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225 results for “aboveground biomass”
Aboveground biomass carbon and nitrogen: Old-Field Chronosequence: Plant Productivity
The goal of this research is to study the change in plant growth and species distribution during succession. Annual plant growth above ground is annually sampled in more than 20 fields from 4 permanently marked 3m x 4m plots in each field. These fields were previously cultivated, but then abandoned from agriculture at various times in the past. The fields were left undisturbed for plants to develop from seeds within the soil or brought into the fields by wind or animals. The fields included in this study are 4, 5, 10, 24, 26, 28, 35, 39, 41, 45, 53, 70, 72, 77 and the Lawrence strip that was abandoned in 1988. This experiment was started in 1987 by lead investigators David Tilman and Johannes Knops. In 2001 new sampling was started in positions similar to the E054 plots in these E014 fields: 21, 27, 32, 40, 44, 47, 76. Past work at CDR and elsewhere has demonstrated an overriding influence of fire frequency in maintaining prairie openings and oak savanna at the prairie-forest border. Fire regimes harm some types of species while favoring others and drive light and nutrient dynamics, which in turn drive community functional attributes and diversity levels. Ultimately, fire frequency interacts with climate, N deposition, land use, and biotic invasion to determine the outcomes of tree-grass interactions and the dynamics of vegetation at ecotones such as the prairie-forest border in Minnesota. In 2006 each field was divided in half, and one half randomly chosen for periodic prescribed burning (a fire every other year). We anticipate that the burned half will continue succession to prairie grassland while the unburned half will become white pine stands if seed sources are nearby, or will otherwise undergo extremely slow succession to oaks.
Aboveground biomass and nitrogen allocation of ten deciduous southern Appalachian tree species at the Coweeta Hydrologic Laboratory in 1997
Allometric equations were developed for mature trees of 10 deciduous species at the Coweeta Hydrologic Laboratory in western North Carolina, U.S.A. These equations included the following dependent variables: stem wood mass, stem bark mass, branch mass, total wood mass, foliage mass, total biomass, foliage area, stem surface area, sapwood volume, and total tree volume. High correlation coefficients (R2) were observed for all variables versus stem diameter, with the highest being for total tree biomass, which ranged from 0.981 for Oxdendrum arboreum to 0.999 for Quercus coccinea. Foliage area had the lowest R2 values, ranging from 0.555 for Quercus alba to 0.962 for Betula lenta. When all species were combined, correlation coefficients ranged from 0.822 for foliage area to 0.986 for total wood mass, total tree biomass, and total tree volume. Species with ring versus diffuse/semiring porous wood anatomy exhibited higher leaf area with a given cross-sectional sapwood area as well as lower total sapwood volume. Liriodendron tulipifera contained one of the highest foliar nitrogen concentrations and had consistently low branch, bark, sapwood, and heartwood nitrogen contents. For a tree diameter of 50 cm, Carya spp. exhibited the highest total nitrogen content whereas Liriodendron tulipifera exhibited the lowest.
Aboveground biomass of Typha sp. at Upper Parker River brackish marsh site.
Aboveground biomass is determined non-destructively during the growing season at a Typha-dominated brackish marsh on the Parker River within the Plum Island Ecosystems (PIE) LTER site.
Aboveground biomass data from a Spartina alterniflora-dominated salt marsh plots in North Inlet, Georgetown, SC.
Aboveground biomass is determined non-destructively at permanent plots in a Spartina alterniflora-dominated salt marsh in North Inlet, Georgetown, SC. There are five sites. Two sites are low marsh; three sites are high marsh. One site in the high marsh is fertilized with nitrogen and phosphorus.
2009 LENS aboveground biomass results
2009 LENS Geotechnical and Biomass Results from 72 hand auger cores (50 cm depth) retrieved from LENS and TIDE project creeks (Sweeney, West, Clubhead, Nelson) in Rowley, MA. Thirty-six of these cores were processed for aboveground biomass and belowground biomass. Results presented here are aboveground biomass. Results for belowground biomass are presented in file STP-LENS-2009-below-biomass. The remainder 36 auger cores were processed for bulk density and bulk organic content (from loss on ignition, LOI) and results are presented in STP-LENS-2009-geotechnical.
Fiddler crab impacts from observational study 2020-21: Aboveground & diatom biomass, plant height, percent N, burrow & mussel density, belowground biomass, and organic matter
The fiddler crab, Minuca pugnax, expanded its range into the Gulf of Maine recently and was first observed in the Plum Island Estuary in 2014. In 2020 and 2021, we investigated the impact of this burrowing crab on benthic microalgal biomass, sediment properties and the above- and belowground biomass of the cordgrass, Spartina alterniflora. To accomplish this, we conducted a control-impact study in plots with and without fiddler crabs in three marshes in the PIE-LTER: Sawyer, Clubhead, and Metcalf. In its historical range (i.e., south of Cape Cod), M. pugnax, enhances Spartina aboveground biomass. In contrast, we found that, on average, when fiddler crabs were present, aboveground biomass was 40% lower in the PIE-LTER. We also found that belowground biomass was 30% lower and benthic microalgal biomass was 45% lower when fiddler crabs were present, which is in line with our expectations. Because fiddler crabs reduced the biomass of foundational primary producers in its expanded range, our results imply that M. pugnax can influence other saltmarsh functions such as carbon storage and accretion as they expand north. More broadly, our results suggest that as species expand or shift their range with climate change, not only can they have profound impacts in their new ranges, but that those impacts can be the inverse of what is seen in their historical ranges.
Aboveground Biomass (AGB) in the Boreal Region: Dataset for Supporting Biomass Recovery Analysis
<p>This package supplements the following paper submitted to Nature: <strong>Biomass recovery carbon gains surpass fire losses over boreal forests during the last decades</strong>.<br>This dataset contains the Aboveground Biomass (AGB) map (unit: Mg/ha) for the boreal region in 2020 at ~100m resolution.</p><p>The global AGB dataset, upon which this regional subset is based, was initially developed by Yu et al. (2023). The preprint version of the corresponding AGB data paper has been made available online and can be accessed at <a href="https://doi.org/10.5281/zenodo.7583611">https://doi.org/10.5281/zenodo.7583611</a>. This paper is currently under peer review. The AGB map for the boreal region, as featured in our Nature submission, is provided here for reference and further research.</p><p> </p><p> </p>
Forest Aboveground Biomass 2000-2022 for Maryland, USA
<p>This dataset provides 30-m resolution maps of estimated forest aboveground biomass (AGB) in the state of Maryland between 2000-2022. This dataset was produced by a novel forest carbon monitoring system which utilizes high resolution remote sensing of contemporary tree cover and canopy height as powerful constraints within a process-based ecosystem model to reconstruct the spatial and temporal dynamics in AGB while considering impacts of spatially and temporally transient meteorology, elevated CO2 and disturbance. This dataset reports AGB in unit of kg C/m2.</p> <p>This forest carbon monitoring system was built on a process-based ecosystem model called Ecosystem Demography (ED) (Hurtt et al 1998; Moorcroft et al 2001; Ma et al 2022), which can simulate plant dynamics including growth, mortality, and reproduction; carbon dynamics within the simulated plants; and dynamics of carbon pools in forest ecosystems.</p> <p>This forest carbon monitoring system ingests transient meteorology from Daymet (Thornton et al 2016) and MERRA2 (Gelaro et al. 2017) and CO2 concentrations from NOAA, remote sensing of forest change from the Global Forest Change (Hansen et al 2013), contemporary tree cover and canopy height from airborne lidar (e.g. Tang et al. 2021) and aerial imagery from National Agriculture Imagery Program (NAIP). More details about the system development can be found in Hurtt et al. 2022.</p> <p>This data is currently utilized in the State of Maryland’s Greenhouse Gas Inventory and is scheduled to be updated at least triennially as part of updates to the State’s inventory. This data is also serves as the basis for calculations within the University of Maryland Peer-Reviewed Offset Protocol for Maryland Reforestation/Afforestation Projects.</p> <p>For questions and support please contact lma6@umd.edu, rachlamb@umd.edu and gchurtt@umd.edu.</p> <p>This work was supported by contract from the Maryland Department of the Environment. We also gratefully acknowledge the support of NASA Carbon Monitoring System project (80NSSC21K1059).</p>
AGB_VM: 0.25-degree gridded annual aboveground biomass estimates in Alaska and northwestern Canada 2003-2017..
<p>Gridded 0.25-degree annual estimates of aboveground vegetation biomass (AGB) for diverse arctic and boreal ecosystems in Alaska and northwestern Canada years 2003-2017. AGB was estimated from integrated microwave vegetation optical depth (VOD) (Vegetation Optical Depth Climate Archive (VODCA) (Moesinger et al., 2020)) and optical-multispectral vegetation indices from the MODerate Resolution Imaging Spectroradiometer (MODIS) satellite remote sensing datasets. This work was conducted through NASA's Arctic and Boreal Vulnerability Experiment (ABoVE). </p>
Forest Aboveground Biomass 1984-2016 for Maryland, USA
<p>This dataset provides 30-m resolution maps of estimated forest aboveground biomass (AGB) in the state of Maryland between 1984-2016. This dataset was produced by a novel forest carbon monitoring system which utilizes high resolution remote sensing of contemporary tree cover and canopy height as powerful constraints within a process-based ecosystem model to reconstruct the spatial and temporal dynamics in AGB while considering impacts of spatially and temporally transient meteorology, elevated CO2 and disturbance. This dataset reports AGB in unit of kg C/m2.</p> <p>This forest carbon monitoring system was built on a process-based ecosystem model called Ecosystem Demography (ED) (Hurtt et al 1998; Moorcroft et al 2001; Ma et al 2022), which can simulate plant dynamics including growth, mortality, and reproduction; carbon dynamics within the simulated plants; and dynamics of carbon pools in forest ecosystems.</p> <p>This forest carbon monitoring system ingests transient meteorology from Daymet (Thornton et al 2016) and MERRA2 (Gelaro et al. 2017) and CO2 concentrations from NOAA, remote sensing of forest change from the North American Forest Dynamics (NAFD), contemporary tree cover and canopy height from airborne lidar (e.g. Tang et al. 2021) and aerial imagery from National Agriculture Imagery Program (NAIP). More details about the system development can be found in Hurtt et al. 2022.</p> <p>This data is currently utilized in the State of Maryland’s Greenhouse Gas Inventory and is scheduled to be updated at least triennially as part of updates to the State’s inventory. This data is also serves as the basis for calculations within the University of Maryland Peer-Reviewed Offset Protocol for Maryland Reforestation/Afforestation Projects.</p> <p>For questions and support please contact lma6@umd.edu, rachlamb@umd.edu and gchurtt@umd.edu.</p> <p><strong>Reference</strong></p> <p>Hurtt, G.C., P.R. Moorcroft, S.W. Pacala, and S.A. Levin. 1998 Terrestrial models and global change: challenges for the future. Global Change Biology 4:581-590. https://doi.org/10.1046/j.1365-2486.1998.t01-1-00203.x</p> <p>Hurtt et al 2022. Beyond Forest Carbon Monitoring: Integrating High-Resolution Remote Sensing and Ecosystem Modeling for Geospatial Assessment and Attribution of Changes in Forest Carbon Stocks Over Maryland, USA (in prep).</p> <p>Ma, L., G. Hurtt, L. Ott, R. Sahajpal, J. Fisk, R. Lamb, H. Tang, S. Flanagan, L. Chini, A. Chatterjee, and J. Sullivan. 2022a. Global evaluation of the Ecosystem Demography model (ED v3.0). Geoscientific Model Development 15:1971–1994. https://doi.org/10.5194/gmd-15-1971-2022 </p> <p>Moorcroft, P. R., G.C. Hurtt. and S.W. Pacala, 2001 A method for scaling vegetation dynamics: the ecosystem demography model (ED) Ecol. Monogr. 71 557–86. https://doi.org/10.1890/0012-9615(2001)071[0557:AMFSVD]2.0.CO;2 </p> <p>Tang, H., L. Ma, A.J. Lister, J. O'Neil-Dunne, J. Lu, R. Lamb, R.O. Dubayah, and G.C. Hurtt. 2021. LiDAR Derived Biomass, Canopy Height, and Cover for New England Region, USA, 2015. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1854.</p> <p>Dr. Pieter Tans, NOAA/GML (gml.noaa.gov/ccgg/trends/) and Dr. Ralph Keeling, Scripps Institution of Oceanography (scrippsco2.ucsd.edu/).</p>
Simulated Forest Aboveground Biomass Dynamics, Northeastern USA
<p>This dataset includes aboveground biomass (AGB) growth trajectories for the first 300 years of forest succession over the Regional Greenhouse Gas Initiative (RGGI) domain, which includes the states of Connecticut, Delaware, Maine, Maryland, Massachusetts, New Hampshire, New Jersey, New York, Pennsylvania, Rhode Island, and Vermont. These data were derived from a process-based ecosystem model called the Ecosystem Demography (ED) model (Hurtt et al 1998; Moorcroft et al. 2001; Ma et al. 2022a). Here, ED was run at a spatial resolution of 1 km with forcings including meteorology from Daymet (Thornton et al 2016) and MERRA2 (Gelaro et al. 2017) and soil hydraulic properties from POLARIS (Chaney et al 2016) and CONUS-SOIL (Miller and White 1998). This dataset is spatially interpolated from its native resolution of 1 km to 30 m to support small scale data analysis. The unit is kg C/m2.</p> <p>This dataset can support multiple applications relevant to reforestation and afforestation planning. Utilizing this stack of annualized and spatially explicit forest growth trajectories, data users can estimate how much carbon could be stored via natural regeneration in any particular geographic location by any point over the next 300 years under current environmental conditions (air temperature, precipitation, CO2, etc). These data are currently being utilized by the State of Maryland to support climate-smart afforestation and serve as the basis for several carbon sequestration calculations in the University of Maryland Peer-Reviewed Offset Protocol for Maryland Reforestation/Afforestation Projects.</p> <p>This dataset is the underlying input to a high-resolution forest carbon modeling system developed for the RGGI region. This modeling system combines modeled AGB growth with forest canopy height from airborne lidar data and tree cover fraction to estimate contemporary AGB, carbon sequestration potential, carbon sequestration potential gap and time to reach carbon sequestration potential. More details about the modeling system and ED simulation can be found Ma et al. 2021 and related data products can be found in Ma et al. 2022b.</p> <p>For questions and support please contact lma6@umd.edu, rachlamb@umd.edu and gchurtt@umd.edu. </p> <p><strong>References</strong></p> <p>Chaney N W, Wood E F, McBratney A B, Hempel J W, Nauman T W, Brungard C W and Odgers N P 2016 POLARIS: a 30-meter probabilistic soil series map of the contiguous United States Geoderma 274 54–67. <br> https://doi.org/10.1016/j.geoderma.2016.03.025 </p> <p>Gelaro R et al 2017 The modern-era retrospective analysis for research and applications, version 2 (MERRA-2) J. Clim. 30 5419–54. https://doi.org/10.1175/JCLI-D-16-0758.1 <br> Hurtt, G.C., P.R. Moorcroft, S.W. Pacala, and S.A. Levin. 1998 Terrestrial models and global change: challenges for the future. Global Change Biology 4:581-590. https://doi.org/10.1046/j.1365-2486.1998.t01-1-00203.x</p> <p>Ma, L., G. Hurtt, H. Tang, R. Lamb, E. Campbell, R. Dubayah, M. Guy, W. Huang, A. Lister, J. Lu, J. O’Neil-Dunne, A. Rudee, Q. Shen, and C. Silva. 2021. High-resolution forest carbon modelling for climate mitigation planning over the RGGI region, USA. Environmental Research Letters 16:045014. https://doi.org/10.1088/1748-9326/abe4f4</p> <p>Ma, L., G. Hurtt, L. Ott, R. Sahajpal, J. Fisk, R. Lamb, H. Tang, S. Flanagan, L. Chini, A. Chatterjee, and J. Sullivan. 2022a. Global evaluation of the Ecosystem Demography model (ED v3.0). Geoscientific Model Development 15:1971–1994. https://doi.org/10.5194/gmd-15-1971-2022 </p> <p>Ma, L., G.C. Hurtt, H. Tang, R. Lamb, E. Campbell, R.O. Dubayah, M. Guy, W. Huang, J. Lu, A. Rudee, Q. Shen, C.E. Silva, and A.J. Lister. 2022b. Forest Aboveground Biomass and Carbon Sequestration Potential, Northeastern USA. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1922 </p> <p>Miller D A and White R A 1998 A conterminous United States multilayer soil characteristics dataset for regional climate and hydrology modeling Earth Interact. 2 1–26 </p> <p>Moorcroft, P. R., G.C. Hurtt. and S.W. Pacala, 2001 A method for scaling vegetation dynamics: the ecosystem demography model (ED) Ecol. Monogr. 71 557–86. https://doi.org/10.1890/0012-9615(2001)071[0557:AMFSVD]2.0.CO;2 </p> <p>Thornton, M.M., Thornton P E, Wei Y, Mayer B W, Cook R B and Vose R S 2016 Daymet: monthly climate summaries on a 1-km grid for North America, version 3 (available at: https://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=1345)</p>
Forest Aboveground Biomass 2001-2100 for New Mexico, USA
<p>This dataset provides 1km resolution maps of estimated forest aboveground biomass (AGB) in the state of New Mexico between 2001-2100. This dataset was produced by a novel forest carbon monitoring and modeling system which utilizes high resolution remote sensing of contemporary tree cover and canopy height as powerful constraints within a process-based ecosystem model to reconstruct the spatial and temporal dynamics in AGB while considering impacts of spatially and temporally transient meteorology, elevated CO2 and disturbance. Specifically, the remote sensing data used includes GEDI and ICESat-2 observations of canopy height at footprint scale, canopy cover from USGS RCMAP and ESA CGLS, and meteorology reanalysis from NASA MERRA2 and climate projection from CMIP6. The ecosystem model used is the Ecosystem Demography model (EDv3.0) which has been developed, calibrated and evaluated at global scale (Ma et al 2022).</p> <p>For questions and support please contact lma6@umd.edu and gchurtt@umd.edu.</p> <p>This work was supported by contract from the Maryland Department of the Environment. We also gratefully acknowledge the support of NASA Carbon Monitoring System project (80NSSC21K1059) and United States Climate Alliance Grant Program for NWL Research.</p> <p> </p>
Forest Aboveground Biomass 2000-2023 for Maryland, USA
<p>This dataset provides 30-m resolution maps of estimated forest aboveground biomass (AGB) in the state of Maryland between 2000-2023. This dataset was produced by a novel forest carbon monitoring system which utilizes high resolution remote sensing of contemporary tree cover and canopy height as powerful constraints within a process-based ecosystem model to reconstruct the spatial and temporal dynamics in AGB while considering impacts of spatially and temporally transient meteorology, elevated CO2 and disturbance. This dataset reports AGB in unit of kg C/m2.</p> <p>This forest carbon monitoring system was built on a process-based ecosystem model called Ecosystem Demography (ED) (Hurtt et al 1998; Moorcroft et al 2001; Ma et al 2022), which can simulate plant dynamics including growth, mortality, and reproduction; carbon dynamics within the simulated plants; and dynamics of carbon pools in forest ecosystems.</p> <p>This forest carbon monitoring system ingests transient meteorology from Daymet (Thornton et al 2016) and MERRA2 (Gelaro et al. 2017) and CO2 concentrations from NOAA, remote sensing of forest change from the Global Forest Change (Hansen et al 2013), contemporary tree cover and canopy height from airborne lidar (e.g. Tang et al. 2021) and aerial imagery from National Agriculture Imagery Program (NAIP). More details about the system development can be found in Hurtt et al. 2022.</p> <p>This data is currently utilized in the State of Maryland’s Greenhouse Gas Inventory and is scheduled to be updated at least triennially as part of updates to the State’s inventory. This data is also serves as the basis for calculations within the University of Maryland Peer-Reviewed Offset Protocol for Maryland Reforestation/Afforestation Projects.</p> <p>For questions and support please contact lma6@umd.edu, rachlamb@umd.edu and gchurtt@umd.edu.</p> <p>This work was supported by contract from the Maryland Department of the Environment. We also gratefully acknowledge the support of NASA Carbon Monitoring System project (80NSSC21K1059).</p>
Aboveground plant biomass in LandKlif experimental plots
<p><span>Aboveground plant biomass of standing vegetation occurring in three random quadrats of size 20x20 cm located inside of the LandKlif experimental plots. Samples were taken between May and July 2019.</span></p> <p><span>LandKlif is funded by the Bavarian State Ministry of Science and the Arts within the Bavarian Climate Research Network (bayklif). Within the five year funding period of bayklif, five interdisciplinary senior research associations and five junior research groups are be financed with a total sum of 18 million Euro. LandKliF, as one of the five interdisciplinary senior research associations, addresses the effects of climate change on biodiversity and ecosystem services in semi-natural, agricultural and urban landscapes.</span></p>
Spatial Feature Engineering Dataset for Forest Aboveground Biomass Estimation Using Landsat Imagery
<p><strong>Study Area:</strong><br>The dataset covers forested regions in Oregon, Washington, Idaho, and eastern Montana, characterized by diverse climatic conditions due to orographic effects. The forests in the Coast Range and western slopes of the Cascades, with high precipitation (800-3000 mm annually), contrast with the drier forests in Idaho and Montana, which receive over 400 mm annually. The dataset includes highly productive Douglas-fir and western hemlock forests, with aboveground biomass (AGB) densities exceeding 1200 Mg ha⁻¹, as well as fire-adapted lodgepole and ponderosa pine forests in the rainshadow regions.</p> <p><strong>LiDAR AGB Estimates:</strong><br>The dataset includes 176 lidar-derived AGB maps from 2002 to 2016, covering various regions in Oregon, Washington, Idaho, and Montana. A Random Forest (RF) model was used to estimate AGB at a 30m² resolution, utilizing lidar height features, DEM features, and climate data. Non-forested areas and buildings were masked using binary forest cover maps from the LCMS dataset and the Microsoft Building Footprints dataset.</p> <p><strong>Reference Dataset:</strong><br>A composited AGB map, derived from the 176 lidar maps, was created to develop Landsat-based AGB models, covering 9,361,622 ha of forested land. The AGB layer was stratified into 30 bins, and training, development, and testing sets were constructed for model validation. The dataset includes 7500 test samples and 300,000 training and development samples, with a 500m buffer around test set locations to prevent spatial autocorrelation.</p> <p><strong>Landsat Satellite Imagery:</strong><br>Landsat imagery from 1990 to 2022 was utilized, with three time series derived: all scenes, scenes from May to November, and annual medoid composites. The imagery was processed using the Google Earth Engine (GEE) platform, focusing on periods of maximum phenological activity.</p> <p><strong>Feature Engineering:</strong><br>Extensive feature engineering was performed, generating spectral, spatial, temporal, and topographic features from Landsat imagery and DEM data. Features were extracted over the reference AGB map's domain, synchronized with the lidar acquisition dates.</p> <ul> <li><strong>LandTrendr Fitted Imagery:</strong> Spectral features were derived from LandTrendr-fitted imagery, smoothing variations in the time series.</li> <li><strong>LandTrendr Disturbance and Recovery Features:</strong> Temporal features were derived from LandTrendr models, characterizing disturbance and recovery events.</li> <li><strong>CCDC Disturbance and Recovery Features:</strong> CCDC algorithm-derived features characterized disturbances and recovery using harmonic models.</li> <li><strong>Buffer Features:</strong> Local variations were captured using buffer statistics around each pixel.</li> <li><strong>GLCM Features:</strong> GLCM texture features summarized the joint distribution of gray-tone values.</li> <li><strong>Edge Detectors:</strong> Various edge detection operators captured spatial derivatives and edges.</li> <li><strong>Morphological Operations:</strong> Morphological features were derived using multi-channel image processing techniques.</li> <li><strong>Neighborhood Vectorization:</strong> Direct vectorization of satellite measurements in pixel neighborhoods.</li> <li><strong>Neighborhood Similarity:</strong> Similarity features characterized the relationship between pixel neighborhoods and their centroids.</li> <li><strong>Topographic Features:</strong> Topography was characterized using elevation, slope, aspect embeddings, and hillshade layers from the NED DEM.</li> </ul> <p>This comprehensive dataset enables robust analysis of AGB models and their performance across diverse forested landscapes in the Pacific Northwest</p>
Dataset for "A stacking ensemble algorithm for improving the biases of forest aboveground biomass estimations from multiple remotely sensed datasets"
<p>This dataset is associated with a research article entitled "A stacking ensemble algorithm for improving the biases of forest aboveground biomass estimations from multiple remotely sensed datasets".</p>
Potential aboveground biomass increase in Brazilian Atlantic Forest fragments with climate change
<p>This file collection contains the estimated spatial distribution of the above-ground biomass density (AGB) by the end of the 21st century across the Brazilian Atlantic Forest domain and the respective uncertanty. To develop the models, we used the maximum entropy method with projected climate data to 2100, based on the Intergovernmental Panel on Climate Change (IPCC) Representative Concentration Pathway (RCP) 4.5 from the fifth Assessment Report (AR5).</p> <p>The dataset is composed of four files in GeoTIFF format:</p> <p><strong>calibrated-AGB-distribution.tif</strong>: raster file representing the present spatial distribution of the above-ground biomass density in the Atlantic Forest from the calibrated model. Unit: Mg/ha </p> <p><strong>estimated-uncertanty-for-calibrated-agb-distribution.tif</strong>: raster file representing the estimated spatial uncertanty distribution of the calibrated above-ground biomass density. Unit: percentage.</p> <p><strong>projected-AGB-distribution-under-rcp45.tif</strong>: raster file representing the projected spatial distribution of the above-ground biomass density in the Atlantic Forest by the end of 2100 under RCP 4.5 scenario. Unit: Mg/ha </p> <p><strong>estimated-uncertanty-for-projected-agb-distribution.tif</strong>: raster file representing the estimated spatial uncertanty distribution of the projected above-ground biomass density. Unit: percentage.</p> <p><strong>Spatial resolution:</strong> 0.0083 degree (ca. 1 km)</p> <p><strong>Coordinate reference system:</strong> Geographic Coordinate System - Datum WGS84</p>
Quadrats were harvested for aboveground biomass from eight plots within a tussock, watertrack, and snowbed community at 3 sites - acidic tundra and nonacidic tundra near Arctic LTER Toolik Plots and acidic tundra near Sagwon,Arctic LTER 1997.
Quadrats were harvested for aboveground biomass from eight plots within a tussock, watertrack, and snowbed community at 3 sites - acidic tundra near Toolik (site of acidic LTER plots), nonacidic tundra near Toolik Lake(site of non-acidic LTER plots), and acidic tundra near Sagwon. All vascular species were sorted, divided into new and old growth, dried, and weighed. Lichens were separated by genus in all quadrats. In half of the quadrats (n=4), mosses were separated by species. Moss and lichen data are presented by species elsewhere (see 97lgmosslichen.txt).
A harvest was conducted to determine productivity of rare species not found in at least 4 quadrats per site in a separate small quadrat aboveground biomass harvest, Arctic LTER 1997.
A harvest was conducted to determine productivity of rare species not found in at least 4 quadrats per site in a separate small quadrat aboveground biomass harvest (see 97lg3sbm.txt). Harvests occurred in a tussock, watertrack, and snowbed community at 3 sites - acidic tundra near Toolik (site of acidic LTER plots), nonacidic tundra near Toolik Lake(site of non-acidic LTER plots), and acidic tundra near Sagwon. Moss and lichen data are presented by species elsewhere (see 97lgmosslichen).
Eight Mile Lake Research Watershed, Carbon in Permafrost Experimental Heating and Drying Research (DryPEHR): Peak growing season aboveground biomass 2011-2017.
This drying and warming experiment addresses the following questions: 1) Does ecosystem drying, warming and permafrost thaw cause a net release or uptake of C from the ecosystem to the atmosphere?, 2) Does the decomposition of old C that comprises the bulk of the soil C pool influence ecosystem C loss? 3) How do drying and warmign affect plant communities and ecosystem properties? We are answering these questions using a combined warming and drying experiment (DryPEHR), which is situated with the Carbon in Permafrost Experimental Heating Research (CiPEHR) project and located in an upland tundra field site near Healy, Alaska in the foothills of the Alaska Range. Warming treatment here refers to growing season air temperature warming (~1C) using open top chambers (OTC) combined with soil 'warming' using snow fences during the snow covered months. Drying is achieve using an automated pumping system that lowers the water table in the dry plots. Soil warming began in 2008; OTCs and drying in 2011. Above ground plant biomass was surveyed non-destructively using a point-intercept method for all vascular and moss species at peak growing season.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.