<title>Fractal geometry for atmospheric correction and canopy simulation</title>

Abstract

Global climate modeling needs a good parameterization of the vegetative surface. Two of the main important parameters are the leaf area index (LAI) and the fraction of absorbed photosynthetically active radiation (FPAR). In order to derive these values from space and airborne spectral radiance measurements one needs information on the actual atmospheric state as well as good canopy models. First we have developed a retrieval method for the optical depth to perform an atmospheric correction of remote sensing data. The atmospheric influence reduces the global image contrast and acts as a low pass filter. We found that the autocorrelation function [ACF(lambda )(h)] of the image depends on the global image contrast C and on the fractal dimension s. Using multiple regression the spectral optical depth in the visible range can be estimated from C and s with an absolute accuracy of 0.021. This method was applied and tested for a number of rural TM scenes. Atmospheric correction allows us to calculate the canopy reflectance from the image data. The relationships between the canopy reflectance and LAI or FPAR can be determined from canopy radiative transfer modeling. Row and shadowing effects influence the bi-directional reflectance distribution function (BRDF) since the leaves and stems are real 3D objects. In order to use a ray tracer for 3D radiative transfer simulation the canopy should be described by simple shapes (discs, cylinders) and polygones. Lindenmayer systems which are based on the ideas of fractal geometry allow the construction of plants and trees in this way. We have created simple artificial plants and arranged them into rows to study shadowing and row effects and compute the BRDF in various spectral channels.© (1996) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.