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.

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