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Description

This dataset provides estimates of aboveground biomass and spatially explicit uncertainty from 53 airborne LiDAR surveys of locations throughout California between 2005 and 2014. Aboveground biomass was estimated by performing individual tree crown detection and applying a customized "remote sensing aware" allometric equation to these individual trees. Aboveground biomass estimates and their uncertainties for each study area are provided in per-tree and gridded format. The canopy height models used for the tree detection and biomass estimation are also provided.

Product Summary

Platforms
Airplane
Instruments
LIDAR
Spatial Extent

N: 41.9828 S: 34.0508 E: -117.739 W: -123.746

Coordinate System
CARTESIAN
Granule Spatial Representation
CARTESIAN
Temporal Extent
2005-01-01 to 2014-12-31
Data Partner
THE OAK RIDGE NATIONAL LABORATORY (ORNL) DISTRIBUTED ACTIVE ARCHIVE CENTER (DAAC) (ORNL_DAAC)
Concept ID
C2398118307-ORNL_CLOUD
Data State
COMPLETE
Number of Files/Granules
9716
Processing Level
3
Published
Updated
Science Keywords
Canopy Characteristics ,
Biomass

Citation

Citation is critically important for dataset documentation and discovery. This dataset is openly shared, without restriction, in accordance with the EOSDIS Data Use and Citation Guidance.

Copy Citation

Xu, Q., & Greenberg, J. A. (2018). LiDAR-Derived Aboveground Biomass and Uncertainty for California Forests, 2005-2014 (Version 1). ORNL Distributed Active Archive Center. https://doi.org/10.3334/ORNLDAAC/1537 Date Accessed: 2026-05-02

Documents

Publications Citing This Dataset

Filters
Title Year Author Topic
Forest aboveground biomass in the southwestern US from MISR and GEDI: Chopping, Mark J., Wang, Zhuosen, Schaaf, Crystal B., Bull, Michael Terrestrial Ecosystems, LIDAR WAVEFORM, Biomass, Canopy Characteristics, Vegetation Cover, Droughts, Land Use/Land Cover, Litter Characteristics, Forests, Carbon
Seeing the Disturbed Forest for the Trees: Remote Sensing Is Hwang, Kyotaek, Harpold, Adrian A., Tague, Christina L., Lowman, Lauren, Boisrame, Gabrielle F. S., Lininger, Katherine B., Sullivan, Pamela L., Manning, Aidan, Graup, Louis, Litvak, Marcy, Lewis, Gabriel, Miller, Kate, Brooks, Paul D., Barnard, Holly R. Soil Moisture/Water Content, Root Zone Soil Moisture, Canopy Characteristics, Biomass
TrueTrees: A Scalable Workflow for the Integration of Airborne LiDAR Scanning Data into Fuel Models for Prescribed Fire Simulations Roten, Daniel, Wells, Lucas, Crawl, Daniel, Parsons, Russell A., Marcozzi, Anthony, Linn, Rodman R., Hiers, Kevin, Altintas, Ilkay Canopy Characteristics, Biomass
Estimating forest aboveground biomass using temporal features extracted from multiple satellite data products and ensemble machine learning algorithm Zhang, Yuzhen, Liu, Jingjing Biomass, Canopy Characteristics, Vegetation Cover
Forest aboveground biomass in the southwestern United States from a MISR multi-angle index, 20002015 Chopping, Mark, Wang, Zhuosen, Schaaf, Crystal, Bull, Michael A., Duchesne, Rocio R. Biomass, Carbon, Evergreen Vegetation, Shrubland/Scrub, Grasslands, Forests, Canopy Characteristics, Deciduous Vegetation, LIDAR WAVEFORM, Plant Diseases/Disorders/Pests, Fire Occurrence, Burned Area, Droughts, Land Use/Land Cover, Litter Characteristics, Bidirectional Reflectance Factor, Reflectance
A Review of Regional and Global Gridded Forest Biomass Datasets Zhang, Yuzhen, Liang, Shunlin, Yang, Lu Biomass, Carbon, Forest Yields, Forests, Biomass Dynamics, Deforestation, Canopy Characteristics, Vegetation Cover
Quantification of uncertainty in aboveground biomass estimates derived from small-footprint airborne LiDAR Xu, Qing, Man, Albert, Fredrickson, Mark, Hou, Zhengyang, Pitkanen, Juho, Wing, Brian, Ramirez, Carlos, Li, Bo, Greenberg, Jonathan A. Canopy Characteristics, Biomass
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