Publication:
Mapping global forest canopy height through integration of GEDI and Landsat data

dc.contributor.authorPotapov, P.
dc.contributor.authorLi, X.
dc.contributor.authorHernandez-Serna, A.
dc.contributor.authorTyukavina, A.
dc.contributor.authorHansen, M.C.
dc.contributor.authorKommareddy, A.
dc.contributor.authorPickens, A.
dc.contributor.authorTurubanova, S.A.
dc.contributor.authorTang, H.
dc.contributor.authorSilva, C.E.
dc.contributor.authorArmston, J.
dc.contributor.authorDubayah, R.
dc.contributor.authorBlair, J.B.
dc.contributor.authorHofton, M.
dc.date.accessioned2022-01-23T18:58:08Z
dc.date.available2022-01-23T18:58:08Z
dc.identifier.urihttps://open.fsc.org/handle/resource/1029
dc.languageen
dc.rightsPaywalled content
dc.titleMapping global forest canopy height through integration of GEDI and Landsat dataen
dcterms.abstractConsistent, large-scale operational monitoring of forest height is essential for estimating forest-related carbon emissions, analyzing forest degradation, and quantifying the effectiveness of forest restoration initiatives. The Global Ecosystem Dynamics Investigation (GEDI) lidar instrument onboard the International Space Station has been collecting unique data on vegetation structure since April 2019. Here, we employed global Landsat analysis-ready data to extrapolate GEDI footprint-level forest canopy height measurements, creating a 30 m spatial resolution global forest canopy height map for the year 2019. The global forest height map was compared to the GEDI validation data (RMSE = 6.6 m; MAE = 4.45 m, R2 = 0.62) and available airborne lidar data (RMSE = 9.07 m; MAE = 6.36 m, R2 = 0.61). The demonstrated integration of GEDI data with time-series optical imagery is expected to enable multidecadal historic analysis and operational forward monitoring of forest height and its dynamics. Such capability is important to support global climate and sustainable development initiatives.en
dcterms.issued2020
dcterms.typeJournal Article
dspace.entity.typePublication
fsc.evidenceCategoryFSC relevant studies
fsc.focus.forestType(not yet curated)
fsc.focus.forestZone(not yet curated)
fsc.focus.sustainDimension(not yet curated)
fsc.focus.tenureManagement(not yet curated)
fsc.focus.tenureOwnership(not yet curated)
fsc.issue.environmental(not yet curated)
fsc.topic.environmental(not yet curated)
fscdoc.hashidden.adminyes
fscdoc.hashidden.useryes
is.coverage.country(not yet curated)
is.coverage.region(not yet curated)
is.evaluation.collection(not yet curated)
is.evidenceSubType(not yet curated)
is.evidenceTypeCase study
is.identifier.doihttps://doi.org/10.1016/j.rse.2020.112165
is.identifier.fscdoihttp://dx.doi.org/10.34800/fsc-international670
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