Résumé
Remote sensing methods, and in particular very high (metric) resolution optical imagery, are essential assets to obtain forest structure data that cannot be measured from the ground, because they are too difficult to measure, or because the areas to sample are too large or inaccessible. To understand what kind of, and how precisely and accurately, information on forest structure can be inverted from RS data, we propose a modeling framework combining a simple 3D forest model, Allostand, based on empirical or theoretically-derived DBH distributions and allometry rules , with a well-established radiative transfer model, DART. This framework allows producing forest canopy images for any type of forest based on widely available information of inventory data. Image texture can then be quantified, for instance using the Fourier Transform Textural Ordination (FOTO) method, and the derived textural indices compared with stand parameters for inversion and sensitivity analyses, as well as to indices from real world remote sensing images. The potential of the approach for the development of quantitative methods to assess forest structure, dynamics, matter and energy budgets and degradation, including in tropical contexts, is illustrated emphasizing broadleaf natural forests and discussed.