This dataset corresponds to the article, 'Towards Prediction and Mapping of Grassland Aboveground Biomass using Handheld LiDAR'.

This dataset consist of: 1. An R script for the Random Forest model. 2. A table containing fifteen metrics with corresponding biomass values for the 30 retrieved samples. 3. An ArcGIS project package with the AGB maps, sample locations, OBIA segments, processed point cloud, and Canopy Height Model. 4. An AGB map. 5. Handheld-LiDAR collection video recordings of area A and B, looped and zigzag trajectory.

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.

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. Provider's note: The UN Environment Programme World Conservation Monitoring Centre (UNEP-WCMC) carbon biomass dataset represents conditions between 1982 and 2010 depending on land cover type. The relative patterns of carbon stocks are well represented with this dataset. The NASA/ORNL carbon biomass dataset represents biomass conditions for 2010, with uncertainty estimates at the pixel-level. Additional biomass of non-dominant land cover types are represented within each pixel. For more detailed information, please refer to the papers describing each dataset: WCMC (Soto-Navarro et al. 2020) and NASA/ORNL (Spawn et al. 2020).

This file provides the global biomass map produced with the EU FP7 GEOCARBON project (www.geocarbon.net) and presented by Avitabile et al. (2014) at the Global Vegetation Monitoring and Modeling, 3-7 February 2014, Avignon (France). The map is obtained by combining and harmonizing the pan-tropical biomass map by Avitabile et al. (2016) with the boreal forest biomass map by Santoro et al. (2015). The map covers only forest areas, where forest are defined as areas with dominance of tree cover in the GLC2000 map (Bartholomé and Belward, 2005). For a proper use and description of this dataset, please refer to the mentioned articles.

Source: Avitabile, V., Herold, M., Lewis, S.L., Phillips, O.L., Aguilar-Amuchastegui, N., Asner, G. P., Brienen, R.J.W., DeVries, B., Cazzolla Gatti, R., Feldpausch, T.R., Girardin, C., de Jong, B., Kearsley, E., Klop, E., Lin, X., Lindsell, J., Lopez-Gonzalez, G., Lucas, R., Malhi, Y., Morel, A., Mitchard, E., Pandey, D., Piao, S., Ryan, C., Sales, M., Santoro, M., Vaglio Laurin, G., Valentini, R., Verbeeck, H., Wijaya, A., Willcock, S., 2014. Comparative analysis and fusion for improved global biomass mapping. Global Vegetation Monitoring and Modeling, 3 – 7 February 2014, Avignon (France) (https://colloque.inra.fr/gv2m)

Based on data from: Avitabile, V., Herold, M., Heuvelink, G. B. M., Lewis, S. L., Phillips, O. L., Asner, G. P., Armston, J., Ashton, P. S., Banin, L., Bayol, N., Berry, N. J., Boeckx, P., de Jong, B. H. J., DeVries, B., Girardin, C. A. J., Kearsley, E., Lindsell, J. A., Lopez-Gonzalez, G., Lucas, R., Malhi, Y., Morel, A., Mitchard, E. T. A., Nagy, L., Qie, L., Quinones, M. J., Ryan, C. M., Ferry, S. J. W., Sunderland, T., Laurin, G. V., Gatti, R. C., Valentini, R., Verbeeck, H., Wijaya, A. and Willcock, S. (2016), An integrated pan-tropical biomass map using multiple reference datasets. Glob Change Biol, 22: 1406–1420. doi:10.1111/gcb.13139

Santoro, M., Beaudoin, A., Beer, C., Cartus, O., Fransson, J.E.S., Hall, R.J., Pathe, C., Schmullius, C., Schepaschenko, D., Shvidenko, A., Thurner, M. and Wegmüller, U. (2015). Forest growing stock volume of the northern hemisphere: Spatially explicit estimates for 2010 derived from Envisat ASAR. Remote Sensing of Environment, Vol. 168, pag. 316-334

Source: Avitabile V, Herold M, Heuvelink G, Lewis SL, Phillips OL, Asner GP et al. (2016). An integrated pan-tropical biomass maps using multiple reference datasets. Global Change Biology, 22: 1406–1420. doi:10.1111/gcb.13139.

The GIS Biomass Atlas of Pakistan is the final output from the biomass resource mapping component of the activity “ Renewable Energy Resource Mapping and Geospatial Planning – Pakistan” [Project ID: P146140]. You can find more information about the project here: https://www.esmap.org/re_mapping_pakistan To visualize the geospatial data generated in this study, please access: https://irena.masdar.ac.ae/GIS/?map=2636 Please cite as: [Data/information/map obtained from the] “World Bank via ENERGYDATA.info, under a project funded by the Energy Sector Management Assistance Program (ESMAP). For more information: Pakistan Biomass GIS Atlas, 2016, https://energydata.info/dataset/pakistan-biomass-gis-atlas"

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.

Remote sensing based maps of tidal marshes, both of their extents and carbon stocks, have the potential to play a key role in conducting greenhouse gas inventories and implementing climate mitigation policies. Our objective was to generate a single remote sensing model of tidal marsh aboveground biomass and carbon that represents nationally diverse tidal marshes within the conterminous United States (CONUS). To meet this objective we developed the first national-scale dataset of aboveground tidal marsh biomass, species composition, and aboveground plant carbon content (%C) from six CONUS regions: Cape Cod, MA, Chesapeake Bay, MD, Everglades, FL, Mississippi Delta, LA, San Francisco Bay, CA, and Puget Sound, WA. We tested how plant community composition and vegetation structure differences across estuaries influence model development, and whether data from multiple sensors, in particular Sentinel-1 C-band synthetic aperture radar and Landsat, can improve model performance. The final model, driven by six Landsat vegetation indices and with the soil adjusted vegetation index as the most important (n=409, RMSE=464 g/m2, 12.2% normalized RMSE), successfully predicted biomass and carbon for a range of marsh plant functional types defined by height, leaf angle and growth form. Model error was reduced by scaling field measured biomass by Landsat fraction green vegetation derived from object-based classification of National Agriculture Imagery Program imagery. We generated 30m resolution biomass maps for estuarine and palustrine emergent tidal marshes as indicated by a modified NOAA Coastal Change Analysis Program map for each region. With a mean plant %C of 44.1% (n=1384, 95% C.I.=43.99% - 44.37%) we estimated mean aboveground carbon densities (Mg/ha) and total carbon stocks for each wetland type for each region. We applied a multivariate delta method to calculate uncertainties in regional carbon estimates that considered standard error in map area, mean biomass and mean %C. The original version 1.0 of the dataset can be obtained by contacting kbyrd@usgs.gov.

DATA OVERVIEW

Mapped attributes are

• above ground biomass, AGB;
• downed wood biomass, i.e., the sum of coarse and fine woody debris, DWB;
• canopy bulk density, CBD;
• canopy height,CH;
• canopy base height, CBH;
• and canopy fuel load, CFL.

Models were fit using auxiliary information that included lidar data from 20 acquisitions in Oregon and climate data. Measurements in plots of the Forest Inventory and Analysis program (FIA) were used to obtain plot-level ground observations for predictive modeling. Tree and transect measurements in FIA plots were respectively used to obtain plot-level values of AGB and DWB. To obtain plot-level values of CBD, CH, CBH and CFL, tree measurements in FIA plots were processed with FuelCalc. Plot level auxiliary variables were obtained intersecting the axiliary information layers with the FIA plots. Predictive models were random forest models in which a parametric component was added to model the error variance. The error variance was modeled as a power function of the predictive value and was used to produce uncertainty maps. A different model was fit for each variable and the resulting models were used to obtain maps of synthetic predictions for all areas covered by the 20 lidar acquisitions. The modeled error variance was used to generate uncertainty maps for the predictions of each response variable. Model accuracy was assessed globally (for the entire dataset) and separately for each one of the 20 lidar acquisitions included in the dataset.

Results from the accuracy assessment can be found in Appendix A and Appendix B of Mauro et al. (2021).

Each variable has two associated maps. These maps are named using the following convention where VARIABLE is the acronym for each variable (AGB, DWB, CBD, CH, CBH or CFL):

Predictions of forest attributes:

VARIABLE.tif

Standard deviation of modeled errors:

SD_VARIABLE.tif

###There are two additional rasters. The first one, year.tif is necessary to obtain the reference year for each lidar acquisition. The second one, forest_mask.tif provides a forest vs non-forest mask. Forested areas are coded as 1s and non-forested areas with no-datas. This mask is a resampled subset of the PALSAR JAXA 2014 ‘New global 25m-resolution PALSAR mosaic and forest/non-forest map (2007-2010) - version 1’ from the Japan Aerospace Exploration Agency Earth Observation Research Center (www.eorc.jaxa.jp/ALOS/en/palsar_fnf/fnf_index.htm). Its reference year is 2009. Models to predict forest attributes were created using ground observations in forested areas. For many applications it is advisable to use the provided mask to excluded non-forested areas from analyses. This can be done, for example, multiplying the desired raster by the forest mask. Exceptions to this may occur in relatively open forested lands where the mask eliminates areas that actually sustain forest. In those areas, the use of an add-hoc forest mask might be more appropriate. ### Reference year: year.tif ### Forest mask: forest_mask.tif ###

UNITS:

For a given variable, both predictions and standard deviation of model errors have the same units. These units are:

• Variable (Abreviation): Units

• Above ground biomass (AGB): Mg/ha

• Downed wood biomass (DWB):Mg/ha

• Canopy bulk density (CBD): Kg/m3 (Kilogram per cubic meter)

• Canopy height (CH): m

• Canopy base height (CBH): m

  Canopy fuel load (CFL):Mg/ha

COORDINATE REFERENCE SYSTEM:

The reference system for all maps is EPSG 5070

USAGE

These data are made freely available to the public and the scientific community in the belief that their wide dissemination will lead to greater understanding and new scientific insights.

Please include the following citation in any publication that uses these data:

Mauro, F., Hudak, A.T., Fekety, P.A., Frank, B., Temesgen, H., Bell, D.M., Gregory, M.J., McCarley, T.R., 2021. Regional Modeling of Forest Fuels and Structural Attributes Using Airborne Laser Scanning Data in Oregon. Remote Sensing 13. https://doi.org/10.3390/rs13020261

Africa Aboveground Biomass (AGB) map at 100m spatial resolution for the year 2017. AGB and its associated uncertainty were mapped for all woody vegetated areas in the continent. This map was developed with funding from the National Centre for Earth Observation (NCEO) Carbon Cycle and Official Development Assistance (ODA) programmes.

This Web Map is a subset of Above and below ground biomass carbon (tonnes/ha)This dataset represents above- and below-ground terrestrial carbon storage (tonnes (t) of C per hectare (ha)) for circa 2010.This layer supports analysis but, if needed, a direct download of the data can be accessed here.The dataset was constructed by combining the most reliable publicly available datasets and overlying them with the ESA CCI landcover map for the year 2010 [ESA, 2017], assigning to each grid cell the corresponding above-ground biomass value from the biomass map that was most appropriate for the grid cell’s landcover type.Input carbon datasets were identified through a literature review of existing datasets on biomass carbon in terrestrial ecosystems published in peer-reviewed literature. To determine which datasets to combine to produce the global carbon density map, identified datasets were evaluated based on resolution, accuracy, biomass definition and reference date (see table 1 for further information on datasets selected).DatasetScopeYearResolutionDefinitionSantoro et al. 2018Global2010100 mAbove-ground woody biomass for trees that are >10 cm diameter-at-breast-height, masked to Landsat-derived canopy cover for 2010; biomass is expressed as oven-dry weight of the woody parts (stem, bark, branches and twigs) of all living trees excluding stump and roots.Xia et al. 2014Global1982-20068 kmAbove-ground grassland biomass.Bouvet et al. 2018Africa201025 mAbove-ground woodland and savannah biomass; low woody biomass areas, which therefore exclude dense forests and deserts.Spawn et al. 2017Global2010300 mSynthetic, global above- and below-ground biomass maps that combine recently released satellite-based data of standing forest biomass with novel estimates for non-forest biomass stocks.After aggregating each selected dataset to a nominal scale of 300 m resolution, forest categories in the CCI ESA 2010 landcover dataset were used to extract above-ground biomass from Santoro et al. 2018 for forest areas. Woodland and savanna biomass were then incorporated for Africa from Bouvet et al. 2018., and from Santoro et al. 2018 for areas outside of Africa and outside of forest. Biomass from croplands, sparse vegetation and grassland landcover classes from CCI ESA, in addition to shrubland areas outside Africa missing from Santoro et al. 2018, were extracted from were extracted from Xia et al. 2014. and Spawn et al. 2017 averaged by ecological zone for each landcover type.Below-ground biomass were added using root-to-shoot ratios from the 2006 IPCC guidelines for National Greenhouse Gas Inventories (IPCC, 2006). No below-ground values were assigned to croplands as ratios were unavailable. Above- and below-ground biomass were then summed together and multiplied by 0.5 to convert to carbon, generating a single above-and-below-ground biomass carbon layer.This dataset has not been validated.

These data represent a portion of the forest inventory data used in Hudak et al. (2020) "A carbon monitoring system for mapping regional, annual aboveground biomass across the northwestern USA". This study used forest inventory data located in lidar units along with Landsat data, topographic metrics, and climate metrics to create maps of forested biomass across the northwestern USA (Washington, Oregon, Idaho, and western Montana.) The data requirements for inclusion in this study included: 1. fixed-area plots, 2. plots centers were recorded using a global navigation satellite system receiver (e.g., a GPS receiver) capable of differential correction, and 3. plots were located in a lidar unit where tree data were collected within 3 years of the lidar collection. A shapefile of the lidar units can be found in Fekety and Hudak (2020, https://doi.org/10.3334/ORNLDAAC/1766). The forest inventory data presented here (n = 2,680 plots) include all data that could be made publicly available and have been compiled from numerous existing datasets. The forest inventory data were collected using project-specific sampling plans and therefore these data have been formatted to be read by the Forest Vegetation Simulator (FVS; https://www.fs.usda.gov/fvs/). The forest inventory data in this dataset were collected between 2002 and 2017 and located in Idaho, Oregon, and Washington, USA.These data were used in combination with lidar, topographic, and climate datasets to create annual aboveground biomass maps across the northwestern United States for the years 2000-2016.These data were published on 04/29/2020. Minor metadata updates were made On 04/18/2024.

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.

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.

The China Annual Forest Aboveground Biomass Time-Series Dataset (CFATD) is the first comprehensive, high-resolution annual dataset specifically developed for monitoring forest aboveground biomass (AGB) across China. Covering the period from 1985 to 2023, this dataset was generated using a combination of multi-source remote sensing data and deep learning techniques, including residual neural networks. CFATD provides 30-meter resolution annual AGB density (AGBD) estimates, enabling a detailed assessment of spatiotemporal biomass dynamics in China’s forests.

CFATD is publicly available on the Google Earth Engine (GEE) platform, with updates planned annually. The dataset can be accessed via the following link:
👉 https://code.earthengine.google.com/4f8ad8d32ddb84e826e941a95f31f9be

Users can explore both AGBD estimates and the corresponding uncertainty layers (available only via GEE), supporting in-depth analysis of forest biomass trends and dynamics. Please note that uncertainty estimates are not included in the Zenodo repository.

The unit of CFATD is megagrams per hectare (Mg/ha). This is a density value, already normalized per hectare, so no further conversion based on pixel area (900 m²) is required when interpreting per-pixel values. Each pixel represents the AGBD of the forest within that 30 m × 30 m grid cell. However, to compute total biomass or carbon stock, AGBD values should be multiplied by the corresponding forest area (e.g., pixel area × tree cover fraction), following the approach outlined in our related publication (Cai et al., essdd, 2025).

The tree cover dataset used in these calculations, the China Annual Tree Cover Dataset (CATCD), is available from our peer-reviewed article:
👉 https://doi.org/10.1016/j.isprsjprs.2024.08.001
This article provides both GEE access and offline download links for CATCD.

Important note: The dataset may contain a small number of unusually high AGB values in certain regions (e.g., >1500 Mg/ha), which may reflect outliers or model artifacts rather than actual forest conditions. While these values occur only in a very limited portion of the dataset, users are advised to apply appropriate filters or quality control procedures when conducting sensitive analyses. These issues will be addressed in future dataset updates.

Citation:
Cai, Y., Zhu, P., Li, X., Liu, X., Chen, Y., Shen, Q., Xu, X., Zhang, H., Nie, S., Wang, C., Wang, J., Li, B., Wu, C., and Zhuang, H.: Dynamics of China’s Forest Carbon Storage: The First 30 m Annual Aboveground Biomass Mapping from 1985 to 2023, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2025-96, in review, 2025.

For data-related inquiries, please contact:
Dr. Yaotong Cai (caiyt33@mail2.sysu.edu.cn)

Woody biomass across the state of Alaska, from the Alaska Energy Gateway.Source: Alaska Energy AuthorityThis data is provided as a service in the DCRA Information Portal by the Alaska Department of Commerce, Community, and Economic Development Division of Community and Regional Affairs (SOA DCCED DCRA), Research and Analysis section. SOA DCCED DCRA Research and Analysis is not the authoritative source for this data. For more information and for questions about this data, see: Alaska Energy Data Gateway

“Biomass Inventory Mapping and Analysis – Business Data” provides a number of datasets related to the yield and production of residues from the agricultural and forestry industry, agricultural crops, and municipal solid wastes across Canada. The datasets contain agricultural residue production information (i.e., straw or stover) for barley, wheat, flax, oats and corn, and crop production information for barley, wheat, flax, oats, corn, canola and soybean. They also include information about amounts of straw required for cattle bedding and feeding, the type of tillage used in an area, and the amount of residue needed for soil conservation purposes. Datasets in the series provide the yield, production and other information for the median year and 1-in-10 year and 1-in-20 year lows. The forestry inventory dataset provides information about the location and quantity of residues from the forestry industry, as well as urban wood waste and potential sites and productivity of plantations of fast-growing trees that are grown as feedstock. Forestry residues include material left at the roadside after harvesting and excess and waste materials from mills. The municipal solid waste inventory dataset provides information about the approximate location and quantity of different types of municipal solid wastes, such as organics (including food and yard), paper and total. A transportation network dataset and datasets that are used to calculate cost to harvest and transport biomass are also included in this series.

This dataset comprises estimates of forest above-ground biomass for the years 2010, 2015, 2016, 2017, 2018, 2019, 2020 and 2021. They are derived from a combination of Earth observation data, depending on the year, from the Copernicus Sentinel-1 mission, Envisat’s ASAR (Advanced Synthetic Aperture Radar) instrument and JAXA’s (Japan Aerospace Exploration Agency) Advanced Land Observing Satellite (ALOS-1 and ALOS-2), along with additional information from Earth observation sources. The data has been produced as part of the European Space Agency's (ESA's) Climate Change Initiative (CCI) programme by the Biomass CCI team. This release of the data is version 5. Compared to version 4, version 5 consists of an update of the three maps of AGB (aboveground biomass) for the years 2010, 2017, 2018, 2019, 2020 and new AGB maps for 2015, 2016 and 2021. New AGB change maps have been created for consecutive years (2015-2016, 2016-2017 and 2020-2021), alongside an update of change maps for years 2010-2020, 2017-2018, 2018-2019 and 2019-2020, and for a decadal interval (2020-2010). The pool of remote sensing data now includes multi-temporal observations at L-band for all biomes and for all years. The AGB maps rely on revised allometries which are now based on a longer record of spaceborne LiDAR data from the GEDI and ICESat-2 missions. Temporal information is now implemented in the retrieval algorithm to preserve biomass dynamics as expressed in the remote sensing data. Biases between 2010 and more recent years have been reduced.The data products consist of two (2) global layers that include estimates of:1) above ground biomass (AGB, unit: tons/ha i.e., Mg/ha) (raster dataset). This is defined as the mass, expressed as oven-dry weight of the woody parts (stem, bark, branches and twigs) of all living trees excluding stump and roots2) per-pixel estimates of above-ground biomass uncertainty expressed as the standard deviation in Mg/ha (raster dataset)Additionally provided in this version release are new aggregated data products. These aggregated products of the AGB and AGB change data layers are available at coarser resolutions (1, 10, 25 and 50km).In addition, files describing the AGB change between two consecutive years (i.e., 2015-2016, 2016-2017, 2018-2017, 2019-2018, 2019-2020, 2020-2021) and over a decade (2020-2010) are provided (labelled as 2015_2016, 2016_2017, 2017_2018, 2018_2019, 2019_2020 and 2020_2010). Each AGB change product consists of two sets of maps: the standard deviation of the AGB change and a quality flag of the AGB change. Note that the change itself can be simply computed as the difference between two AGB maps, so is not provided directly.Data are provided in both netcdf and geotiff format.This version represents an update of v5.0 which was missing a number of tiles covering islands on the Pacific and Indian Ocean and one tile covering Scandinavia north of 70 deg latitude.

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.

This dataset contains the annual woody vegetation's aboveground biomass (AGB) and belowground biomass (BGB) maps in China from 2003 to 2020. The spatial resolution is 1km (1/120 degree), while the uncertainty maps of AGB and BGB are also provided. To develop this dataset, we first integrated the up-to-date satellite-based forest AGB and height maps by referring to large quantities of woodland plots throughout China. Next, we developed an improved vegetation water content (VWC) dataset covering 2003~2020 by fusing the vegetation optical depth retrieved from various microwave remote sensors. VWC and the vegetation cover derived from optical remote sensing were subsequently linked to the calibrated reference AGB map for a continuous AGB mapping in China during 2003~2020, which was downscaled to ~ 1 km resolution. Afterwards, based on the collated AGB and BGB in forest and shrubland plots, we developed the random forest model or regression model that can transform the plot-level AGB into the woodland plots' BGB. Finally, we solved the problem resulting from the scale difference between plot measurements and satellite observations to ensure the application of the plot data-based models.

This file provides the pan-tropical biomass map published by Avitabile et al. (2016) "An integrated pan-tropical biomass map using multiple reference datasets". The data shows the aboveground biomass in Mg per ha in the tropic region at approximately 1 km resolution. For a proper use and description of this dataset, please refer to the mentioned article.

Avitabile, V., Herold, M., Heuvelink, G. B. M., Lewis, S. L., Phillips, O. L., Asner, G. P., Armston, J., Ashton, P. S., Banin, L., Bayol, N., Berry, N. J., Boeckx, P., de Jong, B. H. J., DeVries, B., Girardin, C. A. J., Kearsley, E., Lindsell, J. A., Lopez-Gonzalez, G., Lucas, R., Malhi, Y., Morel, A., Mitchard, E. T. A., Nagy, L., Qie, L., Quinones, M. J., Ryan, C. M., Ferry, S. J. W., Sunderland, T., Laurin, G. V., Gatti, R. C., Valentini, R., Verbeeck, H., Wijaya, A. and Willcock, S. (2016), An integrated pan-tropical biomass map using multiple reference datasets. Glob Change Biol, 22: 1406–1420. doi:10.1111/gcb.13139