首页> 外文会议>International Symposium of Geoscience and Remote Sensing >Analysis of snow surface dynamics in the Darwin mountain through satellite images optical and its relationship with data climate (2004-2016) (Análise da dinamica da superfície de neve na Cordilheira Darwin através de imagens de satélite óticas e sua rela??o com dados climáticos (2004-2016))
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Analysis of snow surface dynamics in the Darwin mountain through satellite images optical and its relationship with data climate (2004-2016) (Análise da dinamica da superfície de neve na Cordilheira Darwin através de imagens de satélite óticas e sua rela??o com dados climáticos (2004-2016))

机译:达尔文山通过卫星图像光学山的雪表面动力学分析及其与数据气候的关系(2004-2016(2004-2016))

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The characterization of snow surface is essential to understand the Cryosphere dynamics of the Southern Patagonia of Chile. It becomes more important when we consider that is an area of difficult access due to its geography and extreme climatic conditions. Through the processing of orbitals images from sensors TM and OLI/TIRS, present on LANDSAT 5 and 8, respectively, and their subsequent classification by an algorithm of Maximum Likelihood Supervised Classification (CSMV) and an application of the threshold to Normalized-Difference Snow Index (NDSI), were obtained for each category binary maps with classes 1 (snow) and 0 (not snow). Images were vectorized and then areas corresponding to the snow cover, in square kilometers (k, 2), were calculated. The variation of snow cover was correlated to precipitation and temperature data (reanalysis II NCEP/NCAR) and with Oceanic Nino Index and Antarctic Oscillation Index. The variation of snow cover indicates a decline over the study period (June 2004 to June 2016), what could be associated with global warming or remote influence, for example, ENSO and Antarctic Oscillation. But there is no homogeneous pattern of cause and consequence between snow cover variations and remote phenomenon's occurrence.
机译:雪表面的表征对于了解智利南塔哥代州巴塔哥尼亚的嘈杂岩体动态至关重要。当我们认为这是由于其地理和极端气候条件,这变得更加重要。通过处理来自传感器TM和OLI / TIRS的轨道图像,分别在Landsat 5和8上存在,以及通过最大似然监控分类(CSMV)的算法以及阈值的应用程序的随后分类,以及归一化差异雪指数的阈值(NDSI)是为每个类别的二进制映射获得,其中包含1(雪)和0(不是雪)。计算图像化,然后计算与平方公里(K,2)相对应的区域。雪覆盖的变化与沉淀和温度数据(Reanalysic II NCEP / NCAR)和海洋NINO指数和南极振荡指数相关。雪覆盖的变异表明,研究期(2004年6月至2016年6月)下降,可能与全球变暖或远程影响有关,例如,enso和南极振荡有关。但在雪覆盖变化和远程现象发生的情况下,没有同种异体的原因和后果。

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