The U.S. has been providing national-scale estimates of forest carbon stocks and stock change to meet United Nations Framework Convention on Climate Change reporting requirements for years. Through application of a nearest-neighbor imputation approach, mapped estimates of forest biomass density were developed for the contiguous United States using the annual forest inventory conducted by the USDA Forest Service Forest Inventory and Analysis (FIA) program, MODIS satellite imagery, and ancillary geospatial datasets. This data product would contain the following 7 raster maps: Aboveground Forest Biomass, Belowground Forest Biomass, Forest Tree Bole Biomass, Forest Sapling Biomass, Forest Stump Biomass, Forest Top Biomass, Woodland Specias Biomass. All layers have a 250 meter pixel resolution and values represent biomass pounds per acre. The paper on which these maps are based may be found here: https://dx.doi.org/10.2737/RDS-2013-0004 Access to full metadata and other information can be accessed here: https://dx.doi.org/10.2737/RDS-2013-0004This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService For complete information, please visit https://data.gov.

The U.S. has been providing national-scale estimates of forest carbon stocks and stock change to meet United Nations Framework Convention on Climate Change reporting requirements for years. Through application of a nearest-neighbor imputation approach, mapped estimates of forest biomass density were developed for the contiguous United States using the annual forest inventory conducted by the USDA Forest Service Forest Inventory and Analysis (FIA) program, MODIS satellite imagery, and ancillary geospatial datasets. This data product would contain the following 7 raster maps: Aboveground Forest Biomass, Belowground Forest Biomass, Forest Tree Bole Biomass, Forest Sapling Biomass, Forest Stump Biomass, Forest Top Biomass, Woodland Specias Biomass. All layers have a 250 meter pixel resolution and values represent biomass pounds per acre. The paper on which these maps are based may be found here: https://dx.doi.org/10.2737/RDS-2013-0004 Access to full metadata and other information can be accessed here: https://dx.doi.org/10.2737/RDS-2013-0004

The U.S. has been providing national-scale estimates of forest carbon stocks and stock change to meet United Nations Framework Convention on Climate Change reporting requirements for years. Through application of a nearest-neighbor imputation approach, mapped estimates of forest biomass density were developed for the contiguous United States using the annual forest inventory conducted by the USDA Forest Service Forest Inventory and Analysis (FIA) program, MODIS satellite imagery, and ancillary geospatial datasets. This data product would contain the following 7 raster maps: Aboveground Forest Biomass, Belowground Forest Biomass, Forest Tree Bole Biomass, Forest Sapling Biomass, Forest Stump Biomass, Forest Top Biomass, Woodland Specias Biomass. All layers have a 250 meter pixel resolution and values represent biomass pounds per acre. The paper on which these maps are based may be found here: https://dx.doi.org/10.2737/RDS-2013-0004 Access to full metadata and other information can be accessed here: https://dx.doi.org/10.2737/RDS-2013-0004

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.

I obtained inventory data from the USDA Forest Inventory and Analysis (FIA) program, covering 1999 to 2022, focusing on eight southeastern states in the U.S. (Alabama, Mississippi, Florida, Georgia, North Carolina, South Carolina, Texas, and Louisiana). For this study, I filtered the data for 2015-2019, during which the FIA captures 20% of plots in each state annually. Using R, I converted aboveground biomass (AGB) from pounds to kg/ha, tree height from feet to meters, and diameter at breast height (DBH) from inches to centimeters to align with the metric system. Structural diversity of tree height and diameter was calculated using the Shannon diversity index based on tree height and diameter classes. Climate data from PRISM and soil carbon to nitrogen data from the World Soil Database were integrated with the FIA data using the nearest neighbors method in the SF package in R. The combined dataset was standardized for Structural Equation Modeling (SEM) analysis.

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.

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.

State and local governments are increasingly interested in understanding the role forests and harvested wood products play in regional carbon sinks and storage, their potential contributions to state-level greenhouse gas (GHG) reductions, and the interactions between GHG reduction goals and potential economic opportunities. We used empirically driven process-based forest carbon dynamics and harvested wood product models in a systems-based approach to project the carbon impacts of various forest management and wood utilization activities in Maryland and Pennsylvania from 2007–2100. To quantify state-wide forest carbon dynamics, we integrated forest inventory data, harvest and management activity data, and remotely-sensed metrics of landuse change and natural forest disturbances within a participatory modeling approach. We accounted for net GHG emissions across (1) forest ecosystems (2) harvested wood products, (3) substitution benefits from wood product utilization, and (4) leakage associated with reduced instate harvesting activities. Based on state agency partner input, a total of 15 management scenarios were modeled for Maryland and 13 for Pennsylvania, along with two climate change scenarios and two bioenergy scenarios for each state. Our findings show that both strategic forest management and wood utilization can provide substantial climate mitigation potential relative to business-as-usual practices, increasing the forest C sink by 29% in Maryland and 38% in Pennsylvania by 2030 without disrupting timber supplies. Key climate-smart forest management activities include maintaining and increasing forest extent, fostering forest resiliency and natural regeneration, encouraging sustainable harvest practices, balancing timber supply and wood utilization with tree growth, and preparing for future climate impacts. This study adds to a growing body of work that quantifies the relationships between forest growth, forest disturbance, and harvested wood products. Methods Primary data inputs to CBM-CFS3 included a detailed forest inventory, growth-yield relations to estimate forest productivity, and estimates of harvest yields and intensity, land-use change, and natural disturbances. Inventory data are categorized by a series of forest classifiers defining relevant characteristics such as spatially referenced boundaries, ownership, forest type, site productivity, or reserve status. Allometric equations are used to predict tree volume-to-biomass relationships (Boudewyn et al., 2007). For this study, forest inventory, growth-yield curves, and harvest data were estimated from the USDA Forest Service Forest Inventory and Analysis (FIA) program, which we accessed through the FIA DataMart (USDA Forest Service, 2019) using the rFIA package (Stanke et al., 2020) in the R programming environment (R Core Team, 2020), which enables data exploration and user-defined spatio-temporal queries and estimation of the FIA database (FIADB). Methodologies derived from Bechtold & Patterson, 2005 and Pugh et al., 2018 were used to estimate each state’s forest inventory by a predetermined list of classifiers. Natural disturbance history was estimated from both the FIADB and LANDFIRE (USGS, 2016) datasets to better constrain initial belowground and soil carbon parameters during what the modeling framework refers to as the model spin-up period (Kurz et al., 2009). Estimates of merchantable volume and corresponding biomass from FIADB were used to calibrate the model’s allometric volume-to-biomass assumptions to match forest type groups and growth conditions in Maryland and Pennsylvania. Harvest removals were estimated as average annual removal of merchantable timber in cubic feet between 2007 and 2019, converted to metric tons of carbon using methodologies and specific gravities reported by Smith et al., 2006. To assign a harvest type and intensity to each record of volumetric removal, stand age at the time of removal was calculated by taking the mid-point average between time t1 and t2 (Bechtold & Patterson, 2005) where t1 is the year the unharvested stand was measured and t2 is the repeat interval year measurement post-harvest. In collaboration with state partners, harvest type and intensity were determined heuristically for each forest type based upon state-level management documentation, peer-reviewed literature, and expert input. A complete list of harvest types and intensities prescribed to each forest type group as a product of stand age can be found in Table S3 of the manuscript. Longer-term averages from 2007-2019 were used to estimate annual area targets for all land-use change (LUC) and natural disturbance events including wind, fire, disease, and insects. Annual LUC average rates by ownership and forest type group were derived by overlaying a geospatial forestland ownership dataset (Sass et al., 2020), the Protected Areas Database of the U.S. (PAD-US), a national geodatabase of protected areas (USGS, 2018), and the National Land Cover Database (NLCD), a remotely-sensed data product used to characterize land cover and land cover change (Wickham et al., 2021). Wind disturbance events were calculated using the LANDFIRE Historic Disturbance dataset (USGS, 2016), a remotely-sensed data product provided by the USGS that estimates annual disturbance events. Annual averages for wildfire disturbances were derived from the LANDFIRE Historic Disturbance dataset (USGS, 2016) and validated through annual reports from the National Interagency Fire Center (NIFC). Annual prescribed fire acres were estimated from reports provided by the Maryland DNR Forest Service and Pennsylvania DCNR Bureau of Forestry and scaled to represent treatments on forestlands only. Annual acreages of insect and disease disturbance were derived from the National Insect & Disease Detection Survey (USDA Forest Service, 2020), a spatial data product produced by USDA that collects and reports data on forest insects, diseases, and other disturbances. For more information on all input and activity data see Supplementary Materials 1.2. A complete list of BAU parameters can be found in Table S2. State-specific trade and commodity data from Resource Planning Act (RPA) assessments (USDA Forest Service, 2021), US Commodity Flow Surveys (US Department of Transportation et al., 2020), US International Trade Commission export data (US International Trade Commission, 2021), and published peer-reviewed data (Howard & Liang, 2019) when available, or US averages from the same sources, were used to adapt and parameterize both HWP models. FAOSTAT data (FAO, 2021) were utilized to determine the commodity distributions of exported roundwood. Softwood products were parameterized and modeled separately from hardwood products, as the two wood types differ in exports and commodities produced as well as their associated product half-lives and displacement (Dymond, 2012; Howard et al., 2017). Published data were used to calculate softwood- and hardwood-specific half-lives for Maryland and Pennsylvania sawn wood and veneer products, while we relied on literature estimates for other products (Skog, 2008; J. E. Smith et al., 2006b). To calculate substitution benefits, we coupled region-specific data (USDA Forest Service, 2021), US consumption rates (Howard et al., 2017), product weights (C. Smyth et al., 2017), and LCA data (Bala et al., 2010; Dylewski & Adamczyk, 2013; Hubbard et al., 2020; Meil & Bushi, 2013; Puettmann, 2020a, 2020b; Puettmann et al., 2016; Puettmann & Salazar, 2018, 2019), following methods developed by Smyth et al. (2017). Landfill CO2 and CH4 emissions rely on PICC defaults for methane generation (k) and landfill half-lives for wet, temperate climates (Pingoud et al., 2006). See Supplementary Materials 1.3 for more details on substitution and leakage calculation methods.

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 2001-2023 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. 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 https://manzanita.forestry.oregonstate.edu/ecoservices/ and described in Underwood et al. (2022). REFERENCES 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 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 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. 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. Methods METHODS We generated spatial estimates of above ground live biomass (AGLBM, in kg/m2) for 2000-2021 for our southern California study area. The study area, totaling 6,441,208 ha, is defined by the 42 Level IV Ecoregions (Bailey 2016) that intersect the four southern US Department of Agriculture (USDA) National Forests in southern California (Figure 1). We created biomass raster layers (30m spatial resolution) by modeling a set of covariates (Normalized Difference Vegetation Index [NDVI], precipitation, solar radiation, actual evapotranspiration, aspect, slope, climatic water deficit, elevation, flow accumulation, landscape facets, hydrological recharge and runoff, and soil type) to predict AGLBM using 766 field plots of biomass from the USDA Forest Service Forest Inventory and Analysis (FIA); the Landfire Reference Database (LFRDB) plot data; and other research plots. The dates of field data spanned 2001-2012. The NDVI raster data were derived from Landsat TM/ETM+/OLI multispectral satellite data (onboard Landsat 5, 7, and 8, respectively). NDVI data were composited from all available Landsat images for the months of July and August for each year 2001-2023. We also downloaded annual precipitation data for each water year (October 1 - September 30) 2001-2021 from PRISM (http://www.prism.oregonstate.edu/). For each field plot, we extracted the raster values for all covariates; NDVI and precipitation data were matched to the year of plot visit. We predicted AGLBM using the set of 17 covariates (Random Forest [RF] algorithm in R statistical computing software). To create an AGLBM raster surface for each year 2001-2023, we used NDVI and precipitation raster data specific to each year in the RF (using predict function in the R raster module) (see Schrader-Patton and Underwood 2021 for details). To estimate other shrubland biomass pools (standing dead, litter, and below ground) we employed a multi-step process: 1) First, we segregated the study area by community type using the California Wildlife Habitat Relationships (CWHR) data (Mayer and Laudenslayer 1988). The wildland vegetation of the study area (excluding agricultural, urban, water, and barren classes) contains 45 CWHR classes, 14 of which are >=0.75% of the wildland vegetation in the study area. We divided these 14 classes into shrubland dominated versus non-shrubland dominated types (annual grass, oak, conifer, mixed hardwood) (Table 1). Table 1. The Community types (WHR class) that are >= 0.75% of all wildland vegetation in the study area and their % area of the southern California ecoregion

Community type (WHR class)

Vegetation type

Percent of wildland vegetation in study area

Mixed Chaparral

Shrub

29.2

Annual Grassland

Annual grass

15.9

Desert Scrub

Shrub

12.7

Coastal Scrub

Shrub

12.5

Coastal Oak Woodland

Oak

6.4

Chamise-Redshank Chaparral

Shrub

6.0

Pinyon-Juniper

Conifer

2.5

Montane Hardwood

Mixed hardwood

2.3

Blue Oak Woodland

Oak

2.0

Sierran Mixed Conifer

Conifer

1.2

Juniper

Conifer

1.1

Montane Hardwood-Conifer

Mixed hardwood-conifer

1.1

Montane Chaparral

Shrub

1.0

Sagebrush

Shrub

0.9

2) Second, for the shrubland types we determined the per pixel proportion of biomass by three plant life forms: tree, shrub, and herb. We further subdivided the shrub life form into three life history classes based on shrub post-fire regeneration strategies: Obligate Resprouters (OR), obligate seeders (OS), and facultative seeders (FS), providing five plant types in total. We created rasters depicting the proportion of biomass in each of the five plant types by first calculating the proportion of biomass in each type for the plots used in Schrader-Patton and Underwood (2021). The plot data contained individual plant species, crown width and height measurements. Using these measurements, we estimated the biomass for each individual plant within the plot by applying published allometric equations (see Schrader-Patton and Underwood 2021 for details). The individual plants in the plots were classified into the five plant types and the proportion of biomass in each type were calculated for each plot. A multinomial model was used to relate these proportions to a suite of geophysical and remote sensing variables which, in turn, was applied to raster surfaces of these predictors. The final outputs were raster maps of the proportion of biomass by life form (tree, shrub, herb) and, for shrubs, the proportion of biomass by life history type (OR, OS, and FS) (Underwood et al. in review). 3) Third, we estimated the standing dead, litter, and below ground biomass pools by either applying fractions of AGLBM gleaned the available published literature or by using biomass estimates in existing spatial datasets. The specific method used was dependent on the percentage of the WHR class in the study area and the vegetation type (shrub or non-shrub) (Figure 2).
a) For shrubland types >= 0.75% of all wildland vegetation in the study area (Mixed Chaparral, Desert Scrub, Coastal Scrub, Chamise Redshank Chaparral, Montane Chaparral, and Sagebrush), we used the proportion of the five plant types as a basis for applying the AGLBM factors from the literature. For litter estimates, we applied AGLBM factor of 0.78 (derived from Bohlman et al. 2018) to Mixed chaparral, Chamise-Redshank Chaparral, and Coastal scrub WHR classes. These shrubland types also contained tree and herb biomass. We estimated the litter and standing dead biomass for these plant types by multiplying the plant type proportion by AGLBM (Tree and herb AGLBM), or by the North American Wildland Fuels Database (NAWFD, Pritchard et al. 2018) litter biomass (Tree and herb litter and standing dead biomass), or by literature-derived factors (Tree and herb belowground biomass). Sagebrush, Montane chaparral, and Desert scrub were assigned litter biomass from the NAWFD data as these WHR types had no litter estimates in the literature.
b) For non-shrubland types >= 0.75% all wildland vegetation in the study area (Coastal Oak Woodland, Pinyon-Juniper, Montane Hardwood, Blue Oak Woodland, Sierran Mixed Conifer, Juniper, and Montane Hardwood-Conifer), the snag and litter NAWFD biomass estimates were used for standing dead and litter estimates, respectively. For belowground biomass, we used AGLBM factors from the literature based on the gross vegetation type (Oak, Conifer, or Mixed) and amount of total per pixel AGLBM. For example, for Oak WHR types (Coastal Oak Woodland, Blue Oak Woodland) <= 7 kg/m2 we used an AGLBM factor of 0.46 (see Mokany et al. 2006 for breakdown by class breaks). c) For all the remaining WHR classes (each < 0.75% of all wildland vegetation in the study area) and Annual Grasslands, we used the NAWFD snag and litter estimates (standing dead and litter biomass), and the California Air Resources Board (CARB, Battles et al. 2014) for our belowground estimates. The above ground, litter, standing dead, and below ground biomass raster layers for each

The NBCD 2000 (National Biomass and Carbon data set for the Year 2000) data set provides a high-resolution (30 m) map of year-2000 baseline estimates of basal area-weighted canopy height, above-ground, 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.

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 data set 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 data set (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.

Maps of the number, size, and species of trees in forests across the western United States are desirable for many applications such as estimating terrestrial carbon resources, predicting tree mortality following wildfires, and for forest inventory. However, detailed mapping of trees for large areas is not feasible with current technologies, but statistical methods for matching the forest plot data with biophysical characteristics of the landscape offer a practical means to populate landscapes with a limited set of forest plot inventory data. We used a modified random forests approach with Landscape Fire and Resource Management Planning Tools (LANDFIRE) vegetation and biophysical predictors as the target data, to which we imputed plot data collected by the USDA Forest Service’s Forest Inventory Analysis (FIA) to the landscape at 30-meter (m) grid resolution (Riley et al. 2016). This method imputes the plot with the best statistical match, according to a “forest” of decision trees, to each pixel of gridded landscape data. In this work, we used the LANDFIRE data set as the gridded target data because it is publicly available, offers seamless coverage of variables needed for fire models, and is consistent with other data sets, including burn probabilities and flame length probabilities generated for the continental United States. The main output of this project (the GeoTIFF available in this data publication) is a map of imputed plot identifiers at 30×30 m spatial resolution for the western United States for landscape conditions circa 2009. The map of plot identifiers can be linked to the FIA databases available through the FIA DataMart or to the ACCDB/CSV files included in this data publication to produce tree-level maps or to map other plot attributes. These ACCDB/CSV files also contain attributes regarding the FIA PLOT CN (a unique identifier for each time a plot is measured), the inventory year, the state code and abbreviation, the unit code, the county code, the plot number, the subplot number, the tree record number, and for each tree: the status (live or dead), species, diameter, height, actual height (where broken), crown ratio, number of trees per acre, and a unique identifier for each tree and tree visit. Application of the dataset to research questions other than those related to aboveground biomass and carbon should be investigated by the researcher before proceeding. The dataset may be suitable for other applications and for use across various scales (stand, landscape, and region), however, the researcher should test the dataset's applicability to a particular research question before proceeding.Geospatial data describing tree species or forest structure are required for many analyses and models of forest landscape dynamics. Forest data must have resolution and continuity sufficient to reflect site gradients in mountainous terrain and stand boundaries imposed by historical events, such as wildland fire and timber harvest. Such detailed forest structure data are not available for large areas of public and private lands in the United States, which rely on forest inventory at fixed plot locations at sparse densities. While direct sampling technologies such as light detection and ranging (LiDAR) may eventually make broad coverage of detailed forest inventory feasible, no such data sets at the scale of the western United States are currently available.When linking the tree list raster (“CN_text” field) to the FIA data via the plot CN field (“CN” in the “PLOT” table and “PLT_CN” in other tables), note that this field is unique to a single visit to a plot. The raster contains a “Value” field, which also appears in the ACCDB/CSV files in the “tl_id” field in order to facilitate this linkage. All plot CNs utilized in this analysis were single condition, 100% forested, physically located in the Rocky Mountain Research Station (RMRS) and Pacific Northwest Research Station (PNW) obtained from FIA in December of 2012.

Original metadata date was 01/03/2018. Minor metadata updates made on 04/30/2019.

Fragmentation transforms the environment along forest edges. The prevailing narrative, driven by tropical research, suggests that edge environments increase tree mortality and structural degradation resulting in net decreases in ecosystem productivity. We show that temperate forest edges exhibit increased forest growth (basal area increment; BAI) and biomass (basal area; BA) with no change in total mortality relative to the forest interior. To assess forest edges, we analyzed more than 48,000 forest inventory plots (USDA FIA) across the north-eastern US using a quasi-experimental matching design. At forest edges adjacent to anthropogenic land covers, we report increases of 36.3% and 24.1% in forest growth and biomass, respectively. We then scale the edge impacts on growth (along anthropogenic edges only) across our study area using maps of land-cover and forest type. We find large variability in the effect of including edges on estimates of total forest growth, largely driven by differences in the prevalence of fragmentation. Estimated increases in forest growth range from a 23% increase in the agricultural-dominated western areas, a 2% increase in the least-fragmented northern regions, and a 15% increase within the metropolitan east coast. Finally, we also quantify forest fragmentation globally, at 30-m resolution, showing that temperate forests contain 52% more edge forest area than tropical forests. We provide two tables containing the post-matched dataset of FIA subplots, including subplot BA, BAI, and edge status. We include the associated environmental covariates, extracted from gridded raster data, and used in our matching and statistical analyses. Due to plot confidentiality restrictions we do not provide spatial locations of the FIA subplots, but we do provide unique plot identifiers that allow users to link each record to the publically-available data provided by the USDA FIA database (https://apps.fs.usda.gov/fia/datamart/). This dataset can be used to recreate our regression modelling results and regional estimates of forest growth. We also provide two polygon shapefile layers: EPA Level IV Ecoregion boundaries with spatially-aggregated estimates of edge and total BAI (m2 yr-1) as well as total forest area and edge forest area (forest less than 30 m from a non-forest pixel)and global terrestrial ecoregions boundaries with spatially-aggregated estimates of total forest area and edge forest area (forest less than 30 m from a non-forest pixel).

The U.S. has been providing national-scale estimates of forest carbon stocks and stock change to meet United Nations Framework Convention on Climate Change reporting requirements for years. Through application of a nearest-neighbor imputation approach, mapped estimates of forest biomass density were developed for the contiguous United States using the annual forest inventory conducted by the USDA Forest Service Forest Inventory and Analysis (FIA) program, MODIS satellite imagery, and ancillary geospatial datasets. This data product would contain the following 7 raster maps: Aboveground Forest Biomass, Belowground Forest Biomass, Forest Tree Bole Biomass, Forest Sapling Biomass, Forest Stump Biomass, Forest Top Biomass, Woodland Specias Biomass. All layers have a 250 meter pixel resolution and values represent biomass pounds per acre. The paper on which these maps are based may be found here: https://dx.doi.org/10.2737/RDS-2013-0004 Access to full metadata and other information can be accessed here: https://dx.doi.org/10.2737/RDS-2013-0004

This product is part of the TreeMap data suite. It provides detailed spatial information on forest characteristics including number of live and dead trees, biomass, and carbon across the entire forested extent of the continental United States in 2016. TreeMap v2016 contains one image, a 22-band 30 x 30m resolution …

This data publication contains forest carbon data estimates developed in part to comply with existing United States (US) commitments for forest carbon stocks and stock-change under the United Nations Framework Convention on Climate Change (UNFCCC). The national greenhouse gas (GHG) inventory is required for UNFCCC Annex 1 parties such as the US, and should be provided to the UNFCCC Secretariat each year by April 15. The estimates were adopted by the US Environmental Protection Agency (EPA), which prepares the official annual inventory of US GHG emissions and sinks. This archive provides the forest carbon estimates underlying the summary numbers of EPA (2008) at the most fundamental level: inventory plots collected by the USDA Forest Service Forest Inventory & Analysis (FIA; US Forest Service 2014) for most of the US, but also at the sub-state level which allows for additional coverage due to data limitations at the plot level. Data at these intermediate scales are determined as essential steps in developing the totals, which are available through the EPA publication. The data in this archive may be used for disaggregated analysis and alternate summaries. These carbon estimates for each year 1990 through 2008 reflect forest inventory data publicly available at mid-2007. Much of these underlying FIA data were also used in EPA national GHG inventories in the mid-2000s, and for USDA (2008). Although a partial set of the underlying FIA datasets used may be publicly available, the specific full underlying datasets used are no longer publicly available. GHG inventories published after 2008 through current year are based on much more recent annualized FIA data.The estimates were developed as a direct extension of the extensive USDA Forest Inventory & Analysis (FIA) inventory data, to characterize stocks and change on US forest lands. The FIA data are the basis for the official forest statistics of the United States, and this approach provides carbon estimates consistent with these data used to provide official US forest resource statistics.Original metadata date was 12/23/2014. Minor metadata updates were made on 12/14/2016 and 05/04/2020.

This dataset includes two products from Martinuzzi et al. (2022): "biomass.tif" is a 26-m resolution forest biomass (AGB) map for Puerto Rico derived from NASA G-LiHT lidar data and forest inventory data (FIA plots), in raster format. "input_multivariate_v2.shp" is a point shapefile with information on forest age, substrate, past land use, topographic wetness, slope, and precipitation, for each forest pixel. These two datasets can be used to evaluate spatial patterns of AGB in second-growth forests across transects of lidar data in humid forests of Puerto Rico, and to analyze relationship(s) between AGB and environmental variables. Additional information on these products can be found on the supporting file called "Readme.txt" included within the data archive, as well as in the original manuscript by Martinuzzi et al (2022).

TreeMap 2016 provides a tree-level model of the forests of the conterminous United States.Metadata and DownloadsWe matched forest plot data from Forest Inventory and Analysis (FIA) to a 30x30 meter (m) grid. TreeMap 2016 is being used in both the private and public sectors for projects including fuel treatment planning, snag hazard mapping, and estimation of terrestrial carbon resources. We used a random forests machine-learning algorithm to impute the forest plot data to a set of target rasters provided by Landscape Fire and Resource Management Planning Tools (LANDFIRE: https://landfire.gov). Predictor variables consisted of percent forest cover, height, and vegetation type, as well as topography (slope, elevation, and aspect), location (latitude and longitude), biophysical variables (photosynthetically active radiation, precipitation, maximum temperature, minimum temperature, relative humidity, and vapour pressure deficit), and disturbance history (time since disturbance and disturbance type) for the landscape circa 2016. The main output of this project (the GeoTIFF included in this data publication) is a raster map of imputed plot identifiers at 30X30 m spatial resolution for the conterminous U.S. for landscape conditions circa 2016. In the attribute table of this raster, we also present a set of attributes drawn from the FIA databases, including forest type and live basal area. The raster map of plot identifiers can be linked to the FIA databases available through the FIA DataMart (https://doi.org/10.2737/RDS-2001-FIADB). The dataset has been validated for applications including percent live tree cover, height of the dominant trees, forest type, species of trees with most basal area, aboveground biomass, fuel treatment planning, and snag hazard. Application of the dataset to research questions other than those for which it has been validated should be investigated by the researcher before proceeding. The dataset may be suitable for other applications and for use across various scales (stand, landscape, and region), however, the researcher should test the dataset's applicability to a particular research question before proceeding.This raster dataset represents model output generated by a random forests method that assigns Forest Inventory Analysis plot identifiers to a 30x30m grid (Riley et al. 2016 and Riley et al. 2021). Some attributes provided have been validated as detailed below, and we have high confidence they would be suitable for stand, county, and national scale analyses. Other attributes have not been validated as of this writing on 2/25/2022. Accuracy may vary regionally. This dataset is for the landscape circa 2016 and does not capture disturbances such as fire and land management after that date. Based on a set of FIA validation plots, these data have moderate to high accuracy at point locations for forest cover, height, vegetation group, and recent disturbance by fire and insects and disease (Riley et al. 2021). Summary statistics at Baileys section and subsection levels indicate high accuracy in most sections and subsections when compared to FIA statistics for live basal area, number of live trees greater than or equal to 1 diameter, live cubic-foot volume, and live-tree biomass. Estimates of number of dead trees greater than or equal to 5 diameter and dead tree above-ground biomass have lower correlations with FIA estimates, which are driven largely by the fact that TreeMap does not include areas where live tree cover is less than 10% while FIA does, meaning that severely disturbed areas are not included in mapping. In general, the TreeMap data are appropriately used for planning and policy-level analyses and decisions. Local map accuracy is suitable for many local-scale decisions regarding questions around forest cover, height, vegetation group, and recent disturbances. For other attributes provided here, formal validation has not been completed, and assessment at local scales is advised and must be driven by project-specific needs. References: Riley, Karin L., Isaac C. Grenfell, and Mark A. Finney. 2016. Mapping Forest Vegetation for the Western United States Using Modified Random Forests Imputation of FIA Forest Plots. Ecosphere 7 (10): e01472. https://doi.org/10.1002/ecs2.1472. Riley, Karin L., Isaac C. Grenfell, Mark A. Finney, and John D. Shaw. 2021. TreeMap 2016: A Tree-Level Model of the Forests of the Conterminous United States circa 2016. https://doi.org/10.2737/RDS-2021-0074.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService For complete information, please visit https://data.gov.

Bu ürün, TreeMap veri paketinin bir parçasıdır. 2016'da ABD'nin kıta üzerindeki ormanlık alanının tamamında canlı ve ölü ağaç sayısı, biyokütle ve karbon gibi orman özellikleriyle ilgili ayrıntılı mekansal bilgiler sağlar. TreeMap v2016, 2016 civarında kıta ABD'sindeki ormanların 22 bantlı 30 x 30 m çözünürlüklü bir haritasını içeren bir görüntü içerir. Her bant, belirli FIA verilerinden elde edilen bir özelliği (ve bir bant TreeMap kimliğini) temsil eder. Özelliklere örnek olarak orman türü, ağaç tepesi örtüsü yüzdesi, canlı ağaç stoğu, canlı/ölü ağaç biyokütlesi ve canlı/ölü ağaçlardaki karbon verilebilir. TreeMap ürünleri, ızgaralı LANDFIRE giriş verilerinin her pikseline en benzer Orman Envanteri Analizi (FIA) arsasını atayan bir rastgele orman makine öğrenimi algoritmasının sonucudur. Amaç, orman özelliklerinin çeşitli ölçeklerde daha iyi tahminlerini üretmek için ayrıntılı ancak mekansal olarak seyrek olan FIA verilerinin tamamlayıcı güçlü yönlerini, daha az ayrıntılı ancak mekansal olarak kapsamlı olan LANDFIRE verileriyle birleştirmektir. Ağaç haritası, yakıt işleme planlaması, tehlikeli ağaç haritalandırması ve karasal karbon kaynaklarının tahmini gibi projelerde hem özel hem de kamu sektöründe kullanılmaktadır. TreeMap, diğer tahmin edilen orman bitki örtüsü ürünlerinden farklı olarak her piksele bir FIA alanı tanımlayıcısı sağlarken diğer veri kümeleri canlı taban alanı gibi orman özellikleri sağlar (ör. Ohmann and Gregory 2002; Pierce Jr et al. 2009; Wilson, Lister, and Riemann 2012). FIA arazisi tanımlayıcısı, FIA DataMart'taki her ağaç ve arazi için kaydedilen yüzlerce değişken ve özellikle bağlantılandırılabilir. FIA DataMart, FIA'nın arazi bilgileriyle ilgili herkese açık deposudur (Forest Inventory Analysis 2022a). 2016 metodolojisi, rahatsızlığı bir yanıt değişkeni olarak içerir. Bu da rahatsızlık olan alanların eşleştirilmesinde doğruluğu artırır. LANDFIRE haritalarıyla karşılaştırıldığında orman örtüsü, yükseklik, bitki grubu ve bozulma kodu için sınıf içi doğruluk% 90'ın üzerindeydi. Doğrulama yarıçapı içindeki en az bir piksel, orman örtüsü için% 57,5, yükseklik için% 80,0, en yüksek taban alanına sahip ağaç türleri için% 80,0 ve rahatsızlık için %87,4 oranında tahmin edilen değerlerin sınıfıyla eşleşti. Ek Kaynaklar Yöntemler ve doğruluk değerlendirmesi hakkında daha ayrıntılı bilgi için lütfen TreeMap 2016 Yayınını inceleyin. TreeMap 2016 Veri Gezgini, kullanıcılara TreeMap özellik verilerini görüntüleme ve indirme olanağı sağlayan web tabanlı bir uygulamadır. Tam veri kümesi indirme, meta veriler ve destek dokümanları için TreeMap Research Data Archive. TreeMap özellik verileri indirmeleri, meta verileri ve destek belgeleri için TreeMap Raster Data Gateway. TreeMap 2016'da yer alan özellikler hakkında daha ayrıntılı bilgi için FIA Database Manual version 8'i inceleyin. Sorularınız veya belirli veri istekleriniz için sm.fs.treemaphelp@usda.gov adresiyle iletişime geçin. Orman Envanter Analizi. 2022a. Forest Inventory Analysis DataMart. Forest Inventory Analysis DataMart FIADB_1.9.0. 2022. https://apps.fs.usda.gov/fia/datamart/datamart.html. Ohmann, Janet L ve Matthew J Gregory. 2002. ABD'nin Oregon eyaletindeki kıyı şeridinde doğrudan gradyan analizi ve en yakın komşu atama ile orman bileşimi ve yapısının tahmini haritası. Can. J. Şunun için: Res. 32:725-741. doi: 10.1139/X02-011. Pierce, Kenneth B Jr, Janet L Ohmann, Michael C Wimberly, Matthew J Gregory ve Jeremy S Fried. 2009. Arazi Yönetimi İçin Yabani Arazi Yakıtlarının ve Orman Yapısının Haritalandırılması: En Yakın Komşu Atama ve Diğer Yöntemlerin Karşılaştırılması. Can. J. Şunun için: Res. 39: 1901-1916. doi:10.1139/X09-102. Wilson, B Tyler, Andrew J Lister ve Rachel I Riemann. 2012. Orman Envanteri Parselleri ve Orta Çözünürlüklü Raster Verileri Kullanarak Ağaç Türlerini Geniş Alanlara Haritalandırmada En Yakın Komşu Atama Yaklaşımı. Forest Ecol. Yönet. 271:182-198. doi: 10.1016/j. foreco.2012.02.002.

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.

Due to the increased frequency of human-tick encounters and expanding ranges of ticks in the United States, there is a critical need to identify environmental conditions associated with tick populations and their likelihood to contact human hosts. In a passive tick surveillance partnership with the United States Department of Agriculture Forest Inventory and Analysis (FIA) program, we identified environmental variables associated with tick encounters by forestry personnel. Ticks were identified to species and life stage and site-specific variables were associated with each tick using FIA forest inventory datasets and generalized linear and zero-inflated models. Of the 55 FIA variables available, we identified biotic and abiotic environmental variables associated with Amblyomma americanum (carbon in litter material and standing dead tree aboveground dry biomass), Dermacentor variabilis (live sapling belowground dry biomass, carbon in litter material, forest stand age, and elevation), and Ixodes scapularis (carbon in dead woody material and seedling species unevenness). We propose that land management decisions not only affect common flora and fauna but changes to these habitats can also alter the way ticks parasitize hosts and use vegetation to find those hosts. Testing of these results can be used with land management decisions to prevent future encounters and highlight risk areas. Foresters that inventory sites encounter ticks, which we can then use to better understand the environmental conditions conducive to increased tick abundance or habitat suitability. Methods Data for this study were obtained through a partnership with the USDA-FIA. Ticks were collected voluntarily according to methods outlined in Trout Fryxell and Vogt 2019 by FIA foresters conducting standard inventory operations over a period of five years (2017–2021) in the southeastern U.S. Vegetation data were collected by USDA-FIA crews on permanent ground sampling plots located across the study area at a sampling intensity of 1 plot per 2,428 ha. Crews indicated where ticks were encountered and those plots were cross-referenced with the USDA FIA database.