Elsevier

Advances in Space Research

Volume 62, Issue 9, 1 November 2018, Pages 2480-2493
Advances in Space Research

Extraction and analysis of geological lineaments combining a DEM and remote sensing images from the northern Baoji loess area

https://doi.org/10.1016/j.asr.2018.07.030Get rights and content

Abstract

Geological lineaments are important reactant of geological structure on the surface, and control the distribution of regional groundwater, geohazards, geothermal and earthquakes. The technology of geological lineaments extraction is of great significance for the analysis of regional plate movement and ore forming prognosis. However, the traditional methods are mainly based on the semi-automatic or manual visual interpretation, which consuming time and labor for the dependence on the rich professional experience and knowledge of the interpreter experts, and involving less in the special geomorphological regions. Taking Loess areas in northern Baoji as an example, this paper proposed a lineaments extraction algorithm based on tensor voting coupled Hough Transform, with the help of DEM and Landsat 8 OLI remote sensing images. Firstly, the best independent band combination of Landsat 8 OLI images for lineaments extraction were selected by using principal component analysis. Secondly, Gaussian high-pass filter was applied to sharpen the edge in the DEM and composite Landsat 8 OLI images. Linear boundary was extracted by tensor voting according to the conspicuousness of the vector sum superposition feature based on edge points. Finally, the Hough Transform was employed to search the edges and extracted the geological lineaments in this region. The experimental results showed that the orientation of lineaments was dominated by NW-SE and NE- SW, supplemented by NNW-SSE. Under the influence of the uplift of the eastern foot of Liupan Mountains and the southern margin of Ordos, the lineaments were mainly distributed over linear landforms, which were better consistent with previous studies about the direction of tectonics in this region. Compared with the segment tracing algorithm, this method has more applicability, efficiency, high practical value and scientific significance in the analysis of tectonic movement and evolution in special landform area.

Introduction

In remote sensing images, geological lineaments show up as lines or linear structures that are significantly brighter or darker than the background pixels. Such lineaments include: faults and fractures that have obvious displacement, ruptures that have no significant fracture displacement (e.g., joint zones, cleavage belts, structural fissures, and tectonic crush zones), large crustal fractures, deep faults, buried faults, linear micro-geomorphological features, and linear traces that reflect abnormal hues (Vassilas et al., 2002). Geological lineaments are the manifestation at the earth’s surface of deeper geological structures, reflecting important tectonic units in the crust and zones favorable for the formation of minerals, and controlling the distribution of groundwater, geological disasters, geothermal resources, earthquakes, and geomorphology. Therefore, in-depth studies on lineament extraction are of theoretical significance and practical value (Masoud and Koike, 2011, Radaideh et al., 2016, Raj et al., 2017). Salehi et al. (2015) integrating DEM, Landsat ETM+ images, magnetic data, and gravity data for structural geology investigation in special areas enhanced the ability of extracting and mapping (Salehi et al., 2015). De Oliveira Andrades Filho and De FáTima Rossetti (2011) conducted an in-depth study on the effects of geologic lineaments on topography control using Shuttle Radar Topography Mission (SRTM) and ALOS-PALSAR data (De Oliveira Andrades Filho and De FáTima Rossetti, 2011); Magesh et al. (2012) integrated remote sensing and GIS techniques to extract geological strands for determination of the regional distribution of potential groundwater (Magesh et al., 2012); Yusof et al. (2011) discussed the relation between the distribution of landslides around a freeway and the density of geologic lineaments (Yusof et al., 2011); Bahiru and Woldai (2016) analyzed gold distribution in Uganda using Landsat ETM+ images and an SRTM-DEM resulting in significant implications for mineral prediction (Bahiru and Woldai, 2016); Elmahdy and Mohamed (2016) applied a Sobel threshold filter to extract lineaments from an SRTM-DEM and explored the regularity of earthquake distribution in Egypt (Elmahdy and Mohamed, 2016). Therefore, research on the automatic extraction of geologic lineaments can add to the understanding of regional plate evolution and tectonic movements, explain the evolution of certain geomorphological features, and indicate the change trend and distribution of various geological elements.

In remote sensing images, geological lineaments are often reflected as ridge and valley lines and traditional methods of lineament extraction are mainly based on artificial visual or semi-automatic interpretation, but these are time consuming and it is difficult to ensure accuracy. In recent years, remote sensing images and DEMs have become easy to acquire. These are rich in visual expression and are limited in time and human factors, therefore a large number of researchers have designed and implemented a series of automatic interpretation algorithms, such as the Segment Tracing Algorithm (STA), the Hough Transform (HT) and hydrological analysis, etc. The core of most methods is edge detection based on filtering. However, by searching for the continuity of pixel values in several directions at the pixel center of an image, some scholars have used geo-statistics to determine line segments according to a threshold value and locate ridge and valley lines based on the variation in the directions of the pixel values. Eventually, linear structure recognition in remote sensing images using an SRTM-DEM was realized (Koike et al., 1995, Raghavan et al., 1995). Lineaments have been extracted from remote sensing images using digital imaging, such as edge detection and the HT (Raghavan et al., 1995, Wang and Howarth, 1990). Others have interpreted lineaments on Landsat TM images based on hydrographic analysis (Bhuiyan, 2015, Ramli et al., 2010). However, remote sensing images are vulnerable to noise at their weak edges, the attitude of the satellite, cloud cover, illumination conditions, and so on, and some results may give a 'false edge' similar to a land-use boundary. DEM data are more practical because of their high precision and the fact that they are not affected by weather or other factors (Mallast et al., 2011). Therefore, experts now tend to use the mountain shading algorithm to render the superimposed slope and slope direction to extract geologic lineaments, but this has high computing requirements (Samy et al., 2012). Therefore, it has become necessary to implement a new algorithm to automatically extract and interpret geologic lineaments using a combination of remote sensing images and a DEM. This will be of scientific significance and practical value in the analysis of the tectonic framework and plate movements in the study area.

The research focus of this paper is the loess-covered area in northern Baoji, which experiences heavy weathering all year round. A large number of mineral resources are buried by thick Quaternary loess and there are few exposed bedrock outcrops on the surface, despite the development of gullies and the frequent geological disasters caused by erosion of the valleys. Because of the instability of the Quaternary loess and an acceleration in the weathering rate, the geological lineaments are masked by new loess and are difficult to represent in remote sensing images in real time. Therefore, extraction results often show obvious discontinuities, which results in difficulty with lineament extraction.

In view of this, in this paper, we design and implement a new extraction algorithm for geological lineaments based on a DEM and supplemented by remote sensing images. First, the best independent band combination of Landsat 8 OLI images for lineaments extraction were selected by using principal component analysis. Second, a high-pass Gaussian filter was used to sharpen the edges in the DEM and the composite Landsat 8 OLI images. Linear boundaries were extracted using tensor voting according to the conspicuousness of the vector sum superposition features of the edge points. Finally, an HT was applied to search the edges and extract the geological lineaments. ArcGIS 10.2 and MATLAB R2014a were employed to analysis the orientation and density of the geological lineaments and evaluate their distribution and tectonic significance in this area.

Section snippets

Geological setting of study area

The study area is in the loess area in the northern part of Baoji (Fig. 1). Its geographical range is 34°30′00″–34°37′00″N, and 107°00′00″–107°11′00″ E, and it covers about 239 km2. The area belongs to the late Paleozoic–Mesozoic Ordos Basin and the central Caledonian orogenic belt of the North Qinling Mountains, and is mainly dominated by active faults. Affected by the movement direction of the block, the NW trending Guguan (F2) and Qishan-Qianyang (F4) faults jointly control the Liupan

Methodology

In this paper, an extraction algorithm for geological lineaments in a loess-covered area is realized and consists of the following steps:

  • i.

    Data pre-processing: radiometric calibration, atmospheric correction, and image fusion were applied to the original remote sensing image, and the best independent band combination for lineament extraction were selected as input data using principal component analysis. Geometric correction and image clipping of the DEM data were performed at the same time to

Results

The best independent band combination of Landsat 8 OLI images for lineaments extraction were selected by using principal component analysis. The eigenvector matrix statistics are shown in Table 2. The sum of variance before and after transformation is the same, that is, the information is assigned to the new component image without the loss of information. PC1 is composed of negative weighted eigenvectors in six bands, accounting for 80.60% of the total variance of the data, and the other

Discussion

Density analysis is an effective statistical analysis method for researching the spatial density distribution characteristics of lineaments and can provide clues to hidden structures and information on deep structures and mineralization (Corgne et al., 2010, Sener et al., 2004). In general, a high-density anomaly area usually represents a fault or fold development area, whereas a low-density anomaly area usually represents a relatively stable tectonic block or Quaternary-covered area. High,

Conclusion

Based on a DEM and Landsat 8 OLI remote sensing images, we rapidly extracted geologic lineaments in a loess area in northern Baoji. To avoid extraneous band noise affecting the final lineament extraction results, appropriate remote sensing image bands were selected using principle component analysis. It removes the correlation between bands and highlights the differences in hue and texture saturation of the image. The edge detection method, combining a high-pass Gaussian filter and tensor

Acknowledgements

This work was financially supported by the 1:50, 000 geological mapping in the loess covered region of the map sheets: Caobizhen (I48E008021), Liangting (I48E008022), Zhaoxian (I48E008023), Qianyang (I48E009021), Fengxiang (I48E009022), & Yaojiagou (I48E009023) in Shaanxi Province, China, under Grant [DD-20160060]. And the project of open fund for key laboratory of land and resources degenerate and unused land remediation, under Grant [SXDJ2017-7].

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