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A combined high and low spatial resolution approach for mapping snow covered areas in the Atlas mountains

机译:高和低空间分辨率的组合方法,用于绘制阿特拉斯山脉的积雪区域

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Dynamics of snow in semi-arid mountains are poorly investigated despite the fact that snow may represent an important source of water for downstream populations especially in the spring and early summer. Data acquired by space-borne optical sensors (i.e. reflectance and derived snow indices) may be suitable for spatial and temporal monitoring of snow cover. However, due to prevailing terrain and climatic conditions, the use of satellite sensor data to monitor snow dynamics is not trivial over such regions. Snowfall as well as precipitation are characterized by strong space-time variability. Indeed snow can fall and melt within one week. Under such conditions, appropriate monitoring of snow dynamics requires space instruments that provide data with high spatial and high temporal resolutions. In this context we developed a new approach based on the combination of two types of instruments: low spatial and high temporal resolution (Systeme Pour l'Observation de la Terre (SPOT)-VEGETATION) and high spatial and low temporal resolution (Landsat Thematic Mapper). This new approach improves the relationship between snow index and snow area. The method is validated against snow maps derived from classification of high spatial resolution data on the Atlas range, in Morocco. It is then applied to a one-year series of SPOT-4 VEGETATION images allowing to derive a temporal snow cover profile at 1 km spatial resolution over the entire Atlas. The yearly snow profile obtained is of great interest for hydrological modelling.
机译:尽管事实上雪可能是下游人口的重要水源,特别是在春季和初夏,但半干旱山区的积雪动力学研究却很少。由星载光学传感器获取的数据(即反射率和导出的降雪指数)可能适用于雪盖的时空监测。但是,由于主要的地形和气候条件,在此类区域使用卫星传感器数据监视积雪动力学并非易事。降雪和降水的特点是时空变化很大。确实,一周之内降雪会融化。在这样的条件下,对雪动力学的适当监视需要空间仪器提供具有高空间分辨率和高时间分辨率的数据。在这种情况下,我们基于两种工具的组合开发了一种新方法:低空间和高时间分辨率(Systeme Pour l'Observation de la Terre(SPOT)-VEGETATION)和高空间和低时间分辨率(Landsat Thematic Mapper) )。这种新方法改善了降雪指数和降雪面积之间的关系。该方法已根据摩洛哥Atlas范围内高空间分辨率数据分类得出的雪图进行了验证。然后将其应用于为期一年的SPOT-4植被图像系列,从而可以在整个地图集上以1 km的空间分辨率导出时空积雪轮廓。获得的年度降雪资料对于水文模拟非常重要。

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