基于遥感影像的平朔矿区植被动态监测研究

发布时间：2018-06-09 01:55

本文选题：矿区 + 遥感影像　；参考：《中国地质大学（北京）》2013年硕士论文

【摘要】：矿区土地复垦工作是一项持续性动态工程，随着复垦工作的广泛开展,土地复垦整理效果的跟踪监测评价日益重要，植被覆盖度是其中一个十分重要的评价参数，是植物群落覆盖地表状况的一个综合量化指标，研究煤矿区植被覆盖度的估算及其变化，可以反映出土地复垦的程度与质量，为煤矿区土地利用规划提供指导，也可为编制煤矿区土地复垦规划提供帮助，对全面了解采矿对区域生态系统的影响和辅助矿区生态重建具有重要的意义。同时，矿区植被动态监测的研究结果可以为区域土地资源集约高效利用和生态环境保护提供支持，可以促使土地利用更趋合理，从而达到土地资源可持续利用的目的。本文首先辨析了植被指数及植被覆盖度等基本概念理论，，以像元二分模型为基础，并对参数的取值方法进行了改进，进而采用归一化植被指数—NDVI来建立估算模型。实证研究以山西平朔煤矿区为例，采用多源多时相的Worldview-2和TM遥感影像数据，分别从横向的年代变化和纵向的季节变化两个角度，利用ENVI软件对各期影像的植被覆盖度进行了估算，之后对结果影像进行分级统计，在计算结果的基础上，对比分析矿区植被覆盖度变化情况，提取出植被变化信息，得出整个露天煤矿以及排土场植被的生长状况及时空变化特征。最后，尝试对比了不同遥感数据在植被动态监测中的效果。结果表明：从1999年到2010年，该煤矿区植被覆盖度总体上保持在稳定水平，表现出轻微的上升趋势，而不同区域的植被覆盖度呈现出不同的变化趋势，土地复垦后的试验区植被覆盖度比复垦前高。一些自然林地及耕地退化为露天矿、未复垦排土场及剥离区，植被退化，覆盖程度降低；同时季节变化对整个矿区的植被影响也很大，夏季和秋季是植被长势最旺盛的季节，是开展动态监测评价的最佳时间段。评价结果符合实际情况，采用遥感动态监测技术手段，对整个矿区以及土地复垦后的矿区植被状况进行大面积动态监测评价是可行的。
[Abstract]:Land reclamation in mining area is a continuous dynamic project. With the extensive development of land reclamation, it is increasingly important to track, monitor and evaluate the effect of land reclamation. Vegetation coverage is one of the most important evaluation parameters. It is a comprehensive quantitative index of plant community covering surface condition. Studying the estimation and change of vegetation cover degree in coal mine area can reflect the degree and quality of land reclamation and provide guidance for land use planning in coal mine area. It can also provide help for the compilation of land reclamation planning in coal mining area, and it is of great significance to understand the impact of mining on regional ecosystem and to assist the ecological reconstruction of mining area. At the same time, the research results of vegetation dynamic monitoring in mining areas can provide support for intensive and efficient utilization of regional land resources and ecological environment protection, and promote more rational land use. In order to achieve the purpose of sustainable utilization of land resources, this paper first discriminates and analyzes the basic concept theory, such as vegetation index and vegetation coverage, based on the pixel dichotomy model, and improves the method of parameter selection. Then the normalized vegetation index-NDVI is used to establish the estimation model. Taking Pingshuo coal mine area of Shanxi Province as an example, this paper uses multi-source and multi-temporal Worldview-2 and TM remote sensing image data, from two angles of lateral and vertical seasonal variation, respectively. The vegetation coverage of each period image was estimated by ENVI software, and the result image was classified and counted. On the basis of the calculated results, the variation of vegetation coverage in mining area was compared and analyzed, and the vegetation change information was extracted. The characteristics of vegetation growth and space-time variation in open-pit coal mine and dump are obtained. Finally, the effects of different remote sensing data on vegetation dynamic monitoring are compared. The results show that from 1999 to 2010, the vegetation coverage in this coal mining area remained at a stable level, showing a slight upward trend. The vegetation coverage of different regions showed different trends, and the vegetation coverage of the experimental area after land reclamation was higher than that before reclamation. Some natural woodlands and cultivated land are degraded into open-pit mines, unreclaimed dump sites and strip areas, and vegetation degradation and coverage are reduced. Meanwhile, seasonal changes also have a great impact on the vegetation of the whole mining area, and summer and autumn are the most prosperous seasons for vegetation growth. It is the best time for dynamic monitoring and evaluation. The evaluation results are in line with the actual situation. It is feasible to use the remote sensing dynamic monitoring technique to monitor and evaluate the vegetation status of the whole mining area and the area after land reclamation.
【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】：2013
【分类号】：TD88;P237;Q948

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