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dc.contributor.authorNelson, Rossen
dc.contributor.authorMargolis, Hanken
dc.contributor.authorMontesano, Paulen
dc.contributor.authorSun, Guoqingen
dc.contributor.authorCook, Bruceen
dc.contributor.authorCorp, Larryen
dc.contributor.authorAndersen, Hans-Eriken
dc.contributor.authordeJong, Benen
dc.contributor.authorPaz Pellat, Fernandoen
dc.contributor.authorFickel, Thaddeusen
dc.contributor.authorKauffman, Jobriathen
dc.contributor.authorPrisley, Stephenen
dc.date.accessioned2019-10-10T17:41:28Z
dc.date.available2019-10-10T17:41:28Z
dc.date.issued2017-01en
dc.identifier.issn0034-4257en
dc.identifier.urihttp://hdl.handle.net/10919/94432
dc.description.abstractExisting national forest inventory plots, an airborne lidar scanning (ALS) system, and a space profiling lidar system (ICESat-GLAS) are used to generate circa 2005 estimates of total aboveground dry biomass (AGB) in forest strata, by state, in the continental United States (CONUS) and Mexico. The airborne lidar is used to link ground observations of AGB to space lidar measurements. Two sets of models are generated, the first relating ground estimates of AGB to airborne laser scanning (ALS) measurements and the second set relating ALS estimates of AGB (generated using the first model set) to GLAS measurements. GLAS then, is used as a sampling tool within a hybrid estimation framework to generate stratum-, state-, and national-level AGB estimates. A two-phase variance estimator is employed to quantify GLAS sampling variability and, additively, ALS-GLAS model variability in this current, three-phase (ground-ALS-space lidar) study. The model variance component characterizes the variability of the regression coefficients used to predict ALS-based estimates of biomass as a function of GLAS measurements. Three different types of predictive models are considered in CONUS to determine which produced biomass totals closest to ground-based national forest inventory estimates - (1) linear (LIN), (2) linear-no-intercept (LNI), and (3) log-linear. For CONUS at the national level, the GLAS LNI model estimate (23.95 +/- 0.45 Gt AGB), agreed most closely with the US national forest inventory ground estimate, 24.17 +/- 0.06 Gt, i.e., within 1%. The national biomass total based on linear ground-ALS and ALS-GLAS models (25.87 +/- 0.49 Gt) overestimated the national ground-based estimate by 7.5%. The comparable log -linear model result (63.29 +/- 1.36 Gt) overestimated ground results by 261%. All three national biomass GLAS estimates, LIN, LNI, and log -linear, are based on 241,718 pulses collected on 230 orbits. The US national forest inventory (ground) estimates are based on 119,414 ground plots. At the US state level, the average absolute value of the deviation of LNI GLAS estimates from the comparable ground estimate of total biomass was 18.8% (range: Oregon, -40.8% to North Dakota, 128.6%). Log-linear models produced gross overestimates in the continental US, i.e., >2.6x, and the use of this model to predict regional biomass using GLAS data in temperate, western hemisphere forests is not appropriate. The best model form, LNI, is used to produce biomass estimates in Mexico. The average biomass density in Mexican forests is 53.10 +/- 0.88 t/ha, and the total biomass for the country, given a total forest area of 688,096 km(2), is 3.65 +/- 0.06 Gt. In Mexico, our GLAS biomass total underestimated a 2005 FAO estimate (4.152 Gt) by 12% and overestimated a 2007/8 radar study's figure (3.06 Gt) by 19%. (C) Published by Elsevier Inc.en
dc.description.sponsorshipNASA's Carbon Cycle Science Program within the Science Mission Directorate Earth Science Division [NNH10ZDA001N-CARBON(2010)]en
dc.format.mimetypeapplication/pdfen
dc.language.isoenen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectICESat/GLASen
dc.subjectHybrid 3-phase samplingen
dc.subjectModel-baseden
dc.subjectForest biomassen
dc.titleLidar-based estimates of aboveground biomass in the continental US and Mexico using ground, airborne, and satellite observationsen
dc.typeArticle - Refereeden
dc.description.notesThis research was funded by NASA's Carbon Cycle Science Program within the Science Mission Directorate Earth Science Division-NNH10ZDA001N-CARBON(2010).en
dc.title.serialRemote Sensing of Environmenten
dc.identifier.doihttps://doi.org/10.1016/j.rse.2016.10.038en
dc.identifier.volume188en
dc.type.dcmitypeTexten
dc.type.dcmitypeStillImageen
dc.identifier.eissn1879-0704en


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Creative Commons Attribution 4.0 International
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