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RCCZO -- Land Cover, LiDAR, Vegetation -- Biomass Estimate of Sagebrush -- Reynolds Creek Experimental Watershed -- (2012-2012)

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Last updated: Apr 24, 2020 at 5:35 p.m.
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Vegetation biomass estimates across drylands at regional scales are critical for ecological modeling, yet the low-lying and sparse plant communities characterizing these ecosystems are challenging to accurately quantify and measure their variability using spectral-based aerial and satellite remote sensing. To overcome these challenges, multi-scale data including field-measured biomass, terrestrial laser scanning (TLS) and airborne laser scanning (ALS) data, were combined in a hierarchical modeling framework. Data derived at each scale were used to validate an increasingly broader index of sagebrush (Artemisia tridentata) aboveground biomass. First, two automatic crown delineation methods were used to delineate individual shrubs across the TLS plots. Second, three models to derive shrub volumes were utilized with TLS data and regressed against destructively-sampled individual shrub biomass measurements. Third, TLS-derived biomass estimates at 5 m were used to calibrate a biomass prediction model with a linear regression of ALS-derived percent vegetation cover (adjusted R2 = 0.87, p < 0.001, RMSE = 3.59 kg). The ALS prediction model was applied to the study watershed and evaluated with independent TLS plots (adjusted R2 = 0.55, RMSE = 4.01 kg, normalized RMSE = 35%). The biomass estimates at the scale of 5 m is sufficient for capturing the variability of biomass needed to initialize models to estimate ecosystem fluxes, and the contiguous estimates across the watershed support analyzing patterns and connectivity of these dynamics. Our model is currently optimized for the sagebrush-steppe environment at the watershed scale and may be readily applied to other shrub-dominated drylands, and especially the Great Basin, U.S., which extends across five western states. Improved derived metrics from ALS data and collection of additional TLS data to refine the relationship between TLS-derived biomass estimates and ALS-derived models of vegetation structure, will strengthen the predictive power of our model and extend its range to similar shrubland ecosystems.

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Resource Level Coverage


Coordinate System/Geographic Projection:
WGS 84 EPSG:4326
Coordinate Units:
Decimal degrees
Place/Area Name:
Reynolds Creek Experimental Watershed, Reynolds Creek
North Latitude
East Longitude
South Latitude
West Longitude


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Additional Metadata

Name Value
DOI http://dx.doi.org/10.18122/B2WC74
Recommended Citation Li, Aihua; Glenn, Nancy F.; Olsoy, Peter J.; Mitchell, Jessica J.; and Shrestha, Rupesh. (2015). Data for Aboveground Biomass Estimates of Sagebrush Using Terrestrial and Airborne LiDAR Data in a Dryland Ecosystem [Data set]. Retrieved from http://dx.doi.org/10.18122/B2WC74
BSU ScholarWorks Link https://scholarworks.boisestate.edu/miles_data/1/


Funding Agencies

This resource was created using funding from the following sources:
Agency Name Award Title Award Number
National Science Foundation Idaho EPSCoR Managing Idaho Landscapes for Ecosystem Services IIA-1301792


People that contributed technically, materially, financially, or provided general support for the creation of the resource's content but are not considered authors.

Name Organization Address Phone Author Identifiers
Boise State University Boise, ID
USDA-ARS Northwest Watershed Research Center Boise, ID
Reynolds Creek Experimental Watershed & Critical Zone Observatory Owyhee County, ID

How to Cite

Li, A., N. F. Glenn, P. J. Olsoy, J. J. Mitchell, R. Shrestha (2020). RCCZO -- Land Cover, LiDAR, Vegetation -- Biomass Estimate of Sagebrush -- Reynolds Creek Experimental Watershed -- (2012-2012), HydroShare, http://www.hydroshare.org/resource/ccb0c4cd1bfd46908da6806ca4a2acfa

This resource is shared under the Creative Commons Attribution CC BY.



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