Modelling and Retrieval of Forest Parameters from Synthetic Aperture Radar Data

Sammanfattning: Frequent, high-resolution mapping of national and global forest resourcesis needed for improved climate modelling, degradation and deforestation detection,natural disaster management, as well as commercial forestry. Synthetic aperture radar(SAR) is an active radio- or microwave-frequency imaging sensor, which can be optimisedto fit specific needs through the choice of the centre frequency. In particular,P-band SAR, with wavelengths around 70 cm, is a promising tool for biomass mappingdue to the high sensitivity to tree trunks, whereas X-band SAR, with wavelengthsaround 3 cm and larger available bandwidths, is a promising tool for high-resolutionmapping of forest canopies.Papers A and B summarise the results obtained within the feasibility study forthe European satellite BIOMASS, which is planned to become the first spaceborneP-band SAR system. In Paper A, a forward model relating relevant forest and systemparameters to SAR observables is presented and evaluated. In Paper B, a new modelfor biomass estimation is proposed, in which the significant influence of topographicand moisture variations is treated using empirical corrections. The new model can beused with the same model parameters in two boreal test sites in Sweden, separatedby 720 km, with a root-mean-square error (RMSE) of 22{33% of the mean biomass.In Papers C, D, and E, X-band SAR data acquired with the twin-satellite, singlepassinterferometric system TanDEM-X are studied. Using the principles of acrosstrackinterferometry, the position of the scattering centre is estimated from the phasedifference between two SAR images. With a high-resolution digital terrain model, theinterferometric data are ground-corrected, and the elevation of the scattering centreabove ground is determined. In Paper C, boreal forest biomass is estimated for onetest site in Sweden from ground-corrected TanDEM-X data using three models withtree canopies represented by a random volume, but with different assumptions of theground component. The best results, with an averaged RMSE of 16%, are obtainedwith a model accounting for canopy gaps. Based on this observation, a two-levelmodel (TLM) is introduced, in which forest is modelled as two discrete scatteringlevels: ground and vegetation, the latter with gaps. In Paper D, it is shown thatTLM inversion of single-polarised, ground-corrected TanDEM-X data can provideforest height and canopy density estimates, with RMSE values below 10% for a borealtest site in Sweden. In Paper E, biomass is estimated from the inverted TLMparameters, with an RMSE in the interval 12{19% for eighteen acquisitions from twoboreal test sites in Sweden.