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首页> 外文期刊>International journal of remote sensing >Estimating burned area for Tropical Africa for the year 1990 with the NOAA-NASA Pathfinder AVHRR 8 km land dataset
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Estimating burned area for Tropical Africa for the year 1990 with the NOAA-NASA Pathfinder AVHRR 8 km land dataset

机译:使用NOAA-NASA探路者AVHRR 8公里土地数据集估算1990年非洲热带地区的燃烧面积

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摘要

The international scientific community recognizes the long-term monitoring of biomass burning as important for global climate change, vegetation disturbance and land cover change research on the Earth's surface. Although high spatial resolution satellite images may offer a more detailed view of land surfaces, their limited area coverage and temporal sampling have restricted their use to local research rather than global monitoring. Low spatial resolution images provide an invaluable source for the detection of burned areas in vegetation cover (scars) at global scale along time. However, the automated burned area mapping algorithm applicable at continental or global scale must be sufficiently robust to accommodate the global variation in burned scar signals, Here, the estimation of the percentage of a pixel area affected by a fire is crucial. In a first step, an empirical method is used which is based on a function between the change in Normalized Difference Vegetation Index (NDVI) and the surface area affected by fire. Next, a new statistical method, based on the Monte Carlo algorithm, is applied to compute probabilities of burned pixels percentages in different neighbourhood conditions.
机译:国际科学界认识到对生物质燃烧的长期监测对于全球气候变化,植被扰动和地表覆盖变化研究至关重要。尽管高空间分辨率的卫星图像可能会提供更详细的陆地表面视图,但其有限的区域覆盖范围和时间采样限制了其在本地研究而非全球监测中的使用。低空间分辨率图像可为全球范围内随时间推移检测植被覆盖(疤痕)中的燃烧区域提供宝贵的资源。然而,适用于大陆或全球范围的自动烧伤区域映射算法必须足够健壮,以适应烧伤疤痕信号的整体变化。在此,估算受火灾影响的像素区域的百分比至关重要。第一步,使用经验方法,该方法基于归一化植被指数(NDVI)的变化与受火影响的表面积之间的函数。接下来,基于蒙特卡洛算法的一种新的统计方法被应用于计算在不同邻域条件下被烧像素百分比的概率。

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