The study area is the Sub-Saharian Africa. According to the IGBP vegetation map the major vegetation types present in the area include savanna and woody savanna, evergreen broadleaf forest and limited part of cropland. A mosaic of daily images acquired by SPOT-VGT covers the area ( Figure 1), from October 1999 to March 2000, corresponding to the dry season when fire activity is more intense and spreads across all the major biomes in the area. The data used are daily VGT-S1 products (http://www.spot-vegetation.com/).

Abstract A methodology, based on the use of Multi-Layer Perceptron (MLP) neural network, has been developed in the framework of the GBA-2000 (Global Burnt Areas) project whose aim is to produce global burned area maps for the year 2000 from SPOT-VEGETATION (VGT). The approach allows to exploit not only the spectral information of the observed targets but also the spatial and temporal relationships of the phenomenon. The study area corresponds to the Sub-Saharian Africa, covered by a mosaic of daily SPOT-VGT images. Validation of the burned area maps obtained at different dates has been conducted by the comparison with visual classification of low resolution data and with automatic classification of Landsat-ETM+ scenes acquired over the study area in correspondence with the SPOT-VGT acquisitions.

Classification - The approach proposed is a contextual classification strategy based on Multi-Layer Perceptron (MLP) which is used as a soft classifier considering the values of the output neurons as final partial membership to classes. To detect burned pixels in daily images the MLP model is inserted within a two stages classification strategy (Figure 2). In the first stage each image of the time series Ij…Ij+k is classified separately according to a pixel-by-pixel classification scheme. Results of the first classification stage are soft maps for each image processed indicating the gradual memberships of pixels to the corresponding classes. The series of soft maps related to burnt class are integrated by applying the median operator, pixel-by-pixel. The resulting median map synthesises information about the temporal variation of the pixels and reinforces or attenuates the

MethodsMethods

ReferencesE. Binaghi, P.A. Brivio, P. Ghezzi, A. Rampini and E. Zilioli, “Investigating the behaviour of neural and fuzzy-statistical classifiers in sub-pixel land cover estimations”, Canadian J. Remote Sensing, Vol. 25, n. 2, pp. 171-188, 1999. P. M. Barbosa, J. M. Gregoire and J. M. Pereira, “An algorithm for extracting burned areas form time series of AVHRR GAC data applied at a continental scale”, Remote Sensing of Environment, Vol. 69, pp. 253-263, 1999. J. D. Paola and R. A. Schowengerdt, “A review and analysis of back-propagation neural networks for classification of remotely-sensed multi-spectral imagery”, Int. J. Remote Sensing, Vol. 16, n. 16, pp. 3033-3058, 1995.

Pietro Alessandro Brivio (1), Marta Maggi (1), Elisabetta Binaghi (2), Ignazio Gallo (2), Jean Marie Grégoire (3)

Exploiting spatial and temporal information for extracting burned areas from time series of SPOT-VGT data

(1) Istituto per il Rilevamento Elettromagnetico dell’Ambiente, CNR, Milano, Italy(2) Istituto per le Tecnologie Informatiche Multimediali, CNR, Milano, Italy(3) Institute for Environment and Sustainability, JRC, Ispra, Italy

• To develop a methodology for the detection of burned areas from coarse resolution imagery based on neural networks

• To fully exploit the spatial and temporal information of daily SPOT-VGT data.

ObjectivesObjectives

Spectral profile of two savanna environments

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A fire in a savanna environment Spectral profile of a burnt and unburnt savanna

Figure 2. Diagram showing the classification strategy used for burned area mapping in SPOT-VGT time series of data.

strength of membership to burnt class in the final map. The second stage neural classifier is configured to receive in input I j pixel spectral values and values of a 3x3 window drawn from the median map and centred on the co-ordinates of the pixel to be classified.

probability to be burnt

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SPOT-VGT image (RGB: swir,b3,b2) of 03-03-2000 around Bangui(CAR) and burnt area map.

Validation of the ResultsValidation of the Results

Region A B C

OA% - Kappa 82.3% - 0.57 83.8% - 0.67 87.0% - 0.73

Table 1. Overall accuracy and Kappa coefficient of agreement for three geographic regions of the 03-03-00 burnt area map.

Figure 3. (a) Two examples of burnt areas estimated on VGT (red) and ETM+ (yellow) data (03-03-00) (b) Regression line between low and high burnt area maps

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MLPneural network

Soft maps forburnt class

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Algorithm performance was assessed through visual interpretation of low resolution data. Table 1 shows accuracy indices obtained for three geographic windows covering major vegetation ecosystems.

Final products accuracy was evaluated by comparison with automatic classification of high resolution data (Landsat-ETM+) (Figure 3a). The regression line between proportions of burnt areas in 15x15 km cells extracted from low and high spatial resolution data (Figure 3b) shows a correlation of 0.6 and a angular coefficient of 1.1

y = 1.1391x - 4.1888

R2 = 0.6055

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Figure 1. The mosaic of SPOT-VGT daily images covering the study area. A, B, C validation windows