**3.2.3 Automatic process: TIR- VNIR method**

208 Remote Sensing – Applications

Well class area Real lava flow area ∗ 100

The mean distance separating outlines vectors is calculated from the area which was not correctly classified, divided by the smallest of the two compared lava flow outline's

The « referenced » outlines have been realized on a period from 1980 to 2010, by vectorization on the base of IGN aerial data from 1997 and 2003. As for the eruptions post August 2003, the research was done on a base of SPOT THR, or SPOT 4 AND 5 satellite data (Figure 4). The vectorization of the lava flows by using aerial photos is one of the most precise (Paparoditis et al. 2006), but it is also one of the most binding to realize. It could ask

Fig. 4. Piton de la Fournaise lava flow cartography between 1980 and 2010 (Servadio et al.

For the entire French territories, the temporal recurrence in the IGN's acquisitions is 5 years. It is a problem when dealing with superimposed lava flows. Satellite imaging is then a complementary tool because, even if the spatial resolution of SPOT data is lower (2,5m to 20m with optical SPOT data, or inferior or equal to 1 meter with aerial photos delivered by IGN for the BDORTHOs), the temporal resolution grants data between eruptions and defines outlines for the superimposed zones. Unfortunately, the resolution of the outlines is

Perimeter ∗ 100

�rror � � <sup>∑</sup> Not correctly class area

Pm �lava� �

perimeter:

**3.2.2 Photo interpretation** 

for several worked days.

2008 modified).

The lava flows which implement in zones where spectral properties are different, such as a vegetation zone or a soil that presents high spectral reflectances, are the easiest to identify (figure 5).

Fig. 5. Examples of automatic extraction of the lava flow outline when the reflectance of the substrate is very different from that of the lava. A and B: lava flow from the eruption of September 2009. C and D: lava flow from the eruption of 1986 off enclosures.

To automatically extract an outline, different methods can be used: classifications, threshold, or automatic detection of change (Inglada et al., 2003; Habib et al., 2007). The distance between the referenced outlines and the outlines automatically extracted is then proportional to the pixel's size, and the lava flow's classification precision is between 95% and 99%. On the other hand, lava flows which implement in low spectral reflectance zones, such as the central cone of the Piton de la Fournaise or the upperstream part of the Grandes Pentes, ask for more complex treatments. A data treatment methodology is then put together by using ENVI software (Figure 6). The visible data Principal Composant Analysis (PCA) is applied in order to maximize the data's anti-correlation. The thermal bands, near

Automatic Mapping of the Lava Flows at

Table 3. Continued

Piton de la Fournaise Volcano, by Combining Thermal Data in Near and Visible Infrared 211

Fig. 6. TIR-VNIR Treatment sequences

infrared and the PCA are grouped in a multi-band data. A multi-level binary classification named decisional tree classification is then applied by using the following steps:


The seven lava flows for which ASTER data were available (table 1) were tested. For half of them, the tested eruptions are on a weak slope zone, with a substrate composed of lava flows with similar spectral properties; the other half shows various substrates and slopes (figure 7).

Fig. 7. Localization of the studied lava flows


Table 3. Continued

210 Remote Sensing – Applications

infrared and the PCA are grouped in a multi-band data. A multi-level binary classification

Discretization of the bare soil and covered vegetation zones by using the Near Infra-red

The seven lava flows for which ASTER data were available (table 1) were tested. For half of them, the tested eruptions are on a weak slope zone, with a substrate composed of lava flows with similar spectral properties; the other half shows various substrates and slopes (figure 7).

named decisional tree classification is then applied by using the following steps:

By using thermal data, distinguish « hot » from « cold » zones.

Classify the pixels with low anti-correlation values.

Fig. 6. TIR-VNIR Treatment sequences

Extract and keep only « hot » zones.

Fig. 7. Localization of the studied lava flows

band.


Table 3. Error matrices of outlines obtained by photo-interpretation, comparing those from the photo-interpretation of aerial photographs with those of the different types of satellite data.

Automatic Mapping of the Lava Flows at

Fig. 8. Automatic extraction of lava flows outlines.

on the seven tested objects localized on figure 7.

**4. Results and interpretations** 

hour, once the data collected.

Piton de la Fournaise Volcano, by Combining Thermal Data in Near and Visible Infrared 213

The classification achieved, it is then possible to export the « lava flow » class as a vector that represents the outline of the lava flow (figure 8). The extraction is realized in less than an

In order to validate the outlines automatic extraction method, each extraction result has been compared to the referenced outline. The error matrixes (table 4) represent the tests run

The error matrixes enable calculating global errors of classifications between 77% and 96% (table 4). We can observe a disparity between the summit zone eruptions and those implemented on sloppy substrates with various spectral properties. The first ones show a global precision between 77% and 88%, whereas the others variate from 91% to 96%. This is partly due to low reflectances observed for the substrate at the summit of the volcano. For example, the outlines of a lava flow newly implemented are hardly distinguished from the intra Dolomieu lava effusion zone that presents similar ages. The mean precisions show the same disparity, with values included between 77% and 84% at the summit zone, and included between 86% and 90% for the lava flows situated on the cone's flanks and on the slopes. The area of the lava flows also play a role in the classification precision's difference, The classification achieved, it is then possible to export the « lava flow » class as a vector that represents the outline of the lava flow (figure 8). The extraction is realized in less than an hour, once the data collected.

Fig. 8. Automatic extraction of lava flows outlines.
