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16 2. Remote Sensing with MODIS In 1970, inspired by spatial photographs taken during Mercury and Gemini’s missions, and motivated by the possibility to monitor agriculture crop yelds, NASA launched Landsat 1, the first satellite aiming at observing the earth surface and providing data for scdientists from different disciplines. Following the success of the Landsat series ( 7 sensors since 1972), today’s intense research in remote sensing technologies is motivated by deeper questions. Climate change and its potential disastrous impact on human life urges an improved understanding of earth sciences, ranging from carbon cycle modelling to weather forecasting. Such achievements are stricly limited by the quality of data collected by satellite /aerial sensors and require a continous increase of spatial, temporal and spectral resolution. 2.2 Applications of satellite imagery Satellite and airborne sensors provide a continuous flow of data exploited in fields as diverse as telecommunications, agriculture, deforestation, geology, hydrology, oceanography, urban management and fire monitoring to cite a few. For its abilities to capture wide portions of the globe, satellite derived data offers a high potential for the surveillance of both local and global changes occuring in the Earth main systems. 2.2.1 Monitoring land changes Among many human-induced activites, deforestation is highly responsible for both weather and climate change. Forests play a crucial role in the climate stability as they absorb and trap large quantities of carbon dioxide (CO2) present in the atmopshere. The massive destruction of forests for urbanization and agricultural development, inevitably releases in the atmopshere all the CO2 and other greenhouse gazes stored in trees for decades. Assessing the amount and rate of carbon emissions from deforestation or other activities, remotely sensed data can be used to monitor primary variables of the Earth land surface such as the spectral reflectance, albedo and temperature. In addition, higher order variables in the form of vegetation indices can provide valuable information on land change. A well-known variable is the Normalized Difference Vegetation Index (NDVI) [Rouse et al., 1973], defined as the ratio between the difference and the sum of reflectances in the visible (red) and near-infrared spectral bands : NDVI = ρ NIR − ρ Red ρ NIR + ρ Red (2.1) A major disadvantage of he NDVI index is that it tends to saturate over dense vegetation and is highly dependent on the background soil composition. In the presence of high biomass, the Enhanced Vegetation Index (EVI) [Huete, 2002] provides an optimized vegetation signal able to capture high variability. and minimizes the influence of atmospheric
17 Figure 2.1 – Deforestation in Brazil, in the region of Mato Grosso, monitored using the Normalized Difference Vegetation Index (NDVI) (TL) RGB image from Terra MODIS captured on june 17, 2002 (TR) NDVI index (BL) RGB image from Terra MODIS captured in the same region on june 28 2006 (BR) NDVI index effects. The EVI is defined as : EVI = 2.5ρ NIR − ρ Red L + ρ NIR + C 1 ρ Red − C 2 ρ Blue (2.2) where the coefficient L corrects for canopy background, while C 1 and C 2 reduce the effects of aerosol. The values selected for the MODIS-EVI product are L = 1, C1 = 6 and C 2 =7.5. 2.2.2 Studying the atmosphere dynamics The atmosphere determines the amount of solar radiance actually reaching the Earth surface and therefore acts as a regulator of local and global climate processes. Depending
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130 BIBLIOGRAPHIE A. Chambolle. An
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132 BIBLIOGRAPHIE E. Hochberg, S. A
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134 BIBLIOGRAPHIE P. Perona and J.
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