稀土矿山动态监测及植被覆盖研究

发布时间：2018-03-21 09:02

本文选题：稀土矿　切入点：相对辐射校正　出处：《中国地质大学(北京)》2017年硕士论文　论文类型：学位论文

【摘要】：稀土矿作为世界上的稀缺资源,在国家发展中具有极高的战略价值,特别是我国特有的离子型稀土矿,在航天、军工等高科技领域有着重要的应用。然而,离子型稀土矿多位于交通不便的山区,民间盗采现象普遍存在,这不仅造成资源浪费,而且严重破坏了矿区的植被、土壤等生态环境。随着遥感技术的发展,其在大范围地面信息实时获取上体现出了不可比拟的优势,特别是在交通闭塞、植被茂密的山区,能够节省大量人力物力。本文利用遥感技术,对稀土矿开发状况及矿区地物类型与植被覆盖信息变化进行动态监测。主要研究内容及成果如下:1、探讨研究了遥感影像相对辐射校正中的PIF方法,该方法在很大程度上节省了人工选点的时间,降低了选点的主观性,使PIF选点的相关系数达到了0.98以上,并且利用典型地物波谱曲线、均方根误差和NDVI差值等统计量分析了校正效果,结果表明,校正效果较为理想。2、采用纹理信息辅助分类的方法进行地物信息提取,分类总体精度与kappa系数分别比单纯依靠光谱信息的分类方法提高了4.1%、5.2%;将该方法应用于多时相遥感数据并统计分类结果,分析发现,1990年至2010年间稀土采矿区面积持续扩大,分布模式由初期的零星分布发展成连片分布,虽然有一定的人工复垦和自然恢复,但随着稀土开采规模的扩大,稀土矿区的荒漠化更为突出。3、从植被覆盖度角度研究稀土矿开采对生态环境的影响,并以高分辨无人机数据作参考,评价了不同植被覆盖度估算方法的精度,验证了三波段梯度差样本值模型在稀土矿区植被覆盖度信息提取中具有较好的适用性;利用植被覆盖度分级图进行各项统计及时空演变分析,同时结合稀土采矿信息,进行叠加分析与缓冲分析,进而研究植被覆盖与稀土采矿的空间关系。分析发现,稀土开采初期采矿对植被覆盖的影响并不起主导作用,随着稀土开采规模的扩大,对矿区植被的破坏也越来越大,逐渐成为影响植被覆盖的主要因素。
[Abstract]:Rare earth ore, as a scarce resource in the world, has a high strategic value in the development of our country, especially the ion rare earth ore, which has important applications in aerospace, military industry and other high-tech fields. Ionic rare earth minerals are mostly located in the mountainous areas where transportation is difficult, and the phenomenon of folk stealing and mining is widespread, which not only causes waste of resources, but also seriously destroys the ecological environment such as vegetation and soil in mining areas. With the development of remote sensing technology, It can save a lot of manpower and material resources, especially in the mountainous areas where traffic is blocked and vegetation is dense. In this paper, remote sensing technology is used. Dynamic monitoring of the development of rare earth minerals, the types of objects and the changes of vegetation cover information in mining areas are carried out. The main contents and results are as follows: 1. The PIF method in relative radiation correction of remote sensing images is studied. This method saves the time of artificial selection to a great extent, reduces the subjectivity of spot selection, and makes the correlation coefficient of PIF point selection more than 0.98, and makes use of the spectral curve of typical ground objects. The root mean square error (RMS) and NDVI difference are analyzed. The results show that the correction effect is ideal. The method of texture information aided classification is used to extract the ground object information. The classification accuracy and kappa coefficient are 4.1and 5.2 higher than that of the classification method based on spectral information alone, and the application of this method to multitemporal remote sensing data and the statistical classification results show that the area of rare earth mining areas continues to expand from 1990 to 2010. The distribution pattern has developed from sporadic distribution to continuous distribution. Although there is a certain amount of artificial reclamation and natural recovery, but with the expansion of the scale of rare earth mining, The desertification of rare earth mining area is more prominent. The impact of rare earth mining on ecological environment is studied from the angle of vegetation coverage, and the accuracy of different vegetation coverage estimation methods is evaluated with the reference of high resolution UAV data. It is verified that the three-band gradient difference sample value model is suitable for extracting vegetation coverage information in rare earth mining areas, and the vegetation coverage classification map is used to analyze the statistical and temporal and spatial evolution of vegetation coverage, and the rare earth mining information is combined. Superposition analysis and buffer analysis are carried out to study the spatial relationship between vegetation cover and rare earth mining. It is found that the influence of rare earth mining on vegetation coverage does not play a leading role in the early stage of rare earth mining, and with the expansion of rare earth mining scale, The destruction of vegetation in mining area is becoming more and more serious, which gradually becomes the main factor affecting vegetation cover.
【学位授予单位】：中国地质大学(北京)
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：X87;X753;Q948

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