Improved topographic correction of forest image data using a 3-D canopy reflectance model in multiple forward mode

In most forestry remote sensing applications in steep terrain, simple photometric and empirical (PE) topographic corrections are confounded as a result of stand structure and species assemblages that vary with terrain and the anisotropic reflective properties of vegetated surfaces. To address these problems, we present MFM-TOPO as a new physically-based modelling (PBM) approach for normalising topographically induced signal variance as a function of forest stand structure and sub-pixel scale components. MFM-TOPO uses the Li-Strahler geometric optical mutual shadowing (GOMS) canopy reflectance model in Multiple Forward Mode (MFM) to account for slope and aspect influences directly. MFM-TOPO has an explicit physical-basis and uses sun-canopy-sensor (SCS) geometry that is more appropriate than strictly terrain-based corrections in forested areas since it preserves the geotropic nature of trees (vertical growth with respect to the geoid) regardless of terrain, view and illumination angles. MFM-TOPO is compared against our recently developed SCS+C correction and a comprehensive set of other existing PE and SCS methods (cosine, C correction, Minnaert, statistical-empirical, SCS, and b correction) for removing topographically induced variance and for improving SPOT image classification accuracy in a Rocky Mountain forest in Kananaskis, Alberta Canada. MFM-TOPO removed the most terrain-based variance and provided the greatest improvement in classification accuracy within a species and stand density based class structure. For example, pine class accuracy was increased by 62% over shaded slopes, and spruce class accuracy was increased by 13% over more moderate slopes. In addition to classification, MFM-TOPO is suitable for retrieving biophysical parameters in mountainous terrain.