Preface

This book was born from the desire to offer to a broad audience of interested readers an overview of cognitive approaches to the study of landslides and the control of their evolution, through innovative integrated monitoring techniques. This subject is very topical, considering that landslides are one of the most destructive natural hazards, as they cause significant threats to lives, properties, and natural environments throughout the world, especially in mountainous regions.

This book provides the reader with a comprehensive overview of the current state of the art in landslide disasters, featuring an easy-to-follow format that focuses on the most important evidence-based developments in this critically important area.

Several authors, belonging to research and public institutions from different parts of the world, have collaborated in the technical discussion of this topic, reporting their experience and present advances in the critical research areas. The book contains nine chapters that cover important research aspects inherent to mapping, susceptibility, hazard and risk, monitoring, and modeling.

The introductory chapter, **Chapter 1**, prepared by the editors, consists of an overview of "Landslide Hazards" that includes overarching concepts of importance to the study of landslides, including global landslide information, factors affecting landslides, and recent trends and techniques used to investigate landslides. **Chapter 2**, prepared by Ram L. Ray, Maurizio Lazzari, and Tolulope Olutimehin, discusses remote sensing approaches and techniques used to study landslides and explores the possibilities of potential remote sensing tools that can effectively be used in landslide studies in the future. **Chapter 3**, prepared by Fabio Luino, and Laura Turconi, explains the unique translational rock-block slides located in Tertiary Flyschid Complexes of the Southern Piedmont Region (North-West Italy).

**Chapter 4**, prepared by Rocío N. Ramos-Bernal, René Vázquez-Jiménez, Sulpicio Sánchez Tizapa, and Roberto Arroyo Matus, characterizes susceptible landslide zones by using the accumulated index. This study helps to locate areas vulnerable to landslides and integrate disaster management or prevention plans. **Chapter 5**, prepared by Maurizio Lazzari, Marco Piccarreta, Ram L. Ray, and Salvatore Manfreda, explains the role of antecedent soil moisture on critical rainfall intensityduration thresholds to evaluate the possibility of modifying or improving traditional approaches.

**Chapter 6**, prepared by Miau-Bin Su, I-Hui Chen, Shei-Chen Ho, Yu-Shu Lin, and Jun-Yang Chen, presents time-domain reflectometry (TDR) technology for a long-term landslide monitoring system to explore rock deformation mechanisms and detect the location and magnitude of slope movement. **Chapter 7**, prepared by Jónas Elíasson and Þorsteinn Sæmundsson, presents the use of the translatory wave model to study landslides. This chapter also includes three case studies to study submarine slides (Japan), rock avalanche (SE Iceland), and debris slides (N Iceland) using the translatory wave model.

**II**

**Chapter 8 161**

**Chapter 9 179**

Techniques to Evaluate and Remediate the Slope Stability

Landslide Potential Evaluation Using Fragility Curve Model

*by Yi-Min Huang, Tsu-Chiang Lei, Bing-Jean Lee and Meng-Hsun Hsieh*

in Overconsolidated Clay

*by Herman Peiffer*

**Chapter 8**, prepared by Herman Peiffer, discusses the techniques that can be used to evaluate and remediate the slope stability in overconsolidated Boom Clay in Kruibeke, Belgium, the same origin as London Clay. Finally, **Chapter 9**, prepared by Yi-Min Huang, Tsu-Chiang Lei, Bing-Jean Lee, and Meng-Hsun Hsieh, explains the use of the fragility curve model to evaluate potential landslides.

Overall, this book significantly contributes to understanding the potential causes of landslides, advancing methods in monitoring, investigating, mapping, and mitigating.

The editors wish to express their thanks to all the participants in this book for their valuable contributions, and Ms. Kristina Kardum Cvitan (Author Service Manager) for her assistance in finalizing the work. We also acknowledge the staff at IntechOpen for their help in publishing this book and others.

> **Ram Ray** Prairie View A&M University, Prairie View, Texas, USA

> > **Maurizio Lazzari** CNR-ISPC, Potenza, Tito Scalo, Italy

> > > **1**

situ measurements.

**Chapter 1**

**1. Introduction**

Introductory Chapter: Importance

of Investigating Landslide Hazards

Natural disasters, like earthquakes, volcanic eruptions, intense rainfall, and anthropogenic factors, such as deforestation, contribute to slope failure, either by decreasing resisting forces or increasing driving forces of the soil mass [1]. Landslides are strongly related to steepness of the slope, soil moisture/water content of the soil layer, climate factors that increase the water content of the soil, and other anthropogenic factors and can be triggered by earthquakes, volcanoes, and floods. However, most of the slope failures are proceeded by intense rainfall and wet antecedent soil moisture conditions [2–4]. Often aggravated by rapid and uncontrolled development, landslides, either large or small, which happen every year in

To better understand and manage the potential landslides, it is essential to know the location and size of potential slope failures [7]. However, it is a difficult task to predict precise size and location of the possible landslides. Since slope failure is a complex phenomenon, it requires an in-depth understanding of slope failure

It is necessary to collect/obtain high-resolution spatial information of the soil layer, topography, hydrologic conditions, geotechnical characteristics, and land use/ land cover types to investigate landslide, including mapping, detection, monitoring, analysis, prediction, and others. Since slope failures commonly occur in the hilly region, especially in steep terrain, so it is rather challenging to obtain high-resolu-

It is always recommended to obtain in-situ measurements for an accurate landslide study. However, such in-situ measurements are time-consuming and require complex data collection efforts even on local scales [2, 6]. Recently, remote sensing data and spatial analysis tools are widely used in landslide studies, including landslide detection, assessment, hazard, mapping, and inventories [10–12]. Remote sensing data makes it possible to conduct landslide studies, not only at inaccessible terrain but also at regional to global scale, which otherwise is not possible using in

Data archives of national projects, besides constitute a sort of historical encyclopedia, they also represent a potential operational support tool useful and functional for planning and managing territorial and mitigation policies for landslide risks. The awareness that territorial planning and emergency plans can provide significant information from historical data series on localities and areas previously affected by hydrogeological disasters have recently stimulated the international

*Ram L. Ray and Maurizio Lazzari*

mountainous regions around the world [5, 6].

mechanisms and monitoring techniques.

tion data in conducting landslide studies [8, 9].

**2. Importance of information of historic landslides**

## **Chapter 1**

## Introductory Chapter: Importance of Investigating Landslide Hazards

*Ram L. Ray and Maurizio Lazzari*

## **1. Introduction**

Natural disasters, like earthquakes, volcanic eruptions, intense rainfall, and anthropogenic factors, such as deforestation, contribute to slope failure, either by decreasing resisting forces or increasing driving forces of the soil mass [1]. Landslides are strongly related to steepness of the slope, soil moisture/water content of the soil layer, climate factors that increase the water content of the soil, and other anthropogenic factors and can be triggered by earthquakes, volcanoes, and floods. However, most of the slope failures are proceeded by intense rainfall and wet antecedent soil moisture conditions [2–4]. Often aggravated by rapid and uncontrolled development, landslides, either large or small, which happen every year in mountainous regions around the world [5, 6].

To better understand and manage the potential landslides, it is essential to know the location and size of potential slope failures [7]. However, it is a difficult task to predict precise size and location of the possible landslides. Since slope failure is a complex phenomenon, it requires an in-depth understanding of slope failure mechanisms and monitoring techniques.

It is necessary to collect/obtain high-resolution spatial information of the soil layer, topography, hydrologic conditions, geotechnical characteristics, and land use/ land cover types to investigate landslide, including mapping, detection, monitoring, analysis, prediction, and others. Since slope failures commonly occur in the hilly region, especially in steep terrain, so it is rather challenging to obtain high-resolution data in conducting landslide studies [8, 9].

It is always recommended to obtain in-situ measurements for an accurate landslide study. However, such in-situ measurements are time-consuming and require complex data collection efforts even on local scales [2, 6]. Recently, remote sensing data and spatial analysis tools are widely used in landslide studies, including landslide detection, assessment, hazard, mapping, and inventories [10–12]. Remote sensing data makes it possible to conduct landslide studies, not only at inaccessible terrain but also at regional to global scale, which otherwise is not possible using in situ measurements.

## **2. Importance of information of historic landslides**

Data archives of national projects, besides constitute a sort of historical encyclopedia, they also represent a potential operational support tool useful and functional for planning and managing territorial and mitigation policies for landslide risks.

The awareness that territorial planning and emergency plans can provide significant information from historical data series on localities and areas previously affected by hydrogeological disasters have recently stimulated the international

scientific community to systematically collect data on landslides and floods [13]. Landslides, statistically, represent, after earthquakes and other external driving forces of natural disasters, which cause the most significant number of victims and damages built-up areas, infrastructures, environmental, historical, and cultural assets.

In particular, those which cause the most damage are fast-moving landslides (rockfalls, rapid mudflows, and debris flows), as well as those which involve large volumes of rocks or soils. Several landslide investigation projects, economically supported by public agencies, tended to focus mainly on phenomena that caused significant or evident damages (preferably occurring in urban areas or correspondence with linear infrastructures). They often neglected some landslides, even a substantial entity, which had not affected or interacted with built-up areas, communication routes, or infrastructures.

Historical memory helps play a fundamental and decision-making role that can be recovered and used through integrated methodological approaches. The collection of historical data aimed at knowledge and mapping of the instabilities allows us to complete and improve the information obtained with normal geologicalgeomorphological analysis, better defining fundamental aspects for assessments of danger and vulnerability of the region.

## **3. Global landslide information**

Knowledge of past landslides of a region, in terms of the occurrence of the event, controlling factors, and trigger conditions, are the main needed factors to evaluate spatial and temporal probabilistic hazard [14]. Therefore, the use of published and unpublished historical sources (federal, municipal, provincial, and state archives, publications, newspapers, press reviews, technical reports, photographs, and videos) can provide essential frameworks to understand the impact of hazard events over time in the areas monitored and evaluated during the new surveys.

Historical information can be grouped into four categories:


The types of information available from local archive data of ancient inscriptions, annals, historical chronicles, private funds, ecclesiastical funds, newspapers, iconographies, magazines, monographs, old postcards, cartographies, and videos (**Figure 1**) are broad and may differ from place to place.

The data obtainable from these sources is extensive and vital but requires control and validation of the news and associated information, such as geographical (e.g., location, municipality, street) and temporal ones (exact day, time, etc.) or climatic conditions (rainfalls mm/h, local measurement stations, etc.).

The historical-environmental analysis uses documentary information to reconstruct impact scenarios of natural events of the past on the environmental and

**3**

*Introductory Chapter: Importance of Investigating Landslide Hazards*

anthropic context of the time [15]. A fundamental method of interpreting historical information is to bring it back to its cultural, political, and economic context. The degree of reliability of data is, in fact, directly proportional to the knowledge, more

*Synthesis of the remarkable diversity of historical documents potentially available to carry out geologic hazard* 

There are numerous internal and external forces, natural and anthropogenic factors that trigger landslides [16–19]. The primary natural and anthropogenic

*Morphological*- fluvial/glacial/wind erosion of slope toe, tectonic, volcanism

*Hydrological*- rise in the groundwater table, flooding, rapid snowmelt

*Climate* – cause series of natural disasters (e.g., drought, floods, storm,

*Earthquakes* - ground vibrations created during Earthquakes

*Deforestation* – enhance soil erosion and surface runoff

*Mining and Quarrying Activities* – increase instability

*Overloading Slopes* – increase surcharge load

*Construction* – increase instability

*DOI: http://dx.doi.org/10.5772/intechopen.94279*

or less in-depth, of this context.

*analysis by integrating the present-day scenarios and data.*

**4. Factors affecting landslides**

*Topography* - Steep terrain

*Heavy and Prolonged Rainfall* -

**4.1 Natural factors**

**Figure 1.**

hurricanes, etc.)

**4.2 Anthropogenic factors**

factors that triggered landslides are outlined below.

*Gravity* - slope failure due to gravitational forces *Geological*- weathered/sheared/susceptible materials *Introductory Chapter: Importance of Investigating Landslide Hazards DOI: http://dx.doi.org/10.5772/intechopen.94279*

**Figure 1.**

*Landslides - Investigation and Monitoring*

communication routes, or infrastructures.

danger and vulnerability of the region.

**3. Global landslide information**

and erosion rates;

aquifers to wet seasons).

(**Figure 1**) are broad and may differ from place to place.

conditions (rainfalls mm/h, local measurement stations, etc.).

rainfall series;

cultural assets.

scientific community to systematically collect data on landslides and floods [13]. Landslides, statistically, represent, after earthquakes and other external driving forces of natural disasters, which cause the most significant number of victims and damages built-up areas, infrastructures, environmental, historical, and

In particular, those which cause the most damage are fast-moving landslides (rockfalls, rapid mudflows, and debris flows), as well as those which involve large volumes of rocks or soils. Several landslide investigation projects, economically supported by public agencies, tended to focus mainly on phenomena that caused significant or evident damages (preferably occurring in urban areas or correspondence with linear infrastructures). They often neglected some landslides, even a substantial entity, which had not affected or interacted with built-up areas,

Historical memory helps play a fundamental and decision-making role that can be recovered and used through integrated methodological approaches. The collection of historical data aimed at knowledge and mapping of the instabilities allows us to complete and improve the information obtained with normal geologicalgeomorphological analysis, better defining fundamental aspects for assessments of

Knowledge of past landslides of a region, in terms of the occurrence of the event, controlling factors, and trigger conditions, are the main needed factors to evaluate spatial and temporal probabilistic hazard [14]. Therefore, the use of published and unpublished historical sources (federal, municipal, provincial, and state archives, publications, newspapers, press reviews, technical reports, photographs, and videos) can provide essential frameworks to understand the impact of hazard events over time in the areas monitored and evaluated during the new surveys.

1.Direct recorded changes or natural events, such as droughts, floods, landslides,

2.Indirect data used to determine causes or explain patterns, such as historical

3.Other data that provides additional information, such as geological maps;

4.phenomenological data that may change with time (e.g., the response of

The types of information available from local archive data of ancient inscriptions, annals, historical chronicles, private funds, ecclesiastical funds, newspapers, iconographies, magazines, monographs, old postcards, cartographies, and videos

The data obtainable from these sources is extensive and vital but requires control and validation of the news and associated information, such as geographical (e.g., location, municipality, street) and temporal ones (exact day, time, etc.) or climatic

The historical-environmental analysis uses documentary information to reconstruct impact scenarios of natural events of the past on the environmental and

Historical information can be grouped into four categories:

**2**

*Synthesis of the remarkable diversity of historical documents potentially available to carry out geologic hazard analysis by integrating the present-day scenarios and data.*

anthropic context of the time [15]. A fundamental method of interpreting historical information is to bring it back to its cultural, political, and economic context. The degree of reliability of data is, in fact, directly proportional to the knowledge, more or less in-depth, of this context.

## **4. Factors affecting landslides**

There are numerous internal and external forces, natural and anthropogenic factors that trigger landslides [16–19]. The primary natural and anthropogenic factors that triggered landslides are outlined below.

## **4.1 Natural factors**

*Topography* - Steep terrain *Gravity* - slope failure due to gravitational forces *Geological*- weathered/sheared/susceptible materials *Morphological*- fluvial/glacial/wind erosion of slope toe, tectonic, volcanism *Heavy and Prolonged Rainfall* - *Hydrological*- rise in the groundwater table, flooding, rapid snowmelt *Earthquakes* - ground vibrations created during Earthquakes *Climate* – cause series of natural disasters (e.g., drought, floods, storm, hurricanes, etc.)

## **4.2 Anthropogenic factors**

*Deforestation* – enhance soil erosion and surface runoff *Overloading Slopes* – increase surcharge load *Mining and Quarrying Activities* – increase instability *Construction* – increase instability

The driving forces of landslides are physical/geological, morphological, and human in nature. Globally, the prominent causes of landslides are geological in nature and rainfall-induced. Landslides occurring majorly in mountainous and coastal terrains have also occurred in plains, which can be such as failures of the roadway and building, in addition to quarries and open-pit mines. These are sometimes the resultant effects of heavy rainfalls, volcanic eruptions, earthquakes, and droughts. Some areas are susceptible to landslides due to human activities affecting vegetation and topography. This also happens in places where wildfires occur.

Landslides are unpredictable. For example, landslides can occur due to human activities or non-human activities that affect slope stability [20]. Geological factors may account for 43% of landslides. This includes the impact of gravity on the topography of sloped areas, water pressure, weak soil formation, etc. The morphological causes, such as volcanic pressure, underground erosion, climate factors, vegetation elimination, crest accumulation. Human activities, such as excavation, irrigation, mining, deforestation, and slope encroachment, also enhance slope instability [21, 22]).

Kazmi et al. [21] investigated 11 landslide events in Malaysia and summarized water movements, weak safety management, heavy downpours, inadequate slope protection, damaged drainage, flawed design, and construction were some of the underlying factors that triggered landslides. Singh and Singh [23] found urbanization as a contributing factor for the risk of landslides and hazards. They discussed one of the most famous landslide events in the history known as Frank Slide. The Frank Slide of Turtle Mountain of Canada occurred in 1903 and generated 82 million tons of limestone. The primary cause of this landslide was the geology of the mountain because weak rock and stones were covered by limestone rock. Another factor was the weather event prior to the landslide, which was more snowy than usual, which allowed snowmelt and rain to permeate the mountain. The resistance forces of the rocks were more weakened beyond bearable limits. Pal et al. [24] reviewed several other landslide events and found intense rainfall as a common triggering factor for these landslide events. Since the causes of landslides across the world are multi-faceted, more advanced researches are necessary to investigate the triggering factors of landslide events.

## **5. Landslide investigation: recent trends and techniques**

Landslide investigation has been promoted by the International Decade of Natural Disaster Reduction (IDNDR, 1990–2000) proclaimed by the United Nations, when working groups on landslides were established (e.g., International Landslide Research Group (ILRG)). In addition, recent advances in landslide investigations include real-time monitoring, modeling, prediction, and assessment, which are helping communities and end-users to be better prepared to face potential landslide threats [25]. During the past three decades, tremendous developments have been made to investigate landslides. The investigation of Landslides has been carried out using surface and subsurface methods comprising qualitative and quantitative approaches. Although the wide range of applied geophysical techniques were used in landslide investigations, primarily these are grouped into two main classes: remote sensing techniques, which can be used to characterize the Earth's surface, and sub-surface techniques, which characterize geological surface by using non-destructive approaches [26]. In addition, an integrated approach combining satellite, airborne, and ground-based sensing, is widely used to investigate landslides [27, 28]. The wide range of remote sensing data from various optical

**5**

*Introductory Chapter: Importance of Investigating Landslide Hazards*

and microwave synthetic aperture radar (SAR) sensors and the increased temporal and spatial resolution provides new opportunities to investigate landslides at a

Some other advancements for landslide investigation are the use of in-situ geophysical techniques and electrical resistivity tomography (ERT), which can be used for landslide detection [27]. The interferometric techniques, which include Multi temporary interferometry (MTI) and advanced synthetic aperture radar differential interferometry (DInSAR) techniques, can be used to extract information on ground surface deformations. Also, interferometry SAR (InSAR) data combined with Unmanned Aerial Vehicle (UAV) images and aerial photography can be used to investigate slow-moving landslide [30]. Recently, application of UAV is growing to monitor rapidly occurring landslides and mapping in

Machine learning techniques are also getting popular in evaluating and detecting landslides [33, 34]. In addition, the use of artificial intelligence (AI) technique is growing to investigate landslides, such as landslide susceptibility mapping,

Currently, remote sensing technologies are used in landslide monitoring, mapping, hazard prediction and assessment, inventory and detection, and other investigations. Some of the key technological advancements for investigating landslides outlined by

*Radar satellite sensors* – Radar techniques allow to accurately determine the small

*Terrestrial laser scanners (TLS)*- The TLS, a ground-based LiDAR, mainly used to

*Airborne LiDAR* – A little expensive to collect data; however, it is widely used to

*Engineering geophysics* – This geophysical technique is widely used and applied to

Landslide, a catastrophic disaster, has been on the rise due to the impact of natural and anthropogenic, such as climate and land use/land cover change, and growing population. Landslides are common around the world, especially in mountainous regions. Since landslides are a severe threat to lives and properties, it is essential to understand the physical processes, causes of landslides, movement characteristics, and potential risk factors. It is also vital to study landslides, which helps understand landslide mapping, prediction, monitoring, and risk assessment to reduce the impact of landslides. However, such in-depth landslide investigations require advanced technologies, robust methods, models, and high resolution spatial

data, which includes in-situ and/or remotely sensed measurements globally.

While a high resolution data is required for landslide investigation, the potential landslide area is mostly inaccessible, which limits for in-situ measurements. Although recently, the application of satellite for landslide studies is growing, high spatial and temporal resolution satellite data are still limited on a global scale. Regardless of recent advancements in landslide studies, more research efforts, advanced

*Digital imaging* – Digital photogrammetry to capture image of landslides. *Optical satellite sensors* – Many commercial satellites are providing high spatial resolution data to study landslides (e.g., GeoEye-1 (0.4 m); Quickbird (0.6 m)).

*Google Earth* – An important tool for visualization and analysis.

evaluate the sub-surface environment of landslides in 3D and in real-time.

*DOI: http://dx.doi.org/10.5772/intechopen.94279*

range of scales [26, 29].

inaccessible terrains [31, 32].

Petley [26] are as follows:

landslide movement.

**6. Concluding remarks**

characterization, and prediction [35].

collect field data on steep unstable terrains.

collect high spatial resolution data to study landslides.

*Introductory Chapter: Importance of Investigating Landslide Hazards DOI: http://dx.doi.org/10.5772/intechopen.94279*

*Landslides - Investigation and Monitoring*

enhance slope instability [21, 22]).

factors of landslide events.

The driving forces of landslides are physical/geological, morphological, and human in nature. Globally, the prominent causes of landslides are geological in nature and rainfall-induced. Landslides occurring majorly in mountainous and coastal terrains have also occurred in plains, which can be such as failures of the roadway and building, in addition to quarries and open-pit mines. These are sometimes the resultant effects of heavy rainfalls, volcanic eruptions, earthquakes, and droughts. Some areas are susceptible to landslides due to human activities affecting vegetation and topography. This also happens in places where wildfires occur.

Landslides are unpredictable. For example, landslides can occur due to human

Kazmi et al. [21] investigated 11 landslide events in Malaysia and summarized water movements, weak safety management, heavy downpours, inadequate slope protection, damaged drainage, flawed design, and construction were some of the underlying factors that triggered landslides. Singh and Singh [23] found urbanization as a contributing factor for the risk of landslides and hazards. They discussed one of the most famous landslide events in the history known as Frank Slide. The Frank Slide of Turtle Mountain of Canada occurred in 1903 and generated 82 million tons of limestone. The primary cause of this landslide was the geology of the mountain because weak rock and stones were covered by limestone rock. Another factor was the weather event prior to the landslide, which was more snowy than usual, which allowed snowmelt and rain to permeate the mountain. The resistance forces of the rocks were more weakened beyond bearable limits. Pal et al. [24] reviewed several other landslide events and found intense rainfall as a common triggering factor for these landslide events. Since the causes of landslides across the world are multi-faceted, more advanced researches are necessary to investigate the triggering

**5. Landslide investigation: recent trends and techniques**

Landslide investigation has been promoted by the International Decade of Natural Disaster Reduction (IDNDR, 1990–2000) proclaimed by the United Nations, when working groups on landslides were established (e.g., International Landslide Research Group (ILRG)). In addition, recent advances in landslide investigations include real-time monitoring, modeling, prediction, and assessment, which are helping communities and end-users to be better prepared to face potential landslide threats [25]. During the past three decades, tremendous developments have been made to investigate landslides. The investigation of Landslides has been carried out using surface and subsurface methods comprising qualitative and quantitative approaches. Although the wide range of applied geophysical techniques were used in landslide investigations, primarily these are grouped into two main classes: remote sensing techniques, which can be used to characterize the Earth's surface, and sub-surface techniques, which characterize geological surface by using non-destructive approaches [26]. In addition, an integrated approach combining satellite, airborne, and ground-based sensing, is widely used to investigate landslides [27, 28]. The wide range of remote sensing data from various optical

activities or non-human activities that affect slope stability [20]. Geological factors may account for 43% of landslides. This includes the impact of gravity on the topography of sloped areas, water pressure, weak soil formation, etc. The morphological causes, such as volcanic pressure, underground erosion, climate factors, vegetation elimination, crest accumulation. Human activities, such as excavation, irrigation, mining, deforestation, and slope encroachment, also

**4**

and microwave synthetic aperture radar (SAR) sensors and the increased temporal and spatial resolution provides new opportunities to investigate landslides at a range of scales [26, 29].

Some other advancements for landslide investigation are the use of in-situ geophysical techniques and electrical resistivity tomography (ERT), which can be used for landslide detection [27]. The interferometric techniques, which include Multi temporary interferometry (MTI) and advanced synthetic aperture radar differential interferometry (DInSAR) techniques, can be used to extract information on ground surface deformations. Also, interferometry SAR (InSAR) data combined with Unmanned Aerial Vehicle (UAV) images and aerial photography can be used to investigate slow-moving landslide [30]. Recently, application of UAV is growing to monitor rapidly occurring landslides and mapping in inaccessible terrains [31, 32].

Machine learning techniques are also getting popular in evaluating and detecting landslides [33, 34]. In addition, the use of artificial intelligence (AI) technique is growing to investigate landslides, such as landslide susceptibility mapping, characterization, and prediction [35].

Currently, remote sensing technologies are used in landslide monitoring, mapping, hazard prediction and assessment, inventory and detection, and other investigations. Some of the key technological advancements for investigating landslides outlined by Petley [26] are as follows:

*Digital imaging* – Digital photogrammetry to capture image of landslides.

*Optical satellite sensors* – Many commercial satellites are providing high spatial resolution data to study landslides (e.g., GeoEye-1 (0.4 m); Quickbird (0.6 m)).

*Google Earth* – An important tool for visualization and analysis.

*Radar satellite sensors* – Radar techniques allow to accurately determine the small landslide movement.

*Terrestrial laser scanners (TLS)*- The TLS, a ground-based LiDAR, mainly used to collect field data on steep unstable terrains.

*Airborne LiDAR* – A little expensive to collect data; however, it is widely used to collect high spatial resolution data to study landslides.

*Engineering geophysics* – This geophysical technique is widely used and applied to evaluate the sub-surface environment of landslides in 3D and in real-time.

## **6. Concluding remarks**

Landslide, a catastrophic disaster, has been on the rise due to the impact of natural and anthropogenic, such as climate and land use/land cover change, and growing population. Landslides are common around the world, especially in mountainous regions. Since landslides are a severe threat to lives and properties, it is essential to understand the physical processes, causes of landslides, movement characteristics, and potential risk factors. It is also vital to study landslides, which helps understand landslide mapping, prediction, monitoring, and risk assessment to reduce the impact of landslides. However, such in-depth landslide investigations require advanced technologies, robust methods, models, and high resolution spatial data, which includes in-situ and/or remotely sensed measurements globally.

While a high resolution data is required for landslide investigation, the potential landslide area is mostly inaccessible, which limits for in-situ measurements. Although recently, the application of satellite for landslide studies is growing, high spatial and temporal resolution satellite data are still limited on a global scale. Regardless of recent advancements in landslide studies, more research efforts, advanced

technologies, and tools, high resolution spatial and temporal data, and effective management, awareness, and policy are needed in landslide research. It will help address the impact of future human activity, climate change, land use/land cover change on landslide hazards from local to the global scale.

## **Author details**

Ram L. Ray1 \* and Maurizio Lazzari<sup>2</sup>

1 College of Agriculture and Human Sciences, Prairie View A&M University, Prairie View, Texas, USA

2 CNR-ISPC, C/da S. Loja, Zona Industriale, 85050 Tito Scalo (PZ), Italy

\*Address all correspondence to: raray@pvamu.edu

© 2020 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

**7**

*Introductory Chapter: Importance of Investigating Landslide Hazards*

spatiotemporal variations under dynamic soil moisture conditions. Nat. Hazards. 2011;59:1317-1337. DOI

[10] Ray RL, Jacobs JM, Cosh MH. Landslide susceptibility mapping using downscaled AMSR-E soil moisture: A case study from Cleveland Corral, California, US. Remote Sensing of Environment. 2010;114:2624-2636. DOI:

10.1007/s11069-011-9834-4.

10.1016/j.rse.2010.05.033.

[11] Pradhan B, Singh RP,

asr.2005.03.137.

Buchroithner MF. Estimation of stress and its use in evaluation of landslide prone regions using remote sensing data. Advances in Space Research. 2006;37:698-709. DOI: 10.1016/j.

[12] van Westen CJ. GIS in landslide hazard zonation: A review, with examples from the Andes of Colombia. In: M. F. Price, D. I. Heywood (eds). Mountain environments and geographic information systems. Taylor and Francis

Publishers. 1994. p. 135-165.

[13] Tropeano D, Turconi L. Using Historical Documents for Landslide, Debris Flow and Stream Flood Prevention. Applications in Northern Italy. Natural Hazards. 2004;31(3):663-679. DOI:

10.1023/B:NHAZ.0000024897.71471.f2

Geomorphology. 1996;15:241-28. DOI: 10.1016/0169-555X(95)00073-E

[15] Lazzari M, Geraldi E, Lapenna V, Loperte A. Natural hazards vs human impact: an integrated methodological approach in geomorphological risk assessing on Tursi historical

[14] Ibsen ML, Brunsden D. The nature, use and problems of historical archives for the temporal occurrence

of landslides, with specific references to the south coast of Britain, Ventnor, Isle of Wight.

*DOI: http://dx.doi.org/10.5772/intechopen.94279*

Sharma S, Boyce GM. Slope stability and stabilization methods. Wiley, New York.

[3] Lazzari M, Piccarreta M, Manfreda S. The role of antecedent soil moisture conditions on rainfall-triggered shallow landslides. Nat. Hazards Earth Syst. Sci. 2018. DOI: 10.5194/nhess-2018-371

[4] Ray RL. Jacobs JM. Relationships among remotely sensed soil moisture, precipitation and landslide events. Nat. Hazards. 2007;43:211-222. DOI: 10.1007/

[5] Chuang Y-C, Shiu Y-S. Relationship between landslides and mountain development—Integrating geospatial statistics and a new long-term database. Science of the Total Environment. 2018;622-623:1265-1276. DOI: 10.1016/j.

[6] Ray RL, De Smedt F. Slope stability analysis on a regional scale using GIS: a case study from Dhading, Nepal. Environ Geol. 2009;57:1603-1611. DOI:

[7] Davies T. Landslide Hazards, Risks, and Disasters. IN: Shroder, J.F. (editor), Hzards and Disaster Series, Elsevier.

[8] Singh TN, Gulati A, Dontha L, Bhardwaj V. Evaluating cut slope failure by numerical analysis - a case study. Natural hazards. 2008;47:263. DOI:

[9] Ray RL, Jacobs JM, Ballestero TP. Regional landslide susceptibility:

10.1007/s11069-008-9219-5

[2] Ray RL, Jacobs JM, Douglas EM. Modeling regional landslide susceptibility using dynamic soil moisture profiles. J. Mt. Sci. 2018;15(8):1807-1824. DOI: 10.1007/

**References**

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s11629-018-4896-3.

s11069-006-9095-9.

scitotenv.2017.12.039

2015. 475 pp.

10.1007/s00254-008-1435-5.

[1] Abramson LW, Lee TS,

*Introductory Chapter: Importance of Investigating Landslide Hazards DOI: http://dx.doi.org/10.5772/intechopen.94279*

## **References**

*Landslides - Investigation and Monitoring*

landslide hazards from local to the global scale.

**6**

**Author details**

Prairie View, Texas, USA

\* and Maurizio Lazzari2

\*Address all correspondence to: raray@pvamu.edu

provided the original work is properly cited.

1 College of Agriculture and Human Sciences, Prairie View A&M University,

© 2020 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

technologies, and tools, high resolution spatial and temporal data, and effective management, awareness, and policy are needed in landslide research. It will help address the impact of future human activity, climate change, land use/land cover change on

2 CNR-ISPC, C/da S. Loja, Zona Industriale, 85050 Tito Scalo (PZ), Italy

Ram L. Ray1

[1] Abramson LW, Lee TS, Sharma S, Boyce GM. Slope stability and stabilization methods. Wiley, New York. 1996. 738 pp.

[2] Ray RL, Jacobs JM, Douglas EM. Modeling regional landslide susceptibility using dynamic soil moisture profiles. J. Mt. Sci. 2018;15(8):1807-1824. DOI: 10.1007/ s11629-018-4896-3.

[3] Lazzari M, Piccarreta M, Manfreda S. The role of antecedent soil moisture conditions on rainfall-triggered shallow landslides. Nat. Hazards Earth Syst. Sci. 2018. DOI: 10.5194/nhess-2018-371

[4] Ray RL. Jacobs JM. Relationships among remotely sensed soil moisture, precipitation and landslide events. Nat. Hazards. 2007;43:211-222. DOI: 10.1007/ s11069-006-9095-9.

[5] Chuang Y-C, Shiu Y-S. Relationship between landslides and mountain development—Integrating geospatial statistics and a new long-term database. Science of the Total Environment. 2018;622-623:1265-1276. DOI: 10.1016/j. scitotenv.2017.12.039

[6] Ray RL, De Smedt F. Slope stability analysis on a regional scale using GIS: a case study from Dhading, Nepal. Environ Geol. 2009;57:1603-1611. DOI: 10.1007/s00254-008-1435-5.

[7] Davies T. Landslide Hazards, Risks, and Disasters. IN: Shroder, J.F. (editor), Hzards and Disaster Series, Elsevier. 2015. 475 pp.

[8] Singh TN, Gulati A, Dontha L, Bhardwaj V. Evaluating cut slope failure by numerical analysis - a case study. Natural hazards. 2008;47:263. DOI: 10.1007/s11069-008-9219-5

[9] Ray RL, Jacobs JM, Ballestero TP. Regional landslide susceptibility:

spatiotemporal variations under dynamic soil moisture conditions. Nat. Hazards. 2011;59:1317-1337. DOI 10.1007/s11069-011-9834-4.

[10] Ray RL, Jacobs JM, Cosh MH. Landslide susceptibility mapping using downscaled AMSR-E soil moisture: A case study from Cleveland Corral, California, US. Remote Sensing of Environment. 2010;114:2624-2636. DOI: 10.1016/j.rse.2010.05.033.

[11] 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. 2006;37:698-709. DOI: 10.1016/j. asr.2005.03.137.

[12] van Westen CJ. GIS in landslide hazard zonation: A review, with examples from the Andes of Colombia. In: M. F. Price, D. I. Heywood (eds). Mountain environments and geographic information systems. Taylor and Francis Publishers. 1994. p. 135-165.

[13] Tropeano D, Turconi L. Using Historical Documents for Landslide, Debris Flow and Stream Flood Prevention. Applications in Northern Italy. Natural Hazards. 2004;31(3):663-679. DOI: 10.1023/B:NHAZ.0000024897.71471.f2

[14] Ibsen ML, Brunsden D. The nature, use and problems of historical archives for the temporal occurrence of landslides, with specific references to the south coast of Britain, Ventnor, Isle of Wight. Geomorphology. 1996;15:241-28. DOI: 10.1016/0169-555X(95)00073-E

[15] Lazzari M, Geraldi E, Lapenna V, Loperte A. Natural hazards vs human impact: an integrated methodological approach in geomorphological risk assessing on Tursi historical

site, southern Italy. Landslides. 2006;3(4):275-287, Springer-Verlag.

[16] Gue SS, Tan YC. Landslides: Abuses of the Prescriptive Method, International Conference on Slope, Kuala Lumpur, Malaysia: 2006. p. 34-42.

[17] Jamaludin S, Ali F. An overview of some empirical correlations between rainfall and shallow landslides and their applications in Malaysia. Electron J Geotech Eng. 2011;16:1429-1440.

[18] Lee S, Pradhan B. Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models. Landslides. 2007;4(1):33-41. DOI: 10.1007/s10346-006-0047-y.

[19] Azmi AS, Salleh WA, Nawawi AH. Cognitive behaviour of residents toward living in landslide prone area: Ulu Klang. Procedia - Social and Behavioral Sciences. 2013;101:379-393. DOI: 10.1016/j.sbspro.2013.07.212.

[20] Walker LR, Shiels AB. Physical causes and consequences. In: Walker LR, Shiels AB (eds) Landslide Ecology. Cambridge University Press; New York, NY. 2013. 46-82.

[21] Kazmi D, Qasim S, Harahap I, Baharom S, Imran M, Moin S. A Study on the Contributing Factors of Major Landslides in Malaysia. Civil Engineering journal. 2016;2(12):669- 678. DOI: 10.28991/cej-2016-00000066.

[22] Dlamini W. Analysis of deforestation patterns and drivers in Swaziland using efficient Bayesian multivariate classifiers. Model. Earth Syst. Environ. 2016;2:173. DOI: 10.1007/ s40808-016-0231-6.

[23] Singh K, Singh MP. Causes and remedial measures for rockfall and landslides in Naini lake basin: Uttarakhand, India. Environment Conservation Journal. 2020;21(1&2):95- 102. DOI: 10.36953/ECJ.2020.211211

[24] Pal R, Biswas SS, Mondal B, Pramanik MK. Landslides and Floods in the Tista Basin (Darjeeling and Jalpaiguri Districts): Historical Evidence, Causes and Consequences. J. Ind. Geophys. Union. 2016;20(2):66-72.

[25] Gutierrez F, Soldati M, Audemard F, Balteanu D. Recent advances in landslide investigation: issues and perspectives. Geomorphology. 2010;124(3-4):95-102. DOI: 10.1016/j.geomorph.2010.10.020

[26] Petley DN. Remote sensing techniques and landslides. In: Clague J, Stead D(eds) Landslides types, mechanisms and modeling. Cambridge University Press; New York, NY. 2012. p. 159-71.

[27] Perrone A, Lapenna V, Piscitelli S. Electrical resistivity tomography technique for landslide investigation: A review. Earth-Science Reviews. 2014;135:65-82. DOI: 10.1016/j. earscirev.2014.04.002

[28] Scaioni M, Longoni L, Melillo V, Papini M. Remote sensing for landslide investigations: An overview of recent achievements and perspectives. Remote Sens. 2014; 6:9600-9652. DOI: 10.3390/ rs60x000x.

[29] Hölbling D, Friedl B, Dittrich J, Cigna F, Pedersen G. Combined interpretation of optical and SAR data for landslide mapping. In: Advances in Landslide Research, Proceedings of the 3rd Regional Symposium on Landslides the Adriatic-Balkan Region, Ljubljana, Slovenia, 11-13 October 2017; Jemec Auflic, M., Mikos, M.,Verbovsek, T., Eds.; Geological Survey of Slovenia: Ljubljana, Slovenia, 2018; pp. 11-13.

[30] Ekera R, Aydın A. Long-term retrospective investigation of a large, deep-seated, and slowmoving landslide using InSAR time series, historical aerial photographs, and UAV data: The case of Devrek landslide (NW Turkey). Catena.

**9**

*Introductory Chapter: Importance of Investigating Landslide Hazards*

*DOI: http://dx.doi.org/10.5772/intechopen.94279*

Mansberger R, Hubl J. UAV monitoring and documentation of a large landslide. Applied Geomatics. 2015;8(1). DOI:

2020;196:104895. DOI: 10.1016/j.

[31] Lindner G, Schraml K,

10.1007/s12518-015-0165-0

DOI: 10.5194/nhess-2018-13

[32] Yaprak S, Yildirim O, Susan T, Inyurt S, Oguz I. The role of unmanned aerial vehicles (UAVs) in monitoring rapidly occurring landslides. Nat. hazards Earth Syst. Sci. Discuss. 2018.

[33] Merghadi A, Yunus AP, Dou J, Whiteley J, ThaiPham et al. Machine learning methods for landslide susceptibility studies: A comparative overview of algorithm performance. Earth-Science reviews 2020;207:103225. DOI: 10.1016/j.earscirev.2020.103225

[34] Wang H, Zhang L, Yin K, Luo H, Li J. Landslide identification using machine learning. Geoscience Frontiers. 2020. DOI: 10.1016/j.gsf.2020.02.012

[35] Chen W, Shirzadi A, Shahabi H, Ahmad BB, Zhang et al. A novel hybrid artificial intelligence approachbased on the rotation forest ensemble and naïveBayes tree classifiers for a landslide susceptibility assessment in Langao County, China. Geomatics, Natural Hazards and Risk. 2017;8(2):1955-1977. DOI: 10.1080/19475705.2017.1401560

catena.2020.104895

*Introductory Chapter: Importance of Investigating Landslide Hazards DOI: http://dx.doi.org/10.5772/intechopen.94279*

2020;196:104895. DOI: 10.1016/j. catena.2020.104895

*Landslides - Investigation and Monitoring*

[24] Pal R, Biswas SS, Mondal B, Pramanik MK. Landslides and Floods in the Tista Basin (Darjeeling and Jalpaiguri Districts): Historical

[26] Petley DN. Remote sensing techniques and landslides. In:

[27] Perrone A, Lapenna V, Piscitelli S. Electrical resistivity tomography technique for landslide investigation: A review. Earth-Science Reviews. 2014;135:65-82. DOI: 10.1016/j.

earscirev.2014.04.002

[29] Hölbling D, Friedl B, Dittrich J, Cigna F, Pedersen G. Combined interpretation of optical and SAR data for landslide mapping. In: Advances in Landslide Research, Proceedings of the 3rd Regional Symposium on Landslides the Adriatic-Balkan Region, Ljubljana, Slovenia, 11-13 October 2017; Jemec Auflic, M., Mikos, M.,Verbovsek, T., Eds.; Geological Survey of Slovenia: Ljubljana, Slovenia, 2018; pp. 11-13.

[30] Ekera R, Aydın A. Long-term retrospective investigation of a large, deep-seated, and slowmoving landslide using InSAR time series, historical aerial photographs, and UAV data: The case of Devrek landslide (NW Turkey). Catena.

rs60x000x.

[28] Scaioni M, Longoni L, Melillo V, Papini M. Remote sensing for landslide investigations: An overview of recent achievements and perspectives. Remote Sens. 2014; 6:9600-9652. DOI: 10.3390/

159-71.

Evidence, Causes and Consequences. J. Ind. Geophys. Union. 2016;20(2):66-72.

[25] Gutierrez F, Soldati M, Audemard F, Balteanu D. Recent advances in landslide investigation: issues and perspectives. Geomorphology. 2010;124(3-4):95-102. DOI: 10.1016/j.geomorph.2010.10.020

Clague J, Stead D(eds) Landslides types, mechanisms and modeling. Cambridge University Press; New York, NY. 2012. p.

site, southern Italy. Landslides. 2006;3(4):275-287, Springer-Verlag.

[16] Gue SS, Tan YC. Landslides: Abuses of the Prescriptive Method, International Conference on Slope, Kuala Lumpur, Malaysia: 2006. p. 34-42.

[17] Jamaludin S, Ali F. An overview of some empirical correlations between rainfall and shallow landslides and their applications in Malaysia. Electron J Geotech Eng. 2011;16:1429-1440.

[18] Lee S, Pradhan B. Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models. Landslides. 2007;4(1):33-41. DOI: 10.1007/s10346-006-0047-y.

[19] Azmi AS, Salleh WA, Nawawi AH. Cognitive behaviour of residents toward living in landslide prone area: Ulu Klang. Procedia - Social and Behavioral Sciences. 2013;101:379-393. DOI: 10.1016/j.sbspro.2013.07.212.

[20] Walker LR, Shiels AB. Physical causes and consequences. In: Walker LR, Shiels AB (eds) Landslide Ecology. Cambridge University Press; New York,

[21] Kazmi D, Qasim S, Harahap I, Baharom S, Imran M, Moin S. A Study

on the Contributing Factors of Major Landslides in Malaysia. Civil Engineering journal. 2016;2(12):669- 678. DOI: 10.28991/cej-2016-00000066.

[22] Dlamini W. Analysis of

s40808-016-0231-6.

deforestation patterns and drivers in Swaziland using efficient Bayesian multivariate classifiers. Model. Earth Syst. Environ. 2016;2:173. DOI: 10.1007/

[23] Singh K, Singh MP. Causes and remedial measures for rockfall and landslides in Naini lake basin: Uttarakhand, India. Environment Conservation Journal. 2020;21(1&2):95- 102. DOI: 10.36953/ECJ.2020.211211

NY. 2013. 46-82.

**8**

[31] Lindner G, Schraml K, Mansberger R, Hubl J. UAV monitoring and documentation of a large landslide. Applied Geomatics. 2015;8(1). DOI: 10.1007/s12518-015-0165-0

[32] Yaprak S, Yildirim O, Susan T, Inyurt S, Oguz I. The role of unmanned aerial vehicles (UAVs) in monitoring rapidly occurring landslides. Nat. hazards Earth Syst. Sci. Discuss. 2018. DOI: 10.5194/nhess-2018-13

[33] Merghadi A, Yunus AP, Dou J, Whiteley J, ThaiPham et al. Machine learning methods for landslide susceptibility studies: A comparative overview of algorithm performance. Earth-Science reviews 2020;207:103225. DOI: 10.1016/j.earscirev.2020.103225

[34] Wang H, Zhang L, Yin K, Luo H, Li J. Landslide identification using machine learning. Geoscience Frontiers. 2020. DOI: 10.1016/j.gsf.2020.02.012

[35] Chen W, Shirzadi A, Shahabi H, Ahmad BB, Zhang et al. A novel hybrid artificial intelligence approachbased on the rotation forest ensemble and naïveBayes tree classifiers for a landslide susceptibility assessment in Langao County, China. Geomatics, Natural Hazards and Risk. 2017;8(2):1955-1977. DOI: 10.1080/19475705.2017.1401560

**11**

**1. Introduction**

**Chapter 2**

**Abstract**

Study Landslides

Remote Sensing Approaches and

Related Techniques to Map and

*Ram L. Ray, Maurizio Lazzari and Tolulope Olutimehin*

techniques to conduct landslide studies at a range of scales.

inventory, natural hazards, susceptibility, assessment

**Keywords:** remote sensing, landslide detection, landslide mapping, landslide

Landslides are natural hazards that have a significant impact globally [1, 2]. In comparison to other natural hazards, landslides are one of the costliest and fatal geological hazards, threatening and influencing the socioeconomic conditions of many countries throughout the world [3–5]. A landslide can be triggered by various natural phenomena (e.g., earthquakes, heavy rainfall, tsunami, and flood) and human disturbances (e.g., deforestation, infrastructure developments by cutting slopes, and presence of historical underground cavities) [6–8]. A landslide occurs when the soil layers of the slope get detached either from saturation due to extreme rainfall events or from external forces (e.g., earthquakes) and move downhill causing loss of life, properties, environments, and economic damage. For example, in the U.S. alone, landslides cause approximately \$3.5 billion in damage and kill

Landslide is one of the costliest and fatal geological hazards, threatening and influencing the socioeconomic conditions in many countries globally. Remote sensing approaches are widely used in landslide studies. Landslide threats can also be investigated through slope stability model, susceptibility mapping, hazard assessment, risk analysis, and other methods. Although it is possible to conduct landslide studies using in-situ observation, it is time-consuming, expensive, and sometimes challenging to collect data at inaccessible terrains. Remote sensing data can be used in landslide monitoring, mapping, hazard prediction and assessment, and other investigations. The primary goal of this chapter is to review the existing remote sensing approaches and techniques used to study landslides and explore the possibilities of potential remote sensing tools that can effectively be used in landslide studies in the future. This chapter also provides critical and comprehensive reviews of landslide studies focus¬ing on the role played by remote sensing data and approaches in landslide hazard assessment. Further, the reviews discuss the application of remotely sensed products for landslide detection, mapping, prediction, and evaluation around the world. This systematic review may contribute to better understanding the extensive use of remotely sensed data and spatial analysis

## **Chapter 2**
