CARBON-3D - Biomass Determination


Myneni et al. (2001) and Dong et al. (2003) provide examples of using optical remote sensing data to determine biomass with high spatial resolution at the continental scale using NDVI. Their method of correlating seasonally integrated measures of satellite-observed vegetation greenness with ground-based inventory data amounts to a spatial interpolation and extrapolation of the inventory data. While pioneering, the work is indirect in that direct space-based measurements of biomass are not available. The upscaling used relies on the unknown degree to which the sample of inventories used is representative of the whole of a continental area.

Correlations of Synthetic Aperture Radar (SAR) data to biomass have been proven at low frequencies L- and P-band (Le Toan et al. 1992; Ranson et al. 1997; Rowland et al. 2002, Ericsson et al. 2003) and at C-band using ERS-1/2 repeat-pass interferometry (Santoro et al. 2002) and combining ERS tandem interferometric coherence and JERS backscatter (Balzter et al. 2002; Wagner et al. 2003). Multi-polarimetric SAR data allow interpretation of the canopy structure up to 200t/ha above-ground biomass at P-band and 100t/ha at L-band (Dobson et al. 1992). Recently developed PolInSAR techniques, combining polarimetry and interferometry (Papathanassiou and Cloude 2001; Cloude and Papathanassiou 1998) provide better vegetation characterisation and a sensitivity up to 400 t/ha (Mette et al. 2003). No such system is foreseen yet for operational service in space but the PolInSAR techniques will be tested within the framework of the “Kyoto and Carbon Initiative” with PALSAR (Phased Array type L-band Synthetic Aperture Radar) data from ALOS (Advanced Land Observing Satellite) (Rosenqvist et al. 2001; Rosenqvist et al. 2003). ALOS is planned to be launched in 2005.

In contrast to the frequent attempts of using SAR for biomass estimations only a few airborne light detection and ranging (LIDAR) missions have been developed and validated: the Laser Vegetation Imaging Sensor (LVIS) and the Scanning Lidar Imager of Canopies by Echo Recovery (SLICER). Results of these studies have demonstrated that large-footprint LIDAR instruments show great promise in biomass estimation of tropical as well as temperate forests due to the information about the location of the intercepting surfaces and sub canopy topography (Drake et al. 2002a; Lefsky et al. 1999a; Lefsky et al. 1999b). Due to the correlation between light absorption and net carbon uptake by vegetation, a range of diagnostic methods exist for converting optical satellite observations into estimates of net primary production (e.g., Potter et al. 2003; Veroustraete et al. 2002; Nemani et al. 2003). The relationship between these quantities is considerably moderated by effects of temperature and soil moisture, limiting the accuracy of the assessment. The use of satellite data, however, ensures fine spatial and temporal detail. Derivation of biomass from these estimates requires use of a full biogeochemical process model.

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