Extraction and analysis of geological lineaments combining a DEM and remote sensing images from the northern Baoji loess 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].
References (42)
- et al.
Integrated geological mapping approach and gold mineralization in Buhweju area, Uganda
Ore Geol. Rev.
(2016) - et al.
Interactive spatial analysis of lineaments
Comput. Geosci.
(2010) An integrated approach to hydro-geological lineament mapping of a semi-arid region of West Africa using Radarsat-1 and GIS
Remote Sens. Environ.
(2010)Automatic lineament extraction in a heavily vegetated region using Landsat Enhanced Thematic Mapper (ETM+) imagery
Adv. Space Res.
(2013)- et al.
Lineament analysis of satellite images using a Segment Tracing Algorithm (STA)
Comput. Geosci.
(1995) - et al.
A comparison of two algorithms for segmentation using edge detection techniques ☆
Pattern Recognit. Lett.
(1990) Chronology and tectonic significance of Cenozoic faults in the Liupanshan Arcuate Tectonic Belt at the northeastern margin of the Qinghai-Tibet Plateau
J. Asian Earth Sci.
(2013)- et al.
Regional geological and tectonic structures of the North Sea area from potential field modelling
Tectonophysics
(2006) - et al.
Delineation of groundwater potential zones in Theni district, Tamil Nadu, using remote sensing, GIS and MIF techniques
Geosci. Front.
(2012) - et al.
Tectonic architecture through Landsat-7 ETM+/SRTM DEM-derived lineaments and relationship to the hydrogeologic setting in Siwa region, NW Egypt
J. Afr. Earth Sci.
(2006)
Applicability of computer-aided comprehensive tool (LINDA: LINeament Detection and Analysis) and shaded digital elevation model for characterizing and interpreting morphotectonic features from lineaments
Comput. Geosci.
Auto-detection and integration of tectonically significant lineaments from SRTM DEM and remotely-sensed geophysical data
Isprs J. Photogramm.
Identifying geomorphic features using LANDSAT-5/TM data processing techniques on Lesvos, Greece
Geomorphology
Detection and analysis of morphotectonic features utilizing satellite remote sensing and GIS: An example in SW Jordan
Geomorphology
Automatic lineament extraction from digital images using a segment tracing and rotation transformation approach
Comput. Geosci.
Comparison of edge detector performance through use in an object recognition task
Comput. Vis. Image Und.
Lineaments frequencies from Landsat ETM + of the Middle Atlas Plateaus (Morocco)
Earth Sci. Res. J.
Lineament Tectonics and Mineralizatin in Tarom Area, North Iran
Open J. Geol.
Hydrological characterisation of geological lineaments: a case study from the Aravalli terrain, India
Hydrol. J.
Effectiveness of SRTM and ALOS-PALSAR data for identifying morphostructural lineaments in northeastern Brazil
Int. J. Remote Sens.
Lineament extraction and analysis, comparison of LANDSAT ETM and ASTER imagery. Case study: Suoimuoi tropical karst catchment, Vietnam
Remote Sens. Environ. Monit., GIS Appl., Geol.
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