KMS XINJIANG INSTITUTE OF ECOLOGY AND GEOGRAPHY，CAS
| 帕米尔山区结构和岩性的遥感制图方法研究 | |
| Alternative Title | A REMOTE SENSING APPROACH FOR STRUCTURAL AND LITHOLOGICAL MAPPING IN THE PAMIR MOUNTAINS |
| JAVHAR AMINOV | |
| Subtype | 博士 |
| Thesis Advisor | 陈曦 |
| 2019-06-30 | |
| Degree Grantor | 中国科学院大学 |
| Place of Conferral | 北京 |
| Degree Discipline | 理学博士 |
| Keyword | 遥感 岩性测绘 图像增强 图像处理 自动线性提取 Landsat OLI Sentinel-1 Sentinel 2 SE 帕米尔 地质学 Remote sensing Lithological mapping Image enhancement Image processing automatic lineament extraction Landsat-8 Sentinel-1 Sentinel-2 SE Pamir Geology |
| Abstract | 此研究对遥感技术和遥感影像在帕米尔高原东南部 Alichur 地区地质测绘的应用前景进行了评估。 Alichur 地区位于塔吉克斯坦东南部的帕米尔地区。从地质学角度来看，它位于帕米尔高原的东南部与西南部的交界地带， 在这里， 西南、 二叠系和侏罗纪沉积序列的前寒武纪变质岩被多个多期深成岩体侵入， 如Bazardara 和 Alichur。由于该地区难以到达，山区的岩性，构造线以及蚀变带的绘图一直是一个难题。 现有地质图中岩性边界和构造细节的准确性有待提高。线性检测和制图是结构地质调查的重要组成部分。它还广泛用于其他不同的研究，包括矿物勘探，水文地质和风险评估。 利用 Landsat、 Sentinel 等具有中、高空间分辨率的光学遥感数据和雷达遥感数据，通过先进的遥感技术将这些数据应用于构造线测绘，具有重要的应用价值。 然而，这些多分辨率数据的结果因空间分辨率和土壤占用敏感性的不同而不同。提取的线条的准确性和质量很大程度上取决于图像的空间分辨率。因此，本研究旨在研究一种适用于具有半干旱和高地特征的研究区域的岩性测绘和线性提取方法。首先，我们测试了 Landsat 8 OLI 数据在帕米尔东南部岩性填图中的潜力。利用 landsat8 OLI 卫星数据和图像增强技术，对研究区岩性单元进行了识别。这些方法包括光谱增强，如独立成分分析（ICA），波段比率和假色成分（FCC）。应用光谱增强技术以提取初始岩性信息，其表明了来自陆源和碳酸盐沉积演替的花岗岩的明显区分。 FCC 图像（OLI 波段 6,7 和 5），色比合成图像（OLI 6/5， OLI（7x5） / 7 和 OLI 6/7），以及独立组件的彩色复合（IC6， IC3，IC4）红色，绿色和蓝色分别被发现为岩性信息对比度更高的组合，并被用作监督分类中的输入数据。最大似然分类对结果图像进行分类。通过现场观察验证的结果表明，可以区分和描绘不同类型的花岗岩类，陆源岩和碳质岩，从而保证地质图的收缩具有更好的准确性。本研究的第二个目的是比较光学 Landsat-8、 Sentinel-2A 和雷达 Sentinel-1A卫星数据，用于自动提取轮廓线。该方法的框架包括结合边缘检测和线性连接算法，确定自动提取线性图像的最优参数，并从研究区适合线性图像映射的光学数据中确定合适的波段。为了验证结果，将提取的线形与人工获取的线形进行对比，通过方向滤波和边缘增强技术，并与研究区现有地质图进行数字化处理后的线形进行对比。此外， 利用数字高程模型(DEM)进行精度评估，并进行了现场验证。结果表明，自动提取的轮廓线、人工解释和已有的轮廓线图之间的相关性最好。实验表明，本研究使用的雷达数据分别从 VH 和 VV 分量中提取 5872 条和 5865 条轮廓线，比 Landsat OLI 和 Sentinel 2A 光学成像提取 2338 条和 4745 条轮廓线更有效地进行结构轮廓线测绘。 |
| Other Abstract | In this study the potential of Remote Sensing (RS) techniques and remotelysensed imagery were evaluated for geological mapping in Alichur area, southeastPamir. The Alichur area is located in the South Eastern Pamir of Tajikistan.Geologically it’s situated on the border of the South Eastern with South WesternPamir, where Precambrian metamorphic rocks of the SW and Permian to Jurassicsedimentary sequence of the SE Pamir are intruded by several polychronous pluton -such as Bazardara and Alichur. Mapping of lithology, structural lineaments as well asalteration zones in mountainous areas has always posed a challenge due to theirinaccessibility. The accuracy of lithological boundaries and structural details in theexisting geological maps need to be improved. Lineament detection and mapping isan important part of structural geological investigations. It is also widely used in otherdifferent studies including mineral exploration, hydrogeological and risk assessment.Availability of optical as well as radar remote sensing data such as Landsat andSentinel with medium and high spatial resolution have proved valuable the utilizationof this data for structural lineament mapping through advanced remote sensingtechniques. However, the results from these multi-resolution data vary due to theirdifference in spatial resolution and sensitivity to soil occupation. The accuracy andquality of extracted lineaments depend strongly on the spatial resolution of theimagery. Therefore, this study aimed to investigate a suitable approach for lithologicalmapping and lineament extraction in a study area having semi-arid and highlandcharacteristics.Firstly, we test the Landsat-8 OLI data potential for lithological mapping in theSoutheastern Pamir. Discrimination of lithological units in the study area has beencarried out by utilizing Landsat-8 OLI Satellite data and image enhancementtechniques. The approaches consist of spectral enhancement such as independentcomponent analysis (ICA), band ratioing, and false-color composition (FCC). Thespectral enhancement techniques were applied in order to extract the initiallithological information, which shows a clear discrimination of granitic rocks fromterrigenous and carbonate sedimentary successions. FCC image (OLI bands 6, 7 and5), color-ratio composite image (OLI 6/5, OLI (7x5)/7, and OLI 6/7), and colorcomposite of independent components (IC6, IC3, IC4) in red, green and blue respectively were found as combinations with more contrast on lithologic informationand were used as the input data in supervised classification. Maximum likelihoodclassification was used to classify resultant images. The results, verified with fieldobservations, demonstrate that different kind of granitoids, terrigenous andcarbonaceous rocks can be distinguished and delineated, leading to construction ofgeological maps with a better accuracy.The second aim of this study was to compare the optical Landsat-8, Sentinel-2Aand radar Sentinel-1A satellite data for automatic lineament extraction. Theframework of automatic approach includes defining the optimal parameters forautomatic lineament extraction with a combination of edge detection and line-linkingalgorithms and determining suitable bands from optical data suited for lineamentmapping in the study area.For result validation, the extracted lineaments are compared against the manuallyobtained lineaments through the application of directional filtering and edgeenhancement as well as to the lineaments digitized from the existing geological mapsof the study area. In addition, a digital elevation model (DEM) has been utilized foraccuracy assessment followed by the field verification. The obtained results show thatthe best correlation between automatically extracted lineaments, manual interpretation,and the pre-existing lineament map is achieved from the radar Sentinel-1A images.The tests indicate that the radar data used in this study, with 5872 and 5865lineaments extracted from VH and VV components respectively, is more efficient forstructural lineaments mapping than the Landsat-8 and Sentinel-2A optical imagery,from which 2338 and 4745 lineaments were extracted respectively. |
| Subject Area | 地图学与地理信息系统 |
| Language | 英语 |
| Document Type | 学位论文 |
| Identifier | http://ir.xjlas.org/handle/365004/15290 |
| Collection | 中国科学院新疆生态与地理研究所 研究系统 |
| Affiliation | 中国科学院新疆生态与地理研究所 |
| First Author Affilication | 中国科学院新疆生态与地理研究所 |
| Recommended Citation GB/T 7714 | JAVHAR AMINOV. 帕米尔山区结构和岩性的遥感制图方法研究[D]. 北京. 中国科学院大学,2019. |
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