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STOP FEU, Oran 2010 Orales Thème IV O.4.4 Forest fires smoke monitoring from SeaWiFS sensor images A.Hassini ac , S.Deajean b , A.H.Belbachir a a Laboratory of Application and Analysis of Radiations, Department of Physics USTOMB. El M’nouer B.P.1505 Oran, Algeria. Tel: +213 74374379, Fax: +213 41560350 b Institute of Mathematics of Toulouse, UMR CNRS 5219, University Paul Sabatier, 31062, Toulouse cedex 9, France. c Institute of Maintenance and Industrial Security, University of Oran, Algeria. Correspondence to: A. Hassini (hassini.abdelatif@univ-oran.dz) Monitoring of active fires allows the prediction of spread of fire fronts to areas with values at risk. The forest fires smoke can be used to assist in fire fighting prevention measures by indicating wind patterns, vegetation changes and other cumulative factors. A method for detecting forest fires smoke using SeaWiFS (Sea-viewing Wide- Field-of View Sensor) images is developed in this paper. Because the long field of view in the visible spectrum compared with NOAA – AVHRR (Advanced Very High Resolution Radiometer) sensor , multiregional coverage (polar orbit satellite) compared with GOES and MSG satellites (geostationary satellites), and, large spectral window of each channel compared with MODIS satellite, SeaWiFS sensor is selected better for the extraction of Forest Fires Smoke Plumes product. The colour masking technique is proposed to extract the maximum fires smoke pixels from the SeaStar/SeaWiFS images by using Fusion by Arithmetic Combination (FAC) of spectral bands method. Each image used is converted from RGB (Red, Green, Blue) to HIS (Hue, Saturation, Intensity) system, The results smoke plumes pixels are obtained visually on the images Intensity and Saturation, then one looks at the values taken by intensity and saturation for potentially applying them to other images in routine. In this research, we applied our detecting forest fires smoke algorithm in seven different scenes, for a variety of conditions, including different regions of the planet, and, different times, Smoke Pixel Reference Ratio SPRR is used to test the proposed method. The largest values of this index are observed because of a small area of smoke plumes in scene of image used (1.031% from Saturation image and 0.0950% from Intensity image). We found that the method can detect maximum pixels of smoke plumes in spite of some limitations. Key words: Remote sensing, Forest fires, SeaWiFS Images, Smoke, FAC method. 42
STOP FEU, Oran 2010 Orales Thème IV O.4.5 Quelles essences de reboisement peut –on retenir pour le reboisement ? M.Kaid-Harche Département de biotechnologie ,faculté des sciences ,U.S.T.O. Mohamed Boudiaf. e.m : armoise200@yahoo.fr La forêt fournit à l’être humain l’oxygène, de multiples bienfaits (, bois liège, produits pharmaceutique etc.) et des lieux de détente et de loisir. En Algérie, les forêts et maquis couvrent 4,1 millions d’hectares soit un taux de boisement de 16,4% pour le nord de l’Algérie et de 1,7 % pour les régions du sud. Les essences prédominantes sont représentées par des Gymnospermes (conifères ou résineux) principalement le pin d’Alep, le thuya, le cèdre, le genévrier et par des Angiospermes, nous pouvons citer : le chêne liège, l’ eucalyptus, le chêne vert. Le réchauffement climatique nous incite à diversifier le choix des essences forestières. Des ligneux peu connus en Algérie, constituent au plan morphologique et structurale de bons modèles à introduire dans les programmes de reboisement .Nous donnerons quelques exemples d’arbres susceptibles d’intéresser le forestier. 43
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