The Data hub is a central repository for authoritative climate and forest vulnerability data. It ensures access to high-quality, vetted datasets for conducting assessments.
This page is regularly updated as new datasets become available. Please contact the FIP team if you would like to suggest an edit or a new dataset for inclusion.
40 results
USDA Forest Service
This dataset shows the average total tree carbon — combining live trees, standing dead trees, and downed woody material over 3 inches in diameter — summarized across "firesheds" (fire management planning units) in the western National Forest System, measured in tons per acre for the year 2016. This dataset matters for forests because firesheds are the actual units used for wildfire planning, so linking carbon storage data directly to them lets land managers see exactly which fire-prone areas are also holding the most carbon, helping prioritize fuel treatments and fire management in the places where losing that carbon to wildfire would have the biggest climate impact. It's built from TreeMap 2016, a tree-level model of U.S. forests that used a random forest machine-learning algorithm to combine real Forest Inventory and Analysis (FIA) plot data with LANDFIRE's 30x30 meter rasters, creating a detailed, wall-to-wall carbon map even in areas without direct field measurements. Carbon values were pulled from three separate components — live aboveground carbon, standing dead carbon, and dead/downed carbon — added together and then averaged within each fireshed boundary. The final map groups firesheds into four categories (quartiles), with particular emphasis on highlighting the firesheds in the top 25% for average carbon storage.
USDA Forest Service
This dataset maps areas of Alaska affected by bark beetle mortality from 2000–2021, sourced from the Forest Service's Forest Health Protection program, which collects this data through annual aerial and ground detection surveys of insect and disease damage. This matters for forests because Alaska's boreal spruce forests are warming at roughly twice the global average, and that warming has fueled increasingly severe spruce beetle outbreaks, including one in the 1990s that killed over 90% of large spruce trees across 2.5 million acres, according to the USDA Climate Hubs, making Alaska one of the clearest examples of climate-driven forest disturbance in the country. Spruce beetles normally play a natural role by targeting stressed, dying, or downed trees, but during outbreak conditions, warmer summers and shortened beetle life cycles let them infest large numbers of standing, otherwise healthy trees, rapidly reshaping forest composition and structure. Beyond killing trees directly, these outbreaks compound into other risks: a study published in Forest Ecology and Management found that beetle-killed stands can leave behind dry fuel that raises the likelihood of subsequent wildfire in certain forest conditions, adding another layer of risk to an already warming, fire-prone landscape. Tracking bark beetle mortality over time gives forest managers, including those at places like Chugach National Forest, the ability to anticipate where outbreaks are spreading and target hazard fuel reduction before beetle-killed stands turn into the next wildfire risk.
USDA Forest Service
This dataset maps large wildfire perimeters from 2000–2021 specifically across Alaska, Hawaii, and Puerto Rico, using data from the Monitoring Trends in Burn Severity (MTBS) program, which inventories all known large fires in the U.S. regardless of origin or type using remote sensing combined with state and federal fire occurrence records. This matters for forests because these three regions represent ecologically distinct and often overlooked forest types, boreal forest in Alaska, tropical dry forest in Hawaii, and tropical moist forest in Puerto Rico, that face very different fire dynamics than the continental U.S. but are rarely centered in national wildfire datasets, making a dedicated regional layer essential for understanding fire risk in these landscapes on their own terms. A "large" fire under this dataset follows the same MTBS size thresholds used elsewhere, at least 1,000 acres in the western fire zone (which includes Alaska and Hawaii) and at least 500 acres in the eastern zone (which includes Puerto Rico), keeping it consistent with the methodology used in the continental U.S. wildfire layers. Because fire plays a very different ecological role in each of these regions, for instance Alaska's boreal forests naturally regenerate through periodic wildfire, while introduced fire in Hawaii's forests is a comparatively newer and more disruptive stressor, having this data isolated to these three areas lets managers apply region-appropriate fire and forest management strategies rather than defaulting to continental assumptions. For forest managers in these areas, this dataset is a foundational tool for tracking fire frequency and extent over time and informing restoration and forest health priorities specific to island and northern forest ecosystems.
USDA Forest Service, US Census Bureau
This dataset, developed by Headwaters Economics and credited to GTAC, maps tribal land boundaries across the U.S., built from 2020 Census boundary files and covering nearly every recognized tribal land category, including reservations, trust lands, and Alaska Native and Hawaiian Home Lands. This dataset matters for forests because a significant amount of forested land sits within or near tribal boundaries, and accurate boundary data helps the Forest Service properly consult and co-manage with tribal nations on decisions about forest health, restoration, and resource use. Tribal boundaries were split along state lines for state-level comparisons, and lands belonging to the same tribe (like a reservation and its off-reservation trust lands) were merged into single summary units. The methodology was developed in consultation with the Forest Service's Office of Tribal Relations and the Bureau of Indian Affairs, ensuring it reflects real coordination needs.
USDA Forest Service
This dataset compiles habitat data for roughly 60 at-risk species, subspecies, or distinct population segments across the western U.S. whose habitat is threatened by wildfire and considered a management priority in one or more Western Forest Service regions. This dataset matters for forests because it directly links wildlife conservation priorities to fire management planning, giving land managers a way to see exactly where fuel treatments or fire mitigation work could either protect or endanger critical habitat, so species protection can be factored into fire decisions rather than treated as a separate, disconnected concern. It's organized by fireshed, the same fire-management planning units used elsewhere in Forest Service data, rather than by county or forest boundary. For each fireshed, it tracks several metrics: how many at-risk species are present, what proportion of the fireshed's area overlaps with at-risk species habitat, a combined count-times-proportion score, a summary risk class, and the actual list of species involved. This structure lets users quickly compare firesheds not just by how many species are at risk, but by how much of the landscape that risk actually covers.
USDA Forest Service, NatureServe
This dataset is the USDA Forest Service's hosted copy of NatureServe's Map of Biodiversity Importance, which displays richness of at-risk species outside of existing protected areas using modeled distributions for 2,216 of the most imperiled plants and animals in the United States, with this particular layer showing values in the 75–99 percentile range, essentially isolating the highest-priority areas. This matters for forests because forests host a huge share of the country's imperiled species habitat, and this map was built from habitat models for species listed as Endangered or Threatened under the Endangered Species Act, or identified by NatureServe as Critically Imperiled or Imperiled, so it flags exactly which forested landscapes are quietly holding some of the nation's rarest, most at-risk biodiversity. The underlying models draw on nearly 50 years of standardized biodiversity inventory... provided by the NatureServe Network, meaning the data reflects decades of real, field-collected occurrence records rather than a purely theoretical projection. It's also a genuinely collaborative, technically ambitious product, developed by NatureServe with partners from Esri, The Nature Conservancy, and the NatureServe Network, and covers a comprehensive set of habitat models... for vertebrates, freshwater invertebrates, pollinators, and plants, giving it unusually broad taxonomic coverage compared to older biodiversity maps that focused mainly on birds and mammals. For forest managers, this makes the dataset a practical screening tool for spotting exactly where forest land overlaps with concentrations of rare species that current protections may not yet cover, directly informing where conservation, restoration, or land-use decisions could have an outsized impact on preventing extinctions.
USDA Forest Service
This dataset maps carbon density in 2023 alongside the percent change in carbon stocks from 2005 to 2023 for every National Forest System unit. This dataset matters for forests because it turns nearly two decades of carbon trend data into a simple visual snapshot, helping land managers pinpoint which national forests are thriving as carbon sinks and which ones may need management attention to reverse a declining trend. It's built from five combined carbon pools — aboveground biomass, belowground biomass, soil organic carbon, litter, and dead wood — giving a fuller picture of how much carbon each forest is storing than looking at trees alone would. Each national forest is categorized into one of eight groups, crossing whether it has high or low carbon density with whether its carbon stocks have grown by more than 10%, stayed relatively stable, or dropped by more than 10% over that period. This lets someone quickly see, for example, whether a high-density forest is still gaining carbon or has started losing it, versus a naturally lower-density forest that might actually be improving.
USDA Forest Service
This dataset shows forest carbon density per forested acre for every forested county in the coterminous U.S., combining a 2020 baseline with projections out through 2070 in 10-year steps based on the most recent inventories for each state, with the five carbon pools corresponding to IPCC pool definitions. This dataset matters for forests because tracking density (not just total carbon) over time highlights which forested counties are becoming more or less efficient carbon sinks per acre under future warming and disturbance pressures, giving land managers a clearer, decade-by-decade basis for prioritizing where forest protection, restoration, or management interventions will have the greatest climate benefit. The projections come from the Forest Service's RPA team and include the effects of land use change, climate, socioeconomics, timber harvest, fire, other disturbance, and forest growth, specifically modeled under high warming (RCP 8.5) and moderate growth (SSP2) using the MRI-CGCM3 climate model. Because the data is structured with a value for each county at every decade from 2020 to 2070, it allows users to track how carbon density is expected to shift over roughly 50 years rather than just seeing a single snapshot. This differs from the earlier total-carbon-change layers by focusing specifically on density per forested acre, which normalizes for forest size and makes it easier to compare carbon-storage efficiency across counties of very different sizes.
Centers for Disease Control and Prevention, Agency for Toxic Substances and Disease Registry
Social Vulnerability Index describes how vulnerable communities may be to the impacts of disasters and other environmental or public health stresses. It combines information on factors such as income, household characteristics, racial and ethnic minority status, housing, and transportation access that can affect a community’s ability to prepare for, respond to, and recover from disruptive events. Higher values indicate communities that may face greater barriers during events such as wildfires, floods, extreme heat, or other emergencies.
USDA Forest Service
This dataset maps the projected change in consecutive summer dry days across the continental U.S., comparing a historical baseline (1985–2005) to a future period (2071–2090) under a high-warming scenario (RCP 8.5). For forests, this is a critical dataset because prolonged dry stretches directly drive drought stress, tree mortality, and wildfire risk, so mapping where the biggest increases are expected helps land managers anticipate which forests are most likely to face water stress, prioritize drought-resilient replanting, and prepare fire management resources ahead of time in the areas facing the sharpest shift toward longer dry seasons. Like the frost-free days dataset, it's built from the average of 20 different global climate models rather than a single projection, making the estimate more robust. Values range from a decrease of 113 days to an increase of 142 days, with an average increase of about 25 consecutive dry days across the landscape. That means most areas are expected to see meaningfully longer stretches without rain during the summer months by the end of the century, rather than just isolated dry spells.
Vegetation Futures Tool
Distance (in km) to the nearest location of the projected future primary vegetation type in the climate analog impact models in the Vegetation Futures Tool. This dataset can help to coarsely identify places where dispersal may be a major barrier for establishment of the projected future primary vegetation type (i.e., where distances are quite large). The Vegetation Futures Tool shows projections of plausible future vegetation types for the Western USA based on the results of a method called climate analog impact models. These models use information on projected climatic conditions to find locations on the landscape within a 500-km radius of each pixel that currently have a climate similar to the projected mid-century climate of that pixel. These locations are called “analogs.” Next, vegetation types of the 100 most similar analogs are identified to project the plausible future vegetation type of each pixel. For all pixels within the Western US, the Vegetation Futures Tools shows the most prevalent and second-most prevalent vegetation types among the analogs. The Tool also displays variability in vegetation types among the 100 analogs as vegetation projection agreement which is the percent of the 100 analogs that were the same vegetation type as the most prevalent vegetation type. The Tool also shows the dissimilarity between current and plausible future vegetation, termed “vegetation vulnerability to transformation” as it describes the degree to which the future climate will—or will not—continue to support the same vegetation as the current climate does. Combining this vulnerability metric with how much agreement there is among analogs may help inform decision making surrounding vegetation transformations. Datasets can be downloaded from the "Analysis Tools" menu.
USDA Forest Service
This layer, part of the Fireshed Infrastructure dataset, shows all fire stations and emergency medical service stations across the US.
USDA Forest Service
This dataset summarizes mature and old growth forest area estimates, derived from Forest Inventory Analysis plot data and BIGMAP satellite imputations, organized into the Fireshed Registry's roughly 250,000-acre fireshed accounting units across NFS and BLM lands. This matters for forests because mature and old growth stands hold outsized ecological value, from long-term carbon storage to unique wildlife habitat, so tying their location directly to fireshed boundaries lets managers weigh wildfire risk reduction work against the potential loss of these irreplaceable, slow-to-recover forest stands. The Fireshed Registry itself is a broader decision tool built to map where wildfire risk to communities originates, rather than just where risk exists, using fireshed units sized to contain the vast majority of large fire events. Each fireshed and its nested 25,000-acre project areas get a relative categorical rating (low, intermediate, or high) for old growth, mature forest, and their combination, rather than precise percentages, giving planners a quick comparative sense of where old growth is concentrated. By overlaying old growth presence onto the same units used for fire risk planning, this dataset helps ensure that fuel treatments and fire management investments are designed with old growth protection in mind, rather than inadvertently targeting the areas of highest ecological value.
USDA Forest Service
This dataset, funded by the USDA Forest Service's State & Private Forestry and Rocky Mountain Research Station programs, defines the boundaries of the Fireshed Registry, a geospatial decision tool that organizes wildfire risk by identifying the source of wildfire transmission to buildings rather than just mapping what's at risk. This matters for forests because it flips the usual approach to wildfire planning, tracing exposure back to roughly 250,000-acre "fireshed" units (nested further into 25,000-acre subfiresheds) built from a smoothed national building-exposure map, letting managers see exactly which forested landscapes are generating risk to nearby communities rather than only where structures happen to sit. Firesheds cover the conterminous U.S., Alaska, and Hawaii, generated through separate regional research efforts and then combined into one dataset, with each unit flagged by whether it's "All-Lands" or "USFS-Only" and whether it falls into a designated high-risk category. While built specifically around fire risk, the framework was also designed to be flexible enough to layer in other resource priorities, like water, wildlife habitat, or recreation, without needing a separate accounting structure. For forest managers, this registry functions as the foundational planning grid behind much of the Forest Service's fuel treatment and wildfire mitigation work, giving a consistent, multi-scale basis for deciding where investment in reducing fire risk to forests and communities will have the greatest impact.
USDA Forest Service
This dataset maps where wildfire and bark beetle disturbances overlapped across the U.S. between 2000 and 2022, combining a wildfire footprint layer (fires over 1,000 acres in the western U.S. and 500 acres in the eastern U.S., from the Monitoring Trends in Burn Severity program) with a bark beetle mortality footprint mapped by the Forest Service's Forest Health Protection program. This matters for forests because both disturbance types are driven by the same underlying stressor, atmospheric dryness, which increases vegetation flammability while also weakening trees' natural defenses against beetle attack, so overlapping the two reveals forests caught in a compounding cycle of stress rather than facing just one threat in isolation. Because the layer specifically represents the intersection of these two footprints, it highlights areas where fire and beetle damage occurred in the same place over more than two decades, which are often the forests most structurally weakened and most vulnerable to future disturbance. This differs from the earlier aridity-exposure index by focusing on actual documented disturbance outcomes rather than climate conditions that merely predispose an area to risk, making it a validation layer for where dryness-driven stress has already visibly played out on the ground. For forest managers, this makes it a valuable tool for identifying priority areas for restoration, fuel reduction, and pest monitoring, since these compounded-disturbance forests are the ones most likely to need active intervention to recover.
USDA Forest Service, Monitoring Trends in Burn Severity
This dataset maps where wildfire and bark beetle disturbances overlapped across the U.S. between 2017 and 2022, combining a wildfire footprint layer (fires over 1,000 acres in the western U.S. and 500 acres in the eastern U.S., from the Monitoring Trends in Burn Severity program) with a bark beetle mortality footprint mapped by the Forest Service's Forest Health Protection program. This matters for forests because both disturbance types are driven by the same underlying stressor, atmospheric dryness, which increases vegetation flammability while also weakening trees' natural defenses against beetle attack, so overlapping the two reveals forests caught in a compounding cycle of stress rather than facing just one threat in isolation. This is essentially the same product as the 2000–2022 version of this dataset, just narrowed to the most recent six-year window, which makes it more useful for spotting where compounded disturbance is actively concentrated right now rather than accumulated over two decades. That shorter, more recent window also makes it more responsive to current conditions, useful for tracking whether disturbance patterns are intensifying, shifting geographically, or easing in the near term. For forest managers, this makes it a valuable tool for identifying the most urgent, present-day priority areas for restoration, fuel reduction, and pest monitoring, since these recently compounded-disturbance forests are the ones most likely to need active intervention soon.
USDA Forest Service
This dataset maps the projected change in the number of frost-free days per year across the continental U.S., comparing a historical baseline (1985–2005) to a future period (2071–2090) under a high-warming scenario (RCP 8.5). For forest management, this dataset is important because a longer frost-free season can extend growing conditions for some species while pushing others past their climate tolerance, so knowing where the biggest shifts are expected helps land managers anticipate species range shifts, adjust replanting strategies, and prepare for new pest and fire pressures tied to a longer active season. Rather than relying on one climate model, it averages results from 20 different global climate models to produce a more reliable estimate of how growing seasons are expected to shift. Values range from 0 to 122 additional frost-free days, with an average projected increase of about 49 days across the landscape, meaning most areas are expected to see meaningfully longer growing seasons by the end of the century. This kind of shift affects nearly everything about how a forest functions, from which tree species can survive in a given area to how long pests, disease, and wildfire risk are active each year.
USDA Forest Service
This dataset is the National Register of Historic Places (NRHP) locations layer, mapping the point or polygon boundaries of every building, site, structure, object, or district that possesses historic significance and integrity and has been formally listed on the National Register. This matters for forests because National Forest System land contains a substantial number of historic and archaeological sites, from old fire lookouts to Civilian Conservation Corps structures to Indigenous cultural sites, and under the National Historic Preservation Act, properties listed on or eligible for the National Register must receive consideration prior to any undertaking involving federal monies, meaning forest managers legally have to check this data before approving activities like timber sales, road building, or prescribed burns. Properties are mapped differently depending on their size, with smaller sites less than 10 acres represented as points, while properties or districts larger than 10 acres are delineated as polygons, giving the dataset a mix of precise site markers and larger protected district boundaries. Notably, the attribute tables attached to this data aren't primarily descriptive of the historic resource itself; instead, they mainly document how the data was created, its source, and the mapping parameters used, along with basic status information like whether the resource still exists or carries any access or use restrictions. For forest managers, this dataset functions as a legally necessary screening layer, letting them identify exactly where historic and cultural resources are located before ground-disturbing forest management work begins, helping ensure compliance and avoid inadvertently damaging protected historic sites.
Preview not available
USDA Forest Service
This dataset, part of the Forest Service's Forests to Faucets 2.0 project, projects how much water yield will change in U.S. watersheds between 2010 and 2040 under a moderate warming scenario (RCP 4.5), focusing on watersheds that supply drinking water and are expected to see reduced water quantity. This matters for forests because forests act as natural water filters and reservoirs, and this project exists specifically to map the link between forest cover, clean drinking water, and the people who depend on it, so a decline in water yield signals a direct threat to both the ecosystem and downstream communities. Water yield is just one of four threats the broader project tracks, alongside fire, insects and disease, and land-use change, so this layer is one piece of a larger climate vulnerability picture. Values range widely, from a 100% decrease to a 1,515% increase, though most areas average a modest 7.5% increase, masking a lot of regional variation underneath. This dataset helps managers pinpoint which forested watersheds are most likely to lose water availability in the coming decades, making it a useful tool for prioritizing forest conservation where it matters most for drinking water.
Vegetation Futures Tool
An index that combines vulnerability and projection agreement to help visualize the different combinations of these two metrics across the landscape for the climate analog impact models in the Vegetation Futures Tool. Areas in shades of blue have low values of vegetation vulnerability to transformation and are least likely to experience changes in vegetation due to climate. Areas in shades of red are most vulnerable to climate-driven changes in vegetation. The darkest shades of each color have high vegetation projection agreement - which means that one vegetation type dominates future projections. The lightest shades of each color indicate low projection agreement - which means that multiple future vegetation types are plausible. The Vegetation Futures Tool shows projections of plausible future vegetation types for the Western USA based on the results of a method called climate analog impact models. These models use information on projected climatic conditions to find locations on the landscape within a 500-km radius of each pixel that currently have a climate similar to the projected mid-century climate of that pixel. These locations are called “analogs.” Next, vegetation types of the 100 most similar analogs are identified to project the plausible future vegetation type of each pixel. For all pixels within the Western US, the Vegetation Futures Tools shows the most prevalent and second-most prevalent vegetation types among the analogs. The Tool also displays variability in vegetation types among the 100 analogs as vegetation projection agreement which is the percent of the 100 analogs that were the same vegetation type as the most prevalent vegetation type. The Tool also shows the dissimilarity between current and plausible future vegetation, termed “vegetation vulnerability to transformation” as it describes the degree to which the future climate will—or will not—continue to support the same vegetation as the current climate does. Combining this vulnerability metric with how much agreement there is among analogs may help inform decision making surrounding vegetation transformations. Datasets can be downloaded from the "Analysis Tools" menu.
Preview not available
USDA Forest Service
This dataset shows the ratio of live-to-dead aboveground carbon in each individual National Forest System unit, using annualized carbon estimates from 1990–2023. This dataset matters for forest management because when the live-to-dead ratio drops it often signals recent disturbance — wildfire, insect outbreaks, disease, or drought die-off — helping the Forest Service flag which forests are losing carbon storage capacity and where restoration or fire management is most urgently needed. From the USDA Forest Service's Forest Inventory and Analysis program, it's built from seven measured carbon pools — including live trees, dead standing trees, downed dead wood, and soil carbon — combined through the Forest Service's Carbon Calculation Tool. Each national forest polygon is color-coded on a scale from pale yellow (a low live-to-dead ratio, under 2.5) to dark green (a high ratio, over 15), making it easy to spot forests where dead material makes up an unusually large share of total carbon. Because the data includes both historical and current ratios, it can also reveal how that balance has shifted over time within a given forest, not just its current state.
Preview not available
USDA Forest Service
This layer in the Fireshed Community Infrastructure dataset shows all major roads throughout CONUS.
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USDA Forest Service
This dataset, from the Forest Service's Rocky Mountain Research Station, maps the mean fraction of each stream's annual water flow that originates specifically from National Forest System lands, built from a modeled historical streamflow study (Luce et al., 2017) combined with the 2012 National Hydrography Dataset stream layer. This matters for forests because it directly quantifies how much of the country's stream water actually comes from forested Forest Service land, making the connection between forest cover and water supply concrete and measurable rather than assumed. Since the underlying model draws on historical streamflow patterns, it reflects long-term hydrologic behavior rather than a single year's conditions, giving a more stable picture of which streams depend most heavily on forest-originated runoff. This distinguishes it from related water datasets like Forests to Faucets, since this one is specifically about the fraction of flow within individual streams attributable to Forest Service lands, rather than ranking watershed importance or projecting future climate-driven change. For water and forest managers, this dataset helps identify exactly which streams would be most affected by changes in forest condition or management, making it a useful tool for protecting the streams most reliant on National Forest land for their water supply.
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USDA Forest Service
This dataset, from the Forest Service's Rocky Mountain Research Station, maps the mean fraction of each stream's summer water flow that originates specifically from National Forest System lands, built from a modeled historical streamflow study (Luce et al., 2017) combined with the 2012 National Hydrography Dataset stream layer. This matters for forests because summer is typically the most water-stressed season, when demand for irrigation, drinking water, and wildlife habitat peaks while natural flow is often at its lowest, so knowing which streams rely most heavily on forest-originated water during this critical window highlights exactly where forest cover is doing the most work to sustain water supply when it's needed most. This is a seasonal companion to the related annual-flow dataset, but narrowing the focus to summer specifically captures a very different picture, since forests can contribute disproportionately to summer flow through processes like slower snowmelt release and shaded, reduced evaporation compared to open, non-forested land. For water and forest managers, this makes the dataset especially useful for identifying which specific streams would be most vulnerable to reduced summer flow if the forest cover feeding them were lost to fire, disease, or land-use change. In short, this data pinpoints the streams where the loss of forest cover would be felt most acutely during the exact time of year when water is scarcest.
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USDA Forest Service, PRISM
This dataset, known as the Multidecadal Repeat-Aridity Exposure Index (MRAEI), maps locations across the conterminous U.S. that have repeatedly experienced below-average warm-season (May–September) precipitation during the long-term dry cycle affecting the western U.S. from 2000–2021, built at 4-km resolution using the Standardized Precipitation Index and PRISM climate data. This matters for forests because warm-season aridity has been directly linked to increased wildfire area burned and heightened forest vulnerability to bark beetle infestations, meaning this index essentially flags where forests are most chronically exposed to the dry conditions that drive both disturbances. Values range from 0 to 71, representing the cumulative percent of years each location experienced severe dry conditions (an SPI below -3) relative to a baseline, so higher scores mean a location has repeatedly, not just occasionally, faced severe warm-season drought. Unlike a single drought snapshot, this repeat-exposure approach captures which forested areas are dealing with a pattern of recurring aridity, a more meaningful signal for long-term forest health than any one dry year. For land managers, this makes it a useful targeting tool for prioritizing fire fuel treatments and bark beetle monitoring in the specific forests statistically most prone to repeated dry-season stress.
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USDA Forest Service
This dataset maps how forest carbon storage is projected to change county-by-county across the U.S. between 2020 and 2070 under a moderate climate warming scenario. Carbon storage is a key defense against climate change, and knowing which regions are likely to lose carbon capacity helps land managers and policymakers prioritize where to focus fire prevention, replanting, or protection efforts. It starts from real 2020 forest inventory data, then uses climate and land-use models to project carbon gains or losses by 2070, accounting for factors like timber harvest, wildfire, and land-use change. Counties are color-coded by percent change, so it's easy to see at a glance which areas are expected to become stronger or weaker carbon sinks over time. The projections draw on five different climate models rather than a single estimate, giving a range of likely outcomes instead of one fixed prediction.
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USDA Forest Service
This dataset maps how forest carbon storage is projected to change county-by-county across the U.S. between 2020 and 2070 under a high-warming climate scenario. Carbon storage is a key defense against climate change, and knowing which regions are likely to lose carbon capacity under more severe warming helps land managers and policymakers prioritize where to focus fire prevention, replanting, or protection efforts. It starts from real 2020 forest inventory data, then uses climate and land-use models to project carbon gains or losses by 2070, accounting for factors like timber harvest, wildfire, and land-use change. Counties are color-coded by percent change, so it's easy to see at a glance which areas are expected to become stronger or weaker carbon sinks over time. The projections draw on five different climate models rather than a single estimate, giving a range of likely outcomes instead of one fixed prediction.
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USDA Forest Service
This dataset maps projected changes in forest carbon stocks for each individual National Forest between 2020 and 2070, under a low-warming, moderate-growth climate scenario (RCP 4.5-SSP1). This matters for forests because it lets land managers see, forest by forest, which specific national forests are projected to gain or lose carbon storage capacity over the next 50 years, helping prioritize climate adaptation, fire management, and restoration efforts exactly where they're needed most. It starts from 2020 baseline carbon estimates built from the most recent forest inventories in each state, then layers on future projections developed by the Forest Service's Resources Planning Act (RPA) Assessment team, which account for land-use change, climate shifts, socioeconomics, timber harvest, wildfire, and natural forest growth. Each national forest polygon is color-coded by percent change, ranging from a loss of more than 10% (brown) to a gain of more than 10% (dark green), with gray marking forests where no RPA carbon data is available. Like the county-level version of this data, the projections aren't a single guess — they reflect the mean, minimum, and maximum outcomes across five different climate models, and the dataset also includes a companion high-warming scenario (RCP 8.5) for direct comparison within the same layer.
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USDA Forest Service
This dataset shows how total forest carbon is projected to change by 2070 for each individual National Forest, under a high-warming, moderate-growth climate scenario (RCP 8.5-SSP2). This matters for forests because it lets land managers see, forest-by-forest, which specific national forests are most at risk of losing carbon storage capacity under severe warming, helping prioritize where fire prevention, restoration, or protective management is needed most to keep those forests functioning as carbon sinks. It starts from 2020 baseline carbon estimates built by the FIA carbon team using the most recent state inventories, then applies projections from the Forest Service's Resources Planning Act (RPA) Assessment, which factor in land-use change, climate, socioeconomics, timber harvest, fire, and other disturbances. Each national forest is color-coded by percent change, ranging from a loss of more than 10% (brown) to a gain of more than 10% (dark green), with forests lacking sufficient data marked separately in gray. Like the related layers, it isn't based on a single guess, but reflects the mean, minimum, and maximum outcomes across five different climate models to capture a range of possible futures.
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USDA Forest Service
This dataset, part of the Forest Service's Forests to Faucets 2.0 project, projects how much water yield will change in U.S. watersheds between 2010 and 2040 under a moderate warming scenario (RCP 4.5), focusing on watersheds that supply drinking water and are expected to see reduced water quantity. This matters for forests because forests act as natural water filters and reservoirs, and this project exists specifically to map the link between forest cover, clean drinking water, and the people who depend on it, so a decline in water yield signals a direct threat to both the ecosystem and downstream communities. Water yield is just one of four threats the broader project tracks, alongside fire, insects and disease, and land-use change, so this layer is one piece of a larger climate vulnerability picture. Values range widely, from a 100% decrease to a 1,515% increase, though most areas average a modest 7.5% increase, masking a lot of regional variation underneath. This dataset helps managers pinpoint which forested watersheds are most likely to lose water availability in the coming decades, making it a useful tool for prioritizing forest conservation where it matters most for drinking water.
Preview not available
USDA Forest Service
This dataset, part of the Forest Service's Forests to Faucets 2.0 project, projects how much water yield will change in U.S. watersheds between 2010 and 2040 under a high-warming scenario (RCP 8.5), focusing on watersheds that supply drinking water and are expected to see reduced water quantity. This matters for forests because forests act as natural water filters and reservoirs, and this project exists specifically to map the link between forest cover, clean drinking water, and the people who depend on it, so a decline in water yield under more severe warming signals a heightened threat to both the ecosystem and downstream communities. Water yield is just one of four threats the broader project tracks, alongside fire, insects and disease, and land-use change, so this layer is one piece of a larger climate vulnerability picture. Values range widely, from a 100% decrease to a 1,557% increase, with most areas averaging a modest 6.3% increase, though that average masks a lot of regional variation, and notably shows more spread (a higher standard deviation) than the moderate-warming version, suggesting greater extremes under this scenario. This dataset helps managers pinpoint which forested watersheds are most likely to lose water availability under severe future warming, making it a useful tool for prioritizing forest conservation where it matters most for drinking water.
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USDA Forest Service, Conservation Planning Technologies
This dataset, based on research by Theobald et al. (2015), maps physiographic diversity, also called geodiversity, across the contiguous U.S. by measuring how many different landform and physiographic types (15 landform categories and 269 physiographic types) occur within a given area, using Shannon's Equitability as the unit of measurement. This matters for forests because geodiversity, the physical variety in landscape features like terrain and elevation, sets the "stage" for biological activity, and areas with more physical variety tend to support more ecological niches, higher biodiversity, and often rare or endemic species that depend on specific physical conditions to survive. Since it's built purely from physical landscape features rather than field-observed species data, this dataset can act as a useful stand-in for biodiversity or endemism in places where direct species data is limited or unavailable. Geodiversity is also considered an important marker of climate refugia, the varied terrain (things like north-facing slopes, valleys, or elevation gradients) that can offer species localized shelter from broader climate shifts even as surrounding conditions change. For forest managers, this makes the dataset a useful tool for identifying physically complex landscapes likely to harbor unique or resilient forest communities, helping prioritize conservation in areas that may serve as long-term refuges for biodiversity as the climate continues to change.
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USDA Forest Service
This layer in the Fireshed Community Infrastructure dataset shows all power plants throughout CONUS.
Vegetation Futures Tool
Current (2016) vegetation type modified from the Biophysical Settings (BpS) gridded product (30-m resolution) from LANDFIRE. For the Vegetation Futures Tool, the >200 BpS classes of the western US were aggregated into 50 classes (vegetation “types”), plus an additional class for barren, rock, or perennial snow and ice cover. The Vegetation Futures Tool shows projections of plausible future vegetation types for the Western USA based on the results of a method called climate analog impact models. These models use information on projected climatic conditions to find locations on the landscape within a 500-km radius of each pixel that currently have a climate similar to the projected mid-century climate of that pixel. These locations are called “analogs.” Next, vegetation types of the 100 most similar analogs are identified to project the plausible future vegetation type of each pixel. For all pixels within the Western US, the Vegetation Futures Tools shows the most prevalent and second-most prevalent vegetation types among the analogs. The Tool also displays variability in vegetation types among the 100 analogs as vegetation projection agreement which is the percent of the 100 analogs that were the same vegetation type as the most prevalent vegetation type. The Tool also shows the dissimilarity between current and plausible future vegetation, termed “vegetation vulnerability to transformation” as it describes the degree to which the future climate will—or will not—continue to support the same vegetation as the current climate does. Combining this vulnerability metric with how much agreement there is among analogs may help inform decision making surrounding vegetation transformations. Datasets can be downloaded from the "Analysis Tools" menu.
Vegetation Futures Tool
Vegetation type that was most prevalent among the 100 best analogs for the focal location in the climate analog impact models in the Vegetation Futures Tool. The Vegetation Futures Tool shows projections of plausible future vegetation types for the Western USA based on the results of a method called climate analog impact models. These models use information on projected climatic conditions to find locations on the landscape within a 500-km radius of each pixel that currently have a climate similar to the projected mid-century climate of that pixel. These locations are called “analogs.” Next, vegetation types of the 100 most similar analogs are identified to project the plausible future vegetation type of each pixel. For all pixels within the Western US, the Vegetation Futures Tools shows the most prevalent and second-most prevalent vegetation types among the analogs. The Tool also displays variability in vegetation types among the 100 analogs as vegetation projection agreement which is the percent of the 100 analogs that were the same vegetation type as the most prevalent vegetation type. The Tool also shows the dissimilarity between current and plausible future vegetation, termed “vegetation vulnerability to transformation” as it describes the degree to which the future climate will—or will not—continue to support the same vegetation as the current climate does. Combining this vulnerability metric with how much agreement there is among analogs may help inform decision making surrounding vegetation transformations. Datasets can be downloaded from the "Analysis Tools" menu.
Vegetation Futures Tool
Percentage of the 100 best analogs that project the primary vegetation type in the climate analog impact models in the Vegetation Futures Tool. Values range from 1 to 100, where high values indicate that most analogs project a single plausible future vegetation type (high projection agreement) and low values indicate that future climate may be suitable for two or more vegetation types (low projection agreement). The Vegetation Futures Tool shows projections of plausible future vegetation types for the Western USA based on the results of a method called climate analog impact models. These models use information on projected climatic conditions to find locations on the landscape within a 500-km radius of each pixel that currently have a climate similar to the projected mid-century climate of that pixel. These locations are called “analogs.” Next, vegetation types of the 100 most similar analogs are identified to project the plausible future vegetation type of each pixel. For all pixels within the Western US, the Vegetation Futures Tools shows the most prevalent and second-most prevalent vegetation types among the analogs. The Tool also displays variability in vegetation types among the 100 analogs as vegetation projection agreement which is the percent of the 100 analogs that were the same vegetation type as the most prevalent vegetation type. The Tool also shows the dissimilarity between current and plausible future vegetation, termed “vegetation vulnerability to transformation” as it describes the degree to which the future climate will—or will not—continue to support the same vegetation as the current climate does. Combining this vulnerability metric with how much agreement there is among analogs may help inform decision making surrounding vegetation transformations. Datasets can be downloaded from the "Analysis Tools" menu.
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USDA Forest Service
This layer in the Fireshed Community Infrastructure dataset shows all secondary roads throughout CONUS.
Vegetation Futures Tool
Vegetation type that was the second most prevalent among the 100 best analogs for the focal location in the climate analog impact models in the Vegetation Futures Tool. The Vegetation Futures Tool shows projections of plausible future vegetation types for the Western USA based on the results of a method called climate analog impact models. These models use information on projected climatic conditions to find locations on the landscape within a 500-km radius of each pixel that currently have a climate similar to the projected mid-century climate of that pixel. These locations are called “analogs.” Next, vegetation types of the 100 most similar analogs are identified to project the plausible future vegetation type of each pixel. For all pixels within the Western US, the Vegetation Futures Tools shows the most prevalent and second-most prevalent vegetation types among the analogs. The Tool also displays variability in vegetation types among the 100 analogs as vegetation projection agreement which is the percent of the 100 analogs that were the same vegetation type as the most prevalent vegetation type. The Tool also shows the dissimilarity between current and plausible future vegetation, termed “vegetation vulnerability to transformation” as it describes the degree to which the future climate will—or will not—continue to support the same vegetation as the current climate does. Combining this vulnerability metric with how much agreement there is among analogs may help inform decision making surrounding vegetation transformations. Datasets can be downloaded from the "Analysis Tools" menu.
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USDA Forest Service
This dataset, maintained by the USDA Forest Service's Automated Lands Program and Enterprise Data Warehouse, maps the official boundaries of all 451 National Wilderness Areas on Forest Service land, as designated under the National Wilderness Preservation System, with attributes like wilderness name, legal boundary status (Final, Provisional, or Subject to Change), and calculated acreage, updated weekly to stay current.This dataset matters for forests because a large share of the National Wilderness Preservation System sits within National Forest land, and having precise, current boundary data lets managers avoid unauthorized activity, plan buffer zones around adjacent timber or fire management work, and ensure some of the country's most intact old-growth and undisturbed forest ecosystems remain protected from the disturbances most other Forest Service land is actively managed around. Wilderness designation, established by the 1964 Wilderness Act, is the strongest legal protection a forested landscape can receive in the U.S., barring road-building, logging, mechanized equipment, and most development to keep these areas ecologically intact.
Vegetation Futures Tool
A metric of vulnerability to a climate-drive vegetation transformation in the climate analog impact models in the Vegetation Futures Tool. This metric describes the extent to which the projected future climate supports the same types of vegetation as the current climate. This metric ranges from 0 to 1, with higher values indicating that future climate is more likely to support different types of vegetation than the current climate does, and thus places with high values are more vulnerable to vegetation transformations. The Vegetation Futures Tool shows projections of plausible future vegetation types for the Western USA based on the results of a method called climate analog impact models. These models use information on projected climatic conditions to find locations on the landscape within a 500-km radius of each pixel that currently have a climate similar to the projected mid-century climate of that pixel. These locations are called “analogs.” Next, vegetation types of the 100 most similar analogs are identified to project the plausible future vegetation type of each pixel. For all pixels within the Western US, the Vegetation Futures Tools shows the most prevalent and second-most prevalent vegetation types among the analogs. The Tool also displays variability in vegetation types among the 100 analogs as vegetation projection agreement which is the percent of the 100 analogs that were the same vegetation type as the most prevalent vegetation type. The Tool also shows the dissimilarity between current and plausible future vegetation, termed “vegetation vulnerability to transformation” as it describes the degree to which the future climate will—or will not—continue to support the same vegetation as the current climate does. Combining this vulnerability metric with how much agreement there is among analogs may help inform decision making surrounding vegetation transformations. Datasets can be downloaded from the "Analysis Tools" menu.