HR: 09:15h
AN: B21B-06 INVITED    [Abstracts]
TI: Quantifying Carbon in Savannas: The Role Of Active Sensors
AU: * Lucas, R M
EM: rml@aber.ac.uk
AF: Institute of Geography and Earth Sciences, The University of Wales Aberystwyth, Llandinam Tower, Penglais Campus, Aberystwyth, SY23 3DB, United Kingdom
AU: Lee, A C
EM: alex.lee@anu.edu.au
AF: School of Resources, Environment and Society, Australian National University, Canberra, ACT 0200, United Kingdom
AU: Veirgever, K M
EM: K.Viergever@ed.ac.uk
AF: Institute of Geography, University of Edinburgh, Drummond Street, Edinburgh, EH8 9XP, United Kingdom
AU: Bunting, P J
EM: peter.bunting@aber.ac.uk
AF: Institute of Geography and Earth Sciences, The University of Wales Aberystwyth, Llandinam Tower, Penglais Campus, Aberystwyth, SY23 3DB, United Kingdom
AB: Savannas worldwide are experiencing rapid changes as a consequence of anthropogenic activity, climatic alteration and natural events. Such changes are impacting upon the carbon cycle, with both gains (e.g., through regrowth and woody thickening) and losses (e.g., through deforestation, dieback or burning) occurring. At local to regional scales, optical data from airborne and/or spaceborne sensors (e.g., aerial cameras, Landsat, MODIS) have remained a primary source for characterising savannas and quantifying changes in extent, condition and productivity. However, such data have been limited in quantifying the woody components and changes in these as a function of tree growth or mortality. For these reasons, regional estimates of standing carbon stocks and changes in these over time have also proved difficult to generate. In recent years, technological advances in Synthetic Aperture Radar (SAR) and Light Detection and Ranging (LiDAR) have provided increased opportunities for quantifying the biomass and structure of woody vegetation at local to regional scales. For retreiving biomass, empirical relationships with SAR backscatter have been limited because of saturation of the signal. However, many wooded savannas support a relatively low biomass and their comparative openness leads to a greater diversity of scattering processes between the vegetation components (e.g., branches, trunks) and the ground surface. More information on the forest volume can therefore be extracted which, in turn, has led to the development of new algorithms (e.g., parameter estimation, inversion) for retrieving the biomass and also structure (e.g., stem density) of savannas from SAR, including those that overcome saturation. Structural measures relating to biomass (e.g., vegetation height) have further been retreived using interferometric SAR (InSAR) and polarimetric-interferometry (PolInSAR). In many cases, algorithm development has been advanced using simulation models. LiDAR has provided complementary information on the vertical and horizontal distribution of plant elements within the forest volume and spaceborne data from the ICESAT Geoscience Laser Altimeter System (GLAS) in particular have shown promise for regionally estimating vegetation height in savannas. The use of airborne LiDAR has largely been restricted to local areas because of cost and platform availability. Nevertheless, these and other airborne (e.g., hyperspectral) datasets have played a pivotal role in providing tree to stand level information that support the interpretation of spaceborne LiDAR and SAR data and development of algorithms for biophysical parameter retrieval. Whilst SAR, LiDAR and optical sensors can separately provide unique information on savannas, the integration of data and products from each has provided the greatest opportunities for quantifying the distribution and dynamics of carbon within savannas. Examples include mapping of the growth stage of regenerating vegetation, discriminating forest types, detecting dead standing timber, and quantifying changes in land cover and forest condition. The resulting datasets have provided additional input to carbon models in savanna regions.
DE: 0428 Carbon cycling (4806)
DE: 0439 Ecosystems, structure and dynamics (4815)
DE: 0480 Remote sensing
SC: Biogeosciences [B]
MN: 2007 Fall Meeting