Speaker
Dr
Ludovic Villard
(CESBIO)
Description
To support the selection of Biomass as the 7th ESA Earth Explorer mission, an essential part of the work was to demonstrate that the Above Ground Biomass (AGB) of dense forests (above 300 t/ha) can be retrieved from P-band SAR data, given the importance of tropical forests in the carbon cycle. Using PolSAR data, the retrieval of AGB in tropical forest is a challenging task given the small sensitivity of the radar backscatter to forest biomass with respect to perturbing sources such as environmental effects or terrain topography.
Besides, another challenging aspect of the future retrieval algorithm for a global mission like Biomass lies in its ability to deal with different forest types and natural land covers. Therefore, the mapping of forest Above Ground Biomass (AGB) is not only challenging over tropical dense forests, but also over complex natural landscapes characterized by their high spatial variability (heterogeneous areas, forest patches, clearings, borders...). The foreseen retrieval algorithms have therefore to be parametrized according to the various possible forest types at the global scale, assuming thereby an ad-hoc (adapted) classification prior to the inversion process. As a result, the attributes of the forest classes are more linked to the retrieval algorithm capability than to ecological characteristics.
The retrieval algorithm is based on a Bayesian formulation of the estimated AGB, in which the Gaussian probability density functions are characterized by a mean value derived from the so-called analytical model (power law between a PolSAR indicator and AGB) and by a standard deviation derived by a perturbation model (resulting from electromagnetic simulations of the backscatter). Both analytical model and perturbation model are parametrised using the P-band observations and in-situ data on the forest test plots, but the parametrisation of the analytical model is based on a regression analysis, whereas the parametrisation of the perturbation model is based on its ability to reproduce the complex backscatter, from which the likelihood functions are computed.
A general description of the retrieval algorithm will be first given in the paper, before going more into the details of the three main steps, considering 1/ the ad-hoc classification, 2/ the parametrisation of both analytical and perturbation models and 3/ the pixel-to-pixel inversion. To assess the performance of the proposed algorithm, the TropiSAR P-band data acquired over tropical forests in French Guiana will be used as demonstration case, considering however a limited number of forest classes, which could be further extended with the very recent data from AfriSAR ESA campaign.
Primary author
Dr
Ludovic Villard
(CESBIO)
Co-authors
Dr
Pierre-Louis FRISON
(Universite Paris-Est Marne-la-Vallée - MATIS - IGN / CESBIO)
Dr
Thierry KOLECK
(CNES)
Dr
Thuy Le Toan
(CESBIO)
