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225 results for “aboveground biomass”
Delta-X AVIRIS-NG and UAVSAR L3 Derived Forest Aboveground Biomass, MRD, LA
This dataset contains estimates of forest aboveground biomass (AGB) across the Atchafalaya and Terrebonne Basins, Louisiana, US. AGB was derived from AVIRIS-NG surface reflectance and UAVSAR products. L2B BRDF-adjusted surface reflectance was produced after applying atmospheric correction to L2 Hemispherical-Directional surface reflectance from NASA's AVIRIS-NG instrument. A polarimetric decomposition of the UAVSAR Level 1 (L1) Single Look Complex (SLC) stack product was used. To estimate AGB, local pixel reflectance spectra and radar scattering component pixels coincident with in situ forest AGB plot data from Summer 2015 and Fall 2021 were used to generate a machine learning-based regression model. This model was then scaled to the AVIRIS-NG and UAVSAR mosaic data, in accordance with vegetation type, to map forest AGB across the Atchafalaya and Terrebonne Basins. The data are provided in cloud optimized GeoTIFF format.
GEDI L4A Footprint Level Aboveground Biomass Density, Golden Weeks, Version 1
This dataset contains Global Ecosystem Dynamics Investigation (GEDI) Level 4A (L4A) predictions of the aboveground biomass density (AGBD; in Mg/ha) and estimates of the prediction standard error within each sampled geolocated laser footprint. 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 mission weeks 19, 32, 34 and 38 (a.k.a. Golden Weeks). These weeks cover the range of instrument operating conditions important for calibration and validation of geolocation algorithms, and also include GEDI orbits that are coincident with underflights acquired by the LVIS (Land, Vegetation, and Ice Sensor) airborne lidar instrument. 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 (PFT) 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).
Aboveground Biomass for Howland Forest, Maine, 2012-2023
This dataset holds aboveground biomass (AGB) estimates at 10-m spatial resolution for the Howland Research Forest in central Maine for 2012, 2015, 2017, 2021, and 2023. Forest inventory data were collected using 50 fixed-area plot sampling during the summers of 2021, 2023, and 2024. Plots included permanent inventory plots around existing flux towers and additional plots to ensure representation of various forest conditions. Each plot had a radius of 7.98 m. In addition, leaf-off airborne LiDAR data were collected by the USGS 3DEP project in 2012, 2015, and 2023, and leaf-on data were obtained from the NASA G-LiHT project for 2017 and 2021. The LANDIS-II forest landscape model along with its Biomass Succession extension was used to simulate ecosystem dynamics in Howland Forest. Then, a random forest (RF) model was used to generate wall-to-wall biomass maps for the research forest from the LiDAR data. The RF model was calibrated from in situ AGB measurements from plots and simulated AGB values for the LiDAR acquisition years. Howland Research Forest is a low-elevation transitional forest dominated by spruce and hemlock, with conifer and northern hardwood species. The data are provided in cloud optimized GeoTIFF format.
ABoVE: Annual Aboveground Biomass for Boreal Forests of ABoVE Core Domain, 1984-2014
This dataset provides estimated annual aboveground biomass (AGB) density for live woody (tree and shrub) species and corresponding standard errors at a 30 m spatial resolution for the boreal forest biome portion of the Core Study Domain of NASA's Arctic-Boreal Vulnerability Experiment (ABoVE) Project (Alaska and Canada) over the time period 1984-2014. The data were derived from a time series of Landsat-5 and Landsat-7 surface reflectance imagery and full-waveform lidar returns from the Geoscience Laser Altimeter System (GLAS) flown onboard IceSAT from 2004 to 2008. The Change Detection and Classification (CCDC) model-fitting algorithm was used to estimate the seasonal variability in surface reflectance, and AGB density data were produced by applying allometric equations to the GLAS lidar data. A Gradient Boosted Machines machine learning algorithm was used to predict annual AGB density across the study domain given the seasonal variability in surface reflectance and other predictors. The data received statistical smoothing to reduce noise and uncertainty was estimated at the pixel level. These data contribute to the characterization of how biomass stocks are responding to climate and disturbance in boreal forests.
GEDI L4B Gridded Aboveground Biomass Density, Version 2
This Global Ecosystem Dynamics Investigation (GEDI) L4B product provides 1 km x 1 km (1 km, hereafter) estimates of mean aboveground biomass density (AGBD) based on observations from mission week 19 starting on 2019-04-18 to mission week 138 ending on 2021-08-04. The GEDI L4A Footprint Biomass product converts each high-quality waveform to an AGBD prediction, and the L4B product uses the sample present within the borders of each 1 km cell to statistically infer mean AGBD. The gridding procedure is described in the GEDI L4B Algorithm Theoretical Basis Document (ATBD). Patterson et al. (2019) describes the hybrid model-based mode of inference used in the L4B product. Corresponding 1 km estimates of the standard error of the mean are also provided in the L4B product. Uncertainty is due to both GEDI's sampling of the 1 km area (as opposed to making wall-to-wall observations) and the fact that L4A biomass values are modeled in a process subject to error instead of measured in a process that may be assumed to be error-free.
Forest Aboveground Biomass for Maine, 2023
This dataset holds estimates of forest aboveground biomass (AGB) for Maine, USA, in 2023. AGB was estimated using airborne LiDAR data from the USGS 3DEP project and a deep learning convolutional neural network (CNN) model. The airborne LiDAR datasets used in this mapping were collected in different years. The CNN model was calibrated using plot-level forest inventory data with precise location measurements and spectral indices derived from multiple remote sensing products. Stand-level biomass succession models, developed from the USDA Forest Service Forest Inventory and Analysis (FIA) data, were applied to project biomass estimates to the year 2023 with 10-m spatial resolution. The data are provided in GeoTIFF format.
Aboveground Biomass Change for Amazon Basin, Mexico, and Pantropical Belt, 2003-2016
This dataset provides gridded estimates of aboveground biomass (AGB) for live dry woody vegetation density in the form of both stock for the baseline year 2003 and annual change in stock from 2003 to 2016. Data are at a spatial resolution of approximately 500 m (463.31 m; 21.47 ha) for three geographies: the biogeographical limit of the Amazon Basin, the country of Mexico, and a Pantropical belt from 40 degrees North to 30 degrees South latitudes. Estimates were derived from a multi-step modeling approach that combined field measurements with co-located LiDAR data from NASA ICESat Geoscience Laser Altimeter System (GLAS) to calibrate a machine-learning (ML) algorithm that generated spatially explicit annual estimates of AGB density. ML inputs included a suite of satellite and ancillary spatial predictor variables compiled as wall-to-wall raster mosaics, including MODIS products, WorldClim climate variables reflecting current (1960-1990) climatic conditions, and SoilGrids soil variables. The 14-year time series was analyzed at the grid cell (~500 m) level with a change point-fitting algorithm to quantify annual losses and gains in AGB. Estimates of AGB and change can be used to derive total losses, gains, and the net change in aboveground carbon density over the study period as well as annual estimates of carbon stock.
CMS: Forest Aboveground Biomass from FIA Plots across the Conterminous USA, 2009-2019
This dataset provides forest biomass estimates for the conterminous United States based on data from the USDA Forest Inventory and Analysis (FIA) program. FIA maintains uniformly measured field plots across the conterminous U.S. This dataset, derived from field survey data from 2009-2019, includes statistical estimates of biomass at the finest scale (64,000-hectare hexagons) allowed by FIA's sample density. Estimates include the mean (and standard error of the mean) biomass for both live and dead trees, calculated using three sets of allometric equations. There is also an estimate of the area of forestland in each hexagon. These data can be useful for assessing the accuracy of remotely sensed biomass estimates.
CMS: Aboveground Biomass from Penobscot Experimental Forest, Maine, 2012
This data set includes estimates of aboveground biomass (AGB) in 2012 from the Penobscot Experimental Forest (PEF) in Bradley, Maine. The AGB was modeled using LiDAR data gathered with the LiDAR Hyperspectral and Thermal Imager (G-LiHT) operated by Goddard Space Flight Center and field inventory data from 604 permanent Forest Inventory and Analysis (FIA) plots within the PEF. The estimates were produced through a novel modeling approach that accommodates temporal misalignment between field measurements and remotely sensed data by including multiple time-indexed measurements at plot locations to estimate changes in AGB.
GEDI L4B Country-level Summaries of Aboveground Biomass
This dataset provides country-level estimates of land surface mean aboveground biomass density (AGBD), total aboveground biomass (AGB) stocks, and the associated standard errors of the mean calculated using different versions of the Global Ecosystem Dynamics Investigation (GEDI) Level-4B (L4B) product. The GEDI L4B product provides gridded (1 km x 1 km) estimates of AGBD within the GEDI orbital extent (between 51.6 degrees N and 51.6 degrees S). For comparison purposes, this dataset also includes national-scale National Forest Inventory (NFI) estimates of AGBD from the 2020 Global Forest Resources Assessment (FRA) published by the Food and Agriculture Organization (FAO, 2020) of the United Nations.The GEDI instrument produces high-resolution laser ranging observations of the 3-dimensional structure of the Earth's surface. GEDI was launched on December 5, 2018, and is attached to the International Space Station (ISS). The GEDI instrument consists of three lasers producing a total of eight beam ground transects, which consist of ~25 m footprint samples 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. The data are provided in comma-separated value (CSV) format.
LBA-ECO LC-15 Amazon Basin Aboveground Live Biomass Distribution Map: 1990-2000
The amount and spatial distribution of forest biomass in the Amazon basin is a major source of uncertainty in estimating the flux of carbon released from land-cover and landuse change. Direct measurements of aboveground live biomass (AGLB) are limited to small areas of forest inventory plots and site-specific allometric equations that cannot be readily generalized for the entire basin. Furthermore, there is no space-borne remote sensing instrument that can measure tropical forest biomass directly. To determine the spatial distribution of forest biomass of the Amazon basin, we report a method based on remote sensing metrics representing various forest structural parameters and environmental variables, and more than 500 plot measurements of forest biomass distributed over the basin. A decision tree approach was used to develop the spatial distribution of AGLB for seven distinct biomass classes of lowland old-growth forests with more than 80% accuracy. AGLB for other vegetation types, such as the woody and herbaceous savanna and secondary forests, was directly estimated with a regression based on satellite data. Results show that AGLB is highest in Central Amazonia and in regions to the east and north, including the Guyanas. Biomass is generally above 300 Mg ha-1 here except in areas of intense logging or open floodplains. In Western Amazonia, from the lowlands of Peru, Ecuador, and Colombia to the Andean mountains, biomass ranges from 150 to 300 Mg ha-1. Most transitional and seasonal forests at the southern and northwestern edges of the basin have biomass ranging from 100 to 200 Mg ha-1. The AGLB distribution has a significant correlation with the length of the dry season. We estimate that the total carbon in forest biomass of the Amazon basin, including the dead and belowground biomass, is 86 Pg C with +/- 20% uncertainty.
Aboveground Biomass Estimates for Salt Marsh for the Contiguous United States, 2020
This dataset provides estimates of aboveground biomass (AGB) and salt marsh extent in the contiguous United States for 2020 and includes all coastal watersheds across the contiguous United States at 10-m resolution. Estimates were generated by XGBoost machine learning regression. Salt marsh extent was classified using an ensemble of XGBoost, random forests, and support vector machines, trained with salt marsh location identified with the National Wetland Inventory (NWI). The data are organized by Hydrologic Unit Code (HUC) 6-digit basin. Within each HUC, the spatial extent of salt marsh and its uncertainty were estimated by machine learning and input data from NWI maps, the National Elevation Dataset, along with Sentinel-1 and Sentinel-2 imagery. Estimates were compared to in situ biomass data from salt marshes in Georgia and Massachusetts. The data are provided in cloud-optimized GeoTIFF format.
ABoVE: Gridded 30-m Aboveground Biomass, Shrub Dominance, North Slope, AK, 2007-2016
This dataset includes 30-m gridded estimates of total plant aboveground biomass (AGB), the shrub AGB, and the shrub dominance (shrub/plant AGB) for non-water portions of the Beaufort Coastal Plain and Brooks Foothills ecoregions of the North Slope of Alaska. The estimates were derived by linking biomass harvests from 28 published field site datasets with NDVI from a regional Landsat mosaic derived from Landsat 5 and 7 satellite imagery. The data cover the period 2007-06-01 to 2016-08-31.
Aboveground Biomass High-Resolution Maps for Selected US Tidal Marshes, 2015
This dataset provides maps of aboveground tidal marsh biomass (g/m2) at 30 m resolution for six estuarine regions of the conterminous United States: Cape Cod, MA; Chesapeake Bay, MD, Everglades, FL; Mississippi Delta, LA; San Francisco Bay, CA; and Puget Sound, WA. Estuarine and palustrine emergent tidal marsh areas were based on a 2010 NOAA Coastal Change Analysis Program (C-CAP) map. Aboveground biomass maps were generated from a random forest model driven by Landsat vegetation indices and a national scale dataset of field-measured aboveground biomass. The final model, driven by six Landsat vegetation indices, with the soil adjusted vegetation index as the most important, successfully predicted biomass for a range of marsh plant functional types defined by height, leaf angle, and growth form. Biomass can be converted to carbon stocks using a mean plant carbon content of 44.1%.
Aboveground Biomass, Landcover, and Degradation, Kalimantan Forests, Indonesia, 2014
This dataset provides estimates of aboveground biomass, percent canopy cover, mean canopy height, landcover, and forest degradation index products for forests in Kalimantan, Indonesia (Island of Borneo) representative of conditions in late 2014. Data were combined from several sources including field sampling, airborne lidar, satellite measurements, a forest-type land cover map, and integrated into a random forest algorithm to produce these estimates.
CMS: Pinyon-Juniper Forest Live Aboveground Biomass, Great Basin, USA, 2000-2016
This dataset provides annual maps of live aboveground tree biomass (Mg/ha) for pinyon-juniper forests across the Great Basin of the Western USA for the years 2000-2016 at a spatial resolution of 30 meters. Biomass estimates are limited to areas of the Great Basin defined as a pinyon-juniper ecosystem type by the 2016 Landfire Existing Vegetation Type map. The estimates of biomass were based on a linear relationship with pinyon-juniper canopy cover and crown-based allometrics developed from field data in Nevada and Idaho. Canopy cover was estimated from remote sensing by using annual composites of Landsat imagery, which were temporally segmented with the LandTrendr algorithm, along with biologically-relevant climate variables, and topographic indices in a Random Forest regression model. Models of canopy cover were trained from semi-automatic extraction of tree crowns from 2011 - 2013 high resolution imagery (1 m) from the National Agriculture Imagery Program, which were validated with photo interpretation. Maps of the standard deviation of biomass estimates from decision trees in the Random Forest model are provided as an indicator of uncertainty. Biomass estimates were calibrated to estimates from the Forest Inventory and Analysis program (FIA) on an annual basis and corrections applied.
Forest Aboveground Biomass and Carbon Sequestration Potential for Maryland, USA.
This dataset provides 90-m resolution maps of estimated forest aboveground biomass (Mg/ha) for nominal year 2011 and projections of carbon sequestration potential for the state of Maryland. Estimated biomass and sequestration potential were computed using the Ecosystem Demography (ED) model, which integrates data from multiple sources, including: climate variables from the North American Regional Reanalysis (NARR) Product, soil variables from the Soil Survey Geographic Database (SSURGO), land cover variables 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.
NACP Aboveground Biomass and Carbon Baseline Data, V.2 (NBCD 2000), U.S.A., 2000
The NBCD 2000 (National Biomass and Carbon Dataset for the Year 2000) data set provides a high-resolution (30 m) map of year-2000 baseline estimates of basal area-weighted canopy height, aboveground live dry biomass, and standing carbon stock for the conterminous United States. This data set distributes, for each of 66 map zones, a set of six raster files in GeoTIFF format. There is a detailed README companion file for each map zone. There is also an ArcGIS shapefile (mapping_zone_shapefile.shp) with the boundaries of all the map zones. A mosaic image of biomass at 240 m resolution for the whole conterminous U.S. is also included.Please read this important note regarding the differences of Version 2 from Version 1 of the NBCD 2000 data. With Version 1, in some mapping zones, certain land cover types (in particular Shrubs, NLCD Type 52) were missing from and unaccounted for in modeled estimates because of a lack of reference data. In Version 1, when landcover types were missing in the models, the model for the deciduous tree cover type was applied. While more woody vegetation was mapped, the authors think this had little effect on model performance as in most cases NLCD version 1 cover type was not a strong predictor of modeled estimates (See companion Mapping Zone Readme files). In Version 2, after renewed modeling efforts and user feedback, these previously unaccounted for cover types are now included in modeled estimates.All 66 mapping zones were updated with the previously unmapped land cover types now mapped. The authors recommend use of the new version for all analyses and will only support the updated version.Development of the data set used an empirical modeling approach that combined USDA Forest Service Forest Inventory and Analysis (FIA) data with high-resolution InSAR data acquired from the 2000 Shuttle Radar Topography Mission (SRTM) and optical remote sensing data acquired from the Landsat ETM+ sensor. Three-season Landsat ETM+ data were systematically compiled by the Multi-Resolution Land Characteristics Consortium (MRLC) between 1999 and 2002 for the entire U.S. and were the foundation for development of both the USGS National Land Cover Dataset 2001 (NLCD 2001) and the Landscape Fire and Resource Management Planning Tools Project (LANDFIRE). Products from both the NLCD 2001 (landcover and canopy density) and LANDFIRE (existing vegetation type) projects as well as topographic information from the USGS National Elevation Dataset (NED) were used within the NBCD 2000 project as spatial predictor layers for canopy height and biomass estimation. Forest survey data provided by the USDA Forest Service FIA program were made available to the project under a national Memorandum of Understanding. The response variables (canopy height and biomass) used in model development and validation were derived from the FIA database (FIADB). Production of the NLCD 2001 and LANDFIRE projects was based on a mapping zone approach in which the conterminous U.S. was split into 66 ecoregionally distinct mapping zones. This mapping zone approach was also adopted by the NBCD 2000 project.
GEDI L4B Gridded Aboveground Biomass Density, Version 2.1
This Global Ecosystem Dynamics Investigation (GEDI) L4B product provides 1 km x 1 km (1 km, hereafter) estimates of mean aboveground biomass density (AGBD) based on observations from mission week 19 starting on 2019-04-18 to mission week 223 ending on 2023-03-16. The GEDI L4A Footprint Biomass product converts each high-quality waveform to an AGBD prediction, and the L4B product uses the sample present within the borders of each 1 km cell to statistically infer mean AGBD. The gridding procedure is described in the GEDI L4B Algorithm Theoretical Basis Document (ATBD). Patterson et al. (2019) describes the hybrid model-based mode of inference used in the L4B product. Corresponding 1 km estimates of the standard error of the mean are also provided in the L4B product. Uncertainty is due to both GEDI's sampling of the 1 km area (as opposed to making wall-to-wall observations) and the fact that L4A biomass values are modeled in a process subject to error instead of measured in a process that may be assumed to be error-free.
Annual Removal of Aboveground Plant Biomass Alters Soil Microbial Responses to Warming
GEO Series GSE86527. Fungi; Viruses; Bacteria; Archaea. 24 samples. Type: Genome variation profiling by array.
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