ERS COHERENCE AND SLC IMAGES IN FOREST CHARACTERISATION

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1 ERS COHERENCE AND SLC IMAGES IN FOREST CHARACTERISATION Manninen, T. (1), Parmes, E. (1), Häme, T. (1), Sephton, A. (1), Bach, H. (2) and Borgeaud, M. (3) (1) VTT Automation, Remote Sensing P.O. Box, 134, FIN-244 VTT, Finland Tel , Fax (2) Vista Luisenstr.45, 8333 München, Germany Tel , Fax (3) European Space Agency ESTEC-TOS-EEP, Postbus 299, 22 AG Noordwijk, The Netherlands Tel , Fax INTRODUCTION The coherence of an ERS-1/ERS-2 tandem pair of summer and winter conditions was studied in Finland and Germany in project "The Retrieval of Geo- and Bio-Physical Information from Remote Sensing Data" [1]. Frozen conditions coherence turned out to correlate rather well with many boreal forestry parameters and relationships between the standwise mean coherence and various forest parameters were derived. The root mean square error of stem volume estimate was 54 m 3 /ha for stands larger than 2 ha. Clear cuttings of various ages were easily identified in the coherence image. The results support clearly those obtained in Sweden [2] using a coherence pair of the same days. In the multitemporal ERS SLC-images the clear cuttings can be detected naturally from abrupt decrease in intensity. However, the intensity minimum does not match the time of clear cutting, because after the tree removal most of the clear cuttings are full of logging residue and some of them have been tilled with quite a wide spacing. So the intensity decreases still during a few years, before it starts to increase again with increasing stem volume. The geolocation accuracy turned out to be crucial for both the interferometric and multitemporal analysis in the Finnish test site, because the typical stand size is less than 2 ha and the stand shape is often elongated. In the worst case the stand width was not larger than the coherence image pixel. A new pattern matching method developed in VTT was crucial in getting sufficient geolocation accuracy. TEST SITES Two test sites were used: one boreal and one temperate. The boreal forest test site is in south-eastern Finland (61.3 N, 28.8 E). It is dominated by pine, spruce and mixed forest with stem volume values upto about 45 m 3 /ha. Climatically the test site is situated at the border between the cool-temperate zone of sub-continental climates and the

2 Landsat TM RGB(543) with stand map ( VTT, Stora-Enso and Eurimage 1997) Fig. 1. An extract of about 2.2km x 1.8 km of the Finnish test site. Optical mosaic with stand map ( VTT and Stora-Enso) cool-temperate zone of continental boreal climates with an annual mean temperature of 4 C and an annual mean precipitation rate of 6 mm. The area is mainly in forest production use consisting of forest stands of different age, size and species composition. Besides forests the area contains lakes, peatland and farm fields. Basic forestry parameter values were available for more than 1 stands. Also Landsat TM images and an optical mosaic image with a resolution of 1 m were used (Fig. 1). The temperate forest test site is in the Upper Rhine Valley close to the city of Freiburg in Germany (48 N, 8 E). The forest is mainly deciduous and contains 18 different classes of tree species, for example poplar, oak, spruce, birch and maple. The stem volume values reach up to about 6 m 3 /ha. The mean annual temperature is 1 C and the mean annual precipitation is 65 mm. Besides forest the test site contains agricultural and urban areas. Basic forestry parameter values were available for more than 35 stands. Also Landsat TM images were used. SAR DATA Interferometric ERS SAR image pairs were available both for summer and winter conditions for the German test site and for winter in the Finnish test site. However, the winter temperature of the nearest weather station of the German test site was as high as -1 C, which is so close to the freezing temperature of water, that the soil may not have been completely frozen. Especially since the salts and sugar dissolved in the water to be found in nature decrease the freezing temperature depending on the concentration of the solution. The Finnish winter temperature was well below the freezing point. For the Finnish test site also a multitemporal data set consisting of seven ERS SAR SLC images from February 1993 to August 1997 was available, four for wintertime and three for summertime. Due to the clearly anisotropic topography the only ascending pass image could not be used together with the six descending pass images. In addition, the weather conditions affected the contrast between land and water areas of some of the images (Fig. 3), so that their geolocation uncertainty became too large compared to the stand size. In principle winter images would have been better than summer images due to frozen conditions, but only one of them had good contrast. Therefore in the end only the two best images of the same season were used in the multitemporal analysis. Fortunately the images were from early summer (June 13, 1993 and June 11, 1997) so that they represented the conditions before the actual growth season. The uncertainty of geolocation was a severe complication for both the interferometric and multitemporal analysis. In about half of the images of the Finnish test site the contrast between lakes and land area was too small to permit precise geolocation. On the other hand the typical stand size was less than 2 ha and the shape often elongated (Fig. 2), so that in the worst case the width of a stand was of the order of the coherence image pixel size. The ordinary control point picking approach for rectification turned out to be impossible also for the best SLC images largely due to the speckled

3 Fig. 2. Examples of stand shape in the Finnish test site ( VTT and Stora-Enso). character of SLC images. Therefore a pattern matching technique developed recently in VTT was really crucial in obtaining good enough geolocation. And even then the pattern matching required the use of texture images instead of intensity images. The texture images were obtained from regressing the logarithm of the standard deviation of intensity with the logarithm of distance [4]. The texture parameter used was the constant term log(c 2 ) of that regression. 28 February, 1993, intensity ( ESA) 13 June, 1993, intensity ( ESA) 28 February, 1993, texture parameter c 2 13 June, 1993, texture parameter c 2 ( VTT and Sotra-Enso) ( VTT and Stora-Enso) Fig. 3. A small part of two SLC intensity images and corresponding texture images (Fig. 1). The stand map is overlaid on the texture images. The dark features in the June images are lakes.

4 COHERENCE RESULTS Summer The three summertime coherence images tested for the German test site discriminated easily the forested areas (as expected), but did not reveal any information inside the forest. Not even the discrimination of coniferous and deciduous species was possible. Probably the large number of tree species in the test area complicated this task. The discrimination between forested, agricultural, urban and water areas became more evident by combining the coherence image with the texture (Fig. 4). The SLC intensity image was too speckled for that purpose. Fig. 4. The combination of coherence (red channel) and the logarithm of the texture parameter c 2 (green channel) of the Rhine Valley test-site on 9-1 July The forest appears green, urban areas yellow and water black. ( Vista and VTT.) Winter The wintertime coherence turned out to have a clear relationship with the forest parameters. Relationships between the coherence and various boreal forest parameters were derived on the basis of standwise (partly pointwise) ground measurements of roughly 1 stands. The coherence values to be compared with the forestry data were obtained in two ways. Firstly, they were obtained as means of 3 to 6 pixels within the stand. The use of only large stands excluded cases where these pixels would be at the edge of the stand. This has two advantages: 1) The edge pixels are often mixed pixels and 2) the tree properties at the edges of a stand are typically different from those of the main canopy. Secondly, standwise mean values were calculated using the stand vector map. The correlation of these two mean coherence values is not extremely strong (R 2 =.68), although linear as it should be. Therefore it is probable that slightly different results will be obtained using these two methods. It turned out that the standwise mean coherence value produced slightly better results than the average of 3-6 central pixels, but the difference is not crucial. It is probably due to the rather heterogeneous character of the forest stands. At first the relationships were studied using only stands larger than 5 ha in order to reduce the effect of the coherence pixel size (4 m) at the stand edges and the effect of the geolocation errors (Fig. 5). The diameter, height, basal area, age and stem volume all have a decreasing dependence on the increasing wintertime coherence (Fig. 6 - Fig. 7). The shape of the dependence does not markedly change if only the 159 stands larger than 5 ha or the 417 stands larger than 2 ha are taken into account. The diameter, height and age have rather similar dependence on the coherence: linear except the sparse stands with seed trees. The stem volume and the basal area, on the other hand, behave similarly with each other, but differently from the other parameters: the relationship has so much scatter that the visual impression is not linear, and the sparse stands do not show up as outliers. All these relationships are also affected by changes in other parameters, such as number of trees per ha, which has strongest effect on the age relationship. To exclude clear cuttings

5 Standwise mean coherence (%) clear cut old forest Stand size (ha) Fig. 5. The effect of the stand size on the spread of the values of the standwise mean coherence of the Finnish test site. with just a few seed trees from the data set used for determining the regression parameters, the number of trees per ha has been checked. The lower limit for the number of trees has been derived visually, so that obvious outliers should be excluded. Because the number of trees affects different parameters differently, also the limit varies. The wintertime coherence can also be used as an indicator of clear cuttings [5] due to its dependence on the forestry parameters. No systematic distinction of coniferous and deciduous forests was found in coherence images. Also no tree species could be detected using coherence images. Stand area larger than 5 ha Stand area larger than 2 ha Number of trees >53 per ha Number of trees < 53 per ha Linear (Number of trees >53 per ha) Number of trees >53 per ha Number of trees < 53 per ha Linear (Number of trees >53 per ha) Diameter 1997 (cm) Diameter 1997 (cm) y = x R 2 = y = -.561x R 2 = Mean coherence of the whole stand 1996 (%) Mean coherence of the whole stand 1996 (%) Fig. 6. The relationship between the measured diameter and coherence for stands larger than 5 ha and 2 ha.

6 Stand area larger than 5 ha Stand area larger than 2 ha Stem volume 1997 (m3/ha) Number of trees >53 per ha Number of trees < 53 per ha Linear (Number of trees >53 per ha) 5 y = x R 2 = Mean coherence of the whole stand 1996 (%) Stem volume 1997 (m3/ha) Number of trees >53 per ha Number of trees < 53 per ha Linear (Number of trees >53 per ha) y = x R 2 = Mean coherence of the whole stand 1996 (%) Fig. 7. The relationship between the measured stem volume and the coherence for stands larger than 5 ha and 2 ha. The standwise mean estimates for the forest parameters obtained using standwise mean coherence values are given in Table 1. For the stem volume, height and diameter the errors are rather small. In fact, the stem volume estimation error is of the order of the error of the ground measurements. The variation of the magnitude of the difference between the estimated value and the ground truth is given in Table 2. Even the upper limit of the stem volume error range is of the order of the ground truth accuracy. Both tables reveal the effect of the stand size on the estimation accuracy. the error increases when the stand area is less than 2 ha, which is understandable, since among them there are many stands containing just one or two coherence pixels. Table 1. Root mean square errors of estimated forest parameters using regression equations. Clear cut stands are excluded. Root mean square error Diameter (cm) Height (m) Stem volume (m 3 /ha) Basal area (m 2 /ha) Age (Years) Stands larger than 5 ha Stands larger than 2 ha All forested stands Table 2. The upper and lower limits of the 1 %... 9 % range of the magnitudes of the differences between the estimated and measured forest parameter values. Clear cut stands are excluded. Magnitude of error Diameter (cm) Height (m) Stem volume (m 3 /ha) Basal area (m 2 /ha) Age (Years) 1 % 9 % 1 % 9 % 1 % 9 % 1 % 9 % 1 % 9 % Stands larger than 5 ha Stands larger than 2 ha All forested stands

7 The results of the Finnish test site were compared with a similar study carried out in Sweden [2] using a coherence pair of the same days. Although the stand structure in the Finnish test site is more heterogeneous the results support clearly the results obtained in Sweden: many forest parameters (diameter, height, stem volume, age) are correlated with the coherence. The wintertime results of the German test site do not contradict the results of the Finnish test site, but the support is more or less qualitative, because of large scatter. Reasons for the scatter are: 1) the German test sites contained many more species, 2) the number of stands was so much smaller that it was not possible to exclude small stands from the analysis and 3) the wintertime German interferometric pair corresponded only marginally to freezing conditions. Also the German coherence decreases with increasing diameter, but the regression equations for the Finnish data set do not match directly the German data. But here one has to remember, that the species of the Finnish and German data sets are different, so that also the slope for the linear relationship between height and diameter is completely different for these two data sets. Indeed, from the point of view of wind force, the two data sets behave quite similarly. The main cause for low coherence within a short time range can be assumed to be the movement of the trunks, branches and leaves or needles. The displacement of the top caused by a force acting perpendicularly to a cylinder bound from its bottom depends (besides the force and the elasticity module) on the ratio h 3 /d 4, where h is the height and d the diameter of the cylinder. For a tree trunk this ratio is comparable essentially to 1/d, because of the monotonous almost linear interrelationship of h and d. Thus the larger tree the smaller ratio h 3 /d 4. Since the size and number of branches and volume of leaves or needles is proportional to the size of the trunk, the total movement of the tree should also be related approximately to the ratio h 3 /d 4. And the total movement is naturally the larger the more there are components to move. Hence it is useful to study the relationship of the ratio h 3 /d 4 and the coherence. Obviously the coherence increases with this ratio, except for the very small trees (Fig. 8). This is natural, since their total movement is little compared to the whole stand. The two test sites produce quite similar results, despite the fact, that the Finnish test site is mainly coniferous and the German test site mainly deciduous and the structure of the species is quite different (Fig. 9). Thus it seems that wintertime coherence really has general dependence on forestry parameters. 1 Height 3 /Diameter 4 (1/cm) Finnish test site, tree height m Finnish test site, tree height 1 15 m Finnish test site, tree height 5 1 m Finnish test site, tree height m Finnish test site, tree height m German test site, tree height m German test site, tree height m Mean coherence of the whole stand (%) German test site, tree height m Fig. 8. The relationship between the ratio h 3 /d 4 and the standwise mean coherence for trees of various heights in the Finnish and German test sites. Only stands larger than 5 ha are included in the Finnish data set.

8 Height 3 /Diameter 4 (1/cm) Mean coherence of the whole stand (%) Finnish test site, pine > 5 % Finnish test site, spruce > 5 % Finnish test site, birch > 5 % Finnish test site, mixed German test site, robinia German test site, hornbeam German test site, douglasie German test site, mountain maple German test site, black poplar German test site, ashtree German test site, common alder German test site, red oak German test site, common oak Fig. 9. The relationship between the ratio h 3 /d 4 and the standwise mean coherence for various species in the Finnish and German test sites. Only stands larger than 5 ha are included in the Finnish data set. MULTITEMPORAL SLC IMAGE RESULTS In the ERS SLC-images it was not trivial to distinguish all the clear cuttings, although some of them could be found as easily as agricultural areas [6]. One reason is that many of the clear cuttings are in areas of quite rough topography with rocks and cobble deposit. Another reason is that after the tree removal most of the clear cuttings are full of logging residue and some of them have been tilled with quite a wide spacing. So in many cases there was a natural cause to cancel out the decrease of backscattering due to the clearance of trees. In fact it turned out that right after the cutting the area causes more backscattering than after a few years, because the logging residue and tillage effects disappear with time. Then again the backscattered intensity increases with increasing stem volume (Fig. 1). The two regression equations for the intensity and texture parameter c 2 shown in Fig. 1 were used for clear cutting retrieval in the Finnish test site. Each point was allocated to the class accordin to the regression line it was closer to. The probability of belonging to either class was taken to vary from 5 to 1 % beginning at the middle of the two lines and ending on either line. Both methods misclassify 6 stands out of the 73 stands (i.e. 8 %). Four stands are misclassified by both methods. No marked quality difference between the two methods is detected. A larger data set (and more errors) would be needed for that. The results were checked using an optical mosaic (2 m resolution) of the same area. Thus many causes of possible misclassification have been found. One example is stand number 291, which is classified with a 1 % probability to forest by both the intensity and texture based method (Fig. 1). This stand turned out to contain a few very large rocks, which obviously cause a lot backscattering. Evidently topography causes problems for clear cutting detection, but also very different looking stands result in erroneous clear cutting classification. One evident risk for misclassification is also a complicated shape of the stands. This stresses again the importance of very accurate geolocation.

9 7 6 y =.7799x R 2 = y =.7785x R 2 =.5631 c Intensity y =.4181x R 2 = y =.494x R 2 = c Intensity 1993 Height difference > -5 m Height difference < -5 m old forest Height difference -15 m m, diameter < 3.9 cm Stand 291 Linear (Height difference > -5 m) Linear (Height difference < -5 m) Latter intensity or c2 clear cutting Earlier intensity or c 2 Fig. 1. The relationship between the intensity and the texture parameter c 2 of the years 1993 and The clear cuttings are distinguished by the large decrease of height (at least -15 m). The trees in former clear cuttings have a smaller decrease of height (-5 m or less) and a canopy of small diameter (less than 3.9 m). The clear cut stand number 291 is a very rocky one. The evolution of the backscattered intensity and its texture parameter c 2 of a forest from the time of clear cutting is sketched as well. SUMMARY Regression equations were derived for standwise mean forestry parameter retrieval from wintertime ERS SAR coherence images. For boreal forests the root mean square errors in stands larger than 2 ha were for stem volume, height and diameter estimation 54 m 3 /ha, 3.8 m and 4.7 cm respectively. The dependence of the coherence on a parameter related directly to the movement of the trees caused by wind was similar in the Finnish and the German test sites, which consisted of completely different species. Thus wintertime coherence has potential in forest parameter retrieval, but more data would be needed to verify its usefulness in practice.

10 Clear cuttings can be detected using multitemporal ERS SLC images, in fact just two images suffices, but their detection is easiest a few years after the cutting, because at the time of cutting the stands are full of logging residue and tillage makes the surface very rough. Rough topography and corbelled-out stand shape cause difficulties in clear cutting discrimination. The latter can be helped with improving geolocation methods, but the very rocky stands will still easily be mixed with forests. In the choice of the SLC images the number of images is less stringent than the good contrast. In the Finnish test site 92 % of 73 stands were correctly classified as forest or clear cutting. ACKNOWLEDGEMENTS The authors are grateful to M.Sc. Kaj Andersson for the pattern matching software used for the geolocation. They also wish to thank Ms. Brita Veikkanen and Ms. Gudrun Lampart for the preprocessing of the satellite image data. The forest ground truth for the Upper Rhine Valley was gratefully provided by the University of Freiburg (Prof. B. Koch, Abteilung Fernerkundung und Landschaftsinformationssysteme). The data were gathered in 199 by the "Forstamt Freiburg" in the frame of the routinely conducted "Forsteinrichtung". The forest ground truth for the Finnish test site was provided by Stora-Enso. The ERS SAR data was provided by ESA. The project was funded in ESA Contract Ref.: 1295/98/NL/GD. REFERENCES [1]. H. Bach, K. Schneider, W. Mauser, R. Stolz, T. Manninen, T. Häme, H. van Leeuwen, L. Schouten and W. Verhoef, "The Retrieval of Geo- and Bio-Physical Information from Remote Sensing Data", Final Report, in press. [2] J. Fransson, "Analysis of Synthetic Aperture Radar Images for Forestry Applications", Acta Universitatis Agriculturae Sueciase, Silvestria 1, Doctoral Thesis, Paper III, Umeå, 17 p, [3] G. Smith, P.G.B. Dammert, M. Santoro, J.E.S. Fransson, U. Wegmüller and J.I.H. Askne, Biomass retrieval in boreal forest using ERS and JERS SAR, Proc. Second Int. Workshop on Retrieval of Bio- and Geo-physical Parameters from SAR data for Land Applications, Noordwijk, October, pp , [4] A.T. Manninen, Distinguishing Open Land Areas from Forest Using Single ERS SAR images, Proc. IGARSS 98, Seattle, 6-1 July 1998, pp , [5] T. Strozzi, P. Dammert, U. Wegmüller, J.-M. Martinez, A. Beaudoin, J. Askne and M. Hallikainen, European Forest Mapping with SAR Interferometry, Proc. Second Int. Workshop on Retrieval of Bio- and Geo-physical Parameters from SAR data for Land Applications, Noordwijk, October, pp , [6] S. Quegan, T. Le Toan, J.J. Yu, F. Ribbes and N. Floury, Estimating forest area with multitemporal ERS data, Proc. Second Int. Workshop on Retrieval of Bio- and Geo-physical Parameters from SAR data for Land Applications, Noordwijk, October, pp , 1998.