Improved aboveground biomass estimation and regional assessment with aerial lidar in California’s subalpine forests

Background Understanding the impacts of climate change on forest aboveground biomass is a high priority for land managers. High elevation subalpine forests provide many important ecosystem services, including carbon sequestration, and are vulnerable to climate change, which has altered forest struct...

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Veröffentlicht in:	Carbon balance and management Jg. 19; H. 1; S. 41
Hauptverfasser:	Winsemius, Sara, Babcock, Chad, Kane, Van R., Bormann, Kat J., Safford, Hugh D., Jin, Yufang
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Cham Springer International Publishing 20.12.2024 Springer Nature B.V BMC
Schlagworte:	Aboveground biomass altitude Bayesian analysis Bayesian hierarchical spatial modeling Bayesian theory Biomass California Carbon Carbon footprint Carbon monitoring Carbon sequestration Climate change Dead wood Disturbances Earth and Environmental Science Ecosystem management Ecosystem services Ecosystems Emissions trading Environment Environmental accounting Environmental impact Environmental Management Estimates Forest biomass Forest management forest types Forestry Forests Geostatistics inventories Land management Landsat Lidar Mapping Mathematical models model uncertainty Modelling mountains Regional analysis Remote sensing Satellite data Satellite imagery Standard error Subalpine environments Subalpine forests Sustainability reporting Uncertainty Vegetation structure woody biomass United States > US California Nevada Subalpine forests Bayesian hierarchical spatial modeling Vegetation structure Carbon monitoring Remote sensing Aboveground biomass
ISSN:	1750-0680, 1750-0680
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Abstract	Background Understanding the impacts of climate change on forest aboveground biomass is a high priority for land managers. High elevation subalpine forests provide many important ecosystem services, including carbon sequestration, and are vulnerable to climate change, which has altered forest structure and disturbance regimes. Although large, regional studies have advanced aboveground biomass mapping with satellite data, typically using a general approach broadly calibrated or trained with available field data, it is unclear how well these models work in less prevalent and highly heterogeneous forest types such as the subalpine. Monitoring biomass using methods that model uncertainty at multiple scales is critical to ensure that local relationships between biomass and input variables are retained. Forest structure metrics from lidar are particularly valuable alongside field data for mapping aboveground biomass, due to their high correlation with biomass. Results We estimated aboveground woody biomass of live and dead trees and uncertainty at 30 m resolution in subalpine forests of the Sierra Nevada, California, from aerial lidar data in combination with a collection of field inventory data, using a Bayesian geostatistical model. The ten-fold cross-validation resulted in excellent model calibration of our subalpine-specific model (94.7% of measured plot biomass within the predicted 95% credible interval). When evaluated against two commonly referenced regional estimates based on Landsat optical imagery, root mean square error, relative standard error, and bias of our estimations were substantially lower, demonstrating the benefits of local modeling for subalpine forests. We mapped AGB over four management units in the Sierra Nevada and found variable biomass density ranging from 92.4 to 199.2 Mg/ha across these management units, highlighting the importance of high quality, local field and remote sensing data. Conclusions 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.
AbstractList	BackgroundUnderstanding the impacts of climate change on forest aboveground biomass is a high priority for land managers. High elevation subalpine forests provide many important ecosystem services, including carbon sequestration, and are vulnerable to climate change, which has altered forest structure and disturbance regimes. Although large, regional studies have advanced aboveground biomass mapping with satellite data, typically using a general approach broadly calibrated or trained with available field data, it is unclear how well these models work in less prevalent and highly heterogeneous forest types such as the subalpine. Monitoring biomass using methods that model uncertainty at multiple scales is critical to ensure that local relationships between biomass and input variables are retained. Forest structure metrics from lidar are particularly valuable alongside field data for mapping aboveground biomass, due to their high correlation with biomass.ResultsWe estimated aboveground woody biomass of live and dead trees and uncertainty at 30 m resolution in subalpine forests of the Sierra Nevada, California, from aerial lidar data in combination with a collection of field inventory data, using a Bayesian geostatistical model. The ten-fold cross-validation resulted in excellent model calibration of our subalpine-specific model (94.7% of measured plot biomass within the predicted 95% credible interval). When evaluated against two commonly referenced regional estimates based on Landsat optical imagery, root mean square error, relative standard error, and bias of our estimations were substantially lower, demonstrating the benefits of local modeling for subalpine forests. We mapped AGB over four management units in the Sierra Nevada and found variable biomass density ranging from 92.4 to 199.2 Mg/ha across these management units, highlighting the importance of high quality, local field and remote sensing data.ConclusionsBy 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. Understanding the impacts of climate change on forest aboveground biomass is a high priority for land managers. High elevation subalpine forests provide many important ecosystem services, including carbon sequestration, and are vulnerable to climate change, which has altered forest structure and disturbance regimes. Although large, regional studies have advanced aboveground biomass mapping with satellite data, typically using a general approach broadly calibrated or trained with available field data, it is unclear how well these models work in less prevalent and highly heterogeneous forest types such as the subalpine. Monitoring biomass using methods that model uncertainty at multiple scales is critical to ensure that local relationships between biomass and input variables are retained. Forest structure metrics from lidar are particularly valuable alongside field data for mapping aboveground biomass, due to their high correlation with biomass. We estimated aboveground woody biomass of live and dead trees and uncertainty at 30 m resolution in subalpine forests of the Sierra Nevada, California, from aerial lidar data in combination with a collection of field inventory data, using a Bayesian geostatistical model. The ten-fold cross-validation resulted in excellent model calibration of our subalpine-specific model (94.7% of measured plot biomass within the predicted 95% credible interval). When evaluated against two commonly referenced regional estimates based on Landsat optical imagery, root mean square error, relative standard error, and bias of our estimations were substantially lower, demonstrating the benefits of local modeling for subalpine forests. We mapped AGB over four management units in the Sierra Nevada and found variable biomass density ranging from 92.4 to 199.2 Mg/ha across these management units, highlighting the importance of high quality, local field and remote sensing data. 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. Understanding the impacts of climate change on forest aboveground biomass is a high priority for land managers. High elevation subalpine forests provide many important ecosystem services, including carbon sequestration, and are vulnerable to climate change, which has altered forest structure and disturbance regimes. Although large, regional studies have advanced aboveground biomass mapping with satellite data, typically using a general approach broadly calibrated or trained with available field data, it is unclear how well these models work in less prevalent and highly heterogeneous forest types such as the subalpine. Monitoring biomass using methods that model uncertainty at multiple scales is critical to ensure that local relationships between biomass and input variables are retained. Forest structure metrics from lidar are particularly valuable alongside field data for mapping aboveground biomass, due to their high correlation with biomass.BACKGROUNDUnderstanding the impacts of climate change on forest aboveground biomass is a high priority for land managers. High elevation subalpine forests provide many important ecosystem services, including carbon sequestration, and are vulnerable to climate change, which has altered forest structure and disturbance regimes. Although large, regional studies have advanced aboveground biomass mapping with satellite data, typically using a general approach broadly calibrated or trained with available field data, it is unclear how well these models work in less prevalent and highly heterogeneous forest types such as the subalpine. Monitoring biomass using methods that model uncertainty at multiple scales is critical to ensure that local relationships between biomass and input variables are retained. Forest structure metrics from lidar are particularly valuable alongside field data for mapping aboveground biomass, due to their high correlation with biomass.We estimated aboveground woody biomass of live and dead trees and uncertainty at 30 m resolution in subalpine forests of the Sierra Nevada, California, from aerial lidar data in combination with a collection of field inventory data, using a Bayesian geostatistical model. The ten-fold cross-validation resulted in excellent model calibration of our subalpine-specific model (94.7% of measured plot biomass within the predicted 95% credible interval). When evaluated against two commonly referenced regional estimates based on Landsat optical imagery, root mean square error, relative standard error, and bias of our estimations were substantially lower, demonstrating the benefits of local modeling for subalpine forests. We mapped AGB over four management units in the Sierra Nevada and found variable biomass density ranging from 92.4 to 199.2 Mg/ha across these management units, highlighting the importance of high quality, local field and remote sensing data.RESULTSWe estimated aboveground woody biomass of live and dead trees and uncertainty at 30 m resolution in subalpine forests of the Sierra Nevada, California, from aerial lidar data in combination with a collection of field inventory data, using a Bayesian geostatistical model. The ten-fold cross-validation resulted in excellent model calibration of our subalpine-specific model (94.7% of measured plot biomass within the predicted 95% credible interval). When evaluated against two commonly referenced regional estimates based on Landsat optical imagery, root mean square error, relative standard error, and bias of our estimations were substantially lower, demonstrating the benefits of local modeling for subalpine forests. We mapped AGB over four management units in the Sierra Nevada and found variable biomass density ranging from 92.4 to 199.2 Mg/ha across these management units, highlighting the importance of high quality, local field and remote sensing data.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.CONCLUSIONSBy 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. BACKGROUND: Understanding the impacts of climate change on forest aboveground biomass is a high priority for land managers. High elevation subalpine forests provide many important ecosystem services, including carbon sequestration, and are vulnerable to climate change, which has altered forest structure and disturbance regimes. Although large, regional studies have advanced aboveground biomass mapping with satellite data, typically using a general approach broadly calibrated or trained with available field data, it is unclear how well these models work in less prevalent and highly heterogeneous forest types such as the subalpine. Monitoring biomass using methods that model uncertainty at multiple scales is critical to ensure that local relationships between biomass and input variables are retained. Forest structure metrics from lidar are particularly valuable alongside field data for mapping aboveground biomass, due to their high correlation with biomass. RESULTS: We estimated aboveground woody biomass of live and dead trees and uncertainty at 30 m resolution in subalpine forests of the Sierra Nevada, California, from aerial lidar data in combination with a collection of field inventory data, using a Bayesian geostatistical model. The ten-fold cross-validation resulted in excellent model calibration of our subalpine-specific model (94.7% of measured plot biomass within the predicted 95% credible interval). When evaluated against two commonly referenced regional estimates based on Landsat optical imagery, root mean square error, relative standard error, and bias of our estimations were substantially lower, demonstrating the benefits of local modeling for subalpine forests. We mapped AGB over four management units in the Sierra Nevada and found variable biomass density ranging from 92.4 to 199.2 Mg/ha across these management units, highlighting the importance of high quality, local field and remote sensing data. CONCLUSIONS: 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. Abstract Background Understanding the impacts of climate change on forest aboveground biomass is a high priority for land managers. High elevation subalpine forests provide many important ecosystem services, including carbon sequestration, and are vulnerable to climate change, which has altered forest structure and disturbance regimes. Although large, regional studies have advanced aboveground biomass mapping with satellite data, typically using a general approach broadly calibrated or trained with available field data, it is unclear how well these models work in less prevalent and highly heterogeneous forest types such as the subalpine. Monitoring biomass using methods that model uncertainty at multiple scales is critical to ensure that local relationships between biomass and input variables are retained. Forest structure metrics from lidar are particularly valuable alongside field data for mapping aboveground biomass, due to their high correlation with biomass. Results We estimated aboveground woody biomass of live and dead trees and uncertainty at 30 m resolution in subalpine forests of the Sierra Nevada, California, from aerial lidar data in combination with a collection of field inventory data, using a Bayesian geostatistical model. The ten-fold cross-validation resulted in excellent model calibration of our subalpine-specific model (94.7% of measured plot biomass within the predicted 95% credible interval). When evaluated against two commonly referenced regional estimates based on Landsat optical imagery, root mean square error, relative standard error, and bias of our estimations were substantially lower, demonstrating the benefits of local modeling for subalpine forests. We mapped AGB over four management units in the Sierra Nevada and found variable biomass density ranging from 92.4 to 199.2 Mg/ha across these management units, highlighting the importance of high quality, local field and remote sensing data. Conclusions 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. Background Understanding the impacts of climate change on forest aboveground biomass is a high priority for land managers. High elevation subalpine forests provide many important ecosystem services, including carbon sequestration, and are vulnerable to climate change, which has altered forest structure and disturbance regimes. Although large, regional studies have advanced aboveground biomass mapping with satellite data, typically using a general approach broadly calibrated or trained with available field data, it is unclear how well these models work in less prevalent and highly heterogeneous forest types such as the subalpine. Monitoring biomass using methods that model uncertainty at multiple scales is critical to ensure that local relationships between biomass and input variables are retained. Forest structure metrics from lidar are particularly valuable alongside field data for mapping aboveground biomass, due to their high correlation with biomass. Results We estimated aboveground woody biomass of live and dead trees and uncertainty at 30 m resolution in subalpine forests of the Sierra Nevada, California, from aerial lidar data in combination with a collection of field inventory data, using a Bayesian geostatistical model. The ten-fold cross-validation resulted in excellent model calibration of our subalpine-specific model (94.7% of measured plot biomass within the predicted 95% credible interval). When evaluated against two commonly referenced regional estimates based on Landsat optical imagery, root mean square error, relative standard error, and bias of our estimations were substantially lower, demonstrating the benefits of local modeling for subalpine forests. We mapped AGB over four management units in the Sierra Nevada and found variable biomass density ranging from 92.4 to 199.2 Mg/ha across these management units, highlighting the importance of high quality, local field and remote sensing data. Conclusions 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.
ArticleNumber	41
Author	Winsemius, Sara Bormann, Kat J. Kane, Van R. Babcock, Chad Safford, Hugh D. Jin, Yufang
Author_xml	– sequence: 1 givenname: Sara surname: Winsemius fullname: Winsemius, Sara email: swinsemius@ucdavis.edu organization: Department of Land, Air and Water Resources, University of California – sequence: 2 givenname: Chad surname: Babcock fullname: Babcock, Chad organization: Department of Forest Resources, University of Minnesota – sequence: 3 givenname: Van R. surname: Kane fullname: Kane, Van R. organization: School of Environmental and Forest Sciences, University of Washington – sequence: 4 givenname: Kat J. surname: Bormann fullname: Bormann, Kat J. organization: Airborne Snow Observatories, Inc – sequence: 5 givenname: Hugh D. surname: Safford fullname: Safford, Hugh D. organization: Department of Environmental Science and Policy, University of California, Vibrant Planet – sequence: 6 givenname: Yufang surname: Jin fullname: Jin, Yufang organization: Department of Land, Air and Water Resources, University of California
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/39704861$$D View this record in MEDLINE/PubMed
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CitedBy_id	crossref_primary_10_1016_j_foreco_2025_123002 crossref_primary_10_1371_journal_pone_0330768
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Keywords	Subalpine forests Bayesian hierarchical spatial modeling Vegetation structure Carbon monitoring Remote sensing Aboveground biomass
Language	English
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PublicationTitle	Carbon balance and management
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Snippet	Background Understanding the impacts of climate change on forest aboveground biomass is a high priority for land managers. High elevation subalpine forests... Understanding the impacts of climate change on forest aboveground biomass is a high priority for land managers. High elevation subalpine forests provide many... BackgroundUnderstanding the impacts of climate change on forest aboveground biomass is a high priority for land managers. High elevation subalpine forests... BACKGROUND: Understanding the impacts of climate change on forest aboveground biomass is a high priority for land managers. High elevation subalpine forests... Abstract Background Understanding the impacts of climate change on forest aboveground biomass is a high priority for land managers. High elevation subalpine...
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SubjectTerms	Aboveground biomass altitude Bayesian analysis Bayesian hierarchical spatial modeling Bayesian theory Biomass California Carbon Carbon footprint Carbon monitoring Carbon sequestration Climate change Dead wood Disturbances Earth and Environmental Science Ecosystem management Ecosystem services Ecosystems Emissions trading Environment Environmental accounting Environmental impact Environmental Management Estimates Forest biomass Forest management forest types Forestry Forests Geostatistics inventories Land management Landsat Lidar Mapping Mathematical models model uncertainty Modelling mountains Regional analysis Remote sensing Satellite data Satellite imagery Standard error Subalpine environments Subalpine forests Sustainability reporting Uncertainty Vegetation structure woody biomass
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Title	Improved aboveground biomass estimation and regional assessment with aerial lidar in California’s subalpine forests
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