Comparison of Atmospheric Correction Methods in Mapping Timber Volume with Multitemporal Landsat Images in Kainuu, Finland

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 m3/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. Introduction Material from remote sensing is typically combined with low-intensity field sampling to obtain a general view of forest resources. Earth observation data provide a practical tool for the mapping and frequent monitoring of landcover over large regions. Current optical satellite systems were used in regional forest inventories (Jaakkola and Saukkola, 1979; Jaakkola et al., 1988; Muinonen and Tokola 1990; Tomppo 1993; Bauer et al., 1994). The use of high-resolution data in regional surveys is limited mainly by the cost and difficulty of automatically interpreting detailed and complexly textured information (Hyppänen, 1996). The main methods used for the estimation of forest characteristics have been stratification of digital remotesensing data to homogeneous spectral classes, either according to an unsupervised or supervised scheme (e.g., Poso et al., 1984 and 1987; Horler and Ahern, 1986; Häme, 1991; Brockhaus and Khorram, 1992) and direct estimation of characteristics using regression analysis (e.g., Tomppo, 1987, 1992; Ripple et al., 1991; Ardö, 1992). The nonComparison of Atmospheric Correction Methods in Mapping Timber Volume with Multitemporal Landsat Images in Kainuu, Finland I. Norjamäki and T. Tokola parametric weighted kNN-based method (Kilkki and Päivinen, 1986; Muinonen and Tokola, 1990; Tomppo 1993 and 1998; Tokola et al. 1996; Trotter et al. 1997; Nilsson and Ranneby, 1997) was also used for similar purposes. All estimates derived using these methods will eventually be based on field data, while remote-sensing data are generally used to expand the data by interpolation over non-sampled areas. The variables of interest are modeled separately in the regression approach. In the stratification and weighted kNN approaches, however, several variables can be estimated simultaneously. A nonparametric method, such as the kNN approach, needs extensive reference field data and therefore is very expensive for large-scale forest inventories. The accuracy of satellite image-based forest inventories is highly dependent on the quality of the satellite data interpreted. The average time window for acquiring optical images for forest inventory purposes in Finland is under four months. The relatively long repeat time for alternative satellites, cloud-free images for creating multi-image mosaics are frequently not available. When such data exist, they usually consist of images from many different phases of the growing period of the forest, and the spectral characteristics of different optical satellite systems need to be calibrated. Since the cost of Landsat images has recently decreased, there has been growing interest in the use of multitemporal Landsat imagery, and this imagery type was previously studied (Helmer et al., 2000; Lefsky et al., 2001; Oetter et al., 2001; Song and Woodcock, 2002 and 2003; Hadjimitsis, et al., 2004). Often other satellite data are required to fill the gaps between existing Landsat mosaics. There are many factors that cause uncertainty in the use of multitemporal satellite data: aging of the instrument, atmospheric conditions, topography, phenology, distance of the target to the sun, and sun and view angles. However, the problem is normally avoided using relative normalization among images (Olsson, 1993 and 1995; Tokola et al., 1999; Cohen et al., 2001). Another approach, absolute image calibration, is still an attractive alternative, although there are many difficulties in modeling all the physical conditions required. In optical remote sensing, the atmosphere is the primary source of noise preventing the accurate measurement of surface reflectance PHOTOGRAMMETRIC ENGINEER ING & REMOTE SENS ING Feb r ua r y 2007 155 Department of Forest Resource Management, University of Helsinki, Finland ([email protected]). Photogrammetric Engineering & Remote Sensing Vol. 73, No. 2, February 2007, pp. 155–163. 0099-1112/07/7302–0155/$3.00/0 © 2007 American Society for Photogrammetry and Remote Sensing 05-060 1/11/06 3:08 AM Page 155