Improved aboveground biomass estimation and regional assessment with aerial lidar in California’s subalpine forests
By applying a relatively new Bayesian geostatistical modeling method to a novel forest type, our study produced the most accurate and precise aboveground biomass estimates to date for Sierra Nevada subalpine forests at 30 m pixel and management unit scales. Our estimates of total aboveground biomass within the management units had low uncertainty and can be used effectively in carbon accounting and carbon trading markets.
Authors
- K. J. Bormann (ORCID: https://orcid.org/0000-0003-3697-6550)
- Yufang Jin (ORCID: https://orcid.org/0000-0002-9049-9807)
- Sara Winsemius (ORCID: https://orcid.org/0000-0003-0214-4879)
- Hugh D. Safford (ORCID: https://orcid.org/0000-0002-0608-6728)
- Van R. Kane (ORCID: https://orcid.org/0000-0002-0792-4850)
- Chad Babcock (ORCID: https://orcid.org/0000-0001-9597-4462)
Institutions
- University of Minnesota (US)
- Minnesota Department of Natural Resources (US)
- Thuyloi University (VN)
- University of Washington (US)
- Vibrant Data (United States) (US)
- University of California, Davis (US)
Publication Details
- Journal
- Carbon Balance and Management
- Published
- 2024-12-20
- DOI
- https://doi.org/10.1186/s13021-024-00286-w
- Citations
- 6
- Primary Topic
- Remote Sensing and LiDAR Applications
- Type
- article
- Field-Weighted Citation Impact
- 1.23
Funders
- U.S. Department of Agriculture
- California Air Resources Board
- Department of Water Resources
- U.S. Geological Survey
- National Institute of Food and Agriculture
- U.S. Forest Service