**2.2. Data**

Datasets utilized for the present study included Landsat 8 Operational Land Imager Images. The details of data sources of Landsat 8 OLI imageries for both regions are shown in **Figure 2**: 2A and 2B, respectively, which were acquired from USGS Landsat archives (OLI, http://earthexplorer.usgs.gov/) path: 142, row: 40 for case 1, considering snow and partial cloud coverages and path: 135, row: 35 for case 2, considering partial cloud coverage.

The data product considered for the present research has a 30-m spatial resolution based on different days' interval prior to earthquake occurrence, the absence of earthquake and after the earthquake event. Detailed descriptions regarding the datasets used for this research are shown in **Table 1**.

These satellite images are a digital representation of the Earth's surface for the identification of lineament and its corresponding directional change that may represent the surface expression of geological structures [21, 22]. The digital image processing, thematic mapping of lineament, overlay operation and directions of lineament were obtained using the ENVI 5.3, PCI Geomatica 9.1, ArcGIS 10.5, and Rock Works 16 software, respectively. To investigate the normal behavior of lineament in the absence of earthquake, it has been found suitable imagery of 31 January 2015 only for case 1 (85 days before earthquake strike). From 31 January 2015 to 20 March 2015, all the available imageries suffer with extensive cloud; thus, they were neglected. Meanwhile, in case 2, from 19 March to 30 November 2015, three earthquakes (low category: 4.1–4.3 Mw) were struck in the study area, and most of the available imageries also suffer with cloud; thus, those

> imageries automatically neglected like case 1. Therefore, looking forward, it has been found that suitable imagery of 292 days before earthquake strike contains very less percentage of cloud.

> Brackets terminology in the 1st column OLI refer Operation Land Imager and 2nd column EQ refer Earthquake. *Source*:

**Satellite sensors Date of image acquisition Days interval Path/row Resolution (m)**

Pre-earthquake Anomaly Detection and Assessment through Lineament Changes Observation…

http://dx.doi.org/10.5772/intechopen.72735

153

Landsat 8 OLI 31 January 2015 (absence of EQ) 85 days (before) 142/40 30 Landsat 8 OLI 20 March 2015 (presence of EQ) 36 days (before) 142/40 30 Landsat 8 OLI 5 April 2015 (presence of EQ) 20 days (before) 142/40 30 Landsat 8 OLI 21 April 2015 (presence of EQ) 4 days (before) 142/40 30 Landsat 8 OLI 7 May 2015 (post-EQ) 12 days (after) 142/40 30

Landsat 8 OLI 19 March 2015 (absence of EQ) 292 days (before) 135/43 30 Landsat 8 OLI 30 Nov 2015 (presence of EQ) 36 days (before) 135/43 30 Landsat 8 OLI 16 Dec 2015 (presence of EQ) 20 days (before) 135/43 30 Landsat 8 OLI 1 Jan 2016 (presence of EQ) 4 days (before) 135/43 30 Landsat 8 OLI 17 Jan 2016 (post-EQ) 13 days (after) 135/43 30

**Gorkha of Nepal (case 1): category: major**

**Imphal of Manipur (case-2): category: strong**

USGS-Earth explorer Landsat 8 Archive.

**Table 1.** Details of data used in this research.

From the visual observation of datasets, it has been confirmed that all considered Landsat 8 OLI images used for the automatic lineament feature extractions were partially covered by snow and cloud in case 1 and only by cloud in case 2, which did not affect too much of the lineament data volume. The present test has been conducted based only on satellite data without giving any importance of ground validation data. Traditional research application always required field surveys with ground validation data to match properly with satellite data. However, in this test, it has been used popular traditional technique of automatic line algorithm of PCI Geomatica for lineament data extraction, which clearly highlights the lineament changes in the study areas. The automatic LINE algorithm technique generates sufficient number of lineament data over the two study regions, which were used to know the pre-earthquake anomaly detection and assessment by considering multi-dates Landsat 8 OLI imageries. Though, very limited errors have been observed in extracted lineament data which is further checked by overlaying the highways, railroads, etc. These identified errors were checked with available vector shapefiles of those regions, thus finally neglected and removed from extracted lineament database. The present results which have derived automatically from satellite imageries clearly defined lineament datasets and do not need further validation, as the technique was commonly used by numerous researches. However, if validation required, the extracted data can be verified with superposition of layers with geological map of the specific areas. These are the scientific achievements of space technology application over the traditional surveys, as each satellite scene covered in large area in a single acquisition time, where ground surveys need longer period of time and sometimes not possible due to high rugged terrain or any other natural obstacles present in the Earth's surface.

**Figure 2.** The details of data sources of Landsat 8 OLI imageries for two study areas: (A) Nepal and part of China shown in different time series Landsat 8 OLI data (FLAASH atmospheric corrected image): (a) 31 January 2015 (absence of EQ); (b) 20 March 2015 (presence of EQ); (c) 5 April 2015 (presence of EQ); (d) 21 April 2015 (presence of EQ); and (e) 7 May 2015 (post EQ); (B) Manipur and part of Myanmar shown in different time series Landsat 8 OLI data (FLAASH atmospheric corrected image): (a) 19 March 2015 (absence of EQ); (b) 30 November 2015 (presence of EQ); (c) 16 December 2015 (presence of EQ); (d) 1 January 2016 (presence of EQ), and (e) 17 January 2016 (post EQ).


Brackets terminology in the 1st column OLI refer Operation Land Imager and 2nd column EQ refer Earthquake. *Source*: USGS-Earth explorer Landsat 8 Archive.

**Table 1.** Details of data used in this research.

**2.2. Data**

152 Multi-purposeful Application of Geospatial Data

shown in **Table 1**.

Datasets utilized for the present study included Landsat 8 Operational Land Imager Images. The details of data sources of Landsat 8 OLI imageries for both regions are shown in **Figure 2**: 2A and 2B, respectively, which were acquired from USGS Landsat archives (OLI, http://earthexplorer.usgs.gov/) path: 142, row: 40 for case 1, considering snow and partial cloud cover-

The data product considered for the present research has a 30-m spatial resolution based on different days' interval prior to earthquake occurrence, the absence of earthquake and after the earthquake event. Detailed descriptions regarding the datasets used for this research are

These satellite images are a digital representation of the Earth's surface for the identification of lineament and its corresponding directional change that may represent the surface expression of geological structures [21, 22]. The digital image processing, thematic mapping of lineament, overlay operation and directions of lineament were obtained using the ENVI 5.3, PCI Geomatica 9.1, ArcGIS 10.5, and Rock Works 16 software, respectively. To investigate the normal behavior of lineament in the absence of earthquake, it has been found suitable imagery of 31 January 2015 only for case 1 (85 days before earthquake strike). From 31 January 2015 to 20 March 2015, all the available imageries suffer with extensive cloud; thus, they were neglected. Meanwhile, in case 2, from 19 March to 30 November 2015, three earthquakes (low category: 4.1–4.3 Mw) were struck in the study area, and most of the available imageries also suffer with cloud; thus, those

**Figure 2.** The details of data sources of Landsat 8 OLI imageries for two study areas: (A) Nepal and part of China shown in different time series Landsat 8 OLI data (FLAASH atmospheric corrected image): (a) 31 January 2015 (absence of EQ); (b) 20 March 2015 (presence of EQ); (c) 5 April 2015 (presence of EQ); (d) 21 April 2015 (presence of EQ); and (e) 7 May 2015 (post EQ); (B) Manipur and part of Myanmar shown in different time series Landsat 8 OLI data (FLAASH atmospheric corrected image): (a) 19 March 2015 (absence of EQ); (b) 30 November 2015 (presence of EQ); (c) 16 December

2015 (presence of EQ); (d) 1 January 2016 (presence of EQ), and (e) 17 January 2016 (post EQ).

ages and path: 135, row: 35 for case 2, considering partial cloud coverage.

imageries automatically neglected like case 1. Therefore, looking forward, it has been found that suitable imagery of 292 days before earthquake strike contains very less percentage of cloud.

From the visual observation of datasets, it has been confirmed that all considered Landsat 8 OLI images used for the automatic lineament feature extractions were partially covered by snow and cloud in case 1 and only by cloud in case 2, which did not affect too much of the lineament data volume. The present test has been conducted based only on satellite data without giving any importance of ground validation data. Traditional research application always required field surveys with ground validation data to match properly with satellite data. However, in this test, it has been used popular traditional technique of automatic line algorithm of PCI Geomatica for lineament data extraction, which clearly highlights the lineament changes in the study areas. The automatic LINE algorithm technique generates sufficient number of lineament data over the two study regions, which were used to know the pre-earthquake anomaly detection and assessment by considering multi-dates Landsat 8 OLI imageries. Though, very limited errors have been observed in extracted lineament data which is further checked by overlaying the highways, railroads, etc. These identified errors were checked with available vector shapefiles of those regions, thus finally neglected and removed from extracted lineament database. The present results which have derived automatically from satellite imageries clearly defined lineament datasets and do not need further validation, as the technique was commonly used by numerous researches. However, if validation required, the extracted data can be verified with superposition of layers with geological map of the specific areas. These are the scientific achievements of space technology application over the traditional surveys, as each satellite scene covered in large area in a single acquisition time, where ground surveys need longer period of time and sometimes not possible due to high rugged terrain or any other natural obstacles present in the Earth's surface.
