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Analysis of the impact on vegetation caused by abrupt deforestation via orbital sensor in the environmental disaster of Mariana, Brazil

机译:巴西环境灾害轨道传感器突然砍伐砍伐砍伐植被影响分析

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摘要

The failure of the Fundao Dam in Mariana, more precisely in the subdistrict of Bento Rodrigues, state of Minas Gerais (Brazil) on November 5th, 2015, is considered to be "the biggest environmental tragedy in the country's history." About thirty-four million cubic meters of tailings were dumped into the river where, another 16 million continued to reach the Atlantic ocean. This disaster seriously affected the flora, fauna, economic activities and people's lives, including the loss of human lives. Remote sensing allows mapping the variability of terrain properties, such as vegetation, water and geology, both in space and time, offering a synoptic view and useful environmental information in future decision making. In this way, this research aims to analyze the impacts of the failure of the Fundao Dam in the municipality of Mariana-MG on the vegetation cover, by means of remote sensing techniques and analysis of digital processing of orbital optical images. In order to analyze the soil cover, Unmixing Espectral Linear Model (UELM) was used in order to separate soil, shade and vegetation classes. Subsequently, Artificial Neural Network (ANN) classification method was applied, followed by Enhanced Vegetation Index (EVI) and Normalized Difference Vegetation Index (NDVI). The results showed a loss of 13.02% of the vegetation, about 1289 ha, and a reduction of 68.57% of shade (water), approximately 1347 ha. The UELM showed to be effective in the separation of each image-fraction, being an important stage for the success of the classification. The EVI was the index that best described the vegetation deficit in the affected areas spilling the sludge from waste.
机译:2015年11月5日,Mariana在Mariana的失败,更确切地说,更准确地在Bento Rodrigues,Minas Gerais(巴西)的副名地,被认为是“该国历史上最大的环境悲剧”。大约三百百万立方米的尾矿被倾倒到河里,另外1600万继续到达大西洋。这场灾难严重影响了植物区系,动物群,经济活动和人民的生活,包括人类生命的丧失。遥感允许将地形性质的可变性绘制在空间和时间内,例如在空间和时间内,在未来的决策中提供舞台视图和有用的环境信息。通过这种方式,本研究旨在通过遥感技术和轨道光学图像数字处理分析,分析Mariana-Mg市玛利亚纳-MG市玛利亚纳-MG市的失败的影响。为了分析土壤覆盖,使用了未混合的超声线性模型(UELM)以分离土壤,阴影和植被课程。随后,应用人工神经网络(ANN)分类方法,其次是增强植被指数(EVI)和归一化差异植被指数(NDVI)。结果表明,植被损失为13.02%,约1289公顷,减少了68.57%的阴影(水),约为1347公顷。 UELM显示在每个图像分数的分离中有效,是分类成功的重要阶段。 EVI是最能描述溢出废物污​​泥的受影响区域中植被缺陷的指数。

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