Hyporheic zones act as critical ecological links between terrestrial and aquatic systems where redox-sensitive metals of iron (Fe) and manganese (Mn) significantly impact nutrient cycling and water quality. In order to understand the production, release and speciation of Fe(II) and Mn(II) in groundw

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 3=deciduous meadow and subshrub ( 0.5m) 4=bare rock, soil, gravel and asphalt 5=water 6=snow 7=buildings Source data includes NEON LiDAR and imaging sp

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-processed SDP NEON DEM. Pixels with low NDVI or inside building footprints were set to a value of zero.

This map represents estimated watersheds for stream segments derived from a hydrologically corrected digital elevation model. The flow lines were derived in GRASS GIS using a single direction algorithm that does not allow channel braiding. Each watershed is identified by a unique integer that matche

This map represents estimated watersheds for stream segments derived from a hydrologically corrected digital elevation model. The flow lines were derived in GRASS GIS using a single direction algorithm that does not allow channel braiding. Each watershed is identified by a unique integer that matche

This map represents estimated stream flowlines from a hydrologically corrected digital elevation model. The lines were derived in GRASS GIS using a single flow-direction (D8) algorithm that does not allow channel braiding. Each stream segment is identified by a unique integer. Stream lines were deli

This map represents estimated stream flowlines from a hydrologically corrected digital elevation model. The lines were derived in GRASS GIS using a multi-direction algorithm that allows channel braiding. Each stream segment is identified by a unique integer. Stream lines were delineated for drainage

This map represents estimated stream flowlines from a hydrologically corrected digital elevation model. The lines were derived in GRASS GIS using a single-flow direction algorithm that does not allow channel braiding. Each stream segment is identified by a unique integer.Stream lines were delineated

This map represents estimated flow accumulation from a hydrologically corrected digital elevation model. The map was derived in GRASS GIS using a single flow-direction (D8) algorithm that does not allow channel braiding. Values represent the area (in square meters) of land that contributes flow to a

This map represents the estimated binary presence of surface water during the NEON Airborne Observation Platform campaign in July 2018. Values of 1 represent open water, values of 0 represent no water present. Surface water was mapped using imaging spectrometer data from the AOP, and combined with a

This is a hydrologically corrected digital elevation model derived from the 2018 NEON AOP dataset. It represents the height above sea level for impervious objects attached to the ground, such as buildings, but not vegetation. Bridges and culverts have been "cut" to allow flow, and areas of suface wa

This is a hydrologically corrected digital elevation model derived from the 2018 NEON AOP dataset. It represents the height above sea level for impervious objects attached to the ground, such as buildings, but not vegetation. Bridges and culverts have been "cut" to allow realistic surface flow for h

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 buildings. This version of the data includes the elevation of buildings and other non-vegetation objects such as bridges. Areas of deciduous vegetation

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 buildings. This version of the data includes the elevation of buildings and other non-vegetation objects such as bridges, but areas of vegetation refle

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 buildings and trees. This version of the data includes the elevation of the tops of deciduous vegetation, consistent with the data collection interval

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 with data interpolated from the 3m 2018 Airborne Snow Observatory DEM. The ASO DEM was harmonized with the NEON DEM and filtered to remove small pits a

1m Resolution bare-earth Digital Elevation Model for the Upper East River Derived from 2018 NEON AOP Data. This version has been re-processed to remove artifacts associated with sharp ridges and dense vegetation, and was based on original data from NEON: Goulden T ; Hass B ; Brodie E ; Chadwick K D

Leaf area, wet, and dry weights for leaf samples from sites where field sampling was conducted during the 2018 National Ecological Observatory Network (NEON) Airborne Observation Platform (AOP) imaging spectroscopy and lidar surveys in Gunnison County, Colorado. The sampling sites were located acros

Upper East sub-basins are delineated as area contributing flow to SFA 2016 stream gage locations. Shapefile attributes include sub-basin ID, name, area (m2 and km2) and codes for spatial contributions to EBC and PH used by Carroll et al. (2018). Geospatial reference is UTM 1983 zone 13. JPEG image p

Airborne LiDAR data were acquired over the East River Watershed on June 8, 2015 to August 10, 2015. The area covered was approximately 4933 square kilometers with an average point density of 10-12 points per square meter to comply with USGS's QL1 standard. Additional products include the LiDAR point