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58 results for “above ground biomass”
Below ground root biomass, carbon and nitrogen concentrations by depth increments from the Anaktuvuk River Fire site in 2011
Below ground root biomass was measured by depth increments at three sites at and around the Anaktuvuk River Burn: severely burned, moderately burned and unburned. Roots were also analyzed for carbon and nitrogen concentrations.
Summary of below ground root biomass, carbon and nitrogen concentrations from the Anaktuvuk River Fire site in 2011
A summary of below ground root biomass, carbon and nitrogen concentrations, measured at three sites at and around the Anaktuvuk River Burn: severely burned, moderately burned and unburned.
Ground cover and biomass projection photos for the BBC collapse scar
We used digital photographs to project the biomass over the growing season. This data set contains ground cover photos of plots from the center of the BBC collapse scar (0m) into the surrounding fire scar (30m) of the Survey Line Fire (burned in June-July 2001) for the growing seasons of 2003 and 2004. Also included are ground cover photos of plots along the biomass transect used to create the projected biomass data. We calculated the percent cover of vegetation for the biomass and intensively monitored transects from digital photographs. Photographs were rectified to 10,000 x 10,000 pixels and a 20 x 20 grid was applied to the photograph. The percentage of each plant type was estimated in each grid cell. The percent cover was regressed against the measured dry biomass to project biomass for the intensively measured transect. We did not project moss biomass and instead assumed it to be the same for both transects. We projected the change in biomass over the growing season from the change in greenness determined from digital photographs. We chose five dates throughout the growing season with pictures of equal color saturation, focus and aspect. From these we estimated the percent photosynthetic biomass by selecting areas of green on the photograph and calculating the percentage of the total pixels made up by these areas. We estimated curves for the change in % green vegetation for 0, 6 m and the mean of the remaining distances along the transect (12, 18, 24, and 30 m), as these regions of the transect exhibited different patterns of greenness across the growing season of 2004. To estimate photosynthetic biomass over the growing season, we corrected the biomass estimates for the study transect to account for the change in green vegetation associated with growth and senescence
Sawgrass above ground biomass from the Taylor Slough, just outside Everglades National Park (FCE), South Florida from October 1997 to December 2006
Sawgrass biomass is measured for TS/Ph-1, 2, 3, & 6 since 1999; TS/Ph-4 & 5 since 1997 to 2006; SRS-1b, 2, & 3 since 2000; SRS1c for 2005 to 2006, and SRS1d since 2006. Three 1 m2 plots are placed on each site. The sites are measured every two months. The number of sawgrass plants is counted for each 1 m2 plot and one third or a minimum of fifteen plants is measured. For each measured plant, measurements of the total number of live leaves, length of the live leaves, and culm diameter at the base are taken. From these measurements we calculate the average leaf length, and the sum of the length of the leaves. We created a model for biomass using a stepwise regression to see which of the variables measured and calculated are more correlated to explain plant biomass. Obtaining the mean biomass for the plants within the plot and multiplying it by the number of plants counted in the plot calculates biomass for each plot. The total biomass for each plot is summed with the other two plots in the same site, and they are averaged to obtain one biomass number per site. To validate our model, plant clippings are obtained in which four plants are clipped (small, medium, large, inflorescence) from each site. The same measurements are applied to the clipped plants as those applied to the measured plants in the plots. The plant clippings are oven dried at 70 degrees C and weighed.
The percentage of above-ground biomass carbon carrying capacity reached
<p>This dataset is the percentage of above-ground biomass carbon carrying capacity reached in the eight provinces of southern China from 2002 to 2017 at the resolution of 500m x 500m, with the urban and water areas, cropland, and the southeast margin of the Tibet Plateau masked. The dataset takes values ranging from 0%-100%. 0% represents the highest carbon sequestration potential, while 100% represents carbon sequestration has reached saturation. The dataset can locate areas where vegetation has not yet reached its full potential, which is significant for the implementation and adjustment of ecological engineering. The dataset is publicly available.</p>
Annual biomass data (2001-2021) for southern California: above- and below-ground, standing dead, and litter
<p>Biomass estimates for shrubland-dominated ecosystems in southern California have, to date, been limited to national or statewide efforts which can underestimate the amount of biomass; are limited to one-time snapshots; or estimate aboveground live biomass only. We developed a consistent, repeatable method to assess four vegetative biomass pools from 2001-2021 for our southern California study area (totaling 6,441,208 ha), defined by the Level IV Ecoregions (Bailey 2016) that intersect with USDA Forest Service lands (Figure 1). We first generated aboveground live biomass estimates (Schrader-Patton and Underwood 2021), and then calculated belowground, standing dead, and litter biomass pools using field data in the peer-reviewed literature (Schrader-Patton et al. 2022) (Figure 2). Over half (52.3%) of the study area is shrubland, and our method accounts for three post-fire shrub regeneration strategies: obligate resprouting, obligate seeding, and facultative seeding shrubs. We also generate biomass estimates for trees and herbs, giving a total of five life form/life history types. These data provide an important contribution to the management of shrubland-dominated ecosystems to assess the impacts of wildfire and management activities, such as fuel management and restoration, and for monitoring carbon storage over the long term.</p> <p>The biomass data are a key input into the online web mapping tool SoCal EcoServe, developed for US Department of Agriculture Forest Service resource managers to help evaluate and assess the impacts of wildfire on a suite of ecosystem services including carbon storage. The tool is available at <a href="https://manzanita.forestry.oregonstate.edu/ecoservices/">https://manzanita.forestry.oregonstate.edu/ecoservices/</a> and described in Underwood et al. (2022).</p> <p>REFERENCES</p> <p>Bailey, R.G. 2016. Bailey's ecoregions and subregions of the United States, Puerto Rico, and the U.S. Virgin Islands. Forest Service Research Data Archive. (Fort Collins, Colorado). https://doi.org/10.2737/RDS-2016-0003</p> <p>Schrader-Patton, C.C. and E.C. Underwood. 2021. New biomass estimates for chaparral-dominated southern California landscapes. Remote Sensing, 13, 1581. https://doi.org/10.3390/rs13081581</p> <p>Schrader-Patton et al. 2022. "Estimating Wildfire Impacts on the Biomass of Southern California's Chaparral Shrublands." Proceedings for the Fire and Climate Conference May 23-27, 2022, Pasadena, California, USA and June 6-10, 2022, Melbourne, Australia. Published by the International Association of Wildland Fire, Missoula, Montana, USA.</p> <p>Underwood et al. 2022. "Estimating the Impacts of Wildfire on Chaparral Shrublands in Southern California using an Online Web Mapping Tool." Proceedings for the Fire and Climate Conference May 23-27, 2022, Pasadena, California, USA and June 6-10, 2022, Melbourne, Australia. Published by the International Association of Wildland Fire, Missoula, Montana, USA.</p>
Above and below ground biomass and carbon content in the most common farm crops in Latvia
<p>Field measurements based assessment of above and below ground biomass of the most common farm crops in Latvia, including carbon and nitrogen stock and CO2 input into soil with plant residues under the most common management systems. Organic and integrated farmings are separated. Biomass is expressed as total biomass of all plants growing in the area, not only main species. Data for less common species are extrapolated assuming similarity with similar species or using default factors provided by the IPCC guidelines and other literature sources.</p> <p>Data area partially published (references are provided in the description). Uncertainty, which is not included in the public data set, can be provided separately.</p>
Annual biomass data (2001-2023) for southern California: above- and below-ground, standing dead, and litter
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Above ground plant and below ground stem biomass in the Arctic LTER moist acidic tussock tundra experimental plots, 2006, Toolik Lake, Alaska
Above ground plant and below ground stem biomass, percent nitrogen, and percent carbon were measured in the Arctic LTER moist acidic tundra experimental plots. Treatments included control, and nitrogen and phosphorus amended plots for 10 years, and exclosure plots with and without added nitrogen and phosphorus.
Above ground plant biomass a moist acidic tussock tundra experimental site, 1984, Acric LTER, Toolik Lake, Alaska.
Above ground plant biomass was measured in a tussock tundra experimental site. The plots were set up in 1981 and have been harvested in previous years (See Shaver and Chapin Ecological Monographs, 61(1), 1991 pp.1-31.) This file is the July 26-27, 1984 harvest of the controls and nitrogen + phosphorus treatments.
Above ground biomass in acidic tussock tundra experimental site, 1989, Arctic LTER, Toolik, Alaska.
Above ground plant biomass was measured in a tussock tundra experimental site. The plots were set up in 1981 and have been harvested in previous years (See Shaver and Chapin Ecological Monographs, 61(1), 1991 pp.1-31.) This file contains the biomass numbers for each harvested quadrat.
Percent carbon, percent nitrogen, del13C and del15N of above ground plant and belowground stem biomass samples from experimental plots in moist acidic and moist non-acidic tundra, 2000, Arctic LTER, Toolik Lake, Alaska.
Percent carbon, percent nitrogen, del13C and del15N were measured from above ground plant and belowground stem biomass samples from experimental plots in moist acidic and moist non-acidic tundra. Biomass data are in 2000lgshttbm.dat.
Above ground plant and belowground stem biomass in moist acidic and non-acidic tussock tundra experimental sites, 2001, Arctic LTER, Toolik Lake, Alaska.
Above ground plant and belowground stem biomass was measured in moist acidic and non-acidic tussock tundra experimental sites. Treatments sampled were control plots and plots amended with nitrogen and phosphorus.
Percent carbon and percent nitrogen of above ground plant and belowground stem biomass samples from experimental plots in moist acidic and moist non-acidic tundra, 2001, Arctic LTER, Toolik Lake, Alaska.
Percent carbon and percent nitrogen were measured from above ground plant and belowground stem biomass samples from experimental plots in moist acidic and moist non-acidic tundra. Biomass data are in 2001lgshttbm.dat.
Fertilization Above and Below Ground Biomass and Species Number on Hog Island Dunes, 1991
A one-year study on the accreting north end of Hog Island, VA, provided the opportunity to quantify amounts of plant biomass along a natural dune chronosequence (24, 36, and 120+ year-old dunes) and biomass response to experimental additions of nitrogen. Total aboveground biomass, root biomass, and species number in 1-m2 plots on the dunes across the North Hog Chronosequence. Treatment plots included screened, fertilized, and screened & fertilized.
Data for: A comprehensive dataset of forest above-ground biomass from field observations, machine learning and topographically augmented allometric models over the Kashmir Himalaya
<p>The repository contains observed Above Ground Biomass (AGB) estimates at about 275 sample plots chosen for AGB assessment in the forests of Kashmir Himalaya. The AGB is assessed as a fucntion of dbh using various allometric equations developed specifically for the region. It also contains the AGB for years 1978, 1990, 2000, 2010 and 2021 predicted using topographcally augmeneted multivariate regression model. The extent of forest, delineated using on-screen digitization using Landsat and Sentinel image collection at decadal scale is also provided for the years 1978, 1990, 2000, 2010 and 2021.</p>
Data from: Filtering ground noise from LiDAR returns produces inferior models of forest aboveground biomass in heterogenous landscapes
<p>Airborne LiDAR has become an essential data source for large-scale, high-resolution modeling of forest aboveground biomass and carbon stocks, enabling predictions with much higher resolution and accuracy than can be achieved using optical imagery alone. Ground noise filtering -- that is, excluding returns from LiDAR point clouds based on simple height thresholds -- is a common practice meant to improve the 'signal' content of LiDAR returns by preventing ground returns from masking useful information about tree size and condition contained within canopy returns. However, ground returns may be helpful for making accurate aboveground biomass predictions in heterogeneous landscapes that include a patchy mosaic of vegetation heights and land cover types.<br> <br> In this paper, we applied several ground noise filtering thresholds while mapping forest AGB across New York State (USA), a heterogenous landscape composed of both contiguously forested and highly fragmented areas with mixed land cover types. We fit random forest models to predictor sets derived from each filtering intensity threshold and compared model accuracies, paying attention to how changes in accuracy correlated with landscape structure. We observed that removing ground noise via any height threshold systematically biases many of the LiDAR-derived variables used in AGB modeling, with mean correlation (Spearman's $\rho$) between variables increasing from 0.183 to 0.266. We found that that ground noise filtering yields models of forest AGB with lower accuracy than models trained using predictors derived from unfiltered point clouds, with RMSE increasing by up to 2.2 Mg ha^-1^ statewide. Although we only modeled AGB for forest cover types, models fit to predictors derived from filtered point clouds performed worse as landscape heterogeneity (as measured by patch density and edge density) increased, suggesting ground returns are particularly useful when modeling edge forests. Our results suggest that ground filtering should be a carefully considered decision when mapping forest AGB, particularly when mapping heterogeneous and highly fragmented landscapes, as ground returns are more likely to represent useful 'signal' than extraneous 'noise' in these cases.</p>
Above and below ground biomass in three producing cranberry plantations in Latvia
<p>Results of analyses (biomass and carbon content) of samples collected in three cranberry plantations in Kaigu, Rāķu and Nidas mires. Above and below-ground biomass, leaves and berries separately. Carbon content in mixed sample.</p>
Simulated above ground biomass of forests (larch) aggregated over 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 1800, 1860, 1900, 1990, 2000 and in 5-year steps until 3000 CE and presents the mean over the three repeats of the sum of AGB of the whole study region: extent: 640008.2, 649998.2, 7475006, 7494716 m (xmin, xmax, ymin, ymax).</p> <p>This data set is related to the data set of Kruse (2023).</p> <p>Format: csv, with headers 1-year, Year in CE, 2-average, mean AGB, in kg m<sup>-2</sup> for the study region, 3-upper and 4-lower, is the minimum and maximum value of the three simulations, 5-RCP, is the RCP scenario, 6-Cooling, contains in case of the cooling scenario the string “Cooling”.</p>
Data from: Mangrove above-ground biomass and production are related to forest age at Low Isles, Great Barrier Reef
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