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225 results for “aboveground biomass”
LiDAR-Derived Aboveground Biomass and Uncertainty for California Forests, 2005-2014
This dataset provides estimates of aboveground biomass and spatially explicit uncertainty from 53 airborne LiDAR surveys of locations throughout California between 2005 and 2014. Aboveground biomass was estimated by performing individual tree crown detection and applying a customized "remote sensing aware" allometric equation to these individual trees. Aboveground biomass estimates and their uncertainties for each study area are provided in per-tree and gridded format. The canopy height models used for the tree detection and biomass estimation are also provided.
Pre-Delta-X: Vegetation Species, Structure, Aboveground Biomass, MRD, LA, USA, 2015
This dataset provides vegetation species, height, stem density and diameter, and species aboveground biomass (AGB) measurements collected at herbaceous and forested wetland sites across the Atchafalaya and Terrebonne basins within the Mississippi River Delta (MRD) floodplain in coastal Louisiana, USA. The measurements were made during the Pre-Delta-X campaign in Spring 2015 and Fall 2015. Vegetation height and density and diameter data are only provided for forested Atchafalaya sites during the spring collections. At the nine herbaceous wetland sites, a transect was established perpendicular to the wetland edge with replicate sample plots (0.25 m2, 5 m apart) located at 50, 100, and 150 m from the wetland edge to capture the range of vegetation structure, zonation, and composition. AGB was harvested inside the duplicate plots at each sampling location. At the six forested wetland sites, duplicate circular plots (10 m radius, 50 m apart) were established inside the forest approximately 30 m from the wetland edge. All trees with a diameter at breast height (DBH at 1.3 m) > 2.5 cm were measured within each plot and identified to species. The height of trees was measured with a laser range finder. AGB was estimated using species-specific allometric equations. Measurements were used to generate marsh and forested wetland coverage and biomass in response to seasonality within both basins. The data will be used to calibrate remote sensing data (e.g., UAVSAR, AVIRIS-NG) and hydrodynamics and sediment transport models.
Pre-Delta-X: Aboveground Biomass and Vegetation Maps, Wax Lake Delta, LA, USA, 2016
This dataset includes aboveground biomass (AGB) and vegetation of herbaceous and forest wetland at 5.4 m resolution across the Wax Lake Delta (WLD) in Southern Louisiana, USA, within the Mississippi River Delta (MRD) floodplain. Vegetation classes were derived from Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) imagery acquired over the Atchafalaya Basin and the Terrebonne Basin in October 2016 in combination with a digital elevation model. The AVIRIS-NG surface reflectance data were also combined with L-band Uninhabited Airborne Vehicle Synthetic Aperture Radar (UAVSAR) HV backscatter and scattering component values from coincident vegetation sample sites to develop and test AGB models for emergent herbaceous and forested wetland vegetation. This study used the integrated airborne data from AVIRIS-NG and UAVSAR to assess the instruments' unique capabilities in combination for estimating AGB in coastal deltaic wetlands. The 5.4 m resolution vegetation classification map for the WLD study area was then used to apply the best models to estimate AGB across the WLD.
LiDAR Derived Forest Aboveground Biomass Maps, Northwestern USA, 2002-2016
This dataset provides maps of aboveground forest biomass (AGB) of living trees and standing dead trees in Mg/ha across portions of Northwestern United States, including Washington, Oregon, Idaho, and Montana, at a spatial resolution of 30 m. Forest inventory data were compiled from 29 stakeholders that had overlapping lidar imagery. The collection totaled 3805 field plots with lidar imagery for 176 collections acquired between 2002 and 2016. Plot-level AGB estimates were calculated from tree measurements using the default allometric equations found in the Fire Fuels Extension (FFE) of the Forest Vegetation Simulator (FVS). The random forest algorithm was used to model AGB from lidar height and density metrics that were generated from the lidar returns within fixed-radius field plot footprints, gridded climate metrics obtained from the Climate-FVS Ready Data Server, and topographic estimates extracted from Shuttle Radar Topography Mission (SRTM) 1 Arc-Second Global elevation rasters. AGB was then mapped from the same lidar metrics gridded across the extent of the lidar collections at 30-m resolution. The standard deviation of estimated AGB of the terminal nodes from the random forest predictions was also mapped to show pixel-level model uncertainty. Note that the AGB estimates are, for the most part, a single snapshot in time and that the forest conditions are not necessarily representative of the larger study area.
CMS: LiDAR-derived Estimates of Aboveground Biomass at Four Forested Sites, USA
These data consist of high-resolution maps of aboveground biomass at four forested sites in the US: Garcia River Tract in California, Anne Arundel and Howard Counties in Maryland, Parker Tract in North Carolina, and Hubbard Brook Experimental Forest in New Hampshire. Biomass maps were generated using a combination of field data (forest inventory and Lidar) and modeling approaches. Estimates of uncertainty are also provided for the Maryland site using two different modeling methodologies.These data provide estimates of aboveground biomass for the nominal year of 2011 at 20-50 meter resolution in units of megagrams of carbon per hectare (or acre for the Garcia Tract site).The data are presented as a series of 11 GeoTIFF (*.tif) files.
Global Aboveground and Belowground Biomass Carbon Density Maps for the Year 2010
This dataset provides temporally consistent and harmonized global maps of aboveground and belowground biomass carbon density for the year 2010 at a 300-m spatial resolution. The aboveground biomass map integrates land-cover specific, remotely sensed maps of woody, grassland, cropland, and tundra biomass. Input maps were amassed from the published literature and, where necessary, updated to cover the focal extent or time period. The belowground biomass map similarly integrates matching maps derived from each aboveground biomass map and land-cover specific empirical models. Aboveground and belowground maps were then integrated separately using ancillary maps of percent tree cover and landcover and a rule-based decision tree. Maps reporting the accumulated uncertainty of pixel-level estimates are also provided.
CMS: Aboveground Biomass for Mangrove Forest, Zambezi River Delta, Mozambique
This dataset provides several estimates of aboveground biomass from various regressions and allometries for mangrove forest in the Zambezi River Delta, Mozambique. Plot level estimates of aboveground biomass are based on extensive tree biophysical measurements from field campaigns conducted in September and October of 2012 and 2013. Aboveground biomass estimates for the larger area of mangrove coverage within the delta are based on (1) the plot level data and (2) canopy structure data derived from airborne LiDAR surveys in 2014. The high-resolution canopy height model for the delta region derived from the airborne LiDAR data is also included.
Delta-X: AVIRIS-NG L3 Derived Herbaceous Aboveground Biomass, MRD, Louisiana, USA, V3
This dataset includes high-resolution (~5 m) gridded estimates of aboveground biomass (AGB), aboveground necromass (AGN), and aboveground net primary productivity (ANPP) for herbaceous vegetation in the Atchafalaya and Terrebonne basins of the Mississippi River Delta in coastal Louisiana, USA, for the fall and spring seasons of 2021. AGB, AGN and ANPP were estimated from Bidirectional Reflectance Distribution Function (BRDF)-adjusted surface reflectance products from NASA's Airborne Visible Infrared Imaging Spectrometer - Next Generation (AVIRIS-NG) acquired over the study area in April and August 2021. The BRDF-adjusted reflectance was derived from hemispherical-directional surface reflectance with atmospheric correction. Local pixel reflectance spectra coincident with herbaceous vegetation AGB field samples from both Spring and Fall 2021 collections were used to generate a machine learning-based regression model to estimate AGB. This model was then scaled to the AVIRIS-NG mosaic imagery in accordance with vegetation type to map herbaceous AGB across the Atchafalaya and Terrebonne Basins. The AGN field data, separated from the AGB samples, were also used in conjunction with AVIRIS-NG-derived fractional coverage to derive AGN maps again employing machine learning. The AGB and AGN products were used together to map ANPP across the Atchafalaya and Terrebonne Basins. The data are provided in cloud optimized GeoTIFF (COG) format. This Version 3 dataset replaces the files provided in Version 2.
Delta-X: Aboveground Biomass and Necromass across Wetlands, MRD, Louisiana, 2021, V2
This dataset contains total aboveground biomass (AGB) and necromass (AGN), and total carbon, total nitrogen, and total phosphorus content of aboveground biomass (AGB) and necromass (AGN) samples collected from herbaceous wetlands in the Atchafalaya and Terrebonne basins in southeastern coastal Louisiana during 2021. Field measurements were conducted at three sites in the Atchafalaya basin and three sites in the Terrebonne basin. Five of the sites are adjacent to sites from the Coastwide Reference Monitoring System (CRMS). The other site is located in Wax Lake Delta (WLD) without appropriate adjacent CRMS sites. All AGB and AGN plant material within each plot was clipped at soil level, stored in plastic bags, and transported to the laboratory for further analyses. Herbaceous wetland sites in both basins were chosen to represent a salinity gradient including freshwater, brackish, and saline ecosystems. These data cover the period 2021-03-19 to 2021-03-31 (spring) and 2021-08-19 to 2021-08-27 (fall).
Classification of Global Forests for IPCC Aboveground Biomass Tier 1 Estimates, 2020
This dataset provides classes of global forests delineated by status/condition in 2020 at approximately 30-m resolution. The data support generating Tier 1 estimates for Aboveground dry woody Biomass Density (AGBD) in natural forests in the 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories. Forest classes include primary, young secondary (<=20 years), and old secondary forests (>20 years). Classification was based on a Boolean combination of a suite of existing Earth Observation (EO) products of forest tree cover, height, age, and land use classification layers representing years 2000 to 2020. This forest status/condition classification prioritizes the reduction of potential errors of commission in the delineations by minimizing the inclusion of ambiguous pixels. Hence, it provides a conservative estimate of global forest area, identifying approximately 3.26 billion ha of forests worldwide. The classification was created on the collaborative open-science cloud-computing system, the ESA-NASA Multi-mission Analysis and Algorithm Platform (MAAP). The data are provided in cloud-optimized GeoTIFF format.
Annual Aboveground Biomass Maps for Forests in the Northwestern USA, 2000-2016
This dataset provides annual maps of aboveground biomass (AGB, Mg/ha) for forests in Washington, Oregon, Idaho, and western Montana, USA, for the years 2000-2016, at a spatial resolution of 30 meters. Tree measurements were summarized with the Fire and Fuels Extension of the Forest Vegetation Simulator (FFE-FVS) to estimate AGB in field plots contributed by stakeholders, then lidar was used to predict plot-level AGB using the Random Forests machine learning algorithm. The machine learning outputs were used to predict AGB from Landsat time series imagery processed through LandTrendr, climate metrics generated from 30-year climate normals, and topographic metrics generated from a 30-m Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM). The non-forested pixels were masked using the PALSAR 2009 forest/nonforest mask.
Forest Aboveground Biomass for the Southwestern U.S. from MISR, 2000-2021
This dataset provides estimates of forest aboveground biomass (AGB; in Mg ha-1) at a resolution of 250 m for the southwestern United States over the time period 2000-2021. The AGB estimates were derived from the Jet Propulsion Laboratory Multiangle Imaging Spectro-Radiometer (MISR) Level 1B2 Terrain radiance data and a multi-angle approach that exploits the relationship between forest AGB and a suite of red band reflectance values modeled at viewing angles with respect to the direction of illumination. The year 2000 National Biomass and Carbon Dataset (NBCD 2000) AGB estimates were used to fit a model coefficient for the MISR-derived AGB estimates for the year 2000, with AGB estimates for all subsequent years dependent on both this coefficient and MISR red band bidirectional reflectance factors (BRFs). Quality assurance (QA) files are also provided that allow users to impose criteria of varying stringency. The bidirectional reflectance distribution function (BRDF) model-fitting root mean square error (RMSE) value was used as one of the criteria to determine if the AGB estimates were reasonable. This dataset is the first example of forest AGB estimation based on a multi-angle index applied using MISR data.
Global Mangrove Distribution, Aboveground Biomass, and Canopy Height
This dataset characterizes the global distribution, biomass, and canopy height of mangrove-forested wetlands based on remotely sensed and in situ field measurement data. Estimates of (1) mangrove aboveground biomass (AGB), (2) maximum canopy height (height of the tallest tree), and (3) basal-area weighted height (individual tree heights weighted in proportion to their basal area) for the nominal year 2000 were derived across a 30-meter resolution global mangrove ecotype extent map using remotely-sensed canopy height measurements and region-specific allometric models. Also provided are (4) in situ field measurement data for selected sites across a wide variety of forest structures (e.g., scrub, fringe, riverine and basin) in mangrove ecotypes of the global equatorial region. Within designated plots, selected trees were identified to species and diameter at breast height (DBH) and tree height was measured using a laser rangefinder or clinometer. Tree density (the number of stems) can be estimated for each plot and expressed per unit area. These data were used to derive plot-level allometry among AGB, basal area weighted height (Hba), and maximum canopy height (Hmax) and to validate the remotely sensed estimates.
CMS: LiDAR-derived Aboveground Biomass, Canopy Height and Cover for Maryland, 2011
This data set provides 30-meter gridded estimates of aboveground biomass (AGB), canopy height, and canopy coverage for the state of Maryland in 2011. Leaf-off LiDAR data were combined with high-resolution leaf-on agricultural imagery to select 848 field sampling sites for biomass measurements. The field-based estimates were related to LiDAR height and volume metrics through random forests regression models across three physiographic regions of Maryland.
Aboveground Biomass Density for High Latitude Forests from ICESat-2, 2020
This dataset provides estimates of Aboveground dry woody Biomass Density (AGBD) for high northern latitude forests at a 30-m spatial resolution. It is designed both for boreal-wide mapping and filling the northern spatial data gap from NASA's Global Ecosystem Dynamics Investigation (GEDI) project. Mapping forest aboveground biomass is essential for understanding, monitoring, and managing forest carbon stocks toward climate change mitigation. The AGBD estimates cover the extent of high latitude boreal forests and extend southward to 50 degrees latitude outside the boreal zone. AGBD was predicted using two modeling steps: 1) Ordinary Least Squares (OLS) regression related field plot measurements of AGBD to NASA's ICESat-2 30-m lidar samples, and 2) random forest models were used to extend estimates beyond the field plots by relating ICESat-2 AGBD predictions to wall-to-wall covariate stacks from Harmonized Landsat Sentinel-2 (HLS) and the Copernicus DEM. Per-pixel uncertainties are estimated from bootstrapping both models. Non-vegetated areas (e.g. built up, water, rock, ice) were masked out. HLS composites and ICESat-2 data were from 2019-2021; three years of conditions were aggregated into the circa 2020 map. ICESat-2 data were filtered to include only strong beams, growing seasons (June through September), solar elevations less than 5 degrees, snow free land (snow flag set to 1), and "msw_flag" equal to 0 (clear skies and no observed atmospheric scattering). ICESat-2's ATL08 product was resampled to a 30-m spatial resolution to better match both the field plots and mapped pixels. HLS data (L30HLS) were used to create a greenest pixel composite of growing season multispectral data, which was then used to compute a suite of vegetation indices: NDVI, NDWI, NBR, NBR2, TCW, TCG. These were then used, in combination with the slope and elevation data from the Copernicus DEM product, to predict 30-m AGBD per 90-km tile. Estimates of mean AGBD with standard deviation are provided in cloud-optimized GeoTIFF (CoG) format. Training data are in comma-separated values (CSV) format. A polygon map of data tiles is included as a GeoPackage file and a Shapefile.
Aboveground Biomass from SAR, Great Slave Lake Region, NWT, 2019
This dataset holds aboveground biomass (ABG) estimates for areas in the Great Slave Lake Region in the Northwest Territories of Canada for 2019. ABG was estimated from L-band synthetic aperture radar (SAR) data obtained from JAXA's Advanced Land Observing Satellite-2 (ALOS-2/PALSAR-2) and supplemented with data from NASA's airborne Uninhabited Aerial Vehicle SAR (UAVSAR) instrument. SAR data were collected from 2017 to 2021. In situ AGB measurements at 14 plots sampled in 2019 were used to calibrate a logarithmic regression to relate the radar datasets to in situ AGB data. Then, AGB was mapped over available ALOS-2 tiles. The estimates are provided in 20-Mg ha-1 bins at 100-m resolution in cloud optimized GeoTIFF format.
Forest Aboveground Biomass and Carbon Sequestration Potential, Northeastern USA
This dataset provides 90 m estimates of forest aboveground biomass (Mg/ha) for nominal 2011 and projections of carbon sequestration potential for 11 states in the Regional Greenhouse Gas Initiative (RGGI) domain. The RGGI is a cooperative, market-based effort among States in the eastern United States. Estimated biomass and sequestration potential were computed using the Ecosystem Demography (ED) model. The ED Model integrates several key data including climate variables from Daymet and MERRA2 products; physical soil and hydraulic properties from Probabilistic Remapping of SSURGO (POLARIS) and CONUS-SOIL; land cover characteristics from airborne lidar, the National Agriculture Imagery Program (NAIP), and the National Land Cover Database (NLCD); and vegetation parameters from the Forest Inventory and Analysis (FIA) Program.
LiDAR and PALSAR-Derived Forest Aboveground Biomass, Paragominas, Para, Brazil, 2012
This dataset provides estimates of forest aboveground biomass for three study areas and the entire Paragominas municipality, in Para, Brazil, in 2012. Aboveground biomass (in megagrams of carbon per hectare) was measured for inventory plots within the study (focal) areas, and then assimilated and modeled with LiDAR and PALSAR metrics using gradient boosting machines (GBM) to predict spatially explicit forest aboveground biomass and uncertainties for the entire focal areas. The PALSAR data across the three focal areas was combined and used in a GBM model to predict forest aboveground biomass across the entire Paragominas municipality.
AfriSAR: Aboveground Biomass for Lope, Mabounie, Mondah, and Rabi Sites, Gabon
This dataset provides gridded estimates of aboveground biomass (AGB) for four sites in Gabon at 0.25 ha (50 m) resolution derived with field measurements and airborne LiDAR data collected from 2010 to 2016. The sites represent a mix of forested, savannah, and some agricultural and disturbed landcover types: Lope site, within Lope National Park; Mabounie, mostly forested site; Mondah Forest, protected area; and the Rabi forest site, part of the Smithsonian Institution of Global Earth Observatories world-wide network of forest plots. Plot-level biophysical measurements of tree diameter and tree height (or estimated by allometry) were performed at 1 ha and 0.25 ha scales on multiple plots at each site and used to derive AGB for each tree and then summed for each plot. Aerial LiDAR scans were used to construct digital elevation models (DEM) and digital surface models (DSM), and then the DEM and DSM were used to construct a canopy height model (CHM) at 1 m resolution. After checking site-plot locations against the CHM, mean canopy height (MCH) was computed over each 0.25 ha. A single regression model relating MCH and AGB estimates, incorporating local height based on the trunk DBH (HD) relationships, was produced for all sites and combined with the CHM layer to construct biomass maps at 0.25 ha resolution.
GEDI L4A Footprint Level Aboveground Biomass Density, Version 2.1
This dataset contains Global Ecosystem Dynamics Investigation (GEDI) Level 4A (L4A) Version 2 predictions of the aboveground biomass density (AGBD; in Mg/ha) and estimates of the prediction standard error within each sampled geolocated laser footprint. In this version, the granules are in sub-orbits. The algorithm setting group selection used for GEDI02_A Version 2 has been modified for Evergreen Broadleaf Trees in South America to reduce false positive errors resulting from the selection of waveform modes above ground elevation as the lowest mode. The footprints are located within the global latitude band observed by the International Space Station (ISS), nominally 51.6 degrees N and S and reported for the period 2019-04-18 to 2024-11-27. No acquisitions occurred while the GEDI instrument was in storage on the International Space Station (ISS) from March 2023 to April 2024. The GEDI instrument consists of three lasers producing a total of eight beam ground transects, which instantaneously sample eight ~25 m footprints spaced approximately every 60 m along-track. The GEDI beam transects are spaced approximately 600 m apart on the Earth's surface in the cross-track direction, for an across-track width of ~4.2 km. Footprint AGBD was derived from parametric models that relate simulated GEDI Level 2A (L2A) waveform relative height (RH) metrics to field plot estimates of AGBD. Height metrics from simulated waveforms associated with field estimates of AGBD from multiple regions and plant functional types (PFTs) were compiled to generate a calibration dataset for models representing the combinations of world regions and PFTs (i.e., deciduous broadleaf trees, evergreen broadleaf trees, evergreen needleleaf trees, deciduous needleleaf trees, and the combination of grasslands, shrubs, and woodlands). For each of the eight beams, additional data are reported with the AGBD estimates, including the associated uncertainty metrics, quality flags, model inputs, and other information about the GEDI L2A waveform for this selected algorithm setting group. Model inputs include the scaled and transformed GEDI L2A RH metrics, footprint geolocation variables and land cover input data including PFTs and the world region identifiers. Additional model outputs include the AGBD predictions for each of the six GEDI L2A algorithm setting groups with AGBD in natural and transformed units and associated prediction uncertainty for each GEDI L2A algorithm setting group. Providing these ancillary data products will allow users to evaluate and select alternative algorithm setting groups. Also provided are outputs of parameters and variables from the L4A models used to generate AGBD predictions that are required as input to the GEDI04_B algorithm to generate 1-km gridded products.
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