RADIOMETRIC CORRECTIONS OF TOPOGRAPHICALLY INDUCED EFFECTS ON LANDSAT TM DATA IN ALPINE TERRAIN

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1 RADIOMETRIC CORRECTIONS OF TOPOGRAPHICALLY INDUCED EFFECTS ON LANDSAT TM DATA IN ALPINE TERRAIN Peter Meyer 1, Klaus I. Itten, Tobias Kellenberger, Stefan Sandmeier, and Ruth Sandmeier Remote Sensing Laboratories Department of Geography University of Zurich-Irchel CH-8057 Zurich, Switzerland ABSTRACT Four radiometric correction methods for the reduction of slope-aspect effects in a Landsat TM data set are tested in a mountainous test site with regard to their physical soundness, their influence on a forest classification as well as on the visual appearance of the scene. Excellent ground reference information and a fine resolution DEM allowed a precise assessment of the applicability of the methods under investigation. The results of the study presented here demonstrate the weakness of the classical cosine correction method for the radiometric correction in rugged terrain. The statistical, Minnaert and C-correction approaches however yielded an improvement of the forest classification and an impressive reduction of the visual topography effect. 1 current address: NASA/Jet Propulsion Laboratory MS , 4800 Oak Grove Drive Pasadena, CA USA peterm@gomez.jpl.nasa.gov P. Meyer et al. 1

2 1. INTRODUCTION The overutilization of tropical forests as well as the problems of deterioration of the health of forests in mid latitudes have alarmed mankind that an important ecological factor, an important natural resource is endangered and may not be so renewable as anticipated. In recent years much emphasis was on the mapping and inventorying of forests and forest damages. On a local level colour-infrared airphotographs were used with great success, and on larger scales, be it regional, continental or even global, satellite studies show quite encouraging results. In this study the feasibility of Landsat TM forest classifications in an alpine environment is tested versus excellent ground reference data. It has been shown in the past that terrain induced illumination variations have hindered an easy and straight forward solution of the distinction of forests versus a non-forest background, and also to separate the forest into its major classes. Four methods to correct the impact of illumination have been tested in order to improve the accuracy of forest classifications. Since a correction of the atmospheric effects based on 5S (Tanré et al., 1986) did not result in a significant improvement of the forest classification (Leu, 1991), no atmospheric correction was applied in this study. 2. BASE OF THE STUDY 2.1. Selection of test sites and associated ground reference data The base dataset consists of a 7-band TM scene (194-27) of 3 July Fall, winter or spring imagery is not suited for forest classifications due to the lower sunangles which castshadows, and due to the fact that the foliage of the various forest types is not fully developed. Within the selected cloudfree scene the site "Beckenried" was selected, situated in the mountainous pre-alps of the Canton of Nidwalden in Central Switzerland. The terrain elevation in the 12.0 km by 17.5 km test site varies from 434 to 2404 m with pronounced steep slopes. For this test site the green plates, containing the class forest, of the 1 : 50'000 Swiss Federal Office topographic maps were scanned. Additionally maps on forest stands were P. Meyer et al. 2

3 digitized which had been generated by the Swiss Sanasilva Project using colour-infrared airphotographs at a scale of 1:10'000 flown on 25 July 1985 (353 hectares) and 13 August 1987 (572 hectares), respectively. Thus, well timed ground reference information was available, which in part contained also information on forest type, degree of mixing, crown closure, growth status and vitality. The Beckenried area forest is a heterogeneous mixture of conifers and decidous trees leaving only small patches of clearly discernible stands. Based on a study by DFVLR (1988), stands with less than 20 % conifers were defined to be decidous forest, a ratio between 20 % and 80 % is called mixed forest, and a stand with more than 80 % conifers is named coniferous forest Digital elevation model (DEM) and related datasets For our test site a digital elevation model was available from the Swiss Federal Office of Topography with a resolution of 25 m in x and y and 0.1 m for elevation. It is based on the Swiss topographic map 1:25'000 "Beckenried" with a contour interval of 20 m. From this DEM datasets for slope, aspect and illumination as well as masks for cast-shadow and selfshadow were generated. According to Goodenough et al. (1990), slope and aspect should not be derived at a resolution higher than 4.29 times the contour interval of the source map. This requirement cannot be met in our case, since we would need a contour interval of about 6 m to reach at a grid size of 25 m. Nevertheless we considered it useful to assess the power and applicability of some of todays available correction algorithms. Basic to any radiometric terrain correction is the illumination. It is defined as the cosine of the incident solar angle, thus representing the proportion of the d i r e c t solar radiation hitting a pixel. The illumination is therefore depending on the relative orientation of the pixel towards the sun's actual position (see Figure 1). All datasets used in this study have been geocoded to the rectangular coordinate system of the Swiss topographic maps. This rectification also included geometric correction of relief P. Meyer et al. 3

4 displacement due to variations in terrain elevation (Itten and Meyer, 1993). In contrast to the treatment of data from large field-of-view sensors such as NOAA/AVHRR, with TM geometric and radiometric corrections may be performed independently one after another (Woodham and Lee, 1985). In order to keep the Digital Numbers of the scene unchanged a nearest neighbour resampling algorithm instead of an often applied cubic convolution technique was used (Meyer, 1992). 3. RADIOMETRIC CORRECTIONS Digital Numbers (DN) representing the radiance measured by a sensor such as the Thematic Mapper and registered by its system, cannot only be referred to reflective and emission properties of the observed objects. They are influenced by additional radiometric effects. Teillet (1986) subdivides them into two major categories: sensor induced effects scene related effects Sensor induced effects summarize technical aspects such as calibration of detectors, filtering, platform and system stability etc. Scene related effects include the influence of topography, atmosphere, viewing angle, adjacency effect, position of the sun and the reflectance properties of the objects. Both, sensor and scene related effects were treated in this study. As tested by Sandmeier (1991), however, the radiometric variance of the sensor induced effects in our scene was within a limit of ± 1 DN for the detector calibration mean. The scene could therefore be left unchanged from the side of sensor induced effects (NASA, 1982). Within the scene related effects the study focussed on the impact and correction of illumination which are described with regard to their physical soundness, their influence on classification results as well as on the visual appearance of the scene. P. Meyer et al. 4

5 Extensive tests with the atmospheric correction code 5S (Tanré et al., 1986) did not yield a significant improvement of the forest classification in our scene and were no longer considered. This may be due to the fairly clear weather condition during the overflight with horizontal visibilities of 25 km in the valleys and over 70 km in the mountain area (Leu, 1991). A correction of the viewing angle, a so-called path length correction, was also not necessary. The two paths limiting the rather small test site in the East-West direction differ only 1.7 km from each other. This corresponds to 0.24 % of the average path length. Therefore the influence of this effect was neglected. When processing a full TM scene however, this factor may have to be reexamined Slope-aspect correction Neglecting the atmospheric influence and the adjacency effects we can state that in the visible and near infrared bands the direct sun radiation is the only illuminating factor. If the terrain were additionally completely flat and all objects had a Lambertian reflection characteristic, the reflected energy measured by a sensor (radiance) would only depend on the direct irradiance and the reflectance of the objects on the surface. However, most objects including forest have a non-lambertian reflectance characteristic. Also the effects of topography on scene radiance cannot be neglected in an alpine region and have to be taken into account in several regards: the optical thickness is elevation dependent targets may lay in the cast-shadow of surrounding hills or mountains slopes have a brightening effect on adjacent targets (imagine a deep valley which is brightened by the reflection of snow-covered slopes) the irradiance on a pixel is highly dependent from the sun-target geometry (slope-aspect effect) P. Meyer et al. 5

6 This study focussed on the correction of slope-aspect effects. The influence of adjacent slopes (topographic adjacency correction) and optical thickness are neglected. Also castshadows are not handled and masked out since only few pixels of the test site lay in castshadowed areas during the time of overflight. A Lambertian reflection characteristic, however, is only assumed in one of four approaches under investigation Aim An ideal slope-aspect correction removes all topographically induced illumination variation so that two objects having the same reflectance properties show the same Digital Number despite their different orientation to the sun's position. As a visible consequence the three-dimensional relief impression of a scene gets lost and the image looks flat Correction methods The four following slope-aspect correction methods as described by Teillet et al. (1982) were tested: a statistic-empirical correction method a cosine correction two semi-empirical methods: - the Minnaert method - the C-correction They will be presented in view of their simplicity in application and their effectiveness. P. Meyer et al. 6

7 Statistic-empirical method A statistical approach as used in this study is based on a significant correlation between a dependent and one or several independent variables. With the help of a regression function the influence of the independent variables can be corrected. The quality of such a correction is of course depending on the degree of explanation of the regression function. Assuming a linear correlation between the original band and the illumination, the influence of the direct radiation can be corrected as follows: L H = L T - cos (i) m - b +L T (1) where L H = radiance observed for horizontal surface L T = radiance observed over sloped terrain L T = average of L T for forested pixels (according to ground reference data) i = sun incidence angle in relation to the normal on a pixel (Figure 1) m = inclination of the regression line b = intercept of regression line The application of equation (1) effects a rotation of the regression line into the horizontal at the elevation of L T. A specific object is independent from cos(i) and shows the same Digital Number throughout the scene. The position of L H is secondary. Figures 2 and 3 illustrate the linear regression between the original band 2 and the illumination and between the statistic-empirical corrected band 2 and the illumination, respectively. P. Meyer et al. 7

8 Cosine correction The cosine correction is often applied in flat terrain to equalize illumination differences due to the different sun positions in multitemporal datasets. It is a strongly trigonometric approach based on a basic physical law assuming a Lambertian reflection characteristic of objects and neglecting the presence of an atmosphere. According to Figure 1, the amount of irradiance reaching an inclined pixel is proportional to the cosine of the incidence angle i, where i is defined as the angle between the normal on the pixel in question and the zenith direction (Teillet et al., 1982). Only the part cos(i) * E i of the total incoming irradiance E i reaches the inclined pixel. E i is dependent from the solar constant and the distance between sun and earth. The cosine law, however, only takes the sun's position into account in the form of the sun zenith angle, assuming the solar constant and the distance between sun and earth being constant for all scenes. L H = L T cos (sz) cos (i) (2) where L H = radiance observed for horizontal surface L T = radiance observed over sloped terrain sz = sun zenith angle i = sun incidence angle in relation to the normal on a pixel (Figure 1) Thus for the correction of slope-aspect effects with the cosine approach only data on sun zenith angle and illumination are needed. The cosine correction only models the direct part of the irradiance. Weakly illuminated regions, however, get a considerable amount of diffuse irradiance. On such areas the cosine correction has an unproportional brightening effect. The smaller cos(i) the stronger is this overcorrection (Figure 4). For pixels in complete self-shadow (cos(i) = 0) where a division by P. Meyer et al. 8

9 0 occurs when applying equation (2), and in faintly illuminated areas the Digital Numbers saturate and lead to artefacts in the corrected image as can be seen in the top right image of Figure Minnaert correction The name Minnaert correction is deduced from the Belgian astrophysicist Marell G. J. Minnaert (1941). In connection with his investigations of the lunar light he modified the common cosine correction by adding a constant k: L H = L T [ cos (sz) cos (i) ]k (3) where L H = radiance observed for horizontal surface L T = radiance observed over sloped terrain sz = sun zenith angle i = sun incidence angle in relation to the normal on a pixel (Figure 1) k = Minnaert constant Parameter k has become known as the Minnaert constant and is considered to be a measure of the extent to which a surface is Lambertian, in which case k = 1. In general, k is also a function of the phase angle, but this dependence has only to be considered when using multitemporal or multisensor datasets (Teillet et al., 1982). The values of k varies between 0 and 1. The smaller k, the weaker is the influence of the quotient in equation (3). Especially in areas with a cos(i) near 0, k increases the denominator and prevents a division by small values. Thus one can counteract to an overcorrection as obtained in the common cosine correction (Figures 4 and 5). P. Meyer et al. 9

10 Parameter k can be determined empirically by linearizing equation (3) logarithmically and estimating the slope of a linear regression C-correction Teillet et al. (1982) describe the possibility to bring the original data into the form L T = m cos(i) + b. This corresponds to a regression line used in the statistical-empirical approach with the original Digital Number on the ordinate and cos(i) on the abscissa. Teillet et al. (1982) introduce now a parameter c which is the quotient of b and m of the regression line. Parameter c is built in the cosine law as an additive term: c = b m (4) L H = L T [ cos (sz) + c cos (i) + c ] (5) where c = correction parameter m = inclination of regression line b = intercept of regression line L H = radiance observed for horizontal surface L T = radiance observed over sloped terrain sz = sun zenith angle i = sun incidence angle in relation to the normal on a pixel (Figure 1) According to Teillet et al. (1982) the parameter c might emulate the effect of path radiance on the slope-aspect correction, but the physical analogies are not exact. Mathematically the effect of c is similar to that of the Minnaert constant. It increases the denominator and weakens the P. Meyer et al. 10

11 overcorrection of faintly illuminated pixels as a consequence (Figure 6). Parameter c is determined with the same regression that was used in the statistical-empirical approach Overview of the four methods and their basic differences Table 1 gives a summary on the basic characteristics of the presented slope-aspect correction methods. The parameters given in the third column are the actual correction values used in this study. They are determined empirically by estimating slope and intercept of a linear regression between illumination (cos(i) 100) and the corresponding TM-band as described in section 3.3. Therefore the parameters are specific to the chosen scene and test site and furthermore optimized on forests. They may not be applied to another scene or test site without adaption. 4. EVALUATION OF SLOPE-ASPECT CORRECTIONS The usefulness of the slope-aspect corrections have been tested statistically, visually and in regard to their influence on a forest versus non-forest classification. All tests were based on the whole test area Beckenried ( pixels). In general, it can be stated that only a small difference between the results of the statistical, the Minnaert and the C-method is observable. For the reason of simplicity these three correction approaches will be grouped and called SMC-methods hereafter. The simple cosine correction on the other hand shows remarkable differences to the SMC correction methods and has to be treated separately. P. Meyer et al. 11

12 4.1. Statistical analysis The dynamics of data described by the standard deviation remains more or less constant for the SMC-corrected images compared to the uncorrected original. It is much higher however for the cosine correction. The Pearson correlation coefficient is very high between the uncorrected original band and the SMC-corrected images (r is about 0.97) and rather low between the uncorrected original and the cosine corrected image (r is about 0.14) in all of the three bands under investigation. As the original band shows - apart from the disturbing illumination effect - mainly target information, it seems that the SMC-corrected images have a higher degree of target information than the cosine corrected band. Under the assumption that the level of noise is not influenced by the correction algorithms, the SMC-corrected images are expected to be more authentic than the cosine corrected one. The correlation between the corrected images and the illumination emphasizes this impression. Figures 2, 3 and 4 depict band 2 as a function of illumination for the statisticempirical and the cosine corrected image in comparison with the original image including the corresponding linear regression lines. While the statistic-empirical corrected image (and with it all other SMC-corrected images) shows more or less independency from illumination, in the cosine corrected image (Figure 4), a drastic overcorrection of the weakly illuminated pixels is obvious. This is due to the unrealistic assumption, basic to that correction method, that only direct irradiance is present. Especially weakly illuminated pixels with a small incidence angle i exhibit a large amount of diffuse irradiance and are therefore amplified unproportionally by the cosine correction. Mathematically this can be explained by a division of a constant (cos (sz)) through a very small number (cos(i)) which is zero for a pixel laying in its self-shadow. In this case, the result of the correction is not defined (zero-division) and leads to artefacts in the corrected image. For that reason Teillet et al. (1982) recommend the application of the cosine correction only in cases where the incidence angles are smaller than 55. Table 2 summarizes inclination (m), intercept (b) and coefficient of determination r 2 of the various correlations between the original, the corrected images on the one hand and the illumination file on the other. An ideal correction of the illumination effect would lead to an r 2 P. Meyer et al. 12

13 of zero so that the zero hypothesis "the inclination of the regression line is equal 0" could no longer be rejected. In all three analysed bands 2, 4 and 5, r 2 can be reduced by the SMC-methods in maximum from in the original band 5 to in the Minnaert corrected band 5. The effect of the other SMC-methods are similar in all of the three bands investigated. The success of the illumination correction can therefore be verified statistically for the SMC-methods. The r 2 values for the cosine correction are much higher though they are still smaller than the ones of the original bands. As Figure 4 shows, the cosine correction leads to a regression of second degree rather than to an elimination of the regression. However, the r 2 values are quite small even for the linear regression in spite of the insufficient correction of the illumination effect Visual analysis A comparison between the original band 2 and the statistic-empirical corrected band 2 shows a reduction of the relief effect (Figure 7). This leads to a loss of the three-dimensional impression in the illumination corrected image. It becomes flat and more or less homogeneous in regions of identical objects. The appearance of forests, for instance, becomes much more independent from topography than in the original (uncorrected) bands. This visual effect is more impressive in a near-infrared band than in a visible one. As the impact of the atmosphere is much stronger in the visible part of the electromagnetic spectrum than in the near-infrared, the influence of diffuse irradiance is much more important. Thus, especially in sparsely illuminated pixels the effect of the illumination correction is reduced by the smearing effect of the atmosphere so that the illumination correction could only be examined properly after an atmospheric correction. As expected, the illumination correction with the SMC-methods is much more efficient than the classical cosine correction which is unsuitable in such a rugged terrain. Figure 8 (top right) demonstrates the inadequate correction of the illumination effect and the artefacts along P. Meyer et al. 13

14 the mountain ridges due to the zero division for pixels in self-shadow. These artefacts can be observed in the Minnaert and C-corrected images as well; however, the overcorrection of the cosine approach is reduced to a large amount by the Minnaert constant and parameter c, respectively (Figure 8). Cast-shadows are not handled with this correction methods and appear dark. A special treatment of these pixels would be necessary Impact on classification After illumination correction it is assumed that the influence of scene related effects are reduced in favour of the intrinsic reflection properties of objects. Therefore an increase in classification accuracy can be expected. Based on experiences by Leu (1991) a parallel epiped algorithm was chosen to examine the effect of illumination correction on a forest versus non-forest and forest stand classification using bands 2, 4 and 5. The determination of the parallel epiped limits are fully based on ground reference information. Therefore the classification results are not influenced by operator inaccuracies in choosing training areas. Table 3 shows the results of the classification. The classification accuracy is defined as the sum of all correctly classified pixels (be it forest and non-forest for the forest versus nonforest classification or coniferous, mixed and deciduous for the stands classification) divided by the total number of pixels in the test site. At first glance, the classification accuracies achieved by performing the radiometric corrections seem to be discouraging. It has to be kept in mind however that the radiometric corrections may improve the classification results in small problem regions, such as sparsely or brightly illuminated areas. For an evaluation whether these problem regions were classified better after a correction, the achieved accuracies for no correction and slope-aspect correction were calculated for the forest versus non-forest case (Table 4) as well as for the stand / forest type classification (Table 5). The slope-aspect correction improves the accuracy of the forest versus non forest classification in faintly illuminated areas, without having an adverse effect on the sunny areas. P. Meyer et al. 14

15 The rather small overall improvement of 1 % is due to the fact, that only 27 % of the pixels are faintly illuminated having a cos(i) < 0.6. In fact if areas having a cos(i) < 0.6 are examined, an average classification accuracy improvement of approx. 5 % is achieved. The reason why there is hardly any classification improvement in well illuminated areas may be, that those regions dominate the test site and as a consequence the determination of the parallel epiped limits. Therefore the classification is optimized on bright areas regardless of a slope-aspect correction. In contrast the positive influence of the slope-aspect correction on the stand / forest type classification is not only visible in the fainter illuminated parts as found in the forest versus non forest classification, but even more pronounced in the brighter areas. Again the illumination classes which are underrepresented in the stand ground reference data are improved. A major improvement between 10 and 30 % could be achieved in the classification of stands in brightly illuminated areas with cos(i) > 0.6. Over the whole training area a total improvement of the stand / forest type classification of 7 % could be reached. In summary it can be stated, that the illumination correction markedly increases the accuracies of both the forest versus non forest, and the stand / forest type classifications. 5. CONCLUSIONS Through the correction of scene related radiometric effects a classification accuracy of forests versus non-forests of almost 90 % could be achieved in the Beckenried test area. The semi-empirical C-correction method improved the classification accuracy of faintly illuminated areas by about 5 %, while the statistical-empirical methods' improvement was inferior and the conventional cosine correction even proved to be useless for our test site. The accuracy improvement in the forest stand / type classification using the C-correction method was between impressive 10 % and 30 % for brightly illuminated areas. In the future one should concentrate more on physical models because the improvement potential of empirical and semi-empirical models is somewhat limited, and because the physical P. Meyer et al. 15

16 models allow for a better description of objects in nature (Deering et al., 1990, Woodham, 1989). More research is encouraged and needed on the bidirectional reflection distribution functions (BRDF) of objects, since the assumption of Lambertian surfaces is not adequate, especially for vegetated surfaces. ACKNOWLEDGEMENTS This project has greatly benefited from the experience gained in the SRSFM-Project (Swiss Remote Sensing Forest Mapping Project) at the Remote Sensing Laboratories of the Dept. of Geography, University of Zurich which started in 1988 and is supported by the Swiss Government, UNEP/GRID and ESA. The permission to use the digital elevation model (DHM-25) for this research, issued by the Swiss Federal Institute of Topography, is greatly appreciated. The results reported here mainly base on the M Sc Thesis' of St. Sandmeier (1991) and R. Sandmeier-Leu (1991). The geometric corrections have been carried out by P. Bitter and T. Kellenberger. P. Meyer et al. 16

17 REFERENCES DFVLR, Waldkartierung mit Satellitendaten im Kartenblatt TÜK 200 Regensburg. Forschungsbericht Nr. 2/88, Deutsche Forschungs- und Versuchsanstalt für Luft- und Raumfahrt (DLR), Oberpfaffenhofen, p. 16. Deering, D. W., Eck, T. F., and Otterman, J., 1990: Bidirectional reflectances of selected desert surfaces and their three-parameter soil characterization. Agricultural and Forest Meteorology, 52: Goodenough, D. G., Deguise, J.-C., Robson, M. A., Multiple expert systems for using digital terrain models. In: Proc. of IGARSS'90 Symposium, Washington D.C., pp Itten, K. I., and Meyer, P., Geometric and Radiometric Correction of TM-Data of Mountainous Forested Areas. IEEE Transactions on Geoscience and Remote Sensing, submitted. Leu, R., Digitale Kartierung des Schweizer Waldes mit Landsat TM-Daten - Teil B. M Sc Thesis, Dept. of Geography, University of Zurich, p. 145 Meyer, P., Empirical quality assessment: effect of resampling on geometric and radiometric data quality using a regionbased approach. In: Proc. of IGARSS'92 Symposium, Houston TA, pp Minnaert, N., The Reciprocity Principle in Lunar Photometry. Astrophysical Journal, 93: NASA, Landsat Data Users Notes. Issue No. 23. Sandmeier, St., Digitale Kartierung des Schweizer Waldes mit Landsat TM-Daten - Teil A. M Sc Thesis, Dept. of Geography, University of Zurich, p. 144 P. Meyer et al. 17

18 Tanré, D., Deroo, C., Duhaut, P., Herman, M., Morcrette, J. J., Perbos, J., and Deschamps, P. Y., Description of a computer code to simulate the satellite signal in the solar spectrum: the 5S code. International Journal of Remote Sensing, 11 (4): Teillet, P. M., Guindon, B., Goodenough, D. G., On the slope-aspect correction of multispectral scanner data. Canadian Journal of Remote Sensing, 8 (2): Teillet, P. M., Image correction for radiometric effects in remote sensing. International Journal of Remote Sensing, 7 (12): Woodham, R. J., and Lee, T. K., Photometric method for radiometric correction of multispectral scanner data. Canadian Journal of Remote Sensing, 11 (2): Woodham, R. J., 1989: Determining intrinsic surface reflectance in rugged terrain and changing illumination. In: Proc. of IGARSS'89 Symposium Vancouver, pp P. Meyer et al. 18

19 FIGURE CAPTIONS Figure 1: Representation of the sun incidence angle i and the solar zenith angle sz. Figure 2: Linear regression of illumination versus original band 2 in forest according to ground reference data. Figure 3: Linear regression of illumination versus band 2 statistic-empirical corrected in forest according to ground reference data. Figure 4: Linear regression of illumination versus band 2 cosine corrected in forest according to ground reference data. Figure 5: Linear regression of illumination versus band 2 Minnaert corrected in forest according to ground reference data. Figure 6: Linear regression of illumination versus band 2 C-corrected in forest according to ground reference data. Figure 7: Uncorrected band 4, test site Beckenried (top) and statistic-empirical corrected band 4, test site Beckenried (bottom). Figure 8: Detail presentation of band 4 within the test site Beckenried. Top left: Statistic-empirical corrected Top right: Cosine corrected Bottom left: C-corrected Bottom right: Minnaert corrected P. Meyer et al. I

20 Table 1: Overview on the tested 'slope-aspect' correction methods including parameters used to correct bands 2, 4 and 5. Table 2: Inclination m, intercept b and coefficient of determination r 2 for regressions between illumination and Digital Numbers in bands 2 1), 4 2) and 5 3). Test site Beckenried, forest after ground reference data (95'356 pixels). Table 3: Accuracies for the forest versus non-forest and forest stand classification before and after the four slope-aspect corrections. Table 4: Accuracies of the forest versus non forest classification in the test site Beckenried in relation to the illumination after slope-aspect C-correction (100 % = 338'624 pixel). Table 5: Accuracies of the stand / forest type classification in the test site Beckenried with respect to the illumination after slope-aspect C-correction (100 % = 338'624 pixel). P. Meyer et al. II