Forest aboveground biomass mapping and estimation across multiple spatial scales using model-based inference

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dc.contributor.authorChen, Qien
dc.contributor.authorMcRoberts, Ronald E.en
dc.contributor.authorWang, Changweien
dc.contributor.authorRadtke, Philip J.en
dc.contributor.departmentForest Resources and Environmental Conservationen
dc.date.accessioned2020-04-20T14:48:00Zen
dc.date.available2020-04-20T14:48:00Zen
dc.date.issued2016-10en
dc.description.abstractRemotely sensed data have been widely used in recent years for mapping and estimating biomass. However, the characterization of the uncertainty of mapped or estimated biomass in previous studies was either based on ad-hoc approaches (e.g., using model fitting statistics such root mean square errors derived from purposive samples) or mostly limited to the analysis of mean biomass for the whole study area. This study proposed a novel uncertainty analysis method that can characterize biomass uncertainty across multiple spatial scales and multiple spatial resolutions. The uncertainty analysis method built on model-based inference and can propagate errors from trees to field plots, individual pixels, and small areas or large regions that consist of multiple pixels (up to all pixels within a study area). We developed and tested this method over northern Minnesota forest areas of approximately 69,508 km(2) via a unique combination of several datasets for biomass mapping and estimation: wall-to-wall airborne lidar data, national forest inventory (NFI) plots, and destructive measurements of tree aboveground biomass (AGB). We found that the pixel-level AGB prediction error is dominated by lidar-based AGB model residual errors when the spatial resolution is near 380 m or finer and by model parameter estimate errors when the spatial resolution is coarser. We also found that the relative error of AGB predicted from lidar can be reduced to approximately 11% (or mean 5.1 Mg/ha; max 43.6 Mg/ha) at one-hectare scale (or at 100 m spatial resolution) over our study area. Because our uncertainty analysis method uses model-based inference and does not require probability samples of field plots, our methodology has potential applications worldwide, especially over tropics and developing countries where NFI systems are not well-established.en
dc.description.adminPublic domain – authored by a U.S. government employeeen
dc.description.notesThis research has been supported by the College of Social Sciences (CSS) Research Support Award at the University of Hawaii, Manoa and the science and technology project of Guangdong Province (2013B020314016).en
dc.description.sponsorshipCollege of Social Sciences (CSS) Research Support Award at the University of Hawaii, Manoa; science and technology project of Guangdong Province [2013B020314016]en
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1016/j.rse.2016.07.023en
dc.identifier.eissn1879-0704en
dc.identifier.issn0034-4257en
dc.identifier.urihttp://hdl.handle.net/10919/97840en
dc.identifier.volume184en
dc.language.isoenen
dc.rightsCreative Commons CC0 1.0 Universal Public Domain Dedicationen
dc.rights.urihttp://creativecommons.org/publicdomain/zero/1.0/en
dc.subjectBiomassen
dc.subjectUncertaintyen
dc.subjectLidaren
dc.subjectInventory plotsen
dc.subjectDestructive tree AGB measurementsen
dc.subjectModel-based inferenceen
dc.titleForest aboveground biomass mapping and estimation across multiple spatial scales using model-based inferenceen
dc.title.serialRemote Sensing of Environmenten
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
dc.type.dcmitypeStillImageen

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