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    Author(s): Kristofer D. Johnson; Richard Birdsey; Jason Cole; Anu Swatantran; Jarlath O'Neil-Dunne; Ralph Dubayah; Andrew Lister
    Date: 2015
    Source: Environmental Monitoring and Assessment. 187:623. DOI: 10.1007/s10661-015-4839-1.
    Publication Series: Scientific Journal (JRNL)
    Station: Northern Research Station
    PDF: Download Publication  (1.16 MB)


    Forest inventories are commonly used to estimate total tree biomass of forest land even though they are not traditionally designed to measure biomass of trees outside forests (TOF). The consequence may be an inaccurate representation of all of the aboveground biomass, which propagates error to the outputs of spatial and process models that rely on the inventory data. An ideal approach to fill this data gap would be to integrate TOF measurements within a traditional forest inventory for a parsimonious estimate of total tree biomass. In this study, Light Detection and Ranging (LIDAR) data were used to predict biomass of TOF in all nonforest Forest Inventory and Analysis (FIA) plots in the state of Maryland. To validate the LIDAR-based biomass predictions, a field crew was sent to measure TOF on nonforest plots in three Maryland counties, revealing close agreement at both the plot and county scales between the two estimates. Total tree biomass in Maryland increased by 25.5 Tg, or 15.6 %, when biomass of TOF were included. In two counties (Carroll and Howard), there was a 47 % increase. In contrast, counties located further away from the interstate highway corridor showed only a modest increase in biomass when TOF were added because nonforest conditions were less common in those areas. The advantage of this approach for estimating biomass of TOF is that it is compatible with, and explicitly separates TOF biomass from, forest biomass already measured by FIA crews. By predicting biomass of TOF at actual FIA plots, this approach is directly compatible with traditionally reported FIA forest biomass, providing a framework for other states to follow, and should improve carbon reporting and modeling activities in Maryland.

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    Johnson, Kristofer D.; Birdsey, Richard; Cole, Jason; Swatantran, Anu; O'Neil-Dunne, Jarlath; Dubayah, Ralph; Lister, Andrew. 2015. Integrating LIDAR and forest inventories to fill the trees outside forests data gap. Environmental Monitoring and Assessment. 187:623. DOI: 10.1007/s10661-015-4839-1.


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    Trees outside forest, Carbon management, Nonforest biomass, LIDAR, Forest inventory

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