Topographic Normalization of Landsat TM Images of Forest Based on Subpixel Sun–Canopy–Sensor Geometry

Abstract

Because trees are geotropic (perpendicular to the geoid), topography has no control over the Sun–crown geometry. What topography does influence is the relative positioning of trees and thus the amount of shadowing cast by them within the canopy. As satellite sensors in general measure the collective radiance of many trees inside their instantaneous field of view, the overall canopy brightness at the pixel scale is strongly controlled by canopy shadowing and hence by the topography. The removal of or compensation for topographic effects on forest images should be based on the normalization of mutual shadowing at the subpixel scale, rather than on the normalization of Sun–terrain–sensor geometry at the pixel scale. The Sun–canopy–sensor (SCS) topographic correction model was developed to characterize and hence correct the topographic effects on forest images. Testing with simulated image data showed the SCS model to be accurate (root-mean-squared residual error <0.1) for forest canopies of 50% or higher closure, and testing with Landsat Thematic Mapper images showed that it consistently performs either slightly or significantly better than the widely applied cosine correction, the c-correction and the Minnaert correction models, for forests under different imaging conditions.