Airborne LIDAR-Derived Aboveground Biomass Estimates Using a Hierarchical Bayesian Approach
Airborne LIDAR-Derived Aboveground Biomass Estimates Using a Hierarchical Bayesian Approach
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Publisher
Basel: MDPI AG
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Language
English
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Basel: MDPI AG
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Conventional ground survey data are very accurate, but expensive. Airborne lidar data can reduce the costs and effort required to conduct large-scale forest surveys. It is critical to improve biomass estimation and evaluate carbon stock when we use lidar data. Bayesian methods integrate prior information about unknown parameters, reduce the paramet...
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Full title
Airborne LIDAR-Derived Aboveground Biomass Estimates Using a Hierarchical Bayesian Approach
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TN_cdi_doaj_primary_oai_doaj_org_article_46ae6847369945b8a0f5d86af9d7c4b0
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https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_46ae6847369945b8a0f5d86af9d7c4b0
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ISSN
2072-4292
E-ISSN
2072-4292
DOI
10.3390/rs11091050
