of mid elevation northern hardwood forest. It consists of a total inventory of all trees >=10 cm dbh, within each of four 10 m wide belt transects. The parallel transects are placed approximately 200 m apart and run roughly in an east-west direction for 2200 to 2900 m. In 1991, each live stem >=10 cm dbh was tagged with a unique number. Tree vigor is assessed every two years and diameter is remeasured every ten years. Every two years, new tags are placed on stems that have grown into the 10 cm diameter class. A survey of smaller trees (>=2 to <10 cm dbh) was first taken in 1991 and is resurveyed every ten years. This dataset includes the initial inventory values measured in 1981. The full timeline of tree inventory data for this site is available at https://doi.org/10.6073/pasta/58dfdebfd1b6440510def2394ab92c53 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.

openCC (other)Mar 2024View details →

Forest Inventory of a Northern Hardwood Forest: Bird Area, 1991 - present, Hubbard Brook Experimental Forest

The forest inventory surveys in the bird area were initiated in 1981 and transects were made permanent in 1991 by Tom Siccama who created and designed this tree survey. The inventory is representative of approximately 2.5 km2 of mid elevation northern hardwood forest. The data set is particularly geared toward producing accurate mortality and recruitment estimates. It consists of a total inventory of all trees greater than or equal to 10 cm dbh within each of four 10 m wide belt transects. The parallel transects are placed approximately 200 m apart and 290° bearing in an east-west direction for 2200 to 2900 m. In 1991, each live stem greater than or equal to 10 cm dbh was tagged with a unique number. Tree vigor is assessed every two years and diameter is remeasured every ten years. Every two years, new tags are placed on stems that have grown into the 10 cm diameter class. This dataset includes 1991 and subsequent samplings. These detailed tree status data provide a high resolution assessment of tree vital rates, particularly recruitment and mortality rates. The data show an increase in both mortality and recruitment over the 30 year measure time (1991-2021). The number of unhealthy trees has also increased to about 8% of the live trees. The measure period includes notable disturbances, particularly the 2013 microburst impacted the west end of lines 5 and 9, and a saddled prominent outbreak concurrent with drought conditions in 2020. Data from an earlier sampling in 1981 (pre-tagging of individual trees) can be found in: Sherry, T., D. Holmes, and T. Siccama. 2019. Forest Inventory of a Northern Hardwood Forest: Bird Area at the Hubbard Brook Experimental Forest, 1981 ver 7. Environmental Data Initiative. https://doi.org/10.6073/pasta/206b98f6553f1ff95cf584dd2185554e (Accessed 2021-09-16). 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 ma