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284 results for “Forest Biomass”
Biomass accumulation in trees and downed wood at Bartlett Experimental Forest, Hubbard Brook Experimental Forest, the Bowl Natural Research Area, and the White Mountain National Forest, NH, USA
Standing trees and downed wood were inventoried in all of the chronosequence stands in the White Mountains, New Hampshire to characterize biomass. Live and standing dead trees were inventoried in the chronosequence stands in 1994, 2004, 2012, and 2021. Coarse (≥ 7.6 cm diameter) and fine woody debris (3.0 – 7.6 cm) were inventoried at the same stands in 2004 and 2020. Twigs (FWD < 3.0 cm) were inventoried in 2004 and 2020. The Bowl and Mt. Pond old-growth sites were inventoried (standing trees and downed wood) in 2021.
Long-term measurements of microbial biomass and activity at the Hubbard Brook Experimental Forest 1994 – ongoing
Long-term monitoring of soil nitrate (NO3-) and ammonium (NH4+) concentrations, microbial biomass carbon (C) and nitrogen (N) content, microbial respiration, potential nitrification and N mineralization rates, pH, and denitrification potential has been ongoing at the Hubbard Brook Experimental Forest since 1994. Samples have been collected in the Bear Brook Watershed (west of Watershed 6) beginning in 1994. In 1998, our sampling regime was extended to Watershed 1 in an effort to monitor and quantify microbial response to a whole-watershed calcium addition. 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.
Data from: Trade-off between standing biomass and productivity in species-rich tropical forest: evidence, explanations and implications
<p>These files are the R code and plot data files used for calculating species population turnover of biomass and abundance in a tropical forest plot.</p> <p>This dataset is a processed subset of the original dataset used in our analysis of biomass turnover across tree populations as demonstrated in <a href="https://doi.org/10.1111/1365-2745.13485">the main paper</a>. Readers interested in using the Pasoh 50-ha plot data for purposes other than reviewing our analysis are advised to contact the <a href="https://www.frim.gov.my/">Forest Research Institute Malaysia (FRIM)</a> and the <a href="https://forestgeo.si.edu/">Center for Tropical Forest Science-Forest Global Earth Observatory (CTFS-Forest GEO)</a>, Smithsonian Tropical Research Institute.</p>
Dataset linking to the paper "Exploring characteristics of national forest inventories for integration with global space-based forest biomass data"
<p>The dataset links to the study titled “Exploring characteristics of national forest inventories for integration with global space-based forest biomass data”. This study is published in the journal “Science of the Total Environment” and the publication can be found at <a href="https://doi.org/10.1016/j.scitotenv.2022.157788">https://doi.org/10.1016/j.scitotenv.2022.157788</a>. The dataset contains four csv files that were used to produce the results and other figures in the paper. The description of the individual data files contained in the dataset is given below.</p> <p><strong>NFI availability and characteristics data: </strong>The data file “NFI_availability_characteristics.csv” contains data on the total number of NFIs, the NFI extent, and the year of the most recent NFI in countries with NFI as reported in FRA 2020 country reports. The respective data variables in the data file are termed as Number_of_NFI, Latest_NFI_extent_FRA2020, and Latest_NFI_year_FRA2020 (NFI years generally refer to the years of data collection). In addition, the data file contains data on the region and tropical domain per country. The tropical and subtropical countries were considered tropical in the analysis and interpretation of the results. These data were used to produce Figure 2 of the study. ArcMap 10.7.1 was used for this purpose. </p> <p><strong>National biomass intercomparison data: </strong>The data file “national_biomass_intercomparison.csv” contains national forest AGB data for the year 2018 from FRA 2020 and CCI Biomass product that were used in the national biomass intercomparison analysis. The total (tons) and average space-based AGB (tons/ha) are extracted directly from the CCI Biomass Map 2018 for each country included in the study. The processing is done in Python and R environments. The spatial resolution of the map is 100 m. The average FRA AGB data in tons per ha was compiled from FRA 2020 country reports. The total FRA AGB data (tons) was estimated by multiplying each country's average FRA AGB data with FRA forest area data (in ha).</p> <p>The data unit for total AGB was converted from tons to gigaton (Gt) in intercomparison analysis. The total CCI Map AGB estimates used in the analysis are termed as CCI_MAP_AGB_Gt in the data file and the average as CCI_Map_AGB_tons.ha. Similarly, the total FRA AGB data are termed as FRA_AGB_Gt and the average as FRA_AGB_ton.ha. The NFI availability and temporality were also used in intercomparison analysis and this data is termed as Latest_NFI_year_FRA2020 in the data file. The data were used to produce Figure 3 of the study in the R environment.</p> <p><strong>NFI plot design characteristics: </strong>The data file named “NFI_plot_design_characteristics.csv” contains data on variables that were used in the analysis of NFI plot designs in 46 tropical countries. This data file mainly contains the data that was used to produce Figure 4 and Figure 6 in the R environment. The value “uniform” in the sampling_stratification variable means no stratification was used in the sampling design. The variable name “psu” stands for primary sampling unit (both cluster and single plots), “psu_distance_km” for the distance between primary sampling units in km, “cluster_plotdis_m” for the distance between plots in meter in the cluster, “plotsize_ha” for plot (single and cluster plots ) size in ha, “plotshape” for plot shapes (single and cluster plots), “ILUA” for Integrated Land Use Assessment. The data were compiled from the latest NFI design manuals and NFI reports.</p> <p><strong>NFI years: </strong>The data file “NFI_years_tropical_countries_data.csv” contains data on NFI years of the latest NFI in 46 tropical countries that were used to produce Figure 1 using ArcMap 10.7.1. The years generally refer to the last years of data collection. Data were compiled from the latest country NFI design manual or NFI report. This included both ongoing and completed NFI.</p>
Simulated spatially-explicit above ground biomass of forests (larch) in the vicinity of the Ilirney lake system region, Chukotka, Russia
<p>The model LAVESI (Kruse et al. 2016) was updated (Kruse 2023) and forced with historical and future climate forcing for 3 simulation repeats. The data set contains simulated larch above ground biomass (AGB, in kg m<sup>-2</sup>) for the three climate forcings RCP 2.6, 4.5 and 8.5 and each complemented with a hypothetical cooling scenario from year 2300 CE onwards. The data provided is from years 2020, 2050, 2100 and proceeding in 100-year steps until 3000 CE.</p> <p>Format: Geotiff; projection UTM58N and 30x30 m tiles; extent: 640008.2, 649998.2, 7475006, 7494716 m (xmin, xmax, ymin, ymax)</p>
FORMS: Forest Multiple Source height, wood volume, and biomass maps in France at 10 to 30 m resolution based on Sentinel-1, Sentinel-2, and GEDI data with a deep learning approach.
<p>The products can be vizualized at <a href="https://martinschwartz0.users.earthengine.app/view/forms-height-biomass-volume-viewer">https://martinschwartz0.users.earthengine.app/view/forms-height-biomass-volume-viewer</a></p> <p>- FORMS-H: Canopy height map of France at 10 m resolution. The units are in centimeter (10^-2 m).</p> <p>- FORMS-B: Above-ground biomass density map of France at 30 m resolution. The units are in Mg ha-1</p> <p>- FORMS-V: Wood volume density map of France at 30 m resolution. The units are in m3 ha-1</p> <p>Please refer to the paper <a href="https://doi.org/10.5194/essd-15-4927-2023">https://doi.org/10.5194/essd-15-4927-2023</a> for further details.</p>
Analysis of aboveground biomass in longleaf pine forests in southeast United States, 2015 to 2019
I obtained inventory data from the USDA Forest Inventory and Analysis (FIA) program, covering 1999 to 2022, focusing on eight southeastern states in the U.S. (Alabama, Mississippi, Florida, Georgia, North Carolina, South Carolina, Texas, and Louisiana). For this study, I filtered the data for 2015-2019, during which the FIA captures 20% of plots in each state annually. Using R, I converted aboveground biomass (AGB) from pounds to kg/ha, tree height from feet to meters, and diameter at breast height (DBH) from inches to centimeters to align with the metric system. Structural diversity of tree height and diameter was calculated using the Shannon diversity index based on tree height and diameter classes. Climate data from PRISM and soil carbon to nitrogen data from the World Soil Database were integrated with the FIA data using the nearest neighbors method in the SF package in R. The combined dataset was standardized for Structural Equation Modeling (SEM) analysis.
Aboveground Live Biomass (2008), Andrews Experimental Forest
The biomass raster was developed by equations in (Seidl, et al 2012). Lidar variables used to create the raster include 95th percentile height, bare earth elevation, and the standard deviation of height in a 35m moving window. Watershed Sciences, Inc. (WS) collected Light Detection and Ranging (LiDAR) data from HJ Andrews and the Willamette National Forest (NF) on August 10th and 11th 2008. Total area for this AOI is 17,705 acres. The total area of delivered LiDAR including 100 m buffer is 19,493 acres.
Biomass %N, %C, natural abundance 15N and 13C isotopic signatures for common and rare under- and overstory plants in long unburned and burned (1999) boreal forest stands, Caribou-Poker Creek and Delta Junction
This dataset contains leaf, aboveground stem, and fruit carbon and N concentration and natural abundance isotope data for new and old tissue fractions of common and rare plant species in burned and unburned black spruce forest stands. Stands were located in either Caribou-Poker Creek or Delta Junction, in either unburned areas, or in areas burned in 1999 fires. Biomass was collected between 2000 and 2001 in mid-July at peak biomass.
Hubbard Brook Experimental Forest: microbial biomass and activity at Climate Change Across Seasons Experiment (CCASE) plots, 2015
This data set includes measurements of microbial biomass and activity measured on the Climate Change Across Seasons Experiment (CCASE) plots in May 2015. Reference (or control) plots are shared with the collaborating Northern Forest DroughtNet experiment. There are six plots total (each 11 x 14m). Two are warmed 5 degrees C throughout the growing season (Plots 3 and 4). Two others are warmed 5 degrees C in the growing season and have snow removed during winter to induce soil freeze/thaw cycles (Plots 5 and 6). Four kilometers (2.5 mi) of heating cable are buried in the soil to warm these four plots. Two additional plots serve as controls for our experiment (Plots 1 and 2). This data set includes measurements of microbial biomass and activity. 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.
Hubbard Brook Experimental Forest: microbial biomass and activity at climate gradient plots, 2010-2012
In October 2010, as part of a study of the relationships between snow depth, soil freezing and nutrient cycling (http://www.ecostudies.org/people_sci_groffman_snow_summary.html), we established 6 20 x 20 m plots (intensive plots) and 14 10 x 10-m plots (extensive plots) following an elevation gradient, with eight of the plots facing north and twelve facing south. Seasonal (spring, summer autumn) measurements of soil nitrate (NO3-) and ammonium (NH4+) concentrations, microbial biomass carbon (C) and nitrogen (N) content, microbial respiration, potential nitrification and N mineralization rates, and denitrification potential occurred from 2010 – 2012. 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.
Measurements of microbial biomass and activity in two snow manipulation experiments at Hubbard Brook Experimental Forest 1998 – 2004
In 1997, as part of a study of the relationships between snow depth, soil freezing and nutrient cycling (http://www.ecostudies.org/people_sci_groffman_snow_summary.html), we established eight 10 x 10-m plots located within four stands; two dominated (80%) by sugar maple and two dominated by yellow birch, with one snow reduction (freeze) and one reference plot in each stand. In 2001, we established eight new 10-m x 10-m plots (4 treatment, 4 reference) in four new sites; two high elevation, north facing and two low elevation, south facing maple-beech-birch stands. To establish plots for the “freeze” study, we cleared minor amounts of understory vegetation from all (both freeze and reference) plots (to facilitate shoveling). We then installed soil solution samplers (zero tension lysimeters), thermistors for soil temperature monitoring, water content (time domain) reflectometers (for measuring soil moisture), soil atmosphere sampling probes, minirhizotron access tubes, and trace gas flux measurement chambers (described below). All plots were equipped with dataloggers to allow for continuous monitoring of soil moisture and temperature. Treatments (keep plots snow free by shoveling through the end of January) were applied in the winters of 1997/98, 1998/99, 2002/2003 and 2003/2004. Measurements of soil nitrate (NO3 -) and ammonium (NH4 +) concentrations, microbial biomass carbon (C) and nitrogen (N) content, microbial respiration, potential nitrification and N mineralization rates, pH, and denitrification potential were measured on these plots at multiple time points during these studies. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES) using funding from the U.S. National Science Foundation. The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station.
SBC LTER: Reef: Coefficients for estimating biomass from body size or percent cover for kelp forest species
These data provide coefficients to estimate the biomass of macroalgae, invertebrates and fish from field measurements of body size or percent cover. We developed quantitative relationships between mass and length or mass and percent cover, and conversion factors for transforming wet mass into dry mass, shell free and decalcified dry mass, and ash-free dry mass for taxa of benthic macroalgae and macroinvertebrates common to giant kelp forests in southern California. We also compiled literature-based relationships between mass and total length for reef fish common to giant kelp forests in southern California.
SBC LTER: Reef: Kelp Forest Community Dynamics: Kelp Forest Data to support "Estimating biomass of benthic kelp forest invertebrates from body size and percent cover"
These data describe quantitative relationships between wet mass and length or wet mass and percent cover, and conversion factors to transform wet mass into dry mass, shell-free and decalcified dry mass, and ash-free dry mass for 84 species of benthic macroinvertebrates common to giant kelp forests in southern California. Data are based on organisms collected from sites in the Santa Barbara Channel between April 2010 and May 2014. These measurements are intended to facilitate the conversion of invertebrate abundance into common metrics of biomass, for quantitative studies of community dynamics, trophic interactions, energy flow and biodiversity. Converting numerical abundance (i.e., organism density) to biomass requires information on the relationship between individual size and biomass. For colonial and small aggregating taxa that are numerous and indistinct, measures of abundance are usually proportional (e.g., percent cover). Hence, converstions are taxa-specific, based on either size or cover, and a variety of metrics of species biomass are included, e.g., wet mass, shell-free wet mass, ash-free dry mass. Data are published in Reed, D. C, J. C. Nelson, S. L. Harrer, and R. J. Miller, Estimating biomass of benthic kelp forest invertebrates from body size and percent cover data. Marine Biology. DOI: 10.1007/s00227-016-2879-x. From the paper abstract: The inability to compare different measures of species abundance (such as density and percent cover) or different metrics of species biomass (such as wet mass and ash-free dry mass) hampers quantitative studies of community dynamics, trophic interactions, energy flow and biodiversity. This has been especially problematic for the dynamic and highly productive communities inhabiting shallow reefs in temperate seas where varied metrics are commonly used to characterize the abundance and biomass of different suites of species. Regressions for all 84 species were highly significant and regression fits were very good for mos
Non-trophic interactions amplify kelp harvest-induced biomass oscillations and biomass changes in a kelp forest ecological network model
<p><span>Kelp forests are important marine ecosystems providing habitat for numerous species. Despite over 50 years of mechanical harvesting in the Northeast Atlantic, the indirect impacts of kelp harvesting and associated habitat loss on faunal species within kelp forests remain poorly understood. We investigated the consequences of kelp harvesting by developing an allometric trophic network model for a subtidal Northeast Atlantic kelp forest (dominated by <em>Laminaria</em> <em>hyperborea</em>). Additionally, we designed a novel mechanistic model to explore the non-trophic interactions between kelp and age class 0 Atlantic cod (<em>Gadus</em> <em>morhua</em>) and kelp and European lobster (<em>Homarus</em> <em>gammarus</em>), specifically focusing on the increased survival benefits provided by the kelp habitat. Simulations were conducted over a 50-year period, incorporating harvesting cycles of 2, 5, and 9 years, as well as low and high harvesting intensities. Our findings reveal the complex dynamics resulting from kelp harvesting. The recovery of kelp biomass was observed with 5- and 9-year harvesting cycles, whereas a decline was observed with a 2-year cycle. Furthermore, the non-trophic interaction facilitated a higher pre-harvest biomass for both the European lobster and the Atlantic cod compared to scenarios without this interaction. These results highlight the multitrophic effects of kelp harvesting and emphasize that the recovery of kelp-associated species may not necessarily align with kelp recovery, depending on harvesting intensity and recovery periods. Importantly, our study contributes to a better understanding of the ecological consequences of kelp harvesting and underscores the need for sustainable management practices to mitigate habitat loss in kelp ecosystems.</span></p>
Estimation of forest height and biomass from open-access multi-sensor satellite imagery and GEDI Lidar data: high-resolution maps of metropolitan France
<p>Maps of forest height, aboveground biomass (AGB)* and volume (VOL)* at 10 m spatial resolution for the year 2020 on France. </p> <p>* AGB and Volume maps are available on request.</p> <p>The methodology and validation of the maps are presented here: https://hal.science/hal-04249151</p> <p>Please cite :</p> <p>David Morin, Milena Planells, Stéphane Mermoz, Florian Mouret. Estimation of forest height and biomass from open-access multi-sensor satellite imagery and GEDI Lidar data: high-resolution maps of metropolitan France. 2023. hal-04249151</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>
Data from: The effect of drainage on the fine root biomass, production, and turnover in hemiboreal old-growth forests on organic soils
<p>Information on the capacity of organic soils to capture and store carbon in old-growth forests in the hemiboreal forest zone is scarce and fragmented. However, fine root data can provide valuable insights into soil carbon fluxes. Thus, the aim of the current study was to provide estimates of the fine root biomass (FRB), fine root production (FRP), and fine root turnover (FRT) rate by tree species and other functional groups in old-growth (stand age 131–179 years) forests on mesotrophic organic soils dominated by Scots pine (Pinus sylvestris L.), with (drained mesotrophic organic soil) and without (undrained mesotrophic organic soil) the effects of forest drainage. The sequential soil coring method was used to estimate the FRB and FRP. The total FRB (sum of the FRB of all functional groups) was significantly higher in the undrained sites (6.8±0.3 t ha 1) than in the drained sites (3.97±0.1 t ha 1). The FRB of Scots pine in the undrained forest was significantly higher (1.7±0.1 t ha 1) than in the drained forest (0.5±0.1 t ha 1), supporting an extensive foraging strategy. The significantly higher mean FRB of Norway spruce (Picea abies [L.] Karst.) (1.4±0.1 t ha 1) in the drained sites than the undrained sites (0.7±0.2 t ha-1) can be explained by there being a higher proportion of spruce in the stand compositions, thus a higher standing volume (cubic meters per hectare) of this species and an increased FRB. The FRB of dwarf shrubs (2.43±0.2 t ha-1) formed the largest part of the total FRB in the undrained sites and the second largest (1.16±0.1 t ha-1), following Norway spruce, in the drained sites. The total FRP was similar between the undrained (2.05±0.31 t ha-1 yr-1) and drained (1.82±0.26 t ha-1 yr-1) stands. However, considerable variability in the FRP was observed between different sites of the same forest site type. The FRT rate of Scots pine was twice as high in the drained sites than the undrained sites, suggesting faster nutrient and carbon input into the drained soil compared to the undrained soil. Estimates of FRB, FRP, and FRT rate for different functional groups can be used in carbon-cycle modeling and in further calculations to estimate the carbon budget (balance) in forests on organic soils.</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>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.