Dataset Listing

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

Using Terrestrial and Airborne LiDAR Data

Variables:  point cloud returns

Date Range:  (2012-2012)

Dataset Creators/Authors:  Li, Aihua; Glenn, Nancy F; Olsoy, Peter J; Mitchell, Jessica J; Shrestha, Rupesh

Contact:  Nancy Glenn, Boise State University, Boise Idaho

Field Area:   Reynolds Creek Experimental Watershed

Description
Keywords & XML
Citation
  • Description

    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.
  • Keywords

    Reynolds Creek, LiDAR, TLS, sagebrush, biomass

    XML Metadata

    criticalzone.org/reynolds/data/xml-metadata-test/6273/

    XML is in ISO-19115 geographic metadata format, compatible with ESRI Geoportal Server.

  • Citation for This Dataset

    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

    Citation for This Webpage

    Li, Aihua; Glenn, Nancy F; Olsoy, Peter J; Mitchell, Jessica J; Shrestha, Rupesh (2012). "CZO Dataset: Reynolds Creek - Land Cover, LiDAR, Vegetation (2012) - Biomass Estimate of Sagebrush." Retrieved 25 Mar 2019, from http://criticalzone.org/reynolds/data/dataset/6273/

Data

Reynolds Creek - Above Ground Biomass Estimates of Sagebrush Using Terrestrial and Airborne LiDAR Data

(a/1/)   Data Level 2,   DOI: 10.18122/B2WC74

Data Use Policy
Data Sharing Policy
  • Data Use Policy

    DRAFT v.0.4.0

    1. Use our data freely. All CZO Data Products* except those labelled Private** are released to the public and may be freely copied, distributed, edited, remixed, and built upon under the condition that you give acknowledgement as described below. Non-CZO data products — like those produced by USGS or NOAA — have their own use policies, which should be followed.

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  • Data Sharing Policy

    DRAFT v.0.2.5

    All CZO investigators and collaborators who receive material or logistical support from a CZO agree to:

    1. Share data privately within 1 year. CZO investigators and collaborators agree to provide CZO Data Products* — including data files and metadata for raw, quality controlled and/or derived data — to CZO data managers within one year of collection of samples, in situ or experimental data. By default, data values will be held in a Private CZO Repository**, but metadata will be made public and will provide full attribution to the Dataset Creators†.

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    † Dataset Creators. Defined as the people who are responsible for designing, collecting, analyzing and providing quality assurance for a dataset. The creators of a dataset are analogous to the authors of a publication, and datasets should be cited in an analogous manner following the emerging international guidelines described at http://www.datacite.org/whycitedata.


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