At 20,310 feet (6.2km) above sea level, the highest point in the United States is Denali, Alaska (formerly known as Mount McKinley). The highest point in the contiguous United States is Mount Whitney, in the Sierra Nevada mountain range in California; followed by Mount Elbert, Colorado - the highest point in the Rocky Mountains. When looking at the highest point in each state, the 13 tallest peaks are all found in the western region of the country, while there is much more diversity across the other regions and territories.

Despite being approximately 6,500 feet lower than Denali, Hawaii's Mauna Kea is sometimes considered the tallest mountain (and volcano) on earth. This is because its base is well below sea level - the mountain has a total height of 33,474 feet, which is almost 4,500 feet higher than Mount Everest.

Comprehensive dataset containing 28 verified High Peak locations in United States with complete contact information, ratings, reviews, and location data.

United States US: Urban Population Living in Areas Where Elevation is Below 5 meters: % of Total Population data was reported at 2.264 % in 2010. This records an increase from the previous number of 2.246 % for 2000. United States US: Urban Population Living in Areas Where Elevation is Below 5 meters: % of Total Population data is updated yearly, averaging 2.264 % from Dec 1990 (Median) to 2010, with 3 observations. The data reached an all-time high of 2.329 % in 1990 and a record low of 2.246 % in 2000. United States US: Urban Population Living in Areas Where Elevation is Below 5 meters: % of Total Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s USA – Table US.World Bank: Land Use, Protected Areas and National Wealth. Urban population below 5m is the percentage of the total population, living in areas where the elevation is 5 meters or less.; ; Center for International Earth Science Information Network (CIESIN)/Columbia University. 2013. Urban-Rural Population and Land Area Estimates Version 2. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). http://sedac.ciesin.columbia.edu/data/set/lecz-urban-rural-population-land-area-estimates-v2.; Weighted Average;

This digital data set consists of contours for 1980 water-level elevations for the High Plains aquifer in the central United States. The High Plains aquifer extends from south of 32 degrees to almost 44 degrees north latitude and from 96 degrees 30 minutes to 106 degrees west longitude. The outcrop area covers 174,000 square miles and is present in Colorado, Kansas, Nebraska, New Mexico, Oklahoma, South Dakota, Texas, and Wyoming. This digital data set was created by digitizing the 1980 water-level elevation contours from a 1:1,000,000-scale base map created by the U.S. Geological Survey High Plains Regional Aquifer Systems-Analysis (RASA) project (Gutentag, E.D., Heimes, F.J., Krothe, N.C., Luckey, R.R., and Weeks, J.B., 1984, Geohydrology of the High Plains aquifer in parts of Colorado, Kansas, Nebraska, New Mexico, Oklahoma, South Dakota, Texas, and Wyoming: U.S. Geological Survey Professional Paper 1400-B, 63 p.) The data are not intended for use at scales larger than 1:1,000,000.

The U.S. Interagency Elevation Inventory (USIEI) displays high-accuracy topographic and bathymetric data for the United States and its territories. The project is a collaborative effort between the National Oceanic and Atmospheric Administration, the U.S. Geological Survey, the Federal Emergency Management Agency, the U.S. Department of Agriculture - Natural Resources Conservation Service and U.S. Forest Service, the National Park Service, and the U.S. Army Corps of Engineers. This resource is a comprehensive, nationwide listing of known high-accuracy topographic data, including lidar and IfSAR, and bathymetric data, including NOAA hydrographic surveys, multibeam data, and bathymetric lidar. This zip file contains the attribute information and footprints about the data sets that are displayed in the Topographic Lidar, Topobathy Shoreline Lidar, IfSAR Data, and Bathymetric Lidar layers in the USIEI viewer. This does not include the elevation data itself. The data are provided in Esri file geodatabase format (gdb) and in the open format of OGC GeoPackage (gpkg). The data is also available via this map service: https://coast.noaa.gov/arcgis/rest/services/USInteragencyElevationInventory/USIEIv2/MapServer. The data is updated quarterly. The information provided for each elevation data set includes many attributes such as vertical accuracy, point spacing, and date of collection. A direct link to access the data or information about the contact organization is also available through the inventory. The footprints in this data set are generalized to represent the coverage of the collection. If the exact data coverage is needed, please contact the data provider for an authoritative footprint. The fields in the gdb and gpkg are in four tables. The fields in each table are listed in the Entity Attribute Overview field.

The National High Altitude Photography (NHAP) program, which was operated from 1980 - 1989, was coordinated by the U.S. Geological Survey as an interagency project to eliminate duplicate photography in various Government programs. The aim of the program was to cover the 48 conterminous states of the USA over a 5-year span. In the NHAP program, black-and-white and color-infrared aerial photographs were obtained on 9-inch film from an altitude of 40,000 feet above mean terrain elevation and are centered over USGS 7.5-minute quadrangles. The color-infrared photographs are at a scale of 1:58,000 (1 inch equals about .9 miles) and the black-and-white photographs are at a scale of 1:80,000 (1 inch equals about 1.26 miles).

A nationwide listing of known publicly available high-accuracy topographic and bathymetric source elevation data for the United States and its territories. The inventory provides a single resource for information about all known completed and in-progress broad-area public domain elevation data. The information provided for each elevation dataset includes many attributes such as vertical accuracy, point spacing, and date of collection. A direct link to access the data or information about the contact organization is also available through the inventory. The United States Interagency Elevation Inventory raises awareness of and increases access to existing elevation data, thereby reducing data duplication efforts. It helps to identify data gaps and informs and encourages collaboration on future data collection efforts. The inventory displays data set boundaries and provides information about the elevation data but does not host the data itself. If available, links to access the data, metadata, and reports are included. The inventory viewer uses map services from multiple sources to provide information both topography and bathymetry. Map services from NOAA NCEI display the footprints and attribute information for the NOAA Hydrographic Surveys, Multibeam Bathymetry, and Trackline Surveys. A map service from USACE provides the USACE Hydrographic Surveys. Map services from NOAA Office for Coastal Management provide the bulk of the topographic and bathymetric lidar information. The NOAA NCEI and USACE service are updated regularly as new data in ingested. The data supporting the NOAA OCM hosted services are maintained by a partnership of federal agencies and supports the federal elevation theme. The agencies include NOAA, the U.S. Geological Survey, the Federal Emergency Management Agency, the U.S. Department of Agriculture, the U.S. Forest Service, the National Park Service and the U.S. Army Corps of Engineers. This service is updated quarterly through an active process of data discovery and validation.

This is a tiled collection of the 3D Elevation Program (3DEP) and is one meter resolution. The 3DEP data holdings serve as the elevation layer of The National Map, and provide foundational elevation information for earth science studies and mapping applications in the United States. Scientists and resource managers use 3DEP data for hydrologic modeling, resource monitoring, mapping and visualization, and many other applications. The elevations in this DEM represent the topographic bare-earth surface. USGS standard one-meter DEMs are produced exclusively from high resolution light detection and ranging (lidar) source data of one-meter or higher resolution. One-meter DEM surfaces are seamless within collection projects, but, not necessarily seamless across projects. The spatial reference used for tiles of the one-meter DEM within the conterminous United States (CONUS) is Universal Transverse Mercator (UTM) in units of meters, and in conformance with the North American Datum of 1983 (NAD83). All bare earth elevation values are in meters and are referenced to the North American Vertical Datum of 1988 (NAVD88). Each tile is distributed in the UTM Zone in which it lies. If a tile crosses two UTM zones, it is delivered in both zones. The one-meter DEM is the highest resolution standard DEM offered in the 3DEP product suite. Other 3DEP products are nationally seamless DEMs in resolutions of 1/3, 1, and 2 arc seconds. These seamless DEMs were referred to as the National Elevation Dataset (NED) from about 2000 through 2015 at which time they became the seamless DEM layers under the 3DEP program and the NED name and system were retired. Other 3DEP products include five-meter DEMs in Alaska as well as various source datasets including the lidar point cloud and interferometric synthetic aperture radar (Ifsar) digital surface models and intensity images. All 3DEP products are public domain.

Digital elevation model used for the conservation assessment of Greater Sage-grouse and sagebrush habitat conducted by the Western Association of Fish and Wildlife Agencies. Digital elevation models were downloaded from the USGS National Elevation Dataset (NED) which was developed by merging the highest-resolution, best quality elevation data available across the United States into a seamless raster format to provide 1:24,000-scale Digital Elevation Model (DEM) data for the conterminous US.

The U.S. Geological Survey (USGS) coordinated the acquisition of high accuracy elevation data (meters) for the Lake Okeechobee Littoral Zone collected on a 400 meter topographic grid with a vertical accuracy of +/- 15 centimeters. The elevations are referenced to the horizontal North American Datum of 1983 (NAD83) and vertical North American Vertical Datum of 1988 (NAVD88). The topographic surveys were performed using differential GPS technology and a USGS developed helicopter-based instrument known as the Airborne Height Finder (AHF).

The data are available for the areas shown on the USGS High Accuracy Elevation Data graphic at http://sofia.usgs.gov/exchange/desmond/desmondelev.html.

The High Accuracy Elevation Data Project collected elevation data (meters) on a 400 meter topographic grid with a vertical accuracy of +/- 15 centimeters to define the topography in South Florida. The data are referenced to the horizontal datum North American Datum 1983 (NAD 83) and the vertical datum North American Vertical Datum 1988 (NAVD 88). The High Accuracy Elevation Data Project began with a pilot study in FY 1995 to determine if the then state-of-the-art GPS technology could be used to perform a topographic survey that would meet the vertical accuracy requirements of the hydrologic modeling community. The initial testing platform was from a truck and met the accuracy requirements. Data were collected in areas near Homestead, Florida. The data are available for the areas shown on the USGS High Accuracy Elevation Data graphic at http://sofia.usgs.gov/exchange/desmond/desmondelev.html.

United States US: Land Area Where Elevation is Below 5 Meters: % of Total Land Area data was reported at 1.168 % in 2010. This stayed constant from the previous number of 1.168 % for 2000. United States US: Land Area Where Elevation is Below 5 Meters: % of Total Land Area data is updated yearly, averaging 1.168 % from Dec 1990 (Median) to 2010, with 3 observations. The data reached an all-time high of 1.168 % in 2010 and a record low of 1.168 % in 2010. United States US: Land Area Where Elevation is Below 5 Meters: % of Total Land Area data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s USA – Table US.World Bank: Land Use, Protected Areas and National Wealth. Land area below 5m is the percentage of total land where the elevation is 5 meters or less.; ; Center for International Earth Science Information Network (CIESIN)/Columbia University. 2013. Urban-Rural Population and Land Area Estimates Version 2. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). http://sedac.ciesin.columbia.edu/data/set/lecz-urban-rural-population-land-area-estimates-v2.; Weighted Average;

The sea level rise (SLR) coastal inundation layers were created using existing federal products: the (1) NOAA Coastal Digital Elevation Models (DEMs) and (2) 2022 Interagency Sea Level Rise Technical Report Data Files. The DEMs for the Continental United States (CONUS) are provided in North American Vertical Datum 1988 (NAVD 88) and were converted to Mean Higher High Water (MHHW) using the NOAA VDatum conversion surfaces; the elevation values are in meters (m). The NOAA Scenarios of Future Mean Sea Level are provided in centimeters (cm). The MHHW DEMs for CONUS were merged and converted to cm and Scenarios of Future Mean Sea Level were subtracted from the merged DEM. Values below 0 represent areas that are below sea level and are “remapped” to 1, all values above 0 are remapped to “No Data”, creating a map that shows only areas impacted by SLR. Areas protected by levees in Louisiana and Texas were then masked or removed from the results.Scenario: For each of the 5 GMSL scenarios (identified by the rise amounts in meters by 2100--0.3 m , 0.5 m. 1.0 m, 1.5 m and 2.0 m), there is a low, medium (med) and high value, corresponding to the 17th, 50th, and 83rd percentiles. Scenarios (15 total): 0.3 - MED, 0.3 - LOW, 0.3 - HIGH, 0.5 - MED, 0.5 - LOW, 0.5 - HIGH, 1.0 - MED, 1.0 - LOW, 1.0 - HIGH, 1.5 - MED, 1.5 - LOW, 1.5 - HIGH, 2.0 - MED, 2.0 - LOW, and 2.0 - HIGH Years (15 total): 2005, 2020, 2030, 2040, 2050, 2060, 2070, 2080, 2090, 2100, 2110, 2120, 2130, 2140, and 2150Report Website: https://oceanservice.noaa.gov/hazards/sealevelrise/sealevelrise-tech-report.htmlGeneral DisclaimerThe data and maps in this tool illustrate the scale of potential flooding, not the exact location, and do not account for erosion, subsidence, or future construction. Water levels are relative to Mean Higher High Water (MHHW) (excludes wind driven tides). The data, maps, and information provided should be used only as a screening-level tool for management decisions. As with all remotely sensed data, all features should be verified with a site visit. Hydroconnectivity was not considered in the mapping process. The data and maps in this tool are provided “as is,” without warranty to their performance, merchantable state, or fitness for any particular purpose. The entire risk associated with the results and performance of these data is assumed by the user. This tool should be used strictly as a planning reference tool and not for navigation, permitting, or other legal purposes.SLR data are not available for Hawaii, Alaska, or U.S. territories at this time.Levees DisclaimerEnclosed levee areas are displayed as gray areas on the maps.Major federal leveed areas were assumed high enough and strong enough to protect against inundation depicted in this viewer, and therefore no inundation was mapped in these regions. Major federal leveed areas were taken from the National Levee Database.Minor (nonfederal) leveed areas were mapped using the best available elevation data that capture leveed features. In some cases, however, breaks in elevation occur along leveed areas because of flood control features being removed from elevation data, limitations of the horizontal and vertical resolution of the elevation data, the occurrence of levee drainage features, and so forth. Flooding behind levees is only depicted if breaks in elevation data occur or if the levee elevations are overtopped by the water surface. At some flood levels, alternate pathways around—not through—levees, walls, dams, and flood gates may exist that allow water to flow into areas protected at lower levels. In general, imperfect levee and elevation data make assessing protection difficult, and small data errors can have large consequences.Citations2022 Sea Level Rise Technical Report - Sweet, W.V., B.D. Hamlington, R.E. Kopp, C.P. Weaver, P.L. Barnard, D. Bekaert, W. Brooks, M. Craghan, G. Dusek, T. Frederikse, G. Garner, A.S. Genz, J.P. Krasting, E. Larour, D. Marcy, J.J. Marra, J. Obeysekera, M. Osler, M. Pendleton, D. Roman, L. Schmied, W. Veatch, K.D. White, and C. Zuzak, 2022: Global and Regional Sea Level Rise Scenarios for the United States: Updated Mean Projections and Extreme Water Level Probabilities Along U.S. Coastlines. NOAA Technical Report NOS 01. National Oceanic and Atmospheric Administration, National Ocean Service, Silver Spring, MD, 111 pp. https://oceanservice.noaa.gov/hazards/sealevelrise/noaa-nostechrpt01-global-regional-SLR-scenarios-US.pdf

The U.S. Geological Survey has been forecasting sea-level rise impacts on the landscape to evaluate where coastal land will be available for future use. The purpose of this project is to develop a spatially explicit, probabilistic model of coastal response for the Northeastern U.S. to a variety of sea-level scenarios that take into account the variable nature of the coast and provides outputs at spatial and temporal scales suitable for decision support. Model results provide predictions of adjusted land elevation ranges (AE) with respect to forecast sea-levels, a likelihood estimate of this outcome (PAE), and a probability of coastal response (CR) characterized as either static or dynamic. The predictions span the coastal zone vertically from -12 meters (m) to 10 m above mean high water (MHW). Results are produced at a horizontal resolution of 30 meters for four decades (the 2020s, 2030s, 2050s and 2080s). Adjusted elevations and their respective probabilities are generated using regional geospatial datasets of current sea-level forecasts, vertical land movement rates, and current elevation data. Coastal response type predictions incorporate adjusted elevation predictions with land cover data and expert knowledge to determine the likelihood that an area will be able to accommodate or adapt to water level increases and maintain its initial land class state or transition to a new non-submerged state (dynamic) or become submerged (static). Intended users of these data include scientific researchers, coastal planners, and natural resource management communities.

These GIS layers provide the probability of observing the forecast of adjusted land elevation (PAE) with respect to predicted sea-level rise or the Northeastern U.S. for the 2020s, 2030s, 2050s and 2080s. These data are based on the following inputs: sea-level rise, vertical land movement rates due to glacial isostatic adjustment and elevation data. The output displays the highest probability among the five adjusted elevation ranges (-12 to -1, -1 to 0, 0 to 1, 1 to 5, and 5 to 10 m) to be observed for the forecast year as defined by a probabilistic framework (a Bayesian network), and should be used concurrently with the adjusted land elevation layer (AE), also available from http://woodshole.er.usgs.gov/project-pages/coastal_response/, which provides users with the forecast elevation range occurring when compared with the four other elevation ranges. These data layers primarily show the distribution of adjusted elevation range probabilities over a large spatial scale and should therefore be used qualitatively.

Great Smoky Mountains National Park High Peaks showing height of Mountains and Knobs measured with Lidar or Survey-grade GPS.

The single value tidal water surface of mean higher high water (MHHW) modeled at the Honolulu tide gauge is used to represent present-day sea level for the urban corridor stretching from Honolulu International Airport to Waikiki and Diamond Head along the south shore of Oahu in the state of Hawaii. Water levels are shown as they would appear during the highest high tides (excluding wind-driven tides). Land elevation was derived using a National Geospatial Agency (NGA)-provided digital elevation model (DEM) based on LiDAR data of the Honolulu area collected in 2009. This "bare earth" DEM (vegetation and structures removed) was used to represent the current topography of the study area above zero elevation. The accuracy of the DEM was validated using a selection of 16 Tidal Benchmarks located within the study area. Data produced in 2014 by Dr. Charles "Chip" Fletcher of the department of Geology & Geophysics (G&G) in the School of Ocean and Earth Science and Technology (SOEST) of the University of Hawaii at Manoa. Supported in part by the NOAA Coastal Storms Program (CSP) and the University of Hawaii Sea Grant College Program. These data should be used strictly as a planning reference and not for navigation, permitting, or other legal purposes.

This is a tiled collection of the 3D Elevation Program (3DEP) covering Alaska only, and is 5-meter resolution. The 3DEP data holdings serve as the elevation layer of The National Map, and provide foundational elevation information for earth science studies and mapping applications in the United States. Scientists and resource managers use 3DEP data for hydrologic modeling, resource monitoring, mapping and visualization, and many other applications. The elevations in this DEM represent the topographic bare-earth surface. USGS standard 5-meter DEMs are produced exclusively from interferometric synthetic aperture radar (Ifsar) source data of 5-meter or higher resolution. Five-meter DEM surfaces are seamless within collection projects, but, not necessarily seamless across projects. This DEM is delivered in the original resolution, with the original spatial reference. All elevation units have been converted to meters. These data may be used as the source of updates to the seamless 1/3 ...

This is a 1 arc-second (approximately 30 m) resolution tiled collection of the 3D Elevation Program (3DEP) seamless data products . 3DEP data serve as the elevation layer of The National Map, and provide basic elevation information for Earth science studies and mapping applications in the United States. Scientists and resource managers use 3DEP data for global change research, hydrologic modeling, resource monitoring, mapping and visualization, and many other applications. 3DEP data compose an elevation dataset that consists of seamless layers and a high resolution layer. Each of these layers consists of the best available raster elevation data of the conterminous United States, Alaska, Hawaii, territorial islands, Mexico and Canada. 3DEP data are updated continually as new data become available. Seamless 3DEP data are derived from diverse source data that are processed to a common coordinate system and unit of vertical measure. These data are distributed in geographic coordinates in units of decimal degrees, and in conformance with the North American Datum of 1983 (NAD 83). All elevation values are in meters and, over the conterminous United States, are referenced to the North American Vertical Datum of 1988 (NAVD 88). The vertical reference will vary in other areas. The elevations in these DEMs represent the topographic bare-earth surface. All 3DEP products are public domain.

This dataset includes data over Canada and Mexico as part of an international, interagency collaboration with the Mexico's National Institute of Statistics and Geography (INEGI) and the Natural Resources Canada (NRCAN) Centre for Topographic Information-Sherbrook, Ottawa. For more details on the data provenance of this dataset, visit here and here.

Click here for a broad overview of this dataset

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Aim Alpine treeline ecotones are influenced by environmental drivers and are anticipated to shift their locations in response to changing climate. Our goal was to determine the extent of recent climate-induced treeline advance in the northeastern United States, and we hypothesized that treelines have advanced upslope in complex ways depending on treeline structure and environmental conditions.

Location White Mountain National Forest (New Hampshire) and Baxter State Park (Maine), USA.

Taxon High-elevation trees – Abies balsamea, Picea mariana, and Betula cordata.

Methods We compared current and historical high-resolution aerial imagery to quantify the advance of treelines over the last four decades, and link treeline changes to treeline form (demography) and environmental drivers. Spatial analyses were coupled with ground surveys of forest vegetation and topographical features to ground-truth treeline classification and provide information on treeline demography and additional potential drivers of treeline locations. We used multiple linear regression models to examine the importance of both topographic and climatic variables on treeline advance.

Results Regional treelines have significantly shifted upslope over the past several decades (on average by 3 m/decade). Diffuse treelines (low tree densities and temperature limited) experienced significantly greater upslope shifts (5 m/decade) compared to other treeline forms, suggesting that both climate warming and treeline demography are important drivers of treeline shifts. Topographical features (slope, aspect) as well as climate (accumulated growing degree days, AGDD) explained significant variation in the magnitude of treeline advance (R2 = 0.32).

Main conclusions The observed advance of regional treelines suggests that climate warming induces upslope treeline shifts particularly at higher elevations where greater upslope shifts occurred in areas with lower AGDD. Overall, our findings suggest that diffuse treelines at high-elevations are more a of a result of climate warming than other alpine treeline ecotones and thus they can serve as key indicators of ongoing climatic changes. Methods Remote sensing analysis Physical copies of true color high resolution historical aerial imagery (sub-meter resolution) were acquired from the Appalachian Mountain Club (AMC) and the USFS White Mountain National Forest Headquarters. Imagery for the Presidential Range was taken in 1978 and Katahdin imagery was taken in 1991. Hard copy images were scanned and converted to TIFF format at 300 dpi (resulting in 0.5 m resolution images). Spatial analyses of change in treeline positions over time were enabled by acquiring high resolution 2018 false-color near-infrared imagery from the National Agriculture Inventory Program (NAIP 2021). Both sets of imagery were taken during summer months (1:40,000 scale). Using ArcGIS 10.8 (ESRI 2011, Redlands, CA, USA), historic imagery was ortho- and georectified to newer imagery via a spline function along 60 ground control points, and then converted into one orthomosaic image (RMSE < 1m). Exact error was always below 5 m for each individual image.

All areas above treeline were manually digitized based on observed tree cover for both sets of images, and the resulting polygons were converted to raster format at 2 m resolution (all raster pixels within each polygon had a value of 1). We identified forest cover only as areas with overlapping crowns and seen as green reflectance in historic imagery and red reflectance in contemporary false-color near-infrared imagery (no visible bare earth or easily identified alpine vegetation). Isolated tree island edges were also digitized and included as treeline if they were >20 m in diameter in any direction (determined in ArcGIS) and included an individual >2 m in height as validated in the field. Alpine rasters were aligned to and multiplied by Lidar-derived digital elevation models (DEMs; 2 m resolution) acquired from New Hampshire and Maine state GIS repositories in order to determine treeline elevations. A total of 400 random sample points (200 for each range, using the ArcGIS random sample point tool) were placed along the outer boundary of the alpine rasters derived from our contemporary imagery, and for each of them we established a paired point at the nearest location along the alpine raster boundary derived from our historic imagery.

Field surveys Field sampling was carried out in the summer of 2021 to characterize tree demography and demographic variation among different treeline forms identified from the current imagery. A subset of contemporary points from our GIS-based sample point pairs (n = 54, 33 in the Presidential Range, 21 in the Katahdin Range, see above) were selected using a random number generator to serve as sites for establishing belt transects. Each belt transect was 100 m in length and 4 m wide (2 m on either side of transect for a total area of 400 m2) and perpendicular to elevation contours, spanning the ecotone between closed forest interior and open alpine habitat. The start of each transect (the lowest elevation on the transect, set as 0 m) was located 50 m downslope (straight-line distance) of contemporary sample points. The start and end of each belt transect were recorded using a Garmin GPSMAP 64 (Garmin, Olathe, Kansas, USA). Each tree > 0.1 m in height with a stem rooted within the transect was recorded noting species, basal diameter (10 cm from the ground), height, horizontal distance from the transect, and distance along the transect (to estimate stem density of trees). Slope, aspect, elevation, and soil depth to bedrock (using a metal soil probe) were recorded at 20 m intervals along the belt transect centerline (0 m, 20 m, 40 m, 60 m, 80 m, 100 m).

For all belt transects, treeline form was assigned based on visual assessments (based on changes in tree height and density across the ecotone). Additionally, we visited a majority of our other accessible contemporary random sample points (~80%) in order to assign treeline form and ground-truth remote sensed treeline classifications. For all visited sample points we took a new GPS point at the field-verified treeline location (continuous canopy cover and at least one individual >2 m in height) nearest to our random sample points (assigned from our treeline delineation procedure). The new points were compared to the original sample point locations and assessed for accuracy (measuring linear distance between points). Eye-level photos of treelines were taken at all sample points to keep a permanent record of treeline appearance. We stress that because tree height could not be extracted or field validated from our historic imagery, some krummholz individuals (<2 m) may have been present above our treeline delineation using our classification scheme. Out of all 400 sample point pairs across both the Presidentials and Katahdin, 88 were classified as abrupt (22%), 70 as diffuse (17.5%), 84 as island (21%), and 162 as krummholz (40.5%).

Spatial data processing To examine the factors potentially influencing the spatial dynamics of treeline advance, both climatological and topographical variables were extracted for the Presidential Range. We could not conduct a similar analysis for Katahdin given the lack of fine-scale climatological data in that area. Elevation was extracted from 2 m state produced DEMs. Using the Spatial Analyst toolbox in ArcGIS, topographical variables such as slope, aspect, and curvature (measure of convex or concave shape of the terrain ranging between -4 and 4) were extracted from our DEMs. Circular aspect data (measured in degrees, 0-360⁰) were converted to radians and linearized (east and west = 1, north and south = 0).

Before linearization, aspect values were used to calculate degree difference from prevailing wind (DDPW - 290˚) and degree difference from south (DDS - 180˚) variables. DDPW is a proxy for exposure to strong winds that can cause both direct physical damage and damage from icing, as well as a proxy for the potential for snow accumulation. The prevailing wind direction for the Presidential range (290˚) was based on wind measurements from the Mount Washington Observatory. DDS is a proxy for the amount of direct solar radiation (in the northern hemisphere). Average monthly mean, maximum, and minimum temperatures as well as annual accumulated growing degree days (AGDD) were calculated from an array of 34 HOBO dataloggers (Onset Computer Corporation, Bourne, MA, USA) placed at various elevations and adjacent to Appalachian Mountain Club buildings in the White Mountains of New Hampshire. HOBO loggers have recorded hourly air temperature at ground level (0 m height) continuously since 2007. Air temperature means and AGDD were calculated from HOBO logger data; for AGDD calculations we used a base temperature of 4˚C, consistent with other studies examining growth patterns of balsam fir, the dominant species within studied treelines. AGDD was calculated as the accumulated maximum value of growing degree days (GDD) in a year.

Gridded maps (90 m spatial resolution) of mean annual temperature (Tmean, between 2007 and 2020) and AGDD for the Presidential Range region were produced using a cokriging interpolation method. To do this, temperatures and AGDD response variables were first checked for normality using qq-plots. Next, correlation between response variables and potential covariates was assessed; both elevation and aspect were highly correlated with HOBO derived temperature and AGDD. We used normal-score simple cokriging with a stable semi-variogram model to interpolate (prediction map) climate variables over the entire spatial extent of the Presidential Range (RMSE ~ 1 for both Tmean and AGDD). Mean annual precipitation was estimated from 30-year normal PRISM climate data (1991-2020; PRISM Climate Group, Oregon State University, https://prism.oregonstate.edu).