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REGIONALLY APPLICABLE EQUATIONS FOR ESTIMATING ABOVEGROUND BIOMASS IN THE AMAZON BASIN FROM GEOSCIENCE LASER ALTIMETER SYSTEM WAVEFORM METRICS

Michael A. Lefsky, Center for Ecological Applications of Lidar, Colorado State, University, lefsky@cnr.colostate.edu
Maria Hunter, Complex Systems Research Center, University of New, Hampshire
Michael Keller, USDA Forest Service, International Institute of Tropical Forestry, and, Complex Systems Research Center, University of New Hampshire
David Turner, Department of Forest Science, Oregon State Univerisity, david.turner@orst.edu (Presenting)
Plinio B. de Camargo, Lab. De Ecologia Isotopica, CENA/USP

Lidar remote sensing has a unique capability for estimating forest canopy height, which has a direct and increasingly well-understood relationship to aboveground carbon storage. In our LBA work, we are using lidar waveforms from the Geoscience Laser Altimeter System (GLAS) to estimate canopy height and aboveground forest carbon storage for each GLAS waveform in the Amazon basin and combining those point estimates with images from the MODIS sensor to develop spatially continuous datasets. We will compare the new biomass estimates with spatially explicit data layers from previous studies in the region and use an existing bookkeeping C flux model to evaluate the sensitivity of C flux estimates associated with land cover change in the region to the new biomass estimates. Three problems must be addressed to accurately estimate aboveground carbon storage for each GLAS waveform. First, equations representing the waveforms to the physical structure of the forest must be developed, so that estimates of forest canopy height can be made. Next, an equation relating forest canopy height to field estimates of aboveground biomass must be developed. We have previously published on these two topics for field plots in the Tapajos National Forest, near Santarem, Brazil. We have extended this analysis to two other sets of field sites in the vicinity of Manaus and Tanguro, Brazil. These sites were picked to increase the range of variability in climate that we considered. Simple regression analysis using SRTM slope indices and GLAS waveform height metrics indicates that a single equation estimates maximum canopy height at all three sites with 87% of variance explained, and RMSE of 6.8 m, and no statistically significant biases at any site. Similarly, there is a single equation that relates maximum canopy height to field estimated aboveground biomass that fits all three sites. Combining the two, a single equation relating GLAS estimates of maximum height and field estimated aboveground biomass has been created which explains 72% of variance, and an RMSE of 54.7 Mgha-1. At one site (Manaus) there exists a statistically significant bias (+ 31.3 Mgha-1 or 12.5% of the site mean) in the estimates of aboveground biomass.

Science Theme:  CD (Carbon Dynamics)

Presentation Type:  Poster

Abstract ID: 146

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