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Title: Retrieval of forest structure and biomass from radar data using backscatter modelling and inversion
Author: Clewley, Daniel
ISNI:       0000 0004 2742 9376
Awarding Body: Aberystwyth University
Current Institution: Aberystwyth University
Date of Award: 2011
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In Australia, as in many countries, there is an increasing requirement for spatial and temporal information on forest structure and particularly biomass (carbon). The use of remote sensing data is well suited for the provision of such data, in particular Synthetic Aperture Radar (SAR) data. However, the majority of studies have concentrated on empirical relationships between above ground biomass (AGB) and radar backscatter. Limitations of such approaches include saturation of the signal (e.g., above certain biomass levels), structural variations, and the influence of environmental conditions (e.g., surface moisture). Recognising these limitations, this study focused on the retrieval of parameters through inversion of a physically based backscatter model (that of Durden et al., 1989) using a non-linear estimation algorithm (Moghaddam and Saatchi, 1999). The study focused on the Brigalow Belt Bioregion of Queensland, Australia, and particularly regrowth forests dominated by Acacia harpophylla. A generalised method of parameterisation has been described and validated using relationships established through fieldwork for three Australian species, used to develop parametric scattering equations for use in a non-linear estimation algorithm for the retrieval of structural and dielectric parameters from AIRSAR and ALOS PALSAR data with Landsat derived Foliage Projective Cover (FPC). Four case studies were presented in which the sensitivity of the estimation algorithm to noise was tested for a variety of scenarios. Following sensitivity analysis the algorithm was applied to real data for the product of structural maps. Although currently only modest levels of accuracy have been obtained with the approach there is much scope for improvement (i.e., better backscatter modelling, more data channels) and enhancement (i.e., inclusion of LiDAR data) of the algorithm.
Supervisor: Lucas, Richard Maxwell ; Bunting, Peter Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available