**2.3. Methodology**

Satellite images (multispectral or digital elevation models) and aerial photography are broadly used to extract lineaments for different purposes, like defining geological structures and tectonics fabrics. Each image consists of 11 spectral bands and scene size is 185 km northsouth by 185 km east-west, which enables the delineation of the geological lineaments in the study area. In this experiment, automatic extraction of geologic lineament performed through different steps including from raw satellite imagery pre-processing like DN value conversion into radiance, radiance to reflectance and later atmospheric correction using the FLAASH (Fast Line-of-sight Atmospheric Analysis of Hypercubes) module. Thereafter, enhancementlinear 5% stretch performed over R, G, B (5, 4, 2) band combinations respectively to get the better visualization image and at the end of this stage, all images were assigned to WGS 1984 datum with projection parameter of UTM zone 45 N (case 1) and 46 N (case 2), respectively.

In the next stage, principal component analysis (PCA) was performed on each atmospheric corrected image and each PC-1 image was created by considering best band selection, based on Eigen number and Eigen value, thus finally help to enhance image discontinuties corresponding to structural lineaments. For the present research, at initial stage, we have developed the theoretical model (**Figure 3**) to demonstrate whether the results are coherent with the theoretical model based on the different output of images.

Lineament distribution maps of Nepal and Manipur were prepared from Landsat 8 OLI satellite imageries by using four different remote sensing and GIS software's integration, where one software output was used by other to obtain the final lineament results. The OLI spectral band in gray scale was used to extract lineament. Out of five images of each case, one image used to observe the normal behavior in the absence of earthquake event, three images for "anomaly" observation (pre-earthquake stage), whereas another single image was used for after earthquake lineament change observation of Nepal and Manipur earthquake, respectively. Before the methodological description breakdown, the overall workflow of the present research is shown in **Figure 4**.

*2.3.1. Lineament extraction, line splitting, and length analysis method*

**Figure 4.** Flowchart of methodology used in this research.

There are two common methods for the extraction of lineaments from satellite images: visual extraction and automatic extraction. Most popular traditional methods based on edge filtering techniques, that is, Sobel, PCA, ICA, MNF, band rationing, RGB band combinations with high contrast, different stretch and directional filters are used worldwide for lineament extraction. In this research, PCA is considered, and the most widely used software is deployed for the automatic lineament extraction with the most common popular traditional method, that is, LINE algorithm of PCI Geomatica 9.1 v software [23], which consists of three stages [24] has accentuated and facilitated the detection of lineament in the satellite images. Various computer-aided methods such as edge detection, thresholding, and curve extraction steps [25] were carried out over derived principal component analysis (PCA) image (i.e., PC1) of the study area under default parameter windows, where user defined modification of values can be done using this software. For processing and extraction of lineaments, the following algorithm parameters and its corresponding values were used such as RADI-Radius of the filter in pixels (10), GTHR-Threshold for edge gradient (50), LTHR-Threshold for Curve length, in

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**Figure 3.** Theoretical model developed for the present research.

**Figure 4.** Flowchart of methodology used in this research.

**2.3. Methodology**

154 Multi-purposeful Application of Geospatial Data

Satellite images (multispectral or digital elevation models) and aerial photography are broadly used to extract lineaments for different purposes, like defining geological structures and tectonics fabrics. Each image consists of 11 spectral bands and scene size is 185 km northsouth by 185 km east-west, which enables the delineation of the geological lineaments in the study area. In this experiment, automatic extraction of geologic lineament performed through different steps including from raw satellite imagery pre-processing like DN value conversion into radiance, radiance to reflectance and later atmospheric correction using the FLAASH (Fast Line-of-sight Atmospheric Analysis of Hypercubes) module. Thereafter, enhancementlinear 5% stretch performed over R, G, B (5, 4, 2) band combinations respectively to get the better visualization image and at the end of this stage, all images were assigned to WGS 1984 datum with projection parameter of UTM zone 45 N (case 1) and 46 N (case 2), respectively. In the next stage, principal component analysis (PCA) was performed on each atmospheric corrected image and each PC-1 image was created by considering best band selection, based on Eigen number and Eigen value, thus finally help to enhance image discontinuties corresponding to structural lineaments. For the present research, at initial stage, we have developed the theoretical model (**Figure 3**) to demonstrate whether the results are coherent with

Lineament distribution maps of Nepal and Manipur were prepared from Landsat 8 OLI satellite imageries by using four different remote sensing and GIS software's integration, where one software output was used by other to obtain the final lineament results. The OLI spectral band in gray scale was used to extract lineament. Out of five images of each case, one image used to observe the normal behavior in the absence of earthquake event, three images for "anomaly" observation (pre-earthquake stage), whereas another single image was used for after earthquake lineament change observation of Nepal and Manipur earthquake, respectively. Before the methodological description breakdown, the overall workflow of the present

the theoretical model based on the different output of images.

research is shown in **Figure 4**.

**Figure 3.** Theoretical model developed for the present research.

#### *2.3.1. Lineament extraction, line splitting, and length analysis method*

There are two common methods for the extraction of lineaments from satellite images: visual extraction and automatic extraction. Most popular traditional methods based on edge filtering techniques, that is, Sobel, PCA, ICA, MNF, band rationing, RGB band combinations with high contrast, different stretch and directional filters are used worldwide for lineament extraction. In this research, PCA is considered, and the most widely used software is deployed for the automatic lineament extraction with the most common popular traditional method, that is, LINE algorithm of PCI Geomatica 9.1 v software [23], which consists of three stages [24] has accentuated and facilitated the detection of lineament in the satellite images. Various computer-aided methods such as edge detection, thresholding, and curve extraction steps [25] were carried out over derived principal component analysis (PCA) image (i.e., PC1) of the study area under default parameter windows, where user defined modification of values can be done using this software. For processing and extraction of lineaments, the following algorithm parameters and its corresponding values were used such as RADI-Radius of the filter in pixels (10), GTHR-Threshold for edge gradient (50), LTHR-Threshold for Curve length, in pixels (30), FTHR-Threshold for Line fitting error in pixels (3), ATHR-Threshold for Angular difference in degrees (30), and DTHR-Threshold for linking distance in pixels (20).

For overlay change detection, day wise comparison of temporal lineament data has been per-

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The processing of the orientation of lineaments simply produces a directional diagram, which shows the distribution of lineament features. For lineament direction trend analysis, previously saved lineament data as dxf format was used in the RockWorks 16v software environment, where lineament computation was performed to measure bearing (unidirectional: 0–360°), length (m), line start and ending values, respectively, ultimately helps to create rose diagram. The directional diagram that depicts the orientation of the linear features finally saved it in the required format as a tiff file. Later, following the same process, remaining rose diagrams were prepared for the two study areas to figure out the directional change of lineaments based on three different stages (i.e., in the absence, presence, and after earthquake event, respectively).

The statistical approach of the geometric parameters (number of lineaments and lengths variation) of lineaments is required to describe the structure of a region. The length parameters (i.e., total number of lineament, minimum, maximum, mean, total sum, and standard deviation) are generated based on all attributes of corresponding temporal lineament data of the two impending earthquakes in the absence and the presence of earthquake event. As, the lineament data variations observed in different places, the total number of lineament and length variations, that is, mean and standard deviation values were considered for anomalies identi-

For statistical comparison, the demarcated line has been drawn over the line graph to represent the change behavior of lineament in three different situations with respect to earthquake occurrence day. The left black vertical dotted line used to represent the absence of earthquake marked as "no anomaly" and the second black point dash line considered to represent "extreme anomaly" prior to strike, and black solid line indicates the earthquake occurrence day in the corresponding study areas, whereas black dash line plotted in both the graphs to indicate the anomaly still present representing post-earthquake scenario. The X-axis represents the days which considered for earthquake observation and Y-axis represents the number of extracted lineaments and lineament length (mean and standard deviation value in km), respectively. However, number of lineament and mean value of lineament length were fur-

The focused study areas both are tectonically active in nature, and there is no previous research conducted in the two earthquake-stricken areas considering lineament change. This research has been tried to test using Landsat 8 OLI dataset for the first time based on the newly developed theoretical model. To quantitatively evaluate the present methods, lineaments were automatically extracted from each image after principal component analysis

fication of the study areas, which also further compare with post-earthquake data.

ther used to justify the lineament change, observed through scatter diagram.

formed, which ultimately help to monitor the lineament changes of the study areas.

*2.3.3. Lineament direction analysis method*

*2.3.4. Statistical analysis method*

**3. Results and discussion**

In the first stage, RADI parameter (filter radius) specifies the size of the Gaussian kernel used as a filter during edge detection. The edge strength image was threshold to obtain a binary image. Therefore, the choice of RADI value depends on the condition, like as, the greater the value, the less noise and detail appear in the edge detection image. In the second stage, this image was defined by the GTHR parameter (edge gradient threshold) value after testing with different values, and the suitable one was considered. In the third stage, curves are extracted from binary edge image, which have several substeps. First, a thinning algorithm was applied to the binary edge image to generate pixel-wise skeleton curves, then sequence of pixels for each curve was extracted from that corresponding image. Any curve with the number of pixels less than parameter value LTHR was discarded automatically from further processing. Thereafter, extracted pixel curve was converted to vector form by fitting piece wise line segments to it. The resulting polyline was an approximation to the original pixel curve where the maximum fitting error distance between the two was specified by the FTHR parameter. Finally, the algorithm links pairs of polylines, which satisfy the last two parameters, where the angle between the two segments was less than the parameter ATHR and the distance between end points was less than the parameter DTHR. The lineament extraction algorithm takes these problems into account to extract linear features from the corresponding image.

The main geometric characteristics of a single linear line are orientation and length (continuity) and in case of curved line, curvature [26]. For line split generation, ArcGIS 10.5 Model builder was used to automate GIS processes by linking data input, tools/functions, and data output, which saved into shapefile format. These lineament features extracted as a compound line, which splitted into a single line at their vertices and recorded the polylines in a vector layer.

Thereafter, lineament line length analysis was performed using the ArcGIS 10.5 software through conversion of meters into kilometer unit. The most important factor was that the lineaments in an automated one were shorter in length, so that a few of them could be combined to form one long lineament. In this stage, we are getting lineament length of all the attribute values based on the derived products of lineaments of the two study areas.

#### *2.3.2. Lineament fluctuations change observation method*

To observe the lineament fluctuation change over the two study areas, satellite image-derived vector output, that is, lineaments were considered to perform overlay technique on each temporal data, helps to prepare corresponding lineament fluctuations maps. However, the criteria have the following conditions, if fluctuations of lineament persist over the study areas in the presence of earthquake, those are considered as "anomaly." These anomalous changes of lineament data represent fluctuations over the two study areas in three different phases, that is, initial, middle, and strong phase. On the other hand, if lineament observed less in number along with other statistical information in the absence of earthquake event, it is considered as normal behavior and categorized as "no anomaly" and finally, to know lineament situation after the earthquake, it is indicated as post-earthquake phase. However, the lineament increases or decreases at this phase does not matter, and this has been done only for comparison. For overlay change detection, day wise comparison of temporal lineament data has been performed, which ultimately help to monitor the lineament changes of the study areas.
