Norjamaki, I, Tokola, T (2007). "Comparison of atmospheric correction methods in mapping timber volume with multitemporal landsat images in Kainuu, Finland". PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING, 73(2), 155-163.
Using remote sensing to monitor large forest areas usually requires large field datasets. The need for extensive data collection can be reduced through interpretation of several images simultaneously. This study focused evaluating the accuracy and functionality of stand volume models in overlapping multi-temporal images that could form large areas covering a mosaic of scenes. Various atmospheric correction methods were tested to generalize field information outside the coverage of single images. A dataset consisting of three overlapping Landsat ETM+ images taken on different dates was used to compare atmospheric correction methods with uncorrected raw data. The methods tested were 6S, SMAC, and Dos. Aerosol data from MODIS were used in retrieving parameters for the 6s algorithm. The coefficient of determination values for the regression models used in estimating the total volume of the standing crop varied from 0.46 to 0.62 and standard error from 57 to 77 m(3)/ha, depending on the image calibration method used. All the atmospheric correction methods improved the classification of the multitemporal images. In comparison to the uncorrected data, the relative RMSE values for the multitemporal images decreased by an average of 6 percent on with DOS, 14 percent with SMAC, and 15 percent with 6S.