1 Introduction

Hopkins mapped lineaments in 1841 and was a pioneer in describing the relationship between lineaments and topographical characteristics [1]. The term lineament was first used by Hobbs in 1904 [2] to describe a linear characteristic that represented the ‘significant lines of a landscape’. Lineaments may include linear, lineation, geo-fracture, suture, mega fracture, and shear zones formed by several tectonic processes and the associated activities [3], Lineament magnitude is a scale-dependent parameter, it may range in length from a few centimetres to kilometres and may vary from small as a cleavage in minerals to large as the inter-plate boundaries. The term lineament is, however, most often used for describing Earth’s unidirectional features of larger magnitude [4]. Lineament patterns might be correlated to the structural, geomorphic events and results of tectonic deformation. A lineament represents the features such as faults, fractures, master joints, axial plane fractures, dyke systems, long and linear lithologies, straight courses of streams, vegetation alignment, or topographic linearity [5].

Until the last decade, researchers used aerial photographs and topographic maps to extract lineaments manually. Since the availability of aerial photos is very limited and the process of extracting lineaments through photogrammetry is tedious, the satellite data such as the Digital Terrain Model (DTM) or high-resolution satellite images are also used when the objective of the study is limited to the geological aspect [6, 7]. In the late twentieth century, automatic lineament extraction techniques were developed, which enabled an improved approach to conduct several works [8,9,10,11]. The automatic extraction process using GIS techniques has enabled the extraction of the geological surfaces and subsurface features and the subsequent integration of the derived information [7].

Lineament extraction was performed manually until the introduction of the digital elevation model (DEM). However, lineament extraction from hardcopy geological maps or aerial photo interpretation was tedious the process included observing linear features. Visual identification is a time-consuming process and suffers from a high degree of subjectivity. It needs to be interpreted manually which is subjective and it has restrictions posed due to human eyesight limitations. The advent of remote sensing technology and image processing enabled the extraction of geological features, such as lineament, dike, and fracture zone from satellite images. The advancements in satellite technology, data processing, and data visualization techniques rendered it more convenient to extract lineaments. Lineament extraction and map** are useful in various fields of scientific research and in-demand applications such as geographical information system (GIS) and remote sensing (RS) technology. Accordingly, remote sensing data also have wide applicability in geological map** for various scientific research fields, such as geomorphology, geology, mineral exploration, etc.

The present study is significant with respect to understanding the geological setting and the structural deformation caused by lineaments, which would help to understand the influence of lineaments on the alignment of the drainage network. The present study is also important for improving the understanding of the level of structural control on landscape evolution in general and stream alignment in particular. Such studies also reveal the level of changes that have occurred in the drainage network due to structural deformation caused by lineaments.

The major lineaments, faults, and fractures of a region may be identified and mapped through visual analysis of satellite images. Since the lineaments are straight and linear in nature, Digital Elevation Model (DEM) and satellite images have been applied widely for the identification and extraction of lineaments [7, 12]. The map** of the surface features of the Earth has become further convenient with the availability of huge amounts of remote sensing data and several image processing techniques, including the extensive use of various remote sensing databases and the GIS software for data [7, 13,14,15,16,17,18]. Digital Elevation Model (DEM) with good spatial resolution is the key input for a GIS-based analysis of the lineaments and extraction of drainage networks. Digital Image Processing (DIP) and Geographic information system (GIS) are two important tools that enable a convenient, accurate, and efficient understanding and interpretation of geotectonic activities.

In the present study, lineament extraction was performed using Sentinel-2 satellite data and GIS technology to understand the influence of lineaments on the drainage network in the upper Krishna River basin. The objective of the study was to signify the spatial distribution of the lineaments and their influence on the drainage network in the study region.

1.1 Study area

The Upper Krishna River basin is located in the Deccan Volcanic Province of the Maharashtra plateau, on the eastern side of Western Ghats. The study area extended from 73° 50ʹ E to 75° 05ʹ E longitudes and 16° 02ʹ N to 18° 07ʹ N latitudes (Fig. 1). The total area of the Upper Krishna River basin is approximately 19,331 km2. River Krishna and all its tributaries originate in the Western Ghats region, from where these flow in the North-West to South-East direction. The relief variation of the study area is 505 to 1440 m above the MSL, which indicates steep to moderate relief. The Upper Krishna River basin is characterised by three major physical divisions: (i) the Ghats, hills, and plateaus, (ii) the foothill zones and (iii) the plains.

Fig. 1
figure 1

Location map of the Upper Krishna River Basin. The major streams depicted in the figure are flowing in the North-West to South-East direction

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The climate of the Upper Krishna River basin is of a tropical and sub-tropical monsoon type. The rainfall in the sub-basin region varies from 500 to 6208 mm as per IMD rainfall data for the period 1951 to 2015. The average annual rainfall recorded in the Upper Krishna River basin is 1300 mm. The temperature of the study area varies from 14 °C in winter to 36 °C in summer. The average annual mean temperature in this region is 28 °C (IMD, 2020).

1.2 Geology

The Upper Krishna River basin is formed on the Deccan trap lava flows of the Late Cretaceous Paleocene period. In certain regions of the river basin, extensive alluvium depositions of the Quaternary period are observed along the main river channel [19]. It is in these regions that agricultural practices are dominant.

The broad classification of the geology of the Upper Krishna River basin is alluvium and laterite formation from the Holocene to Pleistocene period and the most prominent Deccan basalt formation from the Upper Cretaceous to Lower Eocene period. (Geological Survey of India, 1975) [20].

Alluvium: Alluvium formations normally lay over the Deccan basalt and comprise loose to semi-consolidated material, such as sand, silt, gravels, clayey, etc. Certain isolated patches of recent alluvium depositions, which vary from 2 to 20 m in thickness, are also formed along the banks of the Krishna River and its major tributaries (Table 1). In total, there is 2% alluvium soil in the Upper Krishna River basin (GSDA, 1975).

Table 1 Lineament categories are based on stream length
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Laterite: Laterite crusts are observed in the study area on the plateau top and the form of tableland with a reddish-brown colour and coarse texture. The laterite formation in the Panchgani region is well exposed (Fig. 2). This has been considered to have been developed on a large plain surface with a gentle gradient in the direction of southerly to southwesterly established on the sequence of the Cretaceous-Eocene Deccan lava flows [21].

Fig. 2
figure 2

Geological map of the Upper Krishna Basin. Source: (based on: www.gsi.gov.in) [20]

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Deccan Basalt: Maximum of the study area belongs to the Deccan trap geological formation of the Late Cretaceous-Paleocene period and comprises basalt rock. In the Deccan basalt region, two types of basalt rocks are present—one massive and the other vesicular basalt. In the study area occurrence of vesicular basalt is limited while massive basalts are common. The Deccan Basalt comprises various thicknesses, which are differentiated based on the presence of a red-bole, a weathered layer of basalt covered with dense basalt, and a vesicular basalt layer covered with dense basalt. In the study area, generally NNE- SSW lineaments are observed (Figs. 4 and 5). Similar observations are made by [22].

2 Database and methodology

The study focused mainly on lineament extraction and visualization of the influence of lineaments on the drainage network of a large river basin area. Therefore, satellite-derived data were required to obtain the output. Remote sensing technology serves as a concrete source for spatial data to be used as an input for GIS and generates a detailed map using other collateral data derived from different sources. A systematic analysis of a satellite image generally involves consideration of the image and terrain elements [23]. In the present study, Sentinel-2 satellite images and DEM were used for the extraction of lineaments and the drainage network. These datasets were also utilized to visualize the terrain of the region and map the features such as lineaments. The details regarding the data and methods used are as below (Fig. 3).

Fig. 3
figure 3

Methodology adopted in lineament extraction and interpretation

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2.1 Lineament extraction

In previous studies use of aerial photogrammetry was a common approach to identifying and extracting lineaments. Satellite images such as multispectral or digital elevation models and aerial photography are commonly used for lineament extraction in different studies, including those concerned with defining geological structures and tectonic activities. In several recent studies, DEM has been used for lineament extraction. In other recent studies, researchers have performed lineament extraction to ensure the map** of the entire set of lineaments available for the study area [6, 7, 24,25,26].

To improve the interpretation and analysis, Digital Image Processing has been used together with various algorithms to enhance the surface or linear features. In the present study, edge detection, threshold setting, curve extraction, and principal component analysis (PCA) were used for image analysis [5, 27], Edge enhancement is useful for delineating the edges and sharpening the lineaments. Spatial filtering techniques were used to sharpen the images with linear features and edge highlighting convolution methods were used. Finally, the PCA technique was used in the lineament extraction.

Sentinel 2 Image data were downloaded, and the atmospheric correction was performed on the retrieved data. Afterwards, the image data were processed for layer stacking and then converted to raster.tiff file format using Erdas Imagine 2014 Software. The raster.tiff format files were then processed using the ENVI 5.0 software to generate PCA imageries, which were then exported with 8-bit grey-scale resolution in the GeoTIFF/TIFF file format. The automatic lineament extraction was performed using the PCI Geomatics 2016 software. The line module in the algorithm library was used for processing the final data product by running the lineament extraction command. The generated lineament map, depicted in Fig. 3, was verified using the lineament data available on the Bhuvan portal of NRSC, India.

2.2 Stream network generation

Water tries to remain in equilibrium and follows the easiest way through the lineaments and fracture zones. In this process, lineaments act as conduits. This causes the water to follow a lineament course that is generally linearly aligned. To better understand the influence of lineament on stream behaviour, the pattern followed by the streams has to be analyzed, which is achieved by generating a stream network.

In the present study, the stream network was generated using data from Alaska Satellite Facility -DEM, which had a spatial resolution of 12.5 m (Fig. 4). ASF-DEM provides high-resolution elevation data and therefore, results in better accuracy as well as better surface details in the outcomes of data analysis. The GIS software was then employed to process these data and generate the stream network in the following steps—‘DEM fills’, ‘flow direction’, ‘flow accumulation’, ‘stream order’, and ‘raster calculation. In the ‘stream order’ step, Strahler’s (1952) method of ‘stream ordering’ was used. Next, the stream order raster was converted to the vector format for the analysis of the linear morphometric properties of the drainage network.

Fig. 4
figure 4

Lineament map of the Upper Krishna River Basin

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3 Results and discussion

Lineament map** and analysis were significant components of the present study. The parameters associated with lineaments and the number of streams influenced by lineaments, along with their respective lengths, are discussed below in detail.

3.1 Lineament analysis

The lineament analysis mainly included the categorization of lineaments based on their respective lengths, lineament density, and influence.

3.2 Lineament categorization

A total of 1314 lineaments were identified for the study area in the present study. The total length of these lineaments was 3983.44 km. The identified lineaments were categorized into three groups based on their length.

Among all lineaments identified, 998 lineaments were categorized into the dominant group of minor lineaments, for which the length ranged from 0.64 to 3.55 km, thereby covering a total length of 1983.82 km (49.80%). The second dominant group of lineaments was the intermediate lineaments, with a length range of 3.59–8.88 km, covering a total length of 1386.94 km (34.82%). A total of 269 lineaments were categorized into this second dominant group. The third group was that of major lineaments, with a length range of 9.31–36.0 km and a total length of 612.68 km (15.36%). A total of 47 lineaments were categorized into this third group. The major lineaments are straight and control the streamflow. The intermediate lineaments are abundant along the regional strike. The minor lineaments are higher in number and smaller in magnitudes, thereby forming a network across the study area (Table 2; Fig. 4).

Table 2 Influence of lineaments on the drainage network in the Upper Krishna River basin
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3.3 Lineament orientation

The lineaments range in length from a few meters to tens of kilometres. Generally, they appear as a rectilinear feature based on the dip of the structural plane. It has been observed that all the lineament in the study area either meet almost perpendicular at large lengths or intersect each other or are parallel to each other. In total 1314 lineaments are identified (Fig. 4). The orientation of maximum lineaments is observed in the direction of NE 45° to SW 225° and minimum orientation in the direction of NW 345°–SE 165° (Fig. 5).

Fig. 5
figure 5

Lineament orientation map of Upper Krishna River Basin

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3.4 Analysis of the influence of lineaments on the drainage network

The study area was drained by the streams of one to seventh order. The total length of all streams in the sub-basin region was 11,145 km, of which approximately 1642.81 km of stream length of all orders was structurally controlled, i.e., it was influenced by lineaments (Fig. 5B). Among all the streams influenced by lineaments, the first-, second-, and third-order streams received the maximum influence of (51.04%), (28.95%) and (11.09%) respectively (Fig. 6). This was mainly because of the loss of stream power, because of which the streams were not able to cross the structurally controlled threshold limit set by the lineaments. The streams from the fourth order to the seventh order received the minimum influence of (5.11%), (3.19%), (0.61%), and (0.003%) respectively. The fourth-, fifth-, and sixth-order streams exhibited minor lineament control, while those of the seventh-order exhibited negligible control by lineament. This might be because of the increased water volume and stream power. Therefore, it was inferred that rather than following the structural control, these streams followed the slope of the terrain.

Fig. 6
figure 6

DEM and stream order network for Upper Krishna River Basin

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The results of the present study distinctly indicated that the maximum influence of lineaments was exerted on the streams of the first, second, and third order all of which are located in the source region. The influence of lineaments on the streams of the fourth order and higher was relatively lower and the influence became negligible for the streams of the seventh order. The structural control of lineaments on the streams could be observed distinctly by overlaying the extracted lineaments on the Koyna Dam region using the Google Earth Pro software (Fig. 7).

Fig. 7
figure 7

The image shows an overlay of extracted lineaments on the Koyna Dam region using Google Earth Pro. The control of lineaments on the streams is distinctly visible. The streams are aligned according to the lineaments

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3.5 Streams influenced by lineaments

The streams influenced by lineaments were identified using a buffer tool. A buffer of 100 m was first drawn along the lineaments. Because lineaments represent potential weakness zones so distance from the lineaments is a predictive factor for any engineering work like dam construction [28]. Distance for the dam construction site should be at least 100 m away from the lineaments [29]. Beyond 100 m distance from the lineament there will be less influence on stream hence we have considered 100 m buffer from the lineament.

The streams located within the buffer limits were considered as the streams influenced by lineaments (Fig. 8A, B). These streams were selected and extracted for further analysis. The respective lengths of the influenced streams were calculated and separated according to the stream order.

Fig. 8
figure 8

A Lineament density map of the Upper Krishna River Basin. B The streams are influenced by lineaments in the Upper Krishna River Basin. The red segment indicates the streams that could be structurally controlled

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The orientation of lineament influenced streams are seems to be maximum in four major East 100°, West 280°, North 10°, South 190° and minimum in four minor NE 45°, SW 225°, NW 315°, SE 135° directions (Fig. 9). All the lineament in the study area either meet almost perpendicular at large lengths or intersect each other or are parallel to each other, which reflects the strong structural control of lineament in the Upper Krishna River basin (Fig. 10).

Fig. 9
figure 9

The rose diagram is used to depict streams and their orientation in a particular direction influenced by the lineaments

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Fig. 10
figure 10

The stream order vs. stream length graph illustrates the influence of lineaments on the length of the streams. It is distinctly visible that more streams of the lower order were influenced by lineaments compared to those of the higher order. Map Data: Google Earth©2020 CNES/Airbus, Maxar Technologies

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4 Conclusion

DEM comprising high-resolution data is an important dataset for analyzing structural landforms. DEM has been proven as a suitable input for lineament extraction. The results of the present study revealed that lineaments guided the flow of streams with low stream power. This was observed mainly for the streams of the lower order, including the streams of the first and second order, which together accounted for approximately 80% of the total length of the whole drainage network in the study area. The streams of the middle and higher orders were relatively less influenced by lineaments. The streams of the third, fourth and fifth order together accounted for only 20% of the influenced streams in terms of total length. In the case of third-order streams, less than 5% of stream length was controlled by lineaments. Negligible control of lineaments was observed on the streams of higher order. This could be attributed to the increased volume of water and steam power in higher-order streams. The water flow and stream power probably overcame the threshold of resistance caused due to the presence of lineaments. Therefore, based on the analysis of lineaments conducted in the present study, it was concluded that the influence of the structural control set by lineaments was higher on the lower-order streams compared to the higher-order streams.

The present study used image analysis techniques for lineament extraction and the study of the influence of lineaments on streams. The change in the course of higher-order streams is mainly attributed to the control caused by the lineaments and the subsurface lithology. The structural complexity of a river basin renders it necessary to understand the level of influence of lineaments. In the present study, the influence of lineaments on lower-order streams was observed to have manifested mainly in the form of stream routing or alignment according to the orientation of the lineaments. The overall landscape development was also influenced by the dense network of lineaments. The findings of the present study are significant as they revealed the geological setting and its structural deformation caused by lineaments, which would, in turn, assist in further exploration of the drainage network and also in water resource management. The present study and other similar ones are important for improving the understanding of the level of structural control on landscape evolution in general and stream alignment in particular. Such studies also reveal the changes that have occurred in the drainage network due to structural deformation caused by lineaments.