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    Author(s): Carlos Alberto Silva; Andrew Thomas Hudak; Lee Alexander Vierling; Carine Klauberg; Mariano Garcia; Antonio Ferraz; Michael Keller; Jan Eitel; Sassan Saatchi
    Date: 2017
    Source: Remote Sensing. 9: 1068.
    Publication Series: Scientific Journal (JRNL)
    Station: Rocky Mountain Research Station
    PDF: View PDF  (4.0 MB)

    Description

    Airborne lidar has become a well-suited technology for predicting and mapping many tropical forest attributes, including aboveground biomass (AGB). However, trade-offs exist between lidar pulse density and acquisition cost. The aim of this study was to evaluate the influence of lidar pulse density on AGB change predictions using airborne lidar and field plot data in a tropical rain forest located near Paragominas, ParĂ¡, Brazil. In the field, AGB was computed at 84 square field plots of 50x50m in 2014. The lidar data were acquired 2012 and 2014, and for each dataset the pulse density was subsampled from its original density to lower densities of 12, 10, 8, 6, 4, 2, 0.8, 0.6, 0.4 and 0.2 pulses m-2. For each pulse density dataset, a power model was used to model AGB from lidar-derived mean height in 2014, and to predict AGB in 2012 and 2014, to compute AGB change at plot and stand levels. We found that AGB change prediction at plot level was only slightly affected by pulse density. However, at the stand level we observed differences in predicted AGB change of >30 Mg ha-1 from 12 to 0.2 pulses m-2 , but just in high slope areas where the lidar data from 2012 and 2014 were normalized to height aboveground using the DTMs created from each pulse density dataset. For a scenario where the DTM generated at highest pulse density from 2012 was used to normalize height-aboveground for all pulse density datasets from 2012 and 2014, the differences in AGB change prediction from 12 to 0.2 pulses m-2 did not exceed 5 Mg ha-1. This study showed that a DTM generated from high pulse density lidar can be used to normalize height aboveground of other lidar data acquisitions for predicting AGB changes in tropical forest. We conclude that there is good potential to monitor carbon pools in Brazilian Tropical Rain Forest using airborne lidar data with low pulse density and DTMs from a single lidar survey acquired at high pulse density.

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    Citation

    Silva, Carlos Alberto; Hudak, Andrew Thomas; Vierling, Lee Alexander; Klauberg, Carine; Garcia, Mariano; Ferraz, Antonio; Keller, Michael; Eitel, Jan; Saatchi, Sassan. 2017. Impacts of airborne lidar pulse density on estimating biomass stocks and changes in a selectively logged tropical forest. Remote Sensing. 9: 1068.

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    Keywords

    lidar, humid tropical forest, biomass change, pulse density, MRV

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