Data from: 'Abiotic influences on continuous conifer forest structure across a subalpine watershed'

Dataset · Wed Jan 01 04:00:00 EST 2025

DOI:https://doi.org/10.15485/2404585· OSTI ID:2404585

Worsham, H. Marshall ^[1]; Wainwright, Haruko ^[2]; Powell, Thomas ^[3]; Falco, Nicola ^[4]; Kueppers, Lara ^[1]

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1. University of California Berkeley
2. Massachusetts Institute of Technology
3. The University of the South
4. Lawrence Berkeley National Laboratory

This package archives the core data used for analysis and inference in 'Abiotic influences on continuous conifer forest structure across a subalpine watershed' (Worsham et al., 2025). All data were collected in the East River, Washington Gulch, Slate River, and Coal Creek watersheds of Colorado. In the paper, we quantified the relative influence of climate, topographic, edaphic, and geologic factors on conifer stand structure and composition, and their functional relationships, at the watershed scale. We used waveform LiDAR data to derive spatially continuous stand structure metrics. We fused these with a species-level classification map to estimate tree species abundance. We applied generalized additive and generalized boosted models to evaluate the covariability of structural and compositional metrics with abiotic variables. The package contains the essential products required for reproducing our analysis and the tables and figures reported in the publication. The products comprise four classes: (1) geospatial data, (2) tabular data used for inferential analysis, (3) tabular data describing analytical results and performance statistics, and (4) a data user guide. (1) includes discretized waveform LiDAR data, locations and attributes of individual tree crowns, sampling locations and domain boundaries, a canopy height model, and raster files of estimated forest structural and compositional metrics at 100 m grid scale. (2) includes all response and explanatory variable values applied in inferential models. Response variables include conifer forest stand density, basal area, 95th percentile height, quadratic mean diameter, and others. Explanatory variables include climatic water deficit, actual evapotranspiration, elevation, heat load, soil available water content, and others. (3) includes results of training and testing several individual tree detection (ITD) algorithms, as well as inferential modeling results. (4) is a PDF user guide for this data package, including detailed descriptions and data dictionaries for all files. The data package root contains 17 assets: 8 compressed tape archive (.tar.gz) files, 5 comma-separated values (.csv) files, 3 Geographic Tagged Image File Format (GeoTIFF) (.tif) files, and 1 Portable Document Format (.pdf) file. The compressed .tar.gz archives contain ESRI shapefiles (.shp) .tif, compressed LASer (.laz), and .csv files. The archives must first be decompressed using the widely distributed command-line software utility TAR. All other files, including constituent files within the .tar.gz archives, can be opened in the open-source R statistical computing environment. Alternatively, .csv files may also be read in any simple text editor software or Microsoft Excel. Geospatial files including .shp and .tif files can also be opened in GIS software, such as QGIS (open-source) or ESRI ArcGIS (proprietary). The .pdf Data User Guide can be read with Adobe Acrobat Reader or other compatible readers.

• Research Organization: Integrating tree hydraulic trait, forest stand structure, and topographic controls on ecohydrologic function in a Rocky Mountain subalpine watershed
• Sponsoring Organization: U.S. DOE > Office of Science > Biological and Environmental Research (BER)
• DOE Contract Number: AC02-05CH11231
• OSTI ID: 2404585
• Country of Publication: United States
• Language: English

Related Subjects

54 ENVIRONMENTAL SCIENCES
Canopy_height: VARIABLE:CF
Climatic water deficit: VARIABLE:NONE
Discretized waveform LiDAR: VARIABLE:NONE
EARTH SCIENCE > AGRICULTURE > SOILS > HYDRAULIC CONDUCTIVITY: VARIABLE:GCMD
EARTH SCIENCE > AGRICULTURE > SOILS > SOIL MOISTURE/WATER CONTENT: VARIABLE:GCMD
EARTH SCIENCE > ATMOSPHERE > ATMOSPHERIC WATER VAPOR > WATER VAPOR PROCESSES > EVAPOTRANSPIRATION: VARIABLE:GCMD
EARTH SCIENCE > BIOSPHERE > VEGETATION
EARTH SCIENCE > BIOSPHERE > VEGETATION > CANOPY CHARACTERISTICS: VARIABLE:GCMD
EARTH SCIENCE > BIOSPHERE > VEGETATION > CROWN: VARIABLE:GCMD
EARTH SCIENCE > BIOSPHERE > VEGETATION > FOREST COMPOSITION/VEGETATION STRUCTURE: VARIABLE:GCMD
EARTH SCIENCE > LAND SURFACE > SOILS > CATION EXCHANGE CAPACITY: VARIABLE:GCMD
EARTH SCIENCE > LAND SURFACE > SOILS > ORGANIC MATTER: VARIABLE:GCMD
EARTH SCIENCE > LAND SURFACE > SOILS > SOIL PH: VARIABLE:GCMD
EARTH SCIENCE > LAND SURFACE > SURFACE RADIATIVE PROPERTIES > REFLECTANCE: VARIABLE:GCMD
EARTH SCIENCE > LAND SURFACE > TOPOGRAPHY
EARTH SCIENCE > LAND SURFACE > TOPOGRAPHY > TERRAIN ELEVATION > DIGITAL ELEVATION/TERRAIN MODEL (DEM): VARIABLE:GCMD
EARTH SCIENCE > TERRESTRIAL HYDROSPHERE > SNOW/ICE > SNOW WATER EQUIVALENT: VARIABLE:GCMD
Forest composition
Forest structure
Generalized additive model performance: VARIABLE:NONE
Generalized boosted model performance: VARIABLE:NONE
Geologic substrate: VARIABLE:NONE
Individual tree crowns: VARIABLE:NONE
Individual tree detection algorithm performance: VARIABLE:NONE
LiDAR
NEON AOP
NEON Campaign 2018
Remote sensing
Sampling location identifiers: VARIABLE:NONE
Subalpine