Mapping of Lava Flows Through SPOT Images : an Example

INT. J. REMOTE SENSING, 1997, VOL. 18, NO. 15, 3111-3133

Mapping of lava flows through SPOT images-an example of the Sabancaya volcano (Peru)

A. LEGELEY-PADOVANI?, C. MERING$,$§, R. GUILLANDET and D. HUAMANS 01 ?Office de la Recherche Scientifique et Technique Outre-Mer, laboratoire de Géophysique, 70, route d‘Aulnay, 93170 Bondy France . e $Office de la Recherche Scientifique et Technique Outre-Mer, 213, rue La Fayette, 75010 France §Département de Géotectonique, Université de Pierre et Marie Curie, BP 129, I 75252 Paris Cedex 05, France YGéosciences Consultants, 189, Bd Brune, 75014 Paris, France

(Received Il October 1994; in final fornt 22 May 1997)

Abstract. SPOT XS and Panchromatic images are used jointly in order to map the lava flows of the Nevado Sabancaya volcano (southern Peru). This mapping is achieved through two types of processing: the unsupervised multispectral clustering applied to the XS image, and some specific methods of image analysis (mathematical morphology, convolution filtering) applied to the Panchromatic image. The resulting images allowed us to identify flows and the two main morphological features of the flow areas: lava reliefs and flow lines. Therefore, our experience shows that image analysis can be a tool for thematic mapping which displays more capabilities than photointerpretation.

1. Introduction The mapping of potentially active volcanoes’remainsa basic datum for numerous unstudied volcanic edifices (Bbnneville 1992). Although volcanoes have been mon- itored through remote sensing for a long time, satellite images are used in mapping

*I by geologists and volcanologists mainly through photointerpretation, like the analysis of aerial photographs (Rothery and Francis 1987, Gastellu et al. 1990). The monitoring of volcanoes in eruption is still not widespread. A few publications report the use of thermal infrared sensors to measure temperatures on active volca- al. noes such as Etna (Bonneville and Kerr 1985), Erta Alé, Erebus (Rothery et 1988 a, 1988 b), Lascar (Glaze et al. 1989) and Mount Reboubt (Casadevall 1991). Most of these works are based on the data obtained from the infrared channels of the Landsat Thematic Mapper. At a regional scale, Francis and de Silva (1989) utilized the infrared scenes to identify the potentially active volcanoes in the Central Andes. Few studies have been conducted about mapping. The mapping of the Piton de la Fournaise at La Réunion al. made by Bonneville et (1989) and the works carried out on Etna by Vandemeulebrouck and Parscau (1989) utilized SPOT images for the geological r_ -_ - .__ ! mapping of GTcanoes. it In previous works the-1 Sabancaya volcano has been studied with SPOT imagery r al. P al. i‘ (Chorowicz-%;;-TL et 1992, uaman-Rodrigo et 1993). In particular, the authors 0143-1161/97 $12.00 O 1997 Taylor & Francis Ltd Fbnds Documentaire ORSTQMI 11111 11111 11111 1111 1111 I B Ex: I486 I:. I Cote : *44 4_86 4 31 12 A. Lepeley-Padommi et al. identified the different lava flows on the volcano and their texture by photointerpretation from Panchromatic scenes. These textures produced by the lava reliefs and the flow lines whose orientation is roughly perpendicular to the flow are charac- teristic mainly of Andesitic lavas. The morphological diagram derived from the photointerpretation of SPOT images is given in figure 1. In this paper, we try to demonstrate how image analysis can be helpful for this kind of mapping, and be a complement to photointerpretation and manual drawing. It has been said about mapping of Eolian forms. such as sand dunes, that image al. analysis can be used as a ‘computer-aided photointerpretation’ (Callot et 1994). As a matter of fact, the structuring activity achieved visually by the photointerpreter is replaced to a certain extent by sequences of image transformations. The advantage of image analysis is that the results are reproducible, contrary to those obtained by visual interpretation. Moreover, the results obtained by both methods may be compared systematically from test areas. We suggest that the mapping of the lava flow can be achieved from radiometric and textural information by processing the raw data (XS and panchromatic SPOT scenes) with numerical methods. The resulting map is then compared to the one obtained by photointerpretation.

1991). Figure 1. Morphological diagram of the Nevado Sabancaya (Chorowicz er 01. 1 =lava flow: :=icecap: 3=volcanic dome: -I=crater: 5=rock slope on the Ampato: 6 =scarp: 7 =crest; S =solifluction slope; 9 =drainage network. Mapping lava floivs 3113

2. Study area and satellite data The volcanic structure of southern Peru is aligned NW-SE. Among the numerous quaternary centres of this structure, the historically active volcanoes are: The Huyana Putine whose Plinian eruption on 19 February 1600 spread out over an area of 150km by 60km a volume of tephra reaching 10km3 (Gonzalez-Ferran 1900). The still fumarolic Ubinas entered a very active stage some Uteen times between 1550 and 1969. The fumarolic Misti suffered several eruptions between 1430 and 1878, its fumarolic activities were intense in 1826, 1940 and 1985. The Sabancaya was thought to have suffered an eruption in the seventeenth century and fumarolic activity has been observed since 1986 (Simkin et al. 1981, CERESIS 1989, de Silva and Francis 1990), the last eruption occurred in 1990, from 28 May to 5 June SEAN 1990). The Nevado Sabancaya volcano is situated in Peru (15’47’s and 71’51’W) (figure 2). It is practically coupled with the Ampato (figure 3). It is composed of two juxtaposed domes (Sabancaya I South and Sabancaya II North). The recent morphology of domes, lava domes, blocks of lava flows and crater characterizes it as a young volcano. It covers an area of about 65 to 70km2, the summit represents a volume of 20 to 25 km3 whose lava seems to be essentially basic. The Ampato and . the Sabancaya are covered with icecaps whose areas amount respectively to 8 km2 and 3.4km2. The images and aerial photographs acquired in 1986 and 1989 show that they are completely white. With regard to mapping, our example is representative of young active volcanoes rising to 6000m. The results shown correspond to the analysis of two images. The features of the scenes are as follows:

75s 73g C 719 I - igh I M‘I COROPUNA A

Figure 2. Location of the study area, 1=lake and ocean; 2 =main towns; 3 =main volcanoes. 3114 A. Legeley-Padot~aniet al.

1§"§0

71'90 Figure 3. Localization of Ampato and Nevada Sabancaya volcanoes (extract of topographic map at 1 : lOOOOO of Peru. sheet of Chivay).

(0) We selected a pre-eruption SPOT 1, XS mode scene (path/row: 660/381) of 1 July, with oblique shooting (incidence 35' left), (h) Unfortunately, we did not get any Panchromatic SPOT scene on the pre- eruptive period among the available scenes, we selected the Panchromatic scene (path/row: 661/381) of 8 July 1990, where the angle of incidence was the most similar to the XS scene formerly described (incidence 19" left).

The XS scene reveals limits of different lava flows by multispectral analysis while the Panchromatic one was used to analyse the flow texture.

3. Basic traitement for image processing In order to process the images. we used various types of numerical methods, such as unsupervised multispectral classification. automatic labelling. gradient filtering and mat hematical morphology: the main outlines of these methods are briefly presented in the following.

3.1. Unsirpewisetl mirltisprctrd annlysis Unsupervised multidimensional clustering is used classically for the segmentation of SPOT XS scenes into homogeneous regions. As already known, this type of clustering on multi-spectral data enables the differentiation of the lava flows from the other geological units (Bonneville et (11. 1989). Mapping lava flows 3115

We made use of the K-Means method with moviiig centres (Diday, 1971) whose single parameter to be determined is the number of classes to discriminate. This number depends on the definition likely to be given to mapping. This method is used here for the automatic mapping of only a limited number of classes, as it is more robust when the number of classes are limited.

3.2. Labelling of coitnected components On a binary image, that may be obtained by a multispectral clustering, the

9 connected components are identified through the automatic labelling of a binary image which allows us to code each connected component differently. This technique allows us to select components by labels resulting from automatic w labelling. In the digital image analysis, on a square lattice, there are two primary definitions of connectivity, the 4-connectivity and the 8-connectivity (figure 4).

3.3. Gradient filtering Directional filters of gradient type were used in order to enhance local contrasts on a grey-tone image. In fact, one method used for describing anisotropic textures is to calculate a gradient image with specific filters, such as for example, the Robinson filter (Robinson 1977). The grey level of the resulting filtered image is equal to the grey level of the maximum gradient in eight directions of the lattice. This filter produces another image that we call the direction image, that is, the image where pixels are coded from O to 7 as related to the direction of the optimum gradient.

3.4. Morphological analysis Morphological transformations defined by mathematical morphology (Serra 1988) enable the filtering of noisy binary or grey-tone images. In order to define smooth-outlined patterns like those that can be obtained in mapping through photointerpretation, we made use of this kind of transformations applied on binary images such as the ones generated by clustering, namely, dilation and erosion, and opening and closing. The opening was particularly useful to disconnect the components weakly connected on the classified image. By contrast the closing allowed us to join neighbouring components in order to get a convex hull (Serra 1982). Outside outlines were smoothed by iterations of a niediari smoothing up to idempotence (Wilson 1989) which here is called final smoothing. The geodesic reconstruction cleans a binary image by removing the components covering a small area, without modifying the outlines (Coster and Chermant 1989).

&v4 P1 v2

4-connectivity %connectivity 1 Figure 4. A pixel P1 and its neighbours Vi according to the two types of connectivities. 3116 A. Legeley-Padozmi et al.

It was used several times to remove the noise resulting from the use of basic transformations. It is also used to fill the holes of a connected component (Callot et al. 1994).

4. The mapping of the lava flows The first step of image processing is the multispectral clustering on the XS scene which provides a first discrimination of lava flaw patterns from other units. At the second step the precise restitution of each single unit, which is made through visual analysis and drawing in the classical photo-interpretation, is carried out here through morphological transformations and labelling.

4.1. hlethotlology qf image analysis Like photointerpretation, image analysis is aimed at identifying smaller and smaller units by successive images focused on spatial sets. In order to focus the analysis on the area concerned. a convex region corresponding to what we call the 'lava flow area' (LFA) is extracted from the SPOT XS scene. This method is based on the automatic discrimination of surface conditions corresponding to the LFA as related to its surroundings. Such a method is relevant if this zone is not uniformly covered by snow. ice or even ash in the post-eruptive period. As a matter of fact, the different lava flows display very specific spectral signatures. In a true coloured image, lava flows can be easily distinguished according to their specific colour and therefore, they can be differentiated from their surroundings and among them (Bonneville 1992). The LFA is defined through image analysis by removing external entities on one hand, and by tracing its convex and smooth outline on the other hand. Only the image pixels within the LFA are processed for the mapping.

4.7. Spectral analvsis In the standard true coloured image obtained from the XS channels (figure 5 (a)), one can distinguish in dark green the areas corresponding to the lava flows sur- rounding craters. Therefore. we may consider selecting these zones automatically by multispectral clustering. However, it should be observed that the information in the centre of the zone (close to craters) is partly hidden by snow and ice cover. We made a true coloured image based on the three nen-channels resulting from a Principal Component Analysis (PCA) on the three basic channels (Vandemeulebrouck et al. 1993 I. The PCA, which is also called decorrelation stretching (D-stretching),is generally utilized to enhance the global contrast from multispectral and more generally multisource data (Rothery and Francis 1987). As planned. this transformation results in enhancing contrasts on the central zone (figure 5 (F)). By making a clustering on the basis of original channels through the I(-Means method with moving centres and by setting the number of classes at 11, after having tested the clustering of an increasing number of classes, we made an adequate discrimination of the lava flow zone (figure 5 (c)). We made an analysis of the resulting classes by analysing the coordinates of the barycentres of the classes obtained on the three channels (figure 6). This analysis helped us to make some regroupings of radiometrically similar classes; thus classes 1,5 and 6 were regrouped. I

Figure 5. Determination of the flow zone by spectral analysis. (U) True coloured image on XS channels; (b) True coloured image on neo-channels; c-. (c) Clustering on XS channels (1 1 classes); (d) Clustering on neo-channels (5 classes). c-. 4 il. 3118 Legeley-Pudozwni et al. 300 I 250

200

150

100

0 XS f XS2 XS3

Figure h. Means of the radiometric values (RV)for each class of objects for the three channels.

4.3. Iiirliiliiliinlirtition qflailti ~~CIMJS The automatic mapping of lava flows consists of individualizing them into connected regions. This operation can be carried out only through image transformation.

4.3.1. F~cirsingo71 thc LE4 We determine the study area (the LFA) from the previous results. In order to produce a mask. that is to say a single connected and convex component, we make use of the methods of image analysis aiming at removing the components outside the LFA and at re-connecting the components inside the LFA. Thus in the image clustered on the basis of the XS channels, we select the classes within the zone by labelling the connected component (figure 7 (a))and by removing the units outside the zone (figure 7 (b)). At the end of such a procedure. the shape obtained is not connected enough to r

)* I t w Figure 7. Determination of the mask. (U) Components derived from the clustering on XS channels; (b) Extraction of patterns of the zone of activity E from (a); (c) Components derived from the clustering on neo-channels; (d) Extraction of patterns of the zone of activity from (b);(e) Mask of the zone of activity. 3110 A. Legeley-Padovani et al. create a precise outline for LFA. Therefore we extract the central entity by the following: in order to obtain more detailed information within the central zone, we made the same type of clustering on neo-channels by setting the number of classes to 5 (figure 5(d)).From this image, we selected the classes corresponding to the ice and snow cover. Part of the whole resulting pixels is outside the LFA but it is very weakly connected with the part within this zone (figure 7(c)). The central zone was selected after labelling (figure 7 ((i)).We gathered the two resulting sets (figure 7 (h) and 7(d).In order to get a single connected and convex component which will be the measurement mask, we gathered the components by a closing of large size. After having 'filled the remaining holes', we made a smoothing of the outlines by a final srnnothiiig (figure 7 (e)). 4.3.1. ~rl~i2~i~llc7~i~atioii We filtered the result of the clustering on channels (figure 5(c)) using the LFA (figure 7 (e)). From this image, we selected four groups of similar classes. the similarity being based on the proximity of their barycentres (figure 6): group 1: classes 4, 5 and 6 (figure 7(h)),group 7: classes 3 and 10 (figure 8(a)),group 3: classes 3 and 9 (figure S(h)),group 4: classes 1, 7 and 8 (figure 8(c)). For the purpose of image analysis these four groups are treated separately by the same sequence of processing. First of all. the components of minimum size are removed by a geodesic reconstrtrction. On figures 8(a) and 8(d), one can see the results for group 2. In the general case, the lava flows are individualized by disconnecting the corresponding entities through the morphologica/ opening. In some cases, the ~norphological openirig reduces the areas of components too much. If the entities are already disconnected, it is enough to operate a labelling by %connectivity to separate them (figure 9 (a)).In some cases when too large components remain after labelling (see the green component on the right of the image on figure 9(a)). it was necessary to use a 4-connectivity labelling (figure 9 (b)).Then the holes remaining inside the resulting components are filled by a geodesic reconstrzrction and the contours are smoothed by a median filter. The effect of this processing on the image of group 7 at this step (figure 9(0)) is shown on figure 10(b)).The resulting images of groups 1, 3 and 4 are shown in figures lQ(tz, e, d). 4.3.3. hlqping miil rlirant$cation qf the liiiw $ow rea In order to estimate the internal defects of the method. we quantify both areas which have not been mapped and areas ambiguously classified after processing. This evaluation is done inside the LFA. Table 1 gives the percentage of pixels of the groups. This calculation needs a special recoding of the pixels: each group of lava flows was coded by power of 3 4 (column 3 of table 1 ) so that when groups are summed up (column of table 11, there remains no ambiguity in the interpretation of the resulting codes (column 5 of table 1) in terms of combination of groups. The resulting image provided by the final coding (figure 11 (ti)) is composed by pixels belonging to coded sets representing 90 more than 0.1 of the LFA. From Table 1, we observe that groups do not overlap to a large extent, and that the amount of unrecognized pixels inside the IFA is about 4.64b. The individualized lava flows of the four groups were gathered and are shown in figure 11 (ti). It should be noted that some lava flows overlap and that some zones are not individualized (in black in figure 11 (ti)) within the lava flow zone of the ,

(4 (4 Figure 8. Pattern extraction. (a)Group 2; (b) Group 3; (c) Group 4; (d) Reconstructed group 2. 3122 A. Legeley-Padouani et al.

Figure9. Labelling of the components of group 2. (U) by the 8-connexity; (b) by the 4-connexity (of the green component on (U). volcano. Concurrently, the individualized entities of the four groups obtained by the method described above (§4.3.2.),were gathered, and each lava-flow is represented by a specific colour as shown in figure 11 (b).

4.4. Comparison of results of image analysis and photointerpretation When maps are deduced from supervised classification of multi-spectral data, validation is classically achieved by estimation of well classified pixels inside test areas. In the present work, the cartography is not based on a systematical sampling on the ground so that we cannot use this kind of validation. Moreover, we use some other criteria than radiometry to map the lava flow: once the pixels are classified by unsupervised multispectral analysis at the first step, the structuration of the image is the result of a sequence of image processing such as elimination of noise, disconnec- tion of entities, individualization of entities by labelling and the smoothing of the contours. Photointerpretation has been used as a reference to elaborate such a sequence which consists similarly to visual analysis, of a progressive simplification of the image aiming to individualize forms. Therefore in order to obtain a validation of the method, which has been said to be a computer aided photointerpretation, we have compared the mapping of some forms resulting from image processing with the ones resulting from the cartography achieved by a photointerpreter. In practice, two different photointerpreters have drawn concurrently the contours of four different forms from the initial true coloured image (figure 5 (a))displayed on a graphic monitor. The drawing is achieved with the help of a CA0 program. The same forms have been extracted from the labelled images (figure lO(a) and lO(b)). E" L

Figure 10. Flow individualization. (U) group 1; (b) group 2; (c) group 3; @)-group4. 3124 A. Legeley-Padovani et al.

Table 1. Quantitative analysis of the space distribution of flow groups.

Gathered Initial Recoding of Resulting Interpretation of Number of Final classes coding groups codes resulting codes pixels Percentage coding

Back- 0 0 0 0 295,728 65.51 0 ground 4, 5, 6 1 1 1 1 66.439 42.68 1 3.10 2 2 2 2 37.435 24.05 2 3 1+2 1.200 077 3 2.9 3 4 4 4 28.572 18.35 4 1, 7, 8 4 8 8 8 20708 13.30 5 9 8+1 28 0.02 10 8+2 75 0.05

12 8+4 1.216 0.78 ' 6

Grl Gr2

GrJ Gr4

T- Gr3+Gr4:a00 0 Ga

I I Figure 11. Flow mapping. (U) Grouping of the patterns; (b)Individualization of all the flows.

In figure 12, we show the forms obtained by the two photointerpreters (figure 12(a) and 12 (b))and the corresponding entities obtained by image processing (figure 12 (c)). The error of interpretation between the reference map A and the evaluated map B is defined by the following: A and B are associated to sets belonging to the Euclidean Mapping lava jlows 3125

Figure 12. Comparison between photo-interpretation and image processing for four forms. (a) photo-interpretationby operator 1; (b)photo-interpretation by operator 2; (c) image processing; (d) symmetrical difference between two photo-interpretations; (e) symmetrical difference between photo-interpretation 1 and image processing; (f)symmetrical difference between photo-interpretation 2 and image processing; (g) identification of the four entities. space. The symmetrical difference between set A and set B is the set D defined by: D=(AuB)-(AnB) This measure of set D is the error of interpretation of map B relatively to map A. This formula has been applied to each form separately. Both of the maps done by the photointerpreters are utilized to calculate the error of interpretation by automatic processing. We also calculate the error made by each photointerpreter relatively to 3136 A. Legeley-Padnz1ani et al. the other. By doing so, one can verify whether the error of automatic interpretation can he compared to the manual and visual error of the photointerpreter. For each form the following quantities are calculated and displayed in table 3: the percentage of pixels belonging to both sets (respectively columns 1, 5 and 9). the percentage of pixels of the spimetrical djference between the two sets (respectively columns 2* 6 and lo), the percentage of pixels belonging exclusively to one of the set (respectively columns 3 and 4,7 and 8, 11 and 12). The images of the syminetricrrl diflerence for the three couples of studied maps are displayed in figures 13(d-$'). On these images, the pixels belonging exclusively to each initial set have a specific colour. For three of the forms considered (form 1. form 3 and form 4),one can conclude that the error of interpretation between the two photointerpreters is slightly smaller than the one of automatic interpretation. However on form 3, the error is clearly superior. This could be explained by the following observation: the contours of this form extracted from the classification are very complex with many concavities and looks to be composed of two shapes, one part of the entity being although tightly connected to the other one. Inversely, on the corresponding zone of the true coloured image. the two shapes do not seem to be connected and the photointerpreters have drawn convex contours around one of the entities. More generally. the contours drawn by human interpreters from a true coloured image are more simple and convex than those more complex resulting from a multispectral classification. From this example. where the ground knowledge has not been taken into account, it seems that, although human interpretation contains less errors than the automatic one. it is not reproducible and moreover, needs a continuous visual effort that cannot be achieved in an homogeneous way on a big surface. The error of automatic interpretation is not so big to discard this way of mapping, in particular in the aim of building geographic information systems from satellite images.

5. Texture analysis and mapping of flow directions Lava flows are also characterized by their texture visible on Panchromatic SPOT images. This texture is generated mainly by the mark left by lava reliefs and by flow lines. Here we tried to produce a mapping of lava flows in relation to the texture. The flow textures are particularly visible on the Panchromatic image (figure 131. These textures are due mainly to the lava relief roughly perpendicular to the flow direction and to the flow lines which are parallel to this direction. The processing aims at analysing the texture generated by the lava relief within each flow, and not in the whole image where the texture results both from the flow outlines and the lava relief. The Panchromatic SPOT image has been reset on the XS image in order to utilize the informations obtained at the previous step. that is the LFA (lava flow area) and the mapping of each lava flow. Moreover, on the reset Panchromatic image. a wreath of smoke was selected and masked in the subsequent processings.

of 5.1. Extraction qf iueati directioiis testiire The image to be analysed is obtained by filtering the Panchromatic SPOT image through a binary mask corresponding to a group of flows. Among the four groups of connected entities (figure 10) we show here the processing on the example of group 1 (figure 10(il)).The image in the binary forni was filtered with the image of Mapping lava jlows 3127

Figure 13. Panchromatic SPOT image located on the Sabancaya and Ampato volcanoes.

the wreath of smoke (figure 14(a)).The resulting mask is applied to the image of the Panchromatic SPOT channel (figure 14 (b)). i The filtering of the Robinson gradient applied to this image provides an image with eight codes for the eight local directions of the gradient, as defined on figure 14(d). In order to remove the effects due to the external limit of flows, the latter is filtered again with the binary image. On the basis of the resulting image (figure 14(c))each direction was selected in a binary image. It was observed that this last binary image is composed of a scattering of small agglomerates of pixels. Each image direction is reconstructed after an erosion. Directions are gathered two by two according to the diagonals (figure 15(k)).Each diagonal is processed sequentially as follows: closing, inversion, cleaning by geodesic reconstruction, new inversion, cleaning by geodesic reconstruction and final smoothing in order to improve the outlines. The result obtained by this entirely automatized processing is illustrated from diagonal number 2 in figure 15.

5.2. Mapping of mean directions of jlows In order to represent simultaneously the four diagonals on the same map, we have adopted the same method as in the previous part, that is to say, we raised the w c mh) --.

c- I I 3r*

1 3

4 OX7 6 5

Figure 14. Example about the successive automatic processings on group 1. (a) binary image filtered with the wreath of smoke; (b) panchromatic image filtered with (U); (c) result of Robinson filter on (b);(d) Direction codes. Mapping lava flows 3129

Figure 15. Example of the successive automatic processings on diagonal 2 from group 1. (u) original direction 1; (b)original direction 5; (c) clean direction 1; (d)clean direction 5; (e) direction 1and 5 gathered (diagonal 2); (ficlosing of size 2 with element 5 x 5 on (e); (g) filling of d holes by geodesic reconstruction using an erosion of size 6, with element 5 x 5 as marker; (h)cleaning of g by geodesic reconstruction using an erosion of size 3,Lwith element 5 x 5 as marker; (i)final smoothing of h, (j)Direction codes; (k)New codes after combination to four diagonals. 3130 A. Legeley-Padovani et al.

4 4+1 4+2

$3 .+ (3til 4+(3+21 4+(3+IZ+llJ l#OO n

431 (c)

Figure 16. Combination of the four diagonals. (a) of the four groups; (b) of the four groups after reconstruction; (c) Diagonal code. pixels of the images to the second power so that the resulting image of the sum of the four images could be interpreted unambiguously. Each group having been processed separately, the results obtained for the four groups are then gathered. The last operation consisted of selecting colours so that Mapping lava jows 3131

Table 3. Quantitative analysis of the mapped areas of direction by image analysis.

~~ Initial Recoding of Resulting Interpretation of coding directions codes resulting codes Number of pixels Percentage

1 1 1 1 121.427 25.4 2 2 2 2 81.899 17.2 3 13-2 138.455 29.0 3 4 4 3 8.014 1.7 5 3+1 3.784 0.8 6 3+2 14-302 3.0 1 7 3 +( +2) 5.120 1.1 4 8 8 4 14.655 3.1 Y 9 4+ 1 51.157 107 10 4+ 15 5.018 1.0 11 4+(1+2) 18.064 3.8 12 4+3 6.850 1.4 13 4+(3+1) 2.263 0.5 14 4+(3+2) 3.238 0.7 15 4+ (3 +( 1+ 2)) 3.001 0.6 Total number of coded pixels 427.247 100.0

Table 4. Quantitative analysis of mapped areas of directions after smoothing. Initial Recoding of Resulting Interpretation of coding directions codes resulting codes Number of pixels Percentage

1 1 1 1 108.513 228 2 2 2 2 73.979 15.5 3 1+2 161.296 33.9 3 4 4 3 4.783 1.0 5 3+ 1 1.877 0.4 6 3+2 19.850 4.2 7 3 +( 1+2) 4.036 0.8 4 8 8 4 6.729 1.4 9 4+ 1 64.201 13.5 10 4+ 15 3.800 0.8 11 4+(1+2) 13.859 2.9 12 4+3 7-865 1.7 13 4+(3+1) O 0.0 14 4+(3+2) 3.384 0.7 15 43. (3 + (1+ 2)) 1.783 04 Total number of coded pixels 475.955 100.0

the resulting map (image and legend) could be readable (figure 16 (a)).This selection aims at representing the codes of pixels corresponding to one diagonal with a pure colour and ones corresponding to combinations of two or more diagonals with an intermediate colour. Table 3 gives the stepwise coding of the pixels (columns 1, 2 and 3), the interpretation of the resulting codes (column 4) and the distribution of pixels of the image according to the resulting codes (columns 5 and 6). The fourth column shows for each code either a diagonal (a single code) or a combination of diagonals. It can be observed from table 3, that if some combinations of diagonals are statistically significant, for instance, the combination of diagonals 1 and 2 (see code 3), it is far from being the case for all of them (see code 13). 3132 A. Legeleji-Padoilmi et al.

In order to remove the isolated pixels on the resulting image (figure 16(a)),and to make this image more readable, we make use of a iiizilticlass geodesic reconstrirctioii (Simonneaux 1995) (figure 16(h)).We calculated the distribution of pixels within this new image and the results are shown in table 4. By comparing both tables, it should be noted that the distribution of pixels according to classes was not greatly modified by the riiirlticlass geodesic reconstrirctiun.

6. Conclusions The processings used allowed us to individualize the two main morphological features of the flow areas: lava reliefs and flow lines. It sheuld be noted that given the same flow direction, two lava flows will show either lava reliefs (perpendicular to the flow direction) or flow lines (in the flow direction), sometimes even both within the same lava flow. These differences are due to variations in slopes or viscosity which produced a rate of variable flow and chilling. Lava reliefs or curved flow lines are represented by codes showing the rotation of directions. The code transitions correspond once more te transitions of slopes. or of the flow direction, or of the underlying substratum. The use of SPOT scenes to analyse active volcanoes allowed us to obtain some geological information about regions where the distance, the volcano elevation (6900m) and the lack of logistics make it very difficult to access. The methods of scene analysis shown here should help to evaluate hazards in cases of eruption and eventually to reconstruct the history of the volcano. Part of the technique of mapping from image analysis demonstrated here was made by analogy with photointerpretation methods. We show that. although for some details, the results can be less accurate than those obtained by visual interpretation and manual drawing. this approach has the advantage of being entirely reproducible. In order to individualize lava flows. we used some transformations of the image which allowed us to separate components, to smooth outlines as is done with visual interpretation. By contrast, the mapping of lava flows as related to their texture which has been automatized has not been made according to visual perception. Therefore, our experience shows that in some cases, numerical image analysis can be a tool for thematic mapping even when visual analysis is of no use. Generally, we have tried to show here through the example of the mapping of lava flows that image analysis can be an aid to mapping from satellite image.

Acknowledgments Image analysis and display have been produced with the packages PLANETES ( L.I.A. ORSTOM-Bondy) and OSIRIS V2.O-LATlCAL/ESTEL 1.

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