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Remote Inventory for a Northern Temperate Forest: Integrating Waveform Lidar with Hyperspectral Remote Sensing Imagery

Jeanne E. Anderson, University of New Hampshire, jeanne.anderson@unh.edu (Presenting)
Mary E. Martin, University of New Hampshire, mary.martin@unh.edu
Lucie Plourde, University of New Hampshire, lucie.plourde@unh.edu
Marie-Louise Smith, USDA Forest Service - Northeastern Research Station, marielouisesmith@fs.fed.us
Ralph O. Dubayah, University of Maryland, dubayah@umd.edu
Michelle A. Hofton, University of Maryland, michelle@ltpmail@gsfc.nasa.gov
J. Bryan Blair, NASA - Goddard Space Flight Center, bryan@arthur.gsfc.nasa.gov
Rob Braswell, University of New Hampshire, rob.braswell@unh.edu

We describe the integrated and individual capabilities of waveform lidar and hyperspectral data to estimate three common forest measurements - basal area (BA), above-ground biomass (AGBM) and quadratic mean stem diameter (QMSD) - in a northern temperate mixed conifer-deciduous forest. The use of this data to discriminate distribution and abundance patterns of several tree species was also explored. Waveform lidar imagery was acquired in July 2003 over the Bartlett Experimental Forest in New Hampshire using NASA’s airborne Laser Vegetation Imaging Sensor (LVIS). Hyperspectral imagery was acquired in August 2003 using NASA’s Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). Field data (2001-2003) from over 400 USDA Forest Service Northeastern Research Station plots were used to determine actual site conditions. Results suggest that integrated data sets of hyperspectral and waveform lidar provide an advantage over the use of either data set alone in evaluating common forest metrics. Across all forest conditions, 8-9% more of the variation in AGBM, BA, and QMSD was explained by use of the integrated sensor data, with estimated error 5-8% lower for these variables. Notably, in an analysis using integrated data limited to unmanaged forest tracts, AGBM coefficients of determination improved by 25% or more, while corresponding error levels decreased by over 25%. AVIRIS data alone best predicted species-specific patterns of abundance. Nonetheless, use of LVIS and AVIRIS data - in tandem - produced complementary maps of estimated abundance and structure for individual tree species, providing a promising adjunct to traditional forest inventory.

Presentation Type:  Poster

Abstract ID: 52

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