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• W2039433676 abstract "[1] Lidar-based aboveground biomass is derived based on the empirical relationship between lidar-measured vegetation height and aboveground biomass, often leading to large uncertainties of aboveground biomass estimates at large scales. This study investigates whether the use of any additional lidar-derived vegetation structure parameters besides height improves aboveground biomass estimation. The analysis uses data collected in the field and with the Laser Vegetation Imaging Sensor (LVIS), and the Echidna® validation instrument (EVI), a ground-based hemispherical-scanning lidar data in New England in 2003 and 2007. Our field data analysis shows that using wood volume (approximated by the product of basal area and top 10% tree height) and vegetation type (conifer/softwood or deciduous/hardwood forests, providing wood density) has the potential to improve aboveground biomass estimates at large scales. This result is comparable to previous individual-tree based analyses. Our LVIS data analysis indicates that structure parameters that combine height and gap fraction, such as RH100*cover and RH50*cover, are closely related to wood volume and thus biomass particularly for conifer forests. RH100*cover and RH50*cover perform similarly or even better than RH50, a good biomass predictor found in previous study. This study shows that the use of structure parameters that combine height and gap fraction (rather than height alone) improves the aboveground biomass estimate, and that the fusion of lidar and optical remote sensing (to provide vegetation type) will provide better aboveground biomass estimates than using lidar alone. Our ground lidar analysis shows that EVI provides good estimates of wood volume, and thus accurate estimates of aboveground biomass particularly at the stand level." @default.
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• W2039433676 date "2010-06-01" @default.
• W2039433676 modified "2023-10-17" @default.
• W2039433676 title "Assessing general relationships between aboveground biomass and vegetation structure parameters for improved carbon estimate from lidar remote sensing" @default.
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• W2039433676 doi "https://doi.org/10.1029/2009jg000936" @default.
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