1. Characterizing and Mapping Ice Sheet Surface Topography Using a Medium-Footprint, Multi- Beam, Waveform-Recording Lidar
    Hofton, M. A.; Blair, J. B. R. D. L. L. S. B.  AGU  December 2007

    Lidar surveys of the Greenland ice sheet have been used to study mass-balance changes since the early 1990's. Sensors include NASA's ATM system (e.g., Krabill et al., 2000), and the ICESat (Schutz et al., 2002), a large- footprint, spaceborne system launched in 2003 for monitoring long-term trends in ice mass balance. To complement these data sets and prepare for the next-generation of spaceborne measurements, the Laser Vegetation Imaging Sensor (LVIS) was flown onboard the NASA P-3 aircraft over Greenland in September 2007. LVIS is an airborne, medium- footprint (25m diameter), full waveform-recording, airborne, scanning lidar system that has been used extensively for mapping forest structure, habitat, carbon and natural hazards. The system digitally records the shape of the returning laser echo, or waveform, after its interaction with the various reflecting surfaces of the earth, providing a true 3-dimensional record of the surface structure. Data collected included ground elevation and vertical extent measurements for each laser footprint, as well as the vertical distribution of intercepted surfaces (the return waveform) from which surface slope, roughness and other metrics can be extracted. During the mission, data were collected along ICESat repeat ground-track "corridors" that encompass a variety of terrain types (e.g., inland ice, crevasses, ponds, sastrugi, ice/rock margins, and bare earth), over sea- ice in northern Greenland, and at Jakobshavn Isbrae, a fast-flowing outlet glacier where discharge rates have increased in recent years. Data from this mission will be used to assess the ability of 25m-footprint, waveform lidar to precisely and accurately characterize and monitor the surface of the Greenland ice sheet and its margins. The data will also be used to assess the effects of across-track slope corrections currently being used on the ICESat data. The study will highlight the complimentary measurement science that can be achieved using a multi-beam, 25m footprint, contiguous beam laser altimeter such as the one proposed for inclusion in the DESDynI mission, especially in the high-slope, highly dynamic areas of Greenland.



  2. Analysis of Tropical Forest Structural Dynamics Using Medium-footprint Lidar
    Sheldon, S. L.; Dubayah, R. O. C. D. B. H. M. A. B. J. B.  AGU  December 2007

    As a forest canopy recovers from a disturbance event, the vertical structure passes through various stages of biomass distribution until reaching an age at which it approximates the vertical structure of old-growth forest. Quantifying and mapping rates of biomass accumulation and distribution in the forest canopy has important implications for understanding carbon stocks and fluxes.The La Selva Biological Station in Costa Rica contains numerous sections of secondary forest at different stages of recovery from disturbance. We explore the vertical canopy structure of these forests at two different years and the progression of biomass distribution in the canopy over time using canopy information collected by the Laser Vegetation Imaging Sensor (LVIS). LVIS, a medium- footprint airborne scanning lidar, collected vegetation data over La Selva in March of 1998 and March of 2005. Waveforms and waveform-derived metrics are used to obtain canopy heights and vertical biomass distribution patterns and dynamics. We assess the potential of using medium-footprint lidar to determine successional status. The ability to remotely detect and map successional status can greatly improve carbon modeling and management.



  3. SAR, InSAR and Lidar studies for measuring vegetation structure over the Harvard Forest
    Siqueira, P. R.  AGU  December 2007

    In this paper we discuss ongoing studies of utilizing repeat-pass interferometric SAR, full waveform lidar, and radar polarimetry over the Harvard Forest in Massachusetts, for the purpose of characterizing vegetation three- dimensional structure. Polarimetric L-band Repeat-pass InSAR data is available over the region from the Japanese Space Agency's ALOS/PALSAR instrument, with a repeat period of 46 days. In 2003, the Laser Vegetation Imaging Sensor (LVIS) also flew over the area and provided extensive mapping of the regions true- ground surface topography and canopy height. The combination of these observations will provide a powerful combination for exploring the ability of the fundamental data types for estimating characteristics about the vegetation structure



  4. Evaluating the Potential of Waveform Lidar and Hyperspectral Data Fusion for Species Level Biomass Mapping.
    Swatantran, A.; Dubayah, R. H. M. B. J. B.  AGU  December 2007

    Many studies have demonstrated the ability of waveform lidar to map forest structural metrics such as canopy height, canopy cover and above ground biomass with high accuracies over different forest cover and types. Hyperspectral data provides forest attributes complementary to lidar such as vegetation stress, moisture content and land cover at species level. This study explores and evaluates the combined potential of waveform lidar (LVIS) and AVIRIS hyperspectral imagery for species level biomass mapping in the Sierra Nevada spotted owl habitat.LVIS quartile heights and canopy cover along with spectral metrics and endmember fractions from AVIRIS were compared with field biomass measures using linear and stepwise regression.Water band indices and shade fractions from AVIRIS show moderate to strong correlation with LVIS canopy height and biomass for certain species. LVIS variables were found to be consistently good predictors of total biomass as well as species level biomass. The inclusion of AVIRIS metrics in combination with lidar added little explanatory value for biomass. However, biomass prediction at species level lowered residual error by 20% or more in comparison to total biomass estimates, suggesting that its main value for biomass mapping is through species-level stratification.



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