Waiting on a mule deer crossing…
Integrates high-resolution remote sensing, machine learning downscaling, and field soil moisture methods to track snowpack dynamics, growing degree days, and hydrological processes across mountain watersheds.
AI-generated synthesis. An AI-synthesized research primer that reads the publications, datasets, and documents tagged as this neighborhood's members and summarizes the work as a brief literature-review-style narrative.
Read it as a starting point — a synthesized map of what is in this neighborhood and how it connects, not as a peer-reviewed literature review. Cited papers are grounded; the synthesis is the model's reading of them.
Mountain snowpack is the hydrological heartbeat of the Gunnison Basin. Snow accumulates through winter, then melts over spring and early summer, releasing water that sustains streams, soils, plants, and downstream communities across the American West. Understanding how snow is distributed across the landscape, how long it persists, and how it interacts with temperature, terrain, and vegetation is therefore central to predicting how mountain ecosystems will respond to a warming climate. Research in this area links remote sensing, field measurement, and statistical modeling to track snow and its downstream consequences across watersheds.
A few core concepts help readers navigate this work. Snow-covered area is mapped using satellite or aerial imagery, often relying on the normalized difference snow index, a ratio that exploits the fact that snow is bright in visible light but dark in shortwave infrared, making it distinguishable from clouds and bare ground. Where shortwave infrared bands are unavailable, scientists turn to machine learning trained on visible and near-infrared imagery instead. Terrain itself is represented by a digital elevation model, a gridded map of surface elevation from which derived variables like slope, aspect, and northness (how north-facing a slope is) can be calculated. These topographic attributes control topographic shading and therefore how quickly snow disappears on a given hillside. LiDAR, a laser-based remote sensing technology, produces detailed three-dimensional maps of vegetation and ground surfaces, allowing researchers to measure individual tree growth and canopy structure over time.
Multi-temporal soil moisture measurements using TDR probe combined with soil coring and laboratory processing to determine moisture content, bulk dens...
Machine learning approach using Random Forest algorithms to downscale coarse resolution climate data to higher spatial resolution using topographic pr...
Time-series analysis of Landsat and Sentinel satellite imagery to determine seasonal snow disappearance dates using Normalized Difference Snow Index w...
High-resolution airborne hyperspectral and lidar data collection and analysis to classify land cover types and derive vegetation parameters for hydrol...
Rain measurements recorded via tipping bucket rain gauge, though may not be accurate for winter precipitation events like snow.
Calculation of potential solar radiation at study sites using topographic variables including sun angle, latitude, ground slope, local shading, and da...
This package is part of the Watershed Function SFA Data Collection and contains remote sensing data and geophysical measurements acquired at the Pumph...
This dataset provides Daymet Version 4 data as gridded estimates of daily weather parameters for North America, Hawaii, and Puerto Rico. Daymet variab...
This dataset provides Daymet Version 4 R1 data as gridded estimates of daily weather parameters for North America, Hawaii, and Puerto Rico. Daymet var...
Processed LiDAR data and environmental covariates from 2015 and 2019 LiDAR scans in the Vicinity of Snodgrass Mountain (Western Colorado, USA), in a g...
This is a 3m map of snow depth derived from repeat LiDAR data collection by the Airborne Snow Observatory. This dataset has been clipped and resampled...
This dataset contains uncrewed aircraft systems (UAS) high-resolution data of soil moisture at the 0-5 cm soil depth, normalized difference vegetation...
Climate is tracked through air temperature records and derived indices. Growing degree days accumulate temperature above a biological threshold and serve as a proxy for the energy available to plants and insects during the growing season. Snow-free season length, measured by tracking when snow disappears from a pixel using time-series satellite imagery, links snowpack directly to ecological processes. Together, these concepts allow researchers to ask how snow, terrain, and warming jointly shape the Gunnison Basin's forests, alpine plant communities, and rivers.
Early work in this neighborhood established that high-resolution snow mapping in complex mountain terrain is both possible and necessary. Traditional satellite products are coarse and struggle in forested or shaded areas, motivating new approaches. A landmark study demonstrated that snow-covered area could be accurately mapped at meter scales in forested mountain ecosystems using commercial PlanetScope imagery combined with convolutional neural networks, even without a shortwave infrared band (John et al., 2022). Adding vegetation indices and terrain variables derived from a digital elevation model further improved performance, and the approach generalized across geographically distinct sites in Colorado and Switzerland (John et al., 2022).
Parallel foundational work in the East River corridor established the importance of antecedent snowpack conditions for downstream water quality. Long-term, high-frequency monitoring of dissolved oxygen and temperature beneath the streambed showed that snowmelt-driven streamflow regimes propagate into subsurface biogeochemistry, linking winter snow accumulation to summer ecosystem function in the river (Gooseff et al., 2023).
A central finding across recent studies is that snowpack and the temperature regime it controls leave clear fingerprints on both terrestrial and aquatic systems. In the East River, antecedent snowpack conditions shape streamflow and, in turn, govern oxygen dynamics in the riverbed, with low-flow years producing extended periods of oxygen depletion at multiple depths (Gooseff et al., 2023). The daily rhythm of oxygen does not simply track temperature, indicating that snow-driven hydrology and biological respiration interact in complex ways that simple models miss.
On the landscape, snowpack persistence and warming jointly influence vegetation. A Bayesian analysis of repeat LiDAR scans of the Upper Gunnison Watershed found that snowpack persistence and growing degree days significantly influence conifer growth rates, alongside competition from neighboring trees (Drew et al., 2025). The same study demonstrated that careful statistical linkage of trees across overlapping scans is essential: naive matching introduces noise that biases estimates of how topography and climate affect growth toward zero (Drew et al., 2025). This two-stage framework opens the door to landscape-scale demographic studies that were previously infeasible.
In alpine plant communities near Gothic, fine-scale moisture availability — itself a product of snowmelt patterns and terrain — emerges as the strongest predictor of moss prevalence, with slope and elevation acting in combination (Ristau, 2025). Vascular plant cover declined sharply with elevation, while mosses remained patchy and low in cover across all surveyed ridges, suggesting they occupy a narrow niche shaped more by local wetness than by elevation alone (Ristau, 2025). Responses to recent warming trends were inconsistent across sites, with only one ridge showing a positive relationship between increasing growing degree days and moss dominance (Ristau, 2025).
All four primary studies in this area have appeared since 2022, reflecting how rapidly the toolkit is evolving. Recent work has shifted from demonstrating that high-resolution snow mapping is feasible toward integrating snow products with vegetation and hydrological models. Machine learning approaches combining PlanetScope imagery, vegetation indices, and terrain variables are now being applied across diverse mountain regions (John et al., 2022), while Bayesian record linkage methods are enabling researchers to extract individual-tree growth signals from overlapping LiDAR campaigns at watershed scales (Drew et al., 2025). In the East River, continuous in-situ sensor networks are revealing subsurface processes that previously could only be inferred from sparse sampling (Gooseff et al., 2023).
The frontier is increasingly about coupling these data streams. Researchers are asking how snow-disappearance timing, derived from satellites, predicts conifer growth measured by LiDAR, plant community shifts measured in field plots, and stream chemistry measured by sensors. Geological perspectives on long-term climate variability also frame this work, reminding researchers that today's warming is occurring against a backdrop of natural glacial-interglacial cycles A Geologist's View of Global Change.
Many questions remain. How will continued declines in snowpack persistence reshape conifer demography across the Upper Gunnison Watershed, and which slopes and aspects will buffer trees from drought stress longest? Can high-resolution snow products be reliably extended backward in time to reconstruct the snow regimes that shaped today's forests, and forward to project future conditions? Why do alpine moss communities respond so inconsistently to warming across nearby ridges, and what does this tell us about the fine-scale refugia that may persist as climate changes? How tightly coupled are snowmelt timing and subsurface oxygen dynamics in the East River, and do low-flow years like 2018 foreshadow a new baseline for mountain rivers? Answering these will require sustained integration of remote sensing, field ecology, and statistical modeling across the basin.
Drew, L., Kaplan, A., Breckheimer, I. (2025). A Bayesian record linkage approach to applications in tree demography using overlapping LiDAR scans. Annals of Applied Statistics. →
Gooseff, M. et al. (2023). Hyporheic Oxygen Dynamics in the East River, Colorado: Insights From an In-Situ, High Frequency Time Series During Two Distinct Flow Seasons. Water Resources Research. →
John, A., Cannistra, A. F., Yang, K., Tan, A., Shean, D., Hille Ris Lambers, J., Cristea, N. (2022). High-Resolution Snow-Covered Area Mapping in Forested Mountain Ecosystems Using PlanetScope Imagery. Remote Sensing. →
Ristau, E. (2025). Moss and vascular plant cover across elevational gradients in a changing alpine climate. →
Creation of digital surface model from 2018 NEON Airborne Observation Platform dataset representing height above sea level for objects attached to the...
Uses ASTER thermal infrared data to identify areas of anomalous surface temperature. Areas with modeled temperatures between 1° and 2° above the mean ...
Soil moisture measurements using the Lobe Differencing Correlation Radiometer (LDCR) L-Band microwave radiometer flown on aerial platforms.
Flow lines derived in GRASS GIS using a single direction algorithm from hydrologically corrected digital elevation model to delineate watersheds drain...
Uses xESMF package to interpolate different UAS measurements to a common rectilinear grid, with bilinear interpolation for soil moisture and conservat...
UAV flights using DJI Mavic 2 Pro collecting RGB images with 3cm ground sample distance. Images processed via Structure from Motion in Agisoft Metasha...
Resampling of 0.6m resolution imagery to 1m grid resolution using bilinear interpolation.
High-density airborne LiDAR scans collected in August-September 2015 and 2019 to generate normalized point clouds.
Collection and analysis of daily temperature data to calculate deviations from long-term averages. Multi-year averages are computed and compared to da...
Statistical method used to fill missing data areas using relationships between 2018 and 2019 snow depths or snow depth to elevation relationships.
A two-stage Bayesian hierarchical approach that identifies unique individuals across LiDAR scans by modeling observed locations as noisy transformatio...
Processing of discrete-return LiDAR data using lidR package to generate canopy height models. Includes normalization, subsetting, and reprojection.
Applies subcanopy solar radiation model from Bode et al. 2014 to account for vegetation shading greater than 1m in height.
Calculation of Normalized Differential Vegetation Index from 4-band aerial imagery using (NIR-Red)/(NIR+Red) formula.
Records the UTM Y Coordinate for every pixel using the WGS84 UTM Zone 13N Coordinate System (EPSG:32613).
Light intensities in lux recorded using HOBO data-loggers from 2008-2010. Readings taken exactly hourly at trap-nest sites.
Gap-filling areas outside Upper East River domain with interpolated data from 3m ASO DEM, harmonized with NEON DEM and filtered to remove small pits a...
Map generated with the cost distance GRASS GIS module (r.cost) using estimated travel speeds as the cost function.
Deployment of temperature loggers at multiple depths in aquatic habitats with half-hourly recordings over multi-week periods. Includes post-processing...
This is a 3m map of snow depth derived from repeat LiDAR data collection by the Airborne Snow Observatory. This dataset has been clipped and resampled...
These are maps of annual accumulated spring snow-free growing potential (snow-free growing degree days, SFGDD) for the Upper Gunnison domain, derived ...
This is a map of temporal variability in accumulated snow-free growing potential (snow-free growing degree days, SFGDD) for the Upper Gunnison domain,...
This dataset contains uncrewed aircraft systems (UAS) high-resolution data of soil moisture at the 0-5 cm soil depth, normalized difference vegetation...
This is a map of temporal variability in accumulated spring snow-free growing potential (snow-free growing degree days, SFGDD) for the Upper Gunnison ...
This is a map of temporal variability in accumulated fall snow-free growing potential (snow-free growing degree days, SFGDD) for the Upper Gunnison do...
This dataset contains uncrewed aircraft systems (UAS) high-resolution data of soil moisture at the 0-5 cm soil depth, normalized difference vegetation...
This dataset contains uncrewed aircraft systems (UAS) high-resolution data of soil moisture at the 0-5 cm soil depth, normalized difference vegetation...
This dataset contains uncrewed aircraft systems (UAS) high-resolution data of soil moisture at the 0-5 cm soil depth, normalized difference vegetation...
This is a map of the influence of snow on the energy available for plant growth (growing degree days, GDD) for the Upper Gunnison domain, derived from...
This is a map of temporal variability in accumulated fall growing potential (growing degree days, GDD) for the Upper Gunnison domain, derived from dai...
This is a map of accumulated spring snow-free freezing potential (snow-free freezing degree days, SFFDD) for the Upper Gunnison domain, derived from d...
This is a map of temporal variability in accumulated snow-free freezing potential (freezing degree days, FDD) for the Upper Gunnison domain, derived f...
This is a map of temporal variability in accumulated spring snow-free growing potential (snow-free growing degree days, SFGDD) for the Upper Gunnison ...
This is a map of accumulated fall snow-free freezing potential (snow-free freezing degree days, SFFDD) for the Upper Gunnison domain, derived from dai...
This is a map of temporal variability in accumulated fall snow-free freezing potential (snow-free freezing degree days, SFFDD) for the Upper Gunnison ...
This is a map of accumulated snow-free freezing potential (freezing degree days, FDD) for the Upper Gunnison domain, derived from daily minimum temper...
This is a map of accumulated snow-free growing potential (snow-free growing degree days, SFGDD) for the Upper Gunnison domain, derived from daily maxi...
This is a map of temporal variability in accumulated snow-free freezing potential (freezing degree days, FDD) for the Upper Gunnison domain, derived f...
This is a map of temporal variability in accumulated snow-free growing potential (snow-free growing degree days, SFGDD) for the Upper Gunnison domain,...
This is a map of accumulated fall snow-free growing potential (snow-free growing degree days, SFGDD) for the Upper Gunnison domain, derived from daily...
This is a map of accumulated snow-free growing potential (snow-free growing degree days, SFGDD) for the Upper Gunnison domain, derived from daily maxi...
This is a map of temporal variability in accumulated growing potential (growing degree days, GDD) for the Upper Gunnison domain, derived from daily ma...
This is a map of the influence of snow on the energy available for plant growth (growing degree days, GDD) in spring and early summer for the Upper Gu...
This is a map of the influence of snow on the energy available for plant growth (growing degree days, GDD) in late summer and fall for the Upper Gunni...
This report contains details of the National Ecological Observatory Network Airborne Observation Platform (NEON AOP) assignable asset (AA) flight over...
The manual_soil_measurements_2022_2023.csv data set contains all of the manually measured soil CO 2 efflux, volumetric water content and soil temperat...
These are maps of annual accumulated spring snow-free freezing potential (snow-free freezing degree days, SFFDD) for the Upper Gunnison domain, derive...
These are maps of annual accumulated snow-free freezing potential (freezing degree days, FDD) for the Upper Gunnison domain, derived from daily minimu...
These are maps of annual accumulated snow-free growing potential (snow-free growing degree days, SFGDD) for the Upper Gunnison domain, derived from da...
These are maps of accumulated fall snow-free growing potential (snow-free growing degree days, SFGDD) for the Upper Gunnison domain, derived from dail...
*** An error has been caught in time codes. The time codes currently read GMT-5:00. The CORRECT time code is GMT-7:00 for all datasets .*** These data...
This dataset represents potential clear-sky incident solar radiation (in w/m^2) for day of year 355 (winter solstice), taking into account shading fr...
This dataset represents potential clear-sky incident solar radiation (in w/m^2) for day of year 172 (summer solstice), taking into account shading fr...
This dataset represents an estimate of interannual variability in the day of year (i.e. , "Julian Day") of the onset of the seasonal snowpack. Speci...
This dataset represents potential clear-sky incident solar radiation (in w/m^2) for day of year 265 (fall equinox), taking into account shading from ...
This map shows areas of anomalous surface temperature in northern Saguache Counties identified from ASTER and LANDSAT thermal data and spatial based i...
This map represents estimated flow accumulation from a hydrologically corrected digital elevation model. The map was derived in GRASS GIS using a mult...
This is a 1 m resolution map of the relative "southness" of topographic aspect, computed from the cosine of the topographic aspect using the equation:...
This is a 1 m resolution map of the relative "westness" of topographic aspect, computed from the cosine of the topographic aspect using the equation: ...
**Error has been found in Glenwood Springs station data. The 8in depth soil moisture sensor was mislabeled as the 40in depth sensor and vice versa for...
This is a map of temporal variability in spring accumulated freezing potential (freezing degree days, FDD) for the Upper Gunnison domain, derived from...
This dataset represents an estimate of interannual variability in the number of days of continuous seasonal snowpack from 1993 - 2022. This map is der...
This is a hydrologically corrected digital elevation model derived from the 2018 NEON AOP dataset. It represents the height above sea level for imperv...
This map represents estimated stream flowlines from a hydrologically corrected digital elevation model. The lines were derived in GRASS GIS using a mu...
This is a hydrologically corrected digital elevation model derived from the 2018 NEON AOP dataset. It represents the height above sea level for imperv...
This dataset represents potential clear-sky incident solar radiation (in w/m^2) for day of year 80 (spring equinox), taking into account shading from...
This dataset represents field observations of reproductive development (flowering phenology) in 135 species of flowering plants collected at 12 fiel...
This dataset represents an estimate of the day of year (i.e. , "Julian Day") of the persistence of the seasonal snowpack. Specifically these are est...
This dataset represents an estimate of the day of year (i.e. , "Julian Day") of the onset of the seasonal snowpack. Specifically these are estimates...
This "Weakly Anomalous to Anomalous Surface Temperature" dataset differs from the "Anomalous Surface Temperature" dataset for this county (another rem...
This is a map of the influence of fall snow on late season freezing potential (snow-free freezing degree days, SFFDD) for the Upper Gunnison domain, d...
This is a map of the influence of spring snow on early season freezing potential (snow-free freezing degree days, SFFDD) for the Upper Gunnison domain...
This map shows areas of anomalous surface temperature in northern Saguache Counties identified from ASTER and LANDSAT thermal data and spatial based i...
This map shows areas of anomalous surface temperature in northern Saguache Counties identified from ASTER and LANDSAT thermal data and spatial based i...
*** An error has been caught in time codes. The time codes currently read GMT-5:00. The CORRECT time code is GMT-7:00 for all datasets .*** These data...
These are maps of annual accumulated freezing potential (freezing degree days, FDD) for the Upper Gunnison domain, derived from daily minimum temperat...
This is a map of temporal variability in fall accumulated freezing potential (freezing degree days, FDD) for the Upper Gunnison domain, derived from d...
*** An error has been caught in time codes. The time codes currently read GMT-5:00. The CORRECT time code is GMT-7:00 for all datasets .*** These data...
These are maps of accumulated growing potential (growing degree days, GDD) for the Upper Gunnison domain, derived from daily maximum temperature maps...
These are maps of annual accumulated fall growing potential (growing degree days, GDD) for the Upper Gunnison domain, derived from daily maximum tempe...
This is a map of the influence of snow on growing season freezing potential (snow-free freezing degree days, SFFDD) for the Upper Gunnison domain, der...
** Error has been found in Glenwood Springs station data. The 8in depth soil moisture sensor was mislabeled as the 40in depth sensor and vice versa fo...
These are maps of annual accumulated fall snow-free freezing potential (snow-free freezing degree days, SFFDD) for the Upper Gunnison domain, derived ...
These are maps of annual accumulated spring snow-free freezing potential (snow-free freezing degree days, SFFDD) for the Upper Gunnison domain, derive...
This is a map of accumulated fall growing potential (growing degree days, GDD) for the Upper Gunnison domain, derived from daily maximum temperature m...
These are maps of annual accumulated snow-free growing potential (snow-free growing degree days, SFGDD) for the Upper Gunnison domain, derived from da...
These are maps of annual accumulated spring snow-free growing potential (snow-free growing degree days, SFGDD) for the Upper Gunnison domain, derived ...
This is a map of temporal variability in accumulated growing potential (growing degree days, GDD) for the Upper Gunnison domain, derived from daily ma...
This is a map of accumulated growing potential (growing degree days, GDD) for the Upper Gunnison domain, derived from daily maximum temperature maps i...
This is a map of accumulated freezing potential (freezing degree days, FDD) for the Upper Gunnison domain, derived from daily minimum temperature maps...
This is a map of variability in accumulated freezing potential (freezing degree days, FDD) for the Upper Gunnison domain, derived from daily minimum t...
This is a map of spring accumulated freezing potential (freezing degree days, FDD) for the Upper Gunnison domain, derived from daily minimum temperatu...
This is a map of accumulated spring snow-free growing potential (snow-free growing degree days, SFGDD) for the Upper Gunnison domain, derived from dai...
This report contains details of the National Ecological Observatory Network Airborne Observation Platform (NEON AOP) assignable asset (AA) flight over...
This map shows areas of anomalous surface temperature in northern Saguache Counties identified from ASTER and LANDSAT thermal data and spatial based i...
This dataset represents potential clear-sky incident solar radiation (in w/m^2) for day of year 172 (summer solstice), taking into account shading fro...
This dataset represents potential clear-sky incident solar radiation (in w/m^2) for day of year 265 (fall equinox), taking into account shading from t...
This dataset represents potential clear-sky incident solar radiation (in w/m^2) for day of year 355 (winter solstice), taking into account shading fro...
This is a map of fall accumulated freezing potential (freezing degree days, FDD) for the Upper Gunnison domain, derived from daily minimum temperature...
This is a bare-earth digital elevation model from the 2018 NEON AOP dataset. Areas outside the boundaries of the Upper East River domain were filled w...
This is a digital surface model from the 2018 NEON AOP dataset. It represents the height above sea level for objects attached to the ground, such as b...
This is a digital surface model from the 2018 NEON AOP dataset. It represents the height above sea level for objects attached to the ground, such as b...
This is a digital surface model from the 2018 NEON AOP dataset. It represents the height above sea level for objects attached to the ground, such as b...
This map represents estimated flow accumulation from a hydrologically corrected digital elevation model. The map was derived in GRASS GIS using a sing...
This map represents estimated stream flowlines from a hydrologically corrected digital elevation model. The lines were derived in GRASS GIS using a si...
This map represents estimated stream flowlines from a hydrologically corrected digital elevation model. The lines were derived in GRASS GIS using a si...
This is a styled basemap showing snow depth on March 31st, 2018 derived from repeat LiDAR data collection by the Airborne Snow Observatory. This datas...
This is a styled basemap showing snow depth on April 7th 2019 derived from repeat LiDAR data collection by the Airborne Snow Observatory. This dataset...
This is a visible (RGB) orthomosaic derived from UAV imagery via Structure from Motion processing. UAV flights were performed in sunny conditions on J...
This is a visible (RGB) orthomosaic derived from UAV imagery via Structure from Motion processing. UAV flights were performed in cloudy conditions on ...
This is a visible (RGB) orthomosaic derived from UAV imagery via Structure from Motion processing. UAV flights were performed in Sunny conditions on S...
This is a visible (RGB) orthomosaic derived from UAV imagery via Structure from Motion processing. UAV flights were performed in Sunny conditions on A...
This is a visible (RGB) orthomosaic derived from UAV imagery via Structure from Motion processing. UAV flights were performed in Sunny conditions on N...
This is a visible (RGB) orthomosaic derived from UAV imagery via Structure from Motion processing. UAV flights were performed in sunny conditions on M...
This is a map of various vegetation canopy structure metrics derived from high-density airborne LiDAR scans collected in August - September 2015 and 2...
This dataset represents potential clear-sky incident solar radiation (in w/m^2) for day of year 80 (spring equinox), derived from an elevation model ...
This map records the UTM X Coordinate for every pixel in the Upper Gunnison Domain, as measured using the WGS84 UTM Zone 13N Coordinate System (EPSG:3...
This map records the UTM Y Coordinate for every pixel in the Upper Gunnison Domain, as measured using the WGS84 UTM Zone 13N Coordinate System (EPSG:3...
This is a 1 m resolution map of topographic slope computed using a 3*3 pixel kernel and Horn's formula. It is derived from a 1m resolution Digital Ele...
This is a 1m resolution map of Normalized Differential Vegetation Index (NDVI) derived from resampled 0.6m 4-band orthoimagery collected as part of th...
This is a 1m resolution aerial imagery orthomosaic resampled from 0.6m 4-band orthoimagery collected on October 14th 2017 as part of the USDA National...
This is a 3m resolution map of Normalized Differential Vegetation Index (NDVI) derived from resampled 0.6m 4-band orthoimagery collected as part of th...
This is a 1m resolution aerial imagery orthomosaic resampled from 0.6m 4-band orthoimagery collected on September 14th 2019 as part of the USDA Nation...
These maps represent annual estimates of the number of days of continuous seasonal snowpack from 1993 - 2022. The maps are derived from estimates of t...
These are maps of monthly averages of daily average air temperature for the Upper Gunnison domain measured in degrees C. Estimates were derived from w...
These are maps of monthly averages of daily maximum air temperature for the Upper Gunnison domain measured in degrees C. Estimates were derived from w...
These are maps of monthly averages of daily minimum air temperature for the Upper Gunnison domain measured in degrees C. Estimates were derived from w...
These are map of annual fall accumulated freezing potential (freezing degree days, FDD) for the Upper Gunnison domain, derived from daily minimum temp...
These are maps of annual accumulated freezing potential (freezing degree days, FDD) for the Upper Gunnison domain, derived from daily minimum temperat...
This is a bare-earth digital elevation model from the 2018 NEON AOP dataset. The original NEON DEM was processed to correct topographic errors and gap...
This is a map of accumulated growing potential (growing degree days, GDD) for the Upper Gunnison domain, derived from daily maximum temperature maps i...
This dataset represents an estimate of the mean number of days of continuous seasonal snowpack from 1993 - 2022. This map is derived from estimates of...
This dataset represents an estimate of the mean day of year (i.e. , "Julian Day") of the onset of the seasonal snowpack. Specifically these are est...
This dataset represents an estimate of the mean day of year (i.e. , "Julian Day") of the persistence of the seasonal snowpack from 1993 - 2022. Spec...
This dataset represents an estimate of interannual variability in the day of year (i.e. , "Julian Day") of the persistence of the seasonal snowpack. ...
File List temp_data.txt light_data.txt Description Both files are tab-delimited text files. Temperatures (in degrees Celsius) were recorded in 2007–20...
These data are the complete data set from installation through Dec 31, 2017 for AGCI's interactive Roaring Fork Observation Network (iRON). The networ...
*** An error has been caught in time codes. The time codes currently read GMT-5:00. The CORRECT time code is GMT-7:00 for all datasets . Files with Fu...
These data are the complete data set from installation through Dec 31, 2017 for AGCI's interactive Roaring Fork Observation Network (iRON). The networ...
These data are the complete data set from installation through Dec 31, 2017 for AGCI's interactive Roaring Fork Observation Network (iRON). The networ...
This map shows areas of anomalous surface temperature in Alamosa and Saguache Counties identified from ASTER thermal data and spatial based insolation...
This map shows areas of anomalous surface temperature in Alamosa and Saguache Counties identified from ASTER thermal data and spatial based insolation...
This map shows areas of anomalous surface temperature in Alamosa and Saguache Counties identified from ASTER thermal data and spatial based insolation...
This "Weakly Anomalous to Anomalous Surface Temperature" dataset differs from the "Anomalous Surface Temperature" dataset for this county (another rem...
**Error has been found in Glenwood Springs station data. The 8in depth soil moisture sensor was mislabeled as the 40in depth sensor and vice versa for...
**Error has been found in Glenwood Springs station data. The 8in depth soil moisture sensor was mislabeled as the 40in depth sensor and vice versa for...
***An error has been caught in time codes. The time codes currently read GMT-5:00. The CORRECT time code is GMT-7:00 for all datasets. Files with Full...
These data are the complete data set from installation through Dec 31, 2017 for AGCI's interactive Roaring Fork Observation Network (iRON). The networ...
*** An error has been caught in time codes. The time codes currently read GMT-5:00. The CORRECT time code is GMT-7:00 for all datasets . Files with Fu...
*** An error has been caught in time codes. The time codes currently read GMT-5:00. The CORRECT time code is GMT-7:00 for all datasets .*** These data...
These data are the complete data set from installation through Dec 31, 2017 for AGCI's interactive Roaring Fork Observation Network (iRON). The networ...
This map shows areas of anomalous surface temperature in Alamosa and Saguache Counties identified from ASTER thermal data and spatial based insolation...
This map shows areas of anomalous surface temperature in Alamosa and Saguache Counties identified from ASTER thermal data and spatial based insolation...
This map shows areas of anomalous surface temperature in Alamosa and Saguache Counties identified from ASTER thermal data and spatial based insolation...
This dataset is a styled basemap depicting vegetation canopy structure variables in the Upper Gunnison domain overlaid on high-resolution topography, ...
This is a landcover map derived from the 2018 NEON AOP dataset for the upper east river. 1=needle-leaf trees and shrubs 2=deciduous trees and shrubs ...
** Error has been found in Glenwood Springs station data. The 8in depth soil moisture sensor was mislabeled as the 40in depth sensor and vice versa fo...
This is a map of vegetation canopy height suitable for tree detection and crown segmentation, derived from high-density airborne LiDAR scans collected...
These are maps of daily minimum air temperature for the Upper Gunnison domain measured in degrees C. Estimates were derived from weather station and m...
These are maps of daily maximum air temperature for the Upper Gunnison domain measured in degrees C. Estimates were derived from weather station and m...
This is 1 meter resolution landcover map developed for the RMBL Spatial Data Platform. Source datasets include 2017 and 2019 4-band imagery from the N...
This is a map of 80th percentile canopy hieght above the ground for the Upper Gunnison River Basin based on 2015 and 2019 LiDAR data. Height is measur...
This is a map of 20th percentile canopy height above the ground for the Upper Gunnison River Basin based on 2015 and 2019 LiDAR data. Height is measur...
This is a map of vegetation canopy height above the ground for the Upper Gunnison River Basin based on 2015 and 2019 LiDAR data. Height is measured in...
This is a map of vegetation understory cover or density for the Upper Gunnison River Basin based on 2015 and 2019 LiDAR data. Cover is measured as a p...
Gothic Colorado Winter 2022 Snow Depth Lidar from Jan7, Jan 26, and Feb 14 2022. Additionally, includes snow off.
This is a vegetation canopy height map from the 2018 NEON AOP dataset. It was derived from the NEON Lidar-based digital surface model and the re-proce...
This map represents estimated watersheds for stream segments derived from a hydrologically corrected digital elevation model. The flow lines were deri...
This map represents estimated watersheds for stream segments derived from a hydrologically corrected digital elevation model. The flow lines were deri...
<p>This is a 1 m resolution Digital Elevation Model (DEM) for the Upper Gunnison River domain derived from public LiDAR datasets. The primary data sou...
This map represents the estimated on-road and off-road travel time in minutes from Crested Butte via the fastest travel means available (snowmobiles e...
This map represents the estimated on-road and off-road travel time in minutes from Crested Butte via the fastest travel means available (snowmobiles e...
This map represents the estimated binary presence of surface water during the NEON AOP campaign in July 2018. Values of 1 represent open water, values...
This is a 3m resolution binary map representing areas within the Upper Gunnison Domain of the RMBL Spatial Data Platform.
This dataset is a styled basemap depicting topographic slope and aspect of Upper Gunnison domain using a rainbow color scale with contour lines, optim...