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Retrospective mapping of burnt areas in Central Siberia using a modification of the normalised difference water index

机译:使用归一化差异水指数的修改对西伯利亚中部烧伤地区进行回顾性测绘

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The boreal forest contains almost half the total carbon pool of world forest ecosystems, and so has a very significant role in global biogeochemical cycles. The flux of greenhouse gases in and out of these forests is influenced strongly by disturbances such as diseases, logging and predominantly fire. It is important to quantify these disturbances to enable the modelling of major greenhouse gases. However, because of the remoteness and vastness of the boreal forest, little data is available on the type, extent, frequency and severity of these disturbances in Siberia, For burnt areas, two of the more responsive wavelengths are the short wave infra-red (SWIR) and the near infra-red (NIR). These produce a vegetation index, the normalised difference SWIR (NDSWIR) capable of detecting retrospective disturbances. Here we combine the NDSWIR from MODIS imagery acquired in the summer of 2003 with thermal anomaly data from 1992 to 2003 to detect and date areas which burnt at some point between 1992 and 2003. The semi-automated method is called SWIR and Thermal ANomalies for Detecting Disturbances (STANDD) and is complemented by an Normalised Difference Vegetation Index (NDVI) differencing method using MODIS 2002 and 2003 imagery to ensure reliable detection of area burnt in the year of image acquisition (i.e. 2003). The area of this study covers approximately 3 million km{sup}2 stretching from Lake Baikal in the south to the Laptev Sea in the north, above the Arctic Circle. Landsat ETM+ images were used to validate the shape and areal extent of the burnt areas resulting in an 81% overall accuracy with a kappa coefficient of agreement of 0.63.
机译:北方森林几乎占世界森林生态系统总碳库的一半,因此在全球生物地球化学循环中具有非常重要的作用。进入和流出这些森林的温室气体的流量受到疾病,伐木和主要火灾等干扰的强烈影响。重要的是量化这些干扰,以便能够对主要温室气体进行建模。但是,由于北方森林的偏远和广阔,西伯利亚这些扰动的类型,程度,频率和严重程度的数据很少。对于烧伤地区,响应速度更快的两个波长是短波红外( SWIR)和近红外(NIR)。这些会产生植被指数,即能够检测回溯干扰的归一化差异SWIR(NDSWIR)。在这里,我们将2003年夏季获得的MODIS影像中的NDSWIR与1992年至2003年的热异常数据相结合,以检测并记录在1992年至2003年之间某个时刻燃烧的区域。半自动方法称为SWIR和热异常检测干扰(STANDD),并使用MODIS 2002和2003影像进行归一化差异植被指数(NDVI)鉴别方法的补充,以确保可靠地检测图像采集年份(即2003年)内的烧伤面积。这项研究的区域覆盖约300万公里{sup} 2,从南部的贝加尔湖延伸到北极圈上方的北部的拉普捷夫海。 Landsat ETM +图像用于验证烧伤区域的形状和面积,从而使总体准确度达到81%,kappa一致性系数为0.63。

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