Variational multiscale approach to LAI profile inversion based on LiDAR full waveform measurements

摘要

Light Detection and Ranging (LiDAR) is one of the important techniques of remote sensing that is currently attracting considerable interest in description of vegetation structure, obtaining information about the canopy height as well as the foliage density of vegetation layer profiles. In this study, we estimate one of the important structural properties of vegetation: the profile of leaf area index (LAI). It is retrieved based on airborne full waveform LiDAR data inversion. It consists of estimating the value of LAI each time an echo is recorded. The proposed approach approximates the scattering taking into account just the first pulse collision within the vegetation cover. Moreover, to be independent from both the scattering coefficient and the pulse energy, the ratio between two successive echoes is model instead of the echo itself. The proposed algorithm is based on Bayesian modeling. Indeed, we propose a variational approach having two components: data attachment (simulated waveform is close to actual one) and regularity (the LAI value slowly varies from a position to the next). The problem is finally written as a non-linear cost function to optimize. To solve it and overcome non-linearity, we propose a new multiscale gradient technique, which start by solving simpler problem and increases progressively the complexity until converging to the original problem. For evaluating our methods, we parametrize the DART model to simulate Lidar full waveform and vegetation scenes; one with Turbid medium and the other with imported 3D olive tree. The results are promising.