**4. Conclusions**

different geologic condition, structural arrangements, depth, and magnitude variations of the

**Figure 10.** Results of number of lineament and lineament length change observation in the absence and the presence of Imphal-Manipur earthquake (4 January 2016) from 19 March 2015 (292 days before) to 17 January 2016 (12 days after).

The internal details of data representations of both figures are the same as **Figure 9**.

The earthquake occurrence day is represented as a vertical solid black line. The left black vertical dotted line represents the absence of earthquake event (no anomaly) at this stage, and the second black point dash line represents extreme anomaly of 4 days prior to strike and black dash line line indicates the anomaly still present representing post-earthquake scenario. In both cases, the X-axis represents the days, which considered for lineament change observation during the corresponding earthquake, and Y-axis represents the number of extracted lineaments (**Figures 9a** and **10a**), and lineament length (km) represents with the SD and mean value (**Figures 9b** and **10b**).

**Figures 9** and **10** represent data anomaly of two study areas in the presence of earthquake event (prior to earthquake). However, the number of lineament was found stable (in the absence of earthquake event) when the days observed 85 days before the earthquake event (case 1: Gorkha-Nepal) compared to the highest anomalous behavior present prior to earthquake strike (4 days before) and recorded approx. three times higher number of lineaments (**Figure 9a**). However, lineament anomaly was observed 2.5 times higher than stable condition (20 days before strike). In **Figure 10** (Imphal-Manipur case), anomaly exists in the absence of earthquake event, and when the observation day's progresses from initial anomaly stage, lineaments were increased (4 days before: highest abnormality presence in the anomaly stage) than two other anomaly

On the other hand, lineament length (km) mean value was recorded as higher (**Figure 9b**). However, the mean length can be shorter or longer and it can be varied due to different geological settings and underlying geological activities. Furthermore, the SD value of lineament length of the two study areas represents (solid black line with dot symbol) different trend, which is decreasing-increasing-decreasing trend in strong earthquake case (Gorkha of Nepal: 7.8 Mw) compared to the absence of earthquake (**Figure 9b**). Whereas, in major earthquake case (Imphal of Manipur: 6.7 Mw), it follows increasing-decreasing-increasing trend (**Figure 10b**). The results, which observed in each stage based on different analyses method, have individual credit, but all these data are integrated with each other in a sense, like that, first it generated number of lineaments, then measured the lineament length and its overall statistical values. Thereafter,

phases (**Figure 10a**), whereas other two phases were also showing anomaly.

mentioned two earthquakes.

166 Multi-purposeful Application of Geospatial Data

In this study, based on the Landsat 8 OLI satellite-derived lineament data of the two earthquake regions from 2015 to 2016, the spatial fluctuations of lineaments data and their behavioral changes were analyzed in the presence and absence of earthquake event, which categorized into three phases, that is, in the absence of earthquake (no anomaly), the presence of earthquake event (anomaly) and post-earthquake phase, respectively.

The Gorkha earthquake of Nepal was a result of thrusting along the Main Himalayan Thrust (MHT) [27], and the analysis of the SAR interferograms led to the interpretations that the event was a blind thrust and seismogenic fault [28–30]. However, for Imphal the existing literature suggest that the regional plate boundary in eastern India-the Indo-Burmese Arc is oriented approximately south-southwest-north-northeast directions, see [31], matching the orientation of extracted lineaments.

Present research is the first kind of study conducted and applied in both the earthquake prone areas based on the theoretical model concept. This study creates a breeze in between all four softwares, which were deployed from preprocessing to final stage output performed through geointegration techniques of ENVI—PCI Geomatica—ArcGIS-RockWorks software's, respectively. These combined techniques were successfully applied on Landsat 8 OLI optical imageries, which used traditional popular automatic methods and clearly showed its ability to extract different kinds of information based on lineament data.

Open Publishing manager for inviting us to write a book chapter, and book editor, anonymous reviewer for their insightful comments and suggestions which helps to improve the manuscript.

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

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

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\*Address all correspondence to: nath.gis79@gmail.com and niuzheng@radi.ac.cn

Digital Earth (RADI), Chinese Academy of Sciences (CAS), Beijing, China

1 The State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and

2 College of Resource and Environmental Studies, University of Chinese Academy of

3 Department of Geography and Environmental Studies, University of Chittagong,

4 Department of Applied Geology, Dibrugarh University, Dibrugarh, Assam, India

[1] Hobbs WH. Repeating patterns in the relief and in the structure of land. Geological

[2] Pradhan B, Singh RP, Buchroithner MF. Estimation of stress and its use in evaluation of landslide prone regions using remote sensing data. Advances in Space Research.

[3] Elmahdy SI, Mohamed MM. Mapping of tecto-lineaments and investigate their association with earthquakes in Egypt: A hybrid approach using remote sensing data.

[4] Masoud AA, Koike K. Auto-detection and integration of tectonically significant lineaments from SRTM DEM and remotely-sensed geophysical data. ISPRS Journal of

[5] Caponera F. Remote sensing applications to water resources: Remote sensing image interpretation for ground water surveying. Food and Agricultural Organization of the

[6] Koike K, Nagano S, Ohmi M. Lineament analysis of satellite images using a segment

[7] Mah A, Taylor GR, Lennox P, Balia L. Lineament analysis of Landsat thematic mapper images, northern territory, Australia. Photogrammetric Engineering & Remote Sensing.

tracing algorithm (STA). Computers and Geosciences. 1995;**21**:1091-1104

**Author details**

Sciences (UCAS), Beijing, China

Chittagong, Bangladesh

2006;**37**:698-709

1995;**61**:761-773

**References**

Biswajit Nath1,2,3\*, Zheng Niu1,2\* and Shukla Acharjee<sup>4</sup>

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United Nations, Rome. 1989. 234 pp

The automatic lineament delineation using the LINE module of PCI Geomatica was deployed and found great ability of data extraction capacity, as it extracts sufficient numbers of lineaments from Landsat 8 OLI imageries. Different types of information extracted from the lineaments data of the two study areas, where number of lineaments, lineament length change, that is, mean and SD value, and directional change were observed. In both cases, their behavior is abnormal in the presence of earthquake event regarded as anomaly.

The present results also identified that the highest lineament fluctuations and abnormality exist within the anomaly phase, which marked as the highest anomaly (strong phase) just 4 days before earthquake strike. Lineaments behavior was observed quite normal (no anomaly, compared with abnormal situation) in 85 days before (Gorkha of Nepal) and 292 days before (Imphal of Manipur) the earthquake event.

However, data comparison method and lineament fluctuations successfully identified the lineament anomaly change over the two study areas. Due to progress of Earth observing satellites in different parts of the world, similar experiments can also be tested and compared with another high-resolution imagery. From this analysis, the exact position of earthquake epicenter, magnitude, and timing of occurrences was quite difficult to predict, but the extracted data can only able to identify the abnormality before the earthquake strike at least 4–36 days before. Thereafter, this lineament abnormality along with cloud presence in the images over such time period can help to target the zone of probable earthquake epicenter.

Overall, the experimental results have shown positive output, as it has been observed anomaly in pre-earthquake stage. Therefore, the first output concept was considered which developed by theoretical model and regarded as possible earthquake. On the other hand, no abnormal behavior of lineament presents in before, compared to anomaly presence prior to earthquake strike; thus, it is considered as no anomaly and declared as no possible earthquake, which supports the second concept of theoretical model. From this research, it has been observed that Landsat 8 OLI data have some power to extract lineament and helpful for pre-earthquake anomaly detection through lineament change observation. That is the only reason of acceptance of those images for the present study, which also supports the theoretical model. However, present lineament change observation technique using Landsat 8 OLI time series data is found effective for pre-earthquake anomaly study and can be used as an alternative approach for future earthquake monitoring.
