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Quantitative retrieval of land surface biological parameters (e.g. foliage projective cover [FPC] and Leaf Area Index) is crucial for forest management, ecosystem modelling, and global change monitoring applications. Currently, remote sensing is a widely adopted method for rapid estimation of surface biological parameters in a landscape scale. Topographic correction is a necessary pre-processing step in the remote sensing application for topographically complex terrain. Selection of a suitable topographic correction method on remotely sensed spectral information is still an unresolved problem. The purpose of this study is to assess the impact of topographic corrections on the prediction of FPC in hilly terrain using an established regression model. Five established topographic corrections [C, Minnaert, SCS, SCS+C and processing scheme for standardised surface reflectance (PSSSR)] were evaluated on Landsat TM5 acquired under low and high sun angles in closed canopied subtropical rainforest and eucalyptus dominated open canopied forest, north-eastern Australia. The effectiveness of methods at normalizing topographic influence, preserving biophysical spectral information, and internal data variability were assessed by statistical analysis and by comparing field collected FPC data. The results of statistical analyses show that SCS+C and PSSSR perform significantly better than other corrections, which were on less overcorrected areas of faintly illuminated slopes. However, the best relationship between FPC and Landsat spectral responses was obtained with the PSSSR by producing the least residual error. The SCS correction method was poor for correction of topographic effect in predicting FPC in topographically complex terrain

IMPACT OF DIFFERENT TOPOGRAPHIC CORRECTIONS ON PREDICTION ACCURACY OF FOLIAGE PROJECTIVE COVER (FPC) IN A TOPOGRAPHICALLY COMPLEX TERRAIN