Open access peer-reviewed chapter

Remote Sensing Approaches and Related Techniques to Map and Study Landslides

Written By

Ram L. Ray, Maurizio Lazzari and Tolulope Olutimehin

Submitted: February 3rd, 2020 Reviewed: August 21st, 2020 Published: September 23rd, 2020

DOI: 10.5772/intechopen.93681

Chapter metrics overview

1,422 Chapter Downloads

View Full Metrics


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 techniques to conduct landslide studies at a range of scales.


  • remote sensing
  • landslide detection
  • landslide mapping
  • landslide inventory
  • natural hazards
  • susceptibility
  • assessment

1. Introduction

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, 4, 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, 7, 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 between 25 to 50 people each year [9]. Also, in 2014 alone, Nepal had a landslide where livestock loss was many times larger than human loss and infrastructural damage was more expensive in comparison to the economy of the country [10]. Slope failures also cause major sedimentation into streams and lakes, which further represents a major cause of flooding [5, 11].

Landslides are common primarily in the mountainous regions if a slope is unstable or becomes unstable due to external driving forces [10, 12]. Landslide hazard can be classified into high, moderate, and low based on the volume, duration, possible effect in terms of distance, area, and speed at which the slope fails. Since landslides can adversely affect human lives and property, it is essential to monitor, detect, map, and conduct hazard analysis in order to reduce the impact of their hazard and save human lives, property, and environment around the globe. Landslide susceptibility maps can be developed for landslide-prone regions by combining all of the potential predisposing factors, which cause a landslide.

Landslide susceptibility depends on the local terrain, land use, and climatic conditions, which require spatial information [13]. A landslide susceptibility zone includes information of past landslide inventory with an evaluation of future landslide-prone areas, but it does not include assessment of the frequency of landslide occurrence [14]. Also, the temporal probability of a landslide is not included in susceptibility models [15]. High-quality landslide inventory maps can be developed using in situ measurements and field surveying [16, 17]. However, in situ measurements and field surveying are time consuming, expensive, and difficult for local to global scales. On the other hand, landslide susceptibility and inventory maps, as well as landslide hazard analysis, can be possible using remote sensing techniques and data, such as aerial surveys, unmanned aerial vehicles (UAV), light detection and ranging (LiDAR), and satellite imagery [16, 18].

Although remote sensing is continuously used for landslide detection/mapping and monitoring, it is generally considered to have a medium effectiveness/reliability for landslide studies because satellite data are available relatively at coarse resolutions [3]. On the other hand, hazard assessment requires high resolution data to define the spatial distribution of landslides and their state of activity both on a local scale and from studies from regional/global scales [10]. In addition, remotely sensed data are cost-effective because most of the global satellite products are freely available and can cover rugged/complex terrains, which is otherwise not possible to assess with in situ measurements [19]. Even in the late 1990s, stereoscopic air-photo interpretation was the most used remote sensing tool applied to the mapping and monitoring of landslides [20]. Many studies have been carried out on landslide hazard evaluation using geographic information systems (GIS) and geoinformation-related techniques [4]. Recently, GIS and remote sensing tools have become powerful tools for integrating spatial data to conduct landslide studies [10, 21].

Remotely sensed data and techniques are widely used in landslide studies, including landslide inventory/detection, monitoring, and mapping, and hazard analysis (e.g., [16, 22, 23]). Timely and high-quality information derived from space-borne observations helps in managing natural and human-made disasters [1]. Accordingly, landslide risk mapping and management can help reduce disaster risk [10]. Similarly, early landslide predictions and warnings are important in curtailing landslide hazards [4, 24]. Landslide vulnerability assessment is used in identifying what elements are at risk and why; such information helps in landslide disaster mitigation measures [10, 21].

The use of remote sensing data for landslide studies, whether air, satellite, or ground-based measurements, is mainly classified into three main categories: (a) detection and identification, (b) monitoring, and (c) spatial analysis and hazard prediction [20]. However, we have reviewed landslide studies using multiple categories as they appear in the literature.

This chapter aims to provide a critical and comprehensive review of recent landslide studies conducted using remotely sensed data and techniques. It includes an overview of landslide inventory/detection, mapping and monitoring, susceptibility, and hazard analysis using remotely sensed data at a range of scales. It helps to understand the potential benefits of conducting landslide studies using satellite data to save human lives, properties, and environments around the globe.


2. Landslide inventory/detection

Landslides, influenced by several preparatory and triggering factors, are naturally hazardous events causing loss of lives and properties and environmental degradation [25]. Landslide-triggering factors such as intense or prolonged rainfall or rapid snowmelt, earthquakes, or volcanic eruptions are enhanced by human-induced triggering factors such as deforestation, mining, and cutting slopes for road development [8, 26, 27]. Landslides include various movements like flowing, sliding, toppling, or falling, and many landslides combine two or more of these movements at the same time or during the lifetime of a landslide [28]. Traditionally, landslide inventory maps can be retrieved from historical sources, archives documents, and newspapers, which are important but not detailed enough, making quantitative risk assessment challenging [29]. Historical landslide events offer a good opportunity to evaluate landslide detection techniques to develop landslide inventory, which can also be used in developing or validating landslide susceptibility and hazard mappings models [30].

An inventory map identifies landslides in a study area to establish the spatial correlation between landslides and environmental factors [31]. A landslide inventory map is required to quantify landslide occurrence effectively. Landslide inventories generally include the size of landslides, its locations, and volume (preferred) [26]. Inventory maps, which provide information on the location of landslide distributions, help implement necessary mitigation measures [25]. The first step in evaluating landslide hazards is a comprehensive landslide inventory map [32].

Although landslide inventory mapping is a crucial requirement for a thorough hazard and risk analysis, the usefulness of these maps for land management and planning is rather limited due to their inhomogeneous spatial distribution and the use of different mapping and classification criteria and methods [33]. However, large-scale landslide inventory maps, developed using remotely sensed data, can overcome these problems and limitations, thus making susceptibility hazard and risk assessment more efficacious [34].

Since it is time consuming and expensive to develop landslide inventories using in situ measurements, remote sensing data, and tools can be an effective way to develop landslide inventory maps. High-resolution satellite image and advanced remote sensing and spatial analysis techniques allow developing more reliable landslide inventory maps [29, 35]. Methods involved in generating landslide inventories include visual interpretation of multi-temporal aerial photographs and remotely sensed images and geomorphological field mapping, expert knowledge on the geological setting in combination [36, 37].

For example, Harp et al. [38] suggested mapping criteria for landslide inventory as follows:

  1. GIS tool can be used to develop a spatial map for statistical analysis in relation to other spatial variables; and

  2. the entire population of landslides triggered by the external forces or natural and anthropogenic causes must be mapped and plotted so that a complete landslide distribution can be obtained.

However, detailed inventories are rarely available because evidence could have been lost due to various degrees of modification in the past [26]. On the other hand, an inventory map/data can be developed using high-resolution historical remote sensing data, especially if morphological indicators for past landslide activities are present [34].

According to Guzzetti et al. [33] and Malamud et al. [26], landslide inventory maps are categorized into archive inventory (based on records in archives, newspapers, and so on). Similarly, in Guzzetti et al. [33] landslide inventory maps are categorized as geomorphological inventory which can further be classified as follows:

  • historical inventory (showing the cumulative effect of landslide over a long period without further temporal differentiation),

  • event-based inventory (landslides caused by a single triggering event, such as a strong earthquake),

  • seasonal inventory (landslides triggered within one active season) and multi-temporal inventory (continuous monitoring of landslide activity over longer periods independent of particular triggering events), and

  • the multi-temporal inventory is the most labor-intensive inventory type and the only one with the potential for spatio-temporal completeness, and it generally requires the use of remote sensing.

A historical inventory is the most popular approach developing landslide inventories using past landslide events. High spatial resolution and long-term remotely sensed data help rapid mapping and monitoring, especially during an emergency [1]. Based on the available in situ information and remotely sensed data, various landslide inventories can be produced and detailed at different scales covering the entire area affected by landslides, including, wherever possible, all sizes of landslides, and mapping landslides as polygons to depict their exact shapes [38].

In addition, landslide inventory maps are prepared for multiple scopes, including:

  • documenting the extent of landslide phenomena in areas ranging from small to large watersheds and from regions to states or nations,

  • taking a preliminary step toward landslide susceptibility, hazard, and risk assessment,

  • investigating distribution, types, and patterns of landslides in relation to morphological and geological characteristics, and distribution of slope failure processes,

  • studying the evolution of landscapes dominated by mass-wasting processes,

  • investigating the recent and historical relationships between mass movement processes, settlements, and high cultural value areas [39], and

  • extracting thresholds of rainfall-induced landslides [40].

Landslide inventory and detection are technically very close and often used interchangeably. An inventory must be carried out through direct visual inspections (or field surveys) and/or in situ measurements (when possible), which, altogether, identify “detection.” In other words, there is no good and effective inventory without detection.

A complete and accurate landslide inventory is crucial for landslide predictions; therefore, the accuracy of landslide inventory can be maintained by analyzing high-resolution satellite images [41]. Identification of landslide’s boundaries, terrestrial and topography verification, and third-party review are the procedures in interpreting the accuracy of landslide inventory [41].

The systematic information on the type, abundance, and distribution of landslide is still lacking, which helps document essential details on landslide types, patterns, recurrence, and statistics of slope failures. These pieces of information are helpful to identify landslide susceptible zones and determine potential hazards and landslide risk assessment [33]. Multi-temporal inventories are needed, especially in regions of frequently occurring landslide. These regions require high spatio-temporal resolution data and efficient methods for landslide mapping and analysis [42]. An inventory map which contains landslide type, state of activity, depth, volume, date, and place of occurrence, can be used for the calculation of predisposing factors for performance and reliability analysis [30, 37]. In addition to analyze and understand the causes of past landslides, landslide detection is equally useful for monitoring and predicting future hazards [43].

Landslide inventory/detection maps can be developed using the consolidated procedure of photo-interpretation of different sets of stereoscopic aerial photos, that can be integrated with an extensive field control of each recognized landslide. The field control process includes acquiring additional information about the main geomorphic elements and topographic signatures related to mass movement processes and their interpretation in terms of pattern, distribution, state of activity, and evolution of slope processes. In particular, the geomorphological field survey focuses on:

  1. the validation of the information acquired by aerial photo-interpretation,

  2. recognition of landslide types and state of activity,

  3. analysis of deposits involved in slope failures, and

  4. evaluation of damages to infrastructures.

Landslides of different sizes and types also offer to detect landslides, which can be used to develop a landslide inventory map [30]. A landslide can be detected or identified using visual interpretation techniques combined with field investigations as a ground control to develop the most reliable form of inventory maps for scientific studies [44, 45, 46, 47].

Numerous researchers have conducted studies to develop landslide inventories using remotely sensed data (e.g., [35, 37, 48, 49, 50]) (Table 1). For example, Moosavi et al. [37] produced a landslide inventory map using GeoEye remotely sensed data and found that thematic mapping using high spatial resolution satellite imagery necessitates a new methodology. Also, Sun et al. [35] used Geofen-1 remotely sensed data to develop a landslide inventory map in a complex region with numerous gullies, which was otherwise challenging and impractical via field investigations.

Satellites Spatial resolutions (m) Launch date Applications References for examples
Geofen-1 2 2013 Inventory [35]
GeoEye-1 1.65 2008 Inventory [37]
Resources at 2-LISS III
Inventory [48]
TERRA/ASTER 15, 30 1999 Inventory [49, 50]
SPOT-5 2.5 Pan
10 Mult
2002 Detection
[39, 51]
Quickbird-2 0.6 2001 Detection [52, 53]
LiDAR 0.5 NA Detection [33, 45, 54]
IKONOS 0.82 Pan, 3.2 Mult 1999 Mapping [55]
Landsat-8 15, 30 2013 Mapping [56, 57]
SRTM 30, 90 2000 Susceptibility
[16, 57]
Cartosat-2 1.0 2017 Susceptibility [25]
Cartosat-1 2.5 2005 Susceptibility [30]
AMSR-E 25,000 2002 Susceptibility [16]
TRMM 25,000 1997 Susceptibility [58]
GPM-Integrated Approx. 11,000 NA Susceptibility [59, 60]
WorldView-1/Quickbird-2/GeoEye-1 1.85/2.4/1.65 2009/2001/2008 Susceptibility
Landsat MSS
Landsat TM
1984 Interpretation [4, 62]
Landsat 30 1992 Planning [63]
Landsat 7 15, 30 1999 Characterizing
[64, 65]
10 2007 Mapping
[1, 52]
Landsat TM/SPOT-5 15, 30/2.5, 10 1999/2002 Hazard [66]
IRS/Landsat-7 6, 23.6, 188/30 1997/1999 Hazard [67]
ERS SAR 10 1995 Monitoring [69]
ERS-1/2 10 1991/1995 Monitoring [70]
COSMO-SkyMed-1, 3 1, 5, 15, 30, 100 2007/2008 Detection [71]
TerraSAR-X 1 2007 Monitoring [72]

Table 1.

A series of remotely sensed data used for landslide study.

This list is an effort to compile popularly used satellite data to study landslides; it is not meant to be a comprehensive list as there are many more studies that used other satellite products.


3. Landslide susceptibility mapping

Landslide prediction is vital to prevent possible damages and save human lives. Landslide susceptibility map is important in predicting landslides because it helps to identify potential landslide areas and any area susceptible to landslides [307374]. The local topography and hydrological conditions play a significant role in landslide susceptibility [39]. Although a proper landslide inventory may provide both spatial and temporal information on previous landslides over an area, landslide susceptibility map gives information about potential future landslides over an area [60]. However, detailed information on the historical records of previous landslides, rainfall, or earthquake is vital in determining triggering thresholds [74].

Landslide susceptibility can be quantified from stable to highly susceptible. Many researchers categorize slopes into four landslide susceptibility classes; highly susceptible, moderately susceptible, slightly susceptible, and stable (e.g., [12, 16, 58, 75, 76, 77, 78]). Some researchers used slightly different susceptible classes to develop landslide susceptibility maps such as unstable, quasi-stable, moderately stable, and stable (e.g., [12]). Some studies also used susceptibility indices: very high, high, moderate, low, and very low (e.g., [59, 60, 61, 79]).

For disaster prevention, a landslide susceptibility map can be used in land-use planning and decision making [39]. A detailed susceptibility map for land-use mapping helps local authorities manage these landscapes for urban or industrial planning and development [80]. However, developing an effective landslide susceptibility map is always a challenge because it requires multiple spatial information of soil, geology, vegetation, and hydrology. For example, Stanley and Kirschbaum [60] identified four major issues that are important to be addressed for developing landslide susceptibility maps:

  1. lack of detailed inventories,

  2. minimum available input data,

  3. regional differences in the importance of causative factors, and

  4. the dearth of expertise on landscape processes across large regions.

Numerous methods exist in literature developing landslide susceptibility map using in situ measurements, models, remotely sensed data either stand-alone or in combination. For example, landslide inventories and causative factors, along with the statistical approach, are used in developing landslide susceptibility model for predicting potential landslides [39]. In addition, many studies used statistical approaches, physically based models, and deterministic approaches in developing landslide susceptibility maps (e.g., [53, 62, 75, 81]).

Besides the different existing methods for landslide susceptibility analysis and mapping, it is also essential to have advanced tools and detailed spatial information to develop an effective landslide susceptibility map. Digital tools such as GIS and global positioning system (GPS) are mostly used to analyze spatial data and developing landslide susceptibility and hazard maps [29, 81]. Moreover, remotely sensed data and technologies are widely used for effective landslide susceptibility mapping, hazard assessment, and risk assessment, which further helps in awareness, mitigation, and management of potential landslide threats [29].

Many researchers have used remotely sensed data to develop landslide susceptibility maps from local to global scale. For example, Ray and Jacobs [59] and Kirschbaum et al. [60] used remotely sensed precipitation data [TRMM and global precipitation measurement (GPM)] combined with slope, geology, road networks, fault zones, and forest loss to develop landslide susceptibility map at the global scale (Table 1, Figure 1). Ray et al. [16] used remotely sensed soil moisture (AMSR-E) combined with slope, soil, and vegetation characteristics to develop dynamic landslide susceptibility maps at a regional scale.

Figure 1.

Global susceptibility map (slightly modified from Stanley and Kirschbaum [60]).


4. Landslide hazard analysis

Landslide is a major natural hazard that leads to a significant loss to human lives and properties. Landslide hazard requires systematic and objective assessment of the multi-landslide hazard, which includes different characteristics and casual factors of hazard along with their spatial, temporal, and size probabilities [48, 82]. Effective planning and management reduce social and economic losses caused by landslides [30, 83, 84]. A landslide susceptibility map combined with temporal information can be converted into a landslide hazard map for estimating potential losses due to landslides [30]. Landslide risk can be estimated using landslide hazard maps [85]. A useful hazard map should include local geology, geomorphology, lithology, hydrology, vegetation, and climatic factors. These factors affect landslide events, needed for proper hazard analysis [86].

A vital part of hazard assessment is the quantitative estimate of the pre-failure and failure stages of the susceptibility of the slope [87]. Landslide hazard assessment determines slope failure probability [88]. Over the last 30 years, numerous studies have been conducted on landslide hazard zonation as a result of the demand for slope instability hazard for planning purposes [89]. Despite that susceptible slopes triggers or reactivate slope failures, hazard analysis must consider the speed of landslide movement along the slope [88]. Huabin et al. [90] suggested two important aspects of landslide hazard zonation, which are assessing the susceptibility of the terrain for slope failure, and determining the probability of a specific triggering event occurring.

Nadim and Kjekstad [91] used a landslide hazard index (Hlandslide) to classify landslide hazard levels from negligible to very high (Table 2). The Hlandslide was obtained by multiplying a series of factors such as slope factor (Sr), lithology factor (Sl), soil moisture factor (Sh), precipitation trigger factor (Tp), and seismic trigger factor (Ts); the following equation was used to develop Hlandslide.

Value of Hlandslide Class Classification of landslide hazard potential Approximate annual potential frequency in 1 km2 grid
<14 1 Negligible Virtually zero
15–20 2 Very low Negligible
51–100 3 Low Very small
101–168 4 Low to moderate Small
169–256 5 Moderate 0.0025–0.01%
257–360 6 Medium 0.0063–0.025%
360–512 7 Medium to high 0.0125–0.05%
513–720 8 High 0.025–0.1%
>720 9 Very high 0.05–0.2%

Table 2.

Landslide hazard index (Hlandslide), adapted from Nadim and Kjekstad [91].

H landslide = S r S l S h T s + T p E1

where Sr = slope factor, Sl = lithology factor, Sh = soil moisture factor, Ts = seismic trigger factor, and Tp = precipitation trigger factor.

Remotely sensed data provide several essential factors used in the equation to develop landslide hazard zones on the range of scales. It is often difficult to obtain the multi-spatio-temporal information on landslide occurrences needed for landslide hazard analysis [48]. Chau et al. [86] suggested that landslide analysis should include landslide-dynamics-based numerical simulations to prevent subjectivity and bias; incorporation with GIS should result in an adequate hazard map to work for better planning.

Susceptible areas can be assessed and predicted, thereby reducing damage caused by landslides through proper preparation and mitigation because landslide prevention is a severe challenge [4]. Generally, landslide hazard analysis is conducted using aerial photographs, and/or remotely sensed images; therefore, it might contain a large degree of uncertainty (Table 3) [92]. As listed in Table 2, the degree of uncertainty is related to many factors, such as topography, soil, vegetation, and hydrology. On the other hand, the level of uncertainty is strongly related to the degree of susceptibility of a map [92]. Also, the probability of landslide hazard depends on both the intrinsic and extrinsic variables. Intrinsic variables include geological conditions and slope structures, whereas extrinsic variables include rainfall and human activities [90]. Chau et al. [86] explained that a reliable landslide hazard map should include historical landslide events, geomorphological analysis, and mechanical analysis of slides, falls, and flows of earth mass. Since all three aspects of hazard analysis involve handling and interpreting a large amount of data, spatial analysis tools such as GIS is essential for such analysis.

Factors Uncertainty Factors Uncertainty
Slope angle Low Rainfall distribution Intermediate
Slope direction Low Morphological setting Low
Slope convexity Low Detailed geomorphological situation Intermediate/high
General lithological zonation Low Present mass movement distribution Intermediate
General lithological composition High Present mass movement typology Intermediate
General tectonic framework Low Present mass movement activity Intermediate/high
Detailed rock structure High Past mass movement distribution High
Soil type distribution Low/intermediate Land use Low
Soil characteristics Intermediate/high Past climatological condition High
Soil thickness High Earthquake acceleration High
Groundwater conditions High

Table 3.

Main factors in landslide hazard zonation and their estimated degree of uncertainty, adapted from Mantovani et al. [92].

Spatial information from previous landslide events is needed for landslide analysis and evaluating the probability of future landslide occurrence [48]. Therefore, high-resolution spatial information (satellite data) for factors associate in landslide hazards is essential for effective hazard analysis.

Recently, GIS tools and remotely sensed data have proven a vital approach for comparing and analyzing landslide, whereby a probabilistic landslide hazard analysis for the affected region is produced [38, 90]. Multiple layers of information are incorporated into the GIS system for a more accurate and reliable landslide hazard and risk analysis [86]. Geotechnical and safety factor-based models are also recommended for an effective landslide hazard analysis [4]. Various scenarios with different volumes or sliding surfaces should be integrated for hazard analysis of potential unknown landslides [2].

Golovko et al. [48] used multiple satellite data (e.g., Landsat, Spot, Aster, IRS-1C, LISS III, and RapidEye) and automated detection techniques to develop landslide susceptibility map and landslide hazard index. They summarized that their presented approach was based on the extensive use of remote sensing data and geospatial tools (e.g., GIS) to characterize landslide susceptibility and hazard. Ray et al. [16] used satellite soil moisture and hydrologic model in combination to develop landslide susceptibility maps at active Cleveland Corral landslide area in California, U.S. Ray et al. [76] used an integrated approach to combine satellite soil moisture and a hydrologic model to develop susceptibility maps at Dhading, Nepal.


5. Landslide monitoring

Landslide monitoring includes all of the activities discussed earlier, such as developing landslide inventory/detection, landslide susceptibility maps, and conducting a landslide hazard analysis. The easiest way to provide geological information to decision-makers and the public is through maps or visualization, which show locations of the landslide events, or where it might occur, thereby providing information on landslide hazard zones [37]. The most effective way to minimize and reduce the impact of landslide hazards and improve risk management is through landslide monitoring and planning [30, 35].

Landslides occur due to the combined effect, such as intense rainfall, topography, and antecedent soil moisture [93]. However, landslides can also be triggered due to external driving forces, such as earthquake, volcano, and excessive surcharge load on the slopes. Landslide hazard causes enormous infrastructural damages and human casualties in mountainous regions [20], and environmental degradation [94]. The combined effect of surface and sub-surface saturation is critical because landslide trigger is not due to only surface layer saturation [92]. Landslide monitoring technique depends on the type and size of the landslide, and the risks involved; it also differs between countries because of their available technology and expertise in landslide monitoring, past experiences with a landslide, and other factors [94]. For example, monitoring the surface displacements of a slope provides essential data for landslide dynamics [94].

Landslide hazard mitigation measures include hazard mapping and assessment, real-time monitoring systems of active and complex landslides, and emergency planning [94]. Landslide monitoring includes a comparison of the areal extent, speed of debris movement, rate of slope movement, surface topography, etc., concerning landslide conditions from different landslide occurrences to assess the activity of a landslide [92]. Timely and high-quality information received from spatial observations is crucial for managers of natural and human-made disasters, particularly in response to emergencies [1]. Landslide monitoring has improved over the years, with better monitoring equipment, automatic measurements done by machines, and less expensive tools [95].

Remote sensing data and techniques can be used for in-depth hazard mapping and monitoring because of their extensive coverage and frequency of observations, especially in high mountainous regions [20, 41, 92, 96]. Stereoscopic air-photo interpretation as far back as the late 1990s has been the most consistent remote sensing tool for landslide monitoring [20]. Combining aerial photography and infrared imagery gives a better result of terrain conditions rather than from either system separately [94].

Coupled with pre-existing landslide inventory maps and synthetic aperture radar (SAR) imagery and interferometric synthetic aperture radar (InSAR) through the integration of auxiliary data (e.g., detailed geological information) can be an effective method to update landslide inventory [1, 20, 97]. Ground-based optical systems (video cameras) limits monitoring movement of active landslides, in case of fog, rain, and darkness [94]. InSAR is useful to monitor prolonged slope movements [20]. Also, InSAR has been widely used in research because of its broad coverage, high spatial resolution, and ability to operate under all weather conditions [52].

Landslide monitoring includes detailed information on topography, geology, groundwater levels, material properties, possible mass movements [93]. Several types of instruments, such as extensometers, inclinometers, piezometers, strainmeters, pressure cells, geophones, tiltmeters, and crack meters, have been used to monitor slope movements and deformation [92, 93]. Recently, the landslide monitoring system has been improved with the development of less expensive computerized equipment [95]. According to Savvaidis [94], and Macek et al. [95], landslide monitoring systems and techniques include:

  1. Remote sensing techniques with space-derived information. They are of importance for seismic hazards, landslide hazards, and management of earthquake disasters. Also, remote sensing tools provide surface soil moisture data up to 1–5 cm deep, which has been used to develop landslide susceptibility maps [93].

  2. Global positioning system (GPS) technique. It is a flexible and easy operation that uses a series of satellites to determine accuracy in the order of centimeters [92]. A handled GPS provides accurate differential positioning over several kilometers [94].

  3. Photogrammetric technique, combined with digital imaging sensors data. It allows early identification of landslide hazard [96]. A photogrammetric technique, which includes interpretation of aerial photography, is a useful technique to identify and map landslides for an extended period [20, 92, 94, 98]. It is also a valuable technique for identifying and describing the 3D overview of the terrain in determining surface information [96].

  4. Ground-based conventional surveying techniques measure for absolute displacement computations with the use of different instruments, usually employed in an episodic `monitoring program [94].

  5. Geotechnical methods make use of sensors permanently working on or in the region under consideration, and can also use a telemetric system for real-time transmission of data to a control center [94].


6. Types and role of remote sensing techniques

Several remote sensing data have been used to study landslide processes, including space-borne synthetic aperture radar (SAR) and optical remote sensing, airborne light detection and ranging (LiDAR), ground-based SAR and terrestrial LiDAR, incorporating in situ measurements and observations of environmental factors (Table 1). In particular:

  • SAR data have been widely used in landslide research because of their broad coverage and high spatial resolution and the ability to operate under all weather conditions. Satellite SAR data used include archived ERS and Envisat ASAR [46], ALOS/PALSAR [52, 99], COSMO-SkyMed constellation [71, 100], TerraSAR-X [72, 101], TerraSAR-X/TanDEM-X [100] and Sentinel-1 [102] and Envisat 2010+ data (22 October 2010–8 April 2012).

  • Optical remote sensing images were mainly applied to generate landslide inventory, considering long time-series of Landsat TM/ETM, SPOT 1–5, ASTER, IRS-1C LISS III, and RapidEye between 1986 and 2016 [48]. The ZY-3 [103], and GF-1 [35] high spatial resolution satellite images were used to investigate the landslide cinematics with an image correlation algorithm to SPOT-5 images [104].

  • Multi-temporal LiDAR images and ortho-photos can be compared to quantify landscape changes caused by an active landslide [105]. The ground-based terrestrial laser scanner (TLS) LiDAR can produce highly detailed three dimensional (3D) images within minutes, allowing the study of 3D surface changes of landslides [106].

Among the most useful applications derived from the analysis of remote sensing images is the development of digital terrain models (DEMs), such as those generated from Indian remote sensing satellite (IRS) P5 images [107] and TerraSAR-X/TanDEM-X images by InSAR [108]. DEM can then be used to assess erosion, landslide, and topographic multi-temporal differences [107].

Lu et al. [45] used Quickbird remotely sensed data for landslide detection and mapping. They summarized that traditional mapping techniques for landslide detection and mapping, which rely on manual interpretation of aerial photographs and intensive field surveys, are time consuming and not efficient for generating such event-based maps. Guzzetti et al. [33] used aerial photographs, high-resolution DEM (LiDAR), and satellite images (e.g., Landsat-7, IRS, IKONOS-2, Quickbird-2, WorldView-2, and GeoEye-1/2) to develop multi-temporal landslide inventory maps. Holbling et al. [51] used SPOT-5 remotely sensed data for landslide change detection, whereas [52] used ALOS/PALSAR imageries and InSAR techniques for landslide detection. On the other hand, Desrues et al. [54] used LiDAR DEM and satellite images for landslide detection and mapping.


7. Global application

There are several studies conducted using remote sensing data and technologies around the globe. This chapter intends to summarize some of the remote-sensing based research undertaken to address landslide issues from selected countries, which are, mainly, more vulnerable to landslides. The studies have shown that several remote sensing techniques can assist in producing landslide inventory and risk assessment maps by providing information on the morphological features of landslides.

These approaches are very useful mainly in very large geographical areas where landslides are the most common yet highly devastating disasters, such as in Nepal, U.S., Philippines, and in many other countries. In the U.S., landslides caused 25 to 50 deaths each year, whereas extreme rainfall is the most common cause of landslides in the Philippines [109]. Ray and Jacobs [93] studied landslides in California, U.S., Leyte, Philippines and, Dhading, Nepal. They established the relationship between landslides, satellite soil moisture (Advanced Microwave Scanning Radiometer (AMSR-E)), and satellite precipitation (Tropical Rainfall Measuring Mission (TRMM)). In Nepal, Amatya et al. [61] used high resolution optical data for landslide mapping and susceptibility analysis along the Karnali Highway in Nepal.

Light detection and ranging (LiDAR) data open unprecedented possibilities for landslide mapping, with potential opportunities for hazard and risk zonation and landscape evolution modeling [33]. Gorsevki et al. [110] used LiDAR data to detect landslides in the Cuyahoga Valley National Park, Ohio, U.S, to generate a susceptibility map using the artificial neural network (ANN). Martha et al. [30] used a semiautomatic approach to develop landslide inventories from post-event satellite images, which they used for landslide susceptibility, hazard, and risk in the High Himalayan terrain in India. Following the 2004 Typhoon Aere, the object-based image analysis approach (OBIA) was adopted to develop landslide inventory in Xiulan, Taiwan [111].

Landslide mapping in the Cameron Highlands area in Malaysia is complicated due to dense vegetation and weather conditions. However, researchers used airborne synthetic aperture radar (AIRSAR) and WorldView-1 satellite data to develop a landslide inventory map for the Cameron Highlands [112, 113]. Also, Bui et al. [34] used synthetic aperture radar (SAR) data for landslide detection and susceptibility mapping in Cameron Highlands, Malaysia. In China, an area near the Three Gorges Reservoir (TGR) along the Yangtze River, which is one of the most landslide-prone regions in the world, was studied using ZY-3 high resolution satellite images to develop landslide inventory map [103].

In Italy, rainfall-induced landslides are serious threats to the population [114]. Since landslides are a national threat and distributed throughout the nation, it is not possible to analyze and monitor landslides using in situ measurements alone. Several researchers have studied landslides using remotely sensed data and technologies (e.g., [102, 115, 116]). Several researchers have also used cartographic thematic and optical data with Persistent Scatterer Interferometry (PSI) to identify slow to very slow moving landslides in a different region of Italy [117, 118, 119, 120, 121, 122]. Boni et al. [115] developed a new methodology to update the landslide state of activity (LAMBDA) using multi-temporal A-DInSAR data at north-western Italy.

Bozzano et al. [46] used field surveys and remote sensing techniques to investigate more than 90 landslides affecting a small river basin in Central Italy. Bardi et al. [116] used GB-InSAR data to monitor the rapid movement of earth flows over the Capriglio landslide in the northern Apennines, Italy. InSAR data were used to conduct a multi-temporal assessment of landslide activity in the basin of Abruzzi, Italy [44]. Fayne et al. [57] used Landsat-8 imagery in identifying potential locations and timing of newly triggered landslides in Italy.

Satellite-born landcover panchromatic images and shuttle radar topography (SRTM) elevation data were used to monitor landslide clusters over a vegetated landscape in Itaoca, Brazil [123]. Landslide is one of the natural hazards that occur each year in Indonesia. Hayati et al. [124] used interferometric synthetic aperture radar (InSAR) data to monitor the slow-moving landslide in Ciloto, Indonesia. The sensors provided useful data in obtaining continuous and area-based information for landslide affected areas. Bravo-Carvajal et al. [125] used SPOT satellite images to develop landslide inventory and susceptibility maps at the village of Nueva Colombia in the state of Chiapas. Also, in northern Pakistan, the SPOT-5 satellite was used to develop a landslide inventory [126]. Shroder and Weihs [127] used ASTER elevation data and Landsat 7 images for landslide mapping at northeastern Badakhshan, Afghanistan. Rainfall-induced landslides are frequent in the mountainous region of Bangladesh. For example, Das and Raja [128] used ASTER elevation data and Landsat 8 images to develop a landslide susceptibility map at Chittagong city, Bangladesh. Singhroy et al. [64] used Landsat TM and SAR images in combination to interpret the retrogressive slope failures on the Shale banks of the Saskatchewan river in Canada. Singhroy et al. [64] used SAR images to identify flow slides on sensitive marine clays in the Ottawa Valley of Canada.


8. Summary and conclusions

Natural disasters such as hurricanes, earthquakes, tsunami, and landslides have been on the rise, causing damage to property and human lives, especially in mountainous regions. Major causes of landslides are conditioning factors, such as lithology, relief, geological structure, geomechanical properties, weathering, and triggering factors such as precipitation, seismicity, temperature change, and static and dynamic loads. Conventional methods for landslide studies mainly rely on the visual interpretation of aerial photographs and field investigation in combination. However, these methods are time consuming and cost-ineffective. On the other hand, remotely sensed data at high spatial and temporal resolutions and advanced techniques could be used for landslide studies at a range of scales, which can reduce the time and resources required for the studies.

This chapter has reviewed the use of remotely sensed data and advanced technologies used for landslide inventory/detection, developing landslide susceptibility maps, conducting a landslide hazard analysis, and monitoring landslide events and slope movement at a range of scales. More robust technologies and high-resolution data are essential to reduce the impact of landslides threats on human lives, properties, and environments. In addition, we also need to develop advanced technologies, which can improve landslide assessment, prediction, and mitigations. According to Singhroy [129], our primary challenge is to have an advanced technology and/or high-resolution data to recognize and interpret detailed geomorphic characteristics of large and small landslides and determine whether or not failure is likely to occur. Although the use of high spatial resolution radar and LiDAR data are very helpful in conducting landslide studies, satellite products at high spatial and temporal resolutions are still limited. In the future, if real-time remotely sensed products at high spatial and temporal resolutions are available, especially in remote and hardly accessible terrain, it would be helpful to study landslide dynamics at a range of scales.



This work was supported by the Evans-Allen project of the United States Department of Agriculture (USDA), National Institute of Food and Agriculture.


Conflict of interest

The authors declare no conflict of interest.


  1. 1. Casagli N, Cigna F, Bianchini S, Hölbling D, et al. Landslide mapping and monitoring by using radar and optical remote sensing: Examples from the EC-FP7 project SAFER. Remote Sensing Applications: Society and Environment. 2016;4:92-108. DOI: 10.1016/j.rsase.2016.07.001
  2. 2. Lari S, Frattini P, Crosta GB. A probabilistic approach for landslide hazard analysis. Engineering Geology. 2014;182:3-14. DOI: 10.1016/j.enggeo.2014.07.015
  3. 3. Tofani V, Segoni S, Agostini A, Catani F, Casagli N. Technical note: Use of remote sensing for landslide studies in Europe. Natural Hazards and Earth System Sciences. 2013;13:299-309. DOI: 10.5194/nhess-13-299-2013
  4. 4. Pradhan B. Remote sensing and GIS-based landslide hazard analysis and cross-validation using multivariate logistic regression model on three test areas in Malaysia. Advances in Space Research. 2010;45:1244-1256. DOI: 10.1016/j.asr.2010.01.006
  5. 5. McKean D, Calkins H, Peuquet D. Remote sensing and landslide hazard assessment. Photogrammetric Engineering and Remote Sensing. 1991;57(9):1185-1193
  6. 6. 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
  7. 7. Lazzari M, Piccarreta M. Landslide disasters triggered by extreme rainfall events: The case of Montescaglioso (Basilicata, southern Italy). Geosciences. 2018;8(10):377
  8. 8. Guzzetti F, Reichenbach P, Cardinali M, Galli M, Ardizzone F. Probabilistic landslide hazard assessment at the basin scale. Geomorphology. 2005;72:272-299. DOI: 10.1016/j.geomorph.2005.06.002
  9. 9. USGS. Landslide Types and Processes. 2004. Available from: [Accessed: 27 April 2020]
  10. 10. Pathak D. Remote sensing and GIS application in landslide risk assessment and management. Nepalese Journal of Geoinformatics. 2016;15:52-57
  11. 11. Gupta M, Ghose MK, Sharma LP. Application of remote sensing and GIS for landslides hazard and assessment of their probabilistic occurrence—A case study of NH31A between Rangpo and Singtam. Journal of Geomatics. 2009;3(1):13-17
  12. 12. Ray RL, De Smedt F. Slope stability analysis on a regional scale using GIS: A case study from Dhading, Nepal. Environmental Geology. 2009;57:1603-1611. DOI: 10.1007/s00254-008-1435-5
  13. 13. Lombardo L, Martin Mai P. Presenting logistic regression-based landslide susceptibility results. Engineering Geology. 2018;244:14-24. DOI: 10.1016/j.enggeo.2018.07.019
  14. 14. Flentje PN, Miner A, Whitt G, Fell R. Guidelines for landslide susceptibility, hazard and risk zoning for land use planning. Australian Geomechanics Journal. 2007;42(1):13-36
  15. 15. Ciampalini A, Raspini F, Lagomarsino D, Catani F, Casagli N. Landslide susceptibility map refinement using PSInSAR data. Remote Sensing of Environment. 2016;184:302-315. DOI: 10.1016/j.rse.2016.07.018
  16. 16. 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
  17. 17. Lazzari M, Gioia D, Anzidei B. Landslide inventory of the Basilicata region (Southern Italy). Journal of Maps. 2018;14(2):348-356
  18. 18. Abdallah C, Chowrowicz J, Bouheir R, Dhont D. Comparative use of processed satellite images in remote sensing of mass movements: Lebanon as a case study. International Journal of Remote Sensing. 2007;28(19):4409-4427. DOI: 10.1080/01431160701241761
  19. 19. Tsai F, Hwang J-H, Chen L-C, Lin T-H. Post-disaster assessment of landslides in southern Taiwan after 2009 typhoon Morakot using remote sensing and spatial analysis. Natural Hazards and Earth System Sciences. 2010;10:2179-2190. DOI: 10.5194/nhess-10-2179-2010
  20. 20. Metternicht G, Hurni L, Gogu R. Remote sensing of landslides: An analysis of the potential contribution to geo-spatial systems for hazard assessment in mountainous environments. Remote Sensing of Environment. 2005;98:284-303. DOI: 10.1016/j.rse.2005.08.004
  21. 21. Sarkar S, Kanungo DP. An integrated approach for landslide susceptibility mapping using remote sensing and GIS. Photogrammetric Engineering and Remote Sensing. 2004;70(5):617-625. DOI: 10.14358/PERS.70.5.617
  22. 22. 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
  23. 23. Gorsevski PV, Gessler PE, Jankowski P. Integrating a fuzzy k-means classification and a Bayesian approach for spatial prediction of landslide hazard. Journal of Geographical Systems. 2003;5:223-251. DOI: 10.1007/s10109-003-0113-0
  24. 24. Scaioni M, Longoni L, Melillo V, Papini M. Remote sensing for landslide investigations: An overview of recent achievements and perspectives. Remote Sensing. 2014;6:9600-9652. DOI: 10.3390/rs60x000x
  25. 25. Khadka A, Dhakal S, Budha PGIS. Based landslide susceptibility mapping along the road section from Bandeu to Barahabise, Sindhupal Chowk District of Nepal. IJSR. 2018;7:465-471. DOI: 10.21275/ART20192474
  26. 26. Malamud BD, Turcotte DL, Guzzetti F, Reichenbach P. Landslide inventories and their statistical properties. Earth Surface Processes and Landforms. 2004;29:687-711. DOI: 10.1002/esp.1064
  27. 27. Naudet V, Lazzari M, Perrone A, Loperte A, Piscitelli S, Lapenna V. Integrated geophysical techniques and geomorphological approach to investigate the snowmelt-triggered landslide of Bosco Piccolo village (Basilicata, southern Italy). Engineering Geology. 2008;98(3-4):156-167
  28. 28. Cruden DM, Varnes DJ. Landslide types and processes. Chapter 3 in landslides-investigation and mitigation. In: Turner AK, Schuster RL, editors. Transportation Research Board Special Report 247. USA: National Research Council; 1996. pp. 36-75
  29. 29. van Westen CJ, van Asch TWJ, Soeters R. Landslide hazard and risk zonation—Why is it still so difficult? Bulletin of Engineering Geology and the Environment. 2006;65:167-184. DOI: 10.1007/s10064-005-0023-0
  30. 30. Martha TR, van Westen CJ, Kerle N, Jetten V, Kumar KV. Landslide hazard and risk assessment using semi-automatically created landslide inventories. Geomorphology. 2012;184:139-150. DOI: 10.1016/j.geomorph.2012.12.001
  31. 31. Pellicani R, Spilotro G. Evaluating the quality of landslide inventory maps: Comparison between archive and surveyed inventories for the Daunia region (Apulia, Southern Italy). Bulletin of Engineering Geology and the Environment. 2015;74:357-367. DOI: 10.1007/s10064-014-0639-z
  32. 32. Booth AM, Roering JJ, Perron JT. Automated landslide mapping using spectral analysis and high-resolution topographic data: Puget Sound lowlands, Washington, and Portland Hills, Oregon. Geomorphology. 2009;109:132-147. DOI: 10.1016/j.geomorph.2009.02.027
  33. 33. Guzzetti F, Mondini AC, Cardinali M, Fiorucci F, Santangelo M, Chang K-T. Landslide inventory maps: New tools for an old problem. Earth-Science Reviews. 2012;112:42-66. DOI: 10.1016/j.earscirev.2012.02.001
  34. 34. Bui Tien D, Shahabi H, Shirzadi A, Chapi K, et al. Landslide detection and susceptibility mapping by AIRSAR data using support vector machine and index of entropy models in Cameron highlands, Malaysia. Remote Sensing. 2018;10:1527. DOI: 10.3390/rs10101527
  35. 35. Sun W, Tian Y, Mu X, Zhai J, Gao P, Zhao G. Loess landslide inventory map based on GF-1 satellite imagery. Remote Sensing. 2017;9:314. DOI: 10.3390/rs9040314
  36. 36. Golovko D, Roessner S, Behling R, Wetzel H-U, et al. Development of multi-temporal landslide inventory information system for Southern Kyrgyzstan using GIS and satellite remote sensing. PFG. 2015;2:157-172
  37. 37. Moosavi V, Talebi A, Shirmohammadi B. Producing a landslide inventory map using pixel-based and object-oriented approaches optimized by Taguchi method. Geomorphology. 2014;204:646-656. DOI: 10.1016/j.geomorph.2013.09.012
  38. 38. Harp E, Keefer D, Sato H, Yagi H. Landslide inventories: The essential part of seismic landslide hazard analyses. Engineering Geology. 2011;122:9-21. DOI: 10.1016/j.enggeo.2010.06.013
  39. 39. Lazzari M, Piccarreta M, Capolongo D. Landslide triggering and local rainfall thresholds in Bradanic Foredeep, Basilicata region (southern Italy). Landslide science and practice. Volume 2. Early warning, instrumentation and modeling. In: Margottini et al., editors. Proceedings of the Second World Landslide Forum, 3-9 October 2011. Rome, Italy: Springer Series; 2013. pp. 671-678
  40. 40. Lazzari M, Gioia D. UAV images and historical aerial-photos for geomorphological analysis and hillslope evolution of the Uggiano medieval archaeological site (Basilicata, southern Italy). Geomatics, Natural Hazards and Risk. 2017;8:104-119
  41. 41. Cheng C-T, Huang C-M, Wei L-W, Lee C-F, Lee C-T. Landslide susceptibility map. In: Sassa K, He B, McSaveney M, Nagai O, editors. International Consortium of Landslides (ICL) Landslide Teaching Tools. Chapter: Part 1. Mapping and Site Prediction. ICL & UNESCO; 2013. pp. 50-55. DOI: 10.1007/s10346-013-0460-y
  42. 42. Behling R, Roessner S, Kaufmann H, Kleinschmit B. Automated spatiotemporal landslide mapping over large areas using RapidEye time series data. Remote Sensing. 2014;6:8026-8055. DOI: 10.3390/rs6098026
  43. 43. Li Z, Shi W, Myint S, Lu P, Wang Q . Semi-automated landslide inventory mapping from bitemporal aerial photographs using change detection and level set method. Remote Sensing of Environment. 2016;175:215-230. DOI: 10.1016/j.rse.2016.01.003
  44. 44. Van Den E, Poesen J, Verstraeten G, Vanacker V, et al. The effectiveness of hillshade maps and expert knowledge in mapping old deep-seated landslides. Geomorphology. 2005;67:351-363. DOI: 10.1016/j.geomorph.2004.11.001
  45. 45. Lu P, Stumpf A, Kerle N, Casagli N. Object-oriented change detection for landslide rapid mapping. IEEE Geoscience and Remote Sensing Letters. 2011;8(4):701-705. DOI: 10.1109/LGRS.2010.2101045
  46. 46. Bozzano F, Mazzanti P, Perissin D, Rocca A. Basin scale assessment of landslides geomorphological setting by advanced InSAR analysis. Remote Sensing. 2017;9:267. DOI: 10.3390/rs9030267
  47. 47. Stumpf A, Kerle N. Object-oriented mapping of landslides using random forests. Remote Sensing of Environment. 2011;115:2564-2577. DOI: 10.1016/j.rse.2011.05.013
  48. 48. Golovko D, Roessner S, Behling R, Wetzel H-U, Kleinschmit B. Evaluation of remote-sensing-based landslide inventories for hazard assessment in southern Kyrgyzstan. Remote Sensing. 2017;9:943. DOI: 10.3390/rs9090943
  49. 49. Gao J, Maro J. Topographic controls on evolution of shallow landslides in pastoral Wairarapa, New Zealand, 1979-2003. Geomorphology. 2010;114:373-381. DOI: 10.1016/j.geomorph.2009.08.002
  50. 50. Alkevli T, Ercanoglu M. Assessment of ASTER satellite images in landslide inventory mapping: Yenice-Gökçebey (Western Black Sea region, Turkey). Bulletin of Engineering Geology and the Environment. 2011;70:607-617. DOI: 10.1007/s10064-011-0353-z
  51. 51. Hölbling D, Friedl B, Eisank C. An object-based approach for semi-automated landslide change detection and attribution of changes to landslide classes in northern Taiwan. Earth Science Informatics. 2015;8:327-335. DOI: 10.1007/s12145-015-0217-3
  52. 52. Kang Y, Zhao C, Zhang Q , Lu Z, Li B. Application of InSAR techniques to an analysis of the Guanling landslide. Remote Sensing. 2017;9(10):1046. DOI: 10.3390/rs9101046
  53. 53. Hearn GJ, Hart AB. Landslide susceptibility mapping: A practitioner’s view. Bulletin of Engineering Geology and the Environment. 2019;78:5811-5826. DOI: 10.1007/s10064-019-01506-1
  54. 54. Desrues M, Lacroix P, Brenguier O. Satellite pre-failure detection and In situ monitoring of the landslide of the tunnel du Chambon, French Alps. Geosciences. 2019;9:313. DOI: 10.3390/geosciences9070313
  55. 55. Fiorucci F, Cardinali M, Carlà R, Rossi M, Mondini AC, Santurri F, et al. Seasonal landslide mapping and estimation of landslide mobilization rates using aerial and satellite images. Geomorphology. 2011;129:59-70. DOI: 10.1016/j.geomorph.2011.01.013
  56. 56. Wang Q , Wang Y, Niu R, Peng L. Integration of information theory, K-means cluster analysis and the logistic regression model for landslide susceptibility mapping in the three gorges area, China. Remote Sensing. 2017;9:938. DOI: 10.3390/rs9090938
  57. 57. Fayne JV, Ahamed A, Roberts-Pierel J, Rumsey AC, Kirschbaum D. Automated satellite-based landslide identification product for Nepal. Earth Interactions. 2019;23:1-21. DOI: 10.1175/EI-D-17-0022.1
  58. 58. Ray RL, Jacobs JM. Landslide susceptibility mapping using remotely sensed soil moisture. IEEE Xplore. 2008;3:47-50. DOI: 10.1109/IGARSS.2008.4779279
  59. 59. Kirschbaum D, Stanley T. Satellite-based assessment of rainfall-triggered landslide hazard for situational awareness. Earth’s Future. 2018;6:505-523. DOI: 10.1002/2017EF000715
  60. 60. Stanley T, Kirschbaum DB. A heuristic approach to global landslide susceptibility mapping. Natural Hazards. 2017;87:145-164. DOI: 10.1007/s11069-017-2757-y
  61. 61. Amatya P, Kirschbaum D, Stanley T. Use of very high-resolution optical data for landslide mapping and susceptibility analysis along the Karnali highway, Nepal. Remote Sensing. 2019;11:2284. DOI: 10.3390/rs11192284
  62. 62. Soeters R, van Westen CJ. Slope instability recognition, analysis and zonation. In: Turner AK, Schuster RL, editors. Landslides: Investigation and Mitigation. Transp. Res. Board. Natl. Acad. Washington, D.C: Press; 1996. pp. 129-177
  63. 63. Bathrellos GD, Gaki-Papanastassiou K, Skilodimou HD, Papanastassiou D, Chousianitis KG. Potential suitability for urban planning and industry development using natural hazard maps and geological–geomorphological parameters. Environment and Earth Science. 2012;66(2):537-548. DOI: 10.1007/s12665-011-1263-x
  64. 64. Singhroy V, Mattar KE, Gray AL. Landslide characterisation in Canada using interferometric SAR and combined SAR and TM images. Advances in Space Research. 1998;21(3):465-476. DOI: 10.1016/S0273-1177(97)00882-X
  65. 65. Zhou C, Lee C, Li J, Xu Z. On the spatial relationship between landslides and causative factors on Lantau Island, Hong Kong. Geomorphology. 2002;43:197-207. DOI: 10.1016/S0169-555X(01)00130-1
  66. 66. Pradhan B, Lee S. Utilization of optical remote sensing data and GIS tools for regional landslide Hazard analysis using an artificial neural network model. Earth Science Frontiers. 2007;14(6):143-152. DOI: 10.1016/S1872-5791(08)60008-1
  67. 67. Lee S, Choi J, Min K. Probabilistic landslide hazard mapping using GIS and remote sensing data at Boun, Korea. International Journal of Remote Sensing. 2004;25(11):2037-2052. DOI: 10.1080/01431160310001618734
  68. 68. Singhroy V, Molch K. Characterizing and monitoring rockslides from SAR techniques. Advances in Space Research. 2004;33:290-295. DOI: 10.1016/S0273-1177(03)00470-8
  69. 69. Berardino P, Costantini M, Franceschetti G, Iodice A, Petranera L, Rizzo V. Use of differential SAR interferometry in monitoring and modelling large slope instability at Maratea (Basilicata, Italy). Engineering Geology. 2003;68:31-51. DOI: 10.1016/S0013-7952(02)00197-7
  70. 70. Squarzoni C, Delacourt C, Allemanda P. Nine years of spatial and temporal evolution of the La Valette landslide observed by SAR interferometry. Engineering Geology. 2003;68:53-66. DOI: 10.1016/S0013-7952(02)00198-9
  71. 71. Konishi T, Suga Y. Landslide detection using COSMO-SkyMed images: A case study of a landslide event on Kii peninsula, Japan. European Journal of Remote Sensing. 2018;51(1):205-221. DOI: 10.1080/22797254.2017.1418185
  72. 72. Zhao F, Mallorqui JJ, Iglesias R, Gili JA, Corominas J. Landslide monitoring using multi-temporal SAR inferometry with advanced persistent scatters identification methods and super high-spatial resolution TerraSAR-X images. Remote Sensing. 2018;10:921. DOI: 10.3390/rs10060921
  73. 73. Vakhshoori V, Pourghasemi HR, Zare M, Blaschke T. Landslide susceptibility mapping using GIS-based data mining algorithms. Watermark. 2019;11:2292. DOI: 10.3390/w11112292
  74. 74. Fressard M, Thiery Y, Maquaire O. Which data for quantitative landslide susceptibility mapping at operational scale? Case study of the pays d’Auge plateau hillslopes (Normandy, France). Natural Hazards and Earth System Sciences. 2014;14:569-588. DOI: 10.5194/nhess-14-569-2014
  75. 75. Ray RL, Jacobs JM, Douglas EM. Modeling regional landslide susceptibility using dynamic soil moisture profiles. Journal of Mountain Science. 2018;15(8):1807-1824. DOI: 10.1007/s11629-018-4896-3
  76. 76. Ray RL, Jacobs JM, Ballestero TP. Regional landslide susceptibility: Spatiotemporal variations under dynamic soil moisture conditions. Natural Hazards. 2011;59:1317-1337. DOI: 10.1007/s11069-011-9834-4
  77. 77. Acharya G, De Smedt F, Long NT. Assessing landslide hazard in GIS: A case study from Rasuwa, Nepal. Bulletin of Engineering Geology and the Environment. 2006;65(1):99-107. DOI: 10.1007/s10064-005-0025-y
  78. 78. Pack RT, Tarboton DG, Goodwin CN. The SINMAP approach to terrain stability mapping. In: Moore D, Hungr O, editors. Proceedings of the 8th IAEG; 21-25 September 1998; Vancouver, British Columbia. Canada: IAEG; 1998. pp. 1157-1165
  79. 79. Pokharel B, Thapa PB. Landslide susceptibility in Rasuwa District of Central Nepal after the 2015 Gorkha earthquake. Journal of Nepal Geological Society. 2019;59:79-88. DOI: 103126/jngs.v59i0.24992
  80. 80. Rozos D, Bathrellos GD, Skillodimou HD. Comparison of the implementation of rock engineering system and analytic hierarchy process methods, upon landslide susceptibility mapping, using GIS: A case study from the eastern Achaia County of Peloponnesus, Greece. Environment and Earth Science. 2011;63:49-63. DOI: 10.1007/s12665-010-0687-z
  81. 81. Yilmaz I. Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: A case study from Kat landslides (Tokat—Turkey). Computers and Geosciences. 2009;35:1125-1138. DOI: 10.1016/j.cageo.2008.08.007
  82. 82. Corominas J, van Westen C, Frattini P, et al. Recommendations for the quantitative analysis of landslide risk. Bulletin of Engineering Geology and the Environment. 2014;73:209-263
  83. 83. Dai FC, Lee CF, Ngai YY. Landslide risk assessment and management: An overview. Engineering Geology. 2002;64:65-87. DOI: 10.1016/S0013-7952(01)00093-X
  84. 84. Lucieer A, de Jong SM, Turner D. Mapping landslide displacements using structure from motion (SfM) and image correlation of multi-temporal UAV photography. Progress in Physical Geography. 2014;38(1):97-116. DOI: 10.1177/0309133313515293
  85. 85. Wu TH, Tang WH, Einstein HE. Landslide hazard and risk assessment. In: Turner AK, Schuster RL, editors. Landslides. Investigation and Mitigation. Transportation Research Board Special Report. Washington: National Academy Press; 1996. pp. 106-118
  86. 86. Chau KT, Sze YL, Fung MK, Wong WY, et al. Landslide hazard analysis for Hong Kong using landslide inventory and GIS. Computers and Geosciences. 2004;30(4):429-443. DOI: 10.1016/j.cageo.2003.08.013
  87. 87. van Asch TWJ, Malet J-P. Flow-type failures in fine-grained soils: An important aspect in landslide hazard analysis. Natural Hazards and Earth System Sciences. 2009;9:1703-1711. DOI: 10.5194/nhess-9-1703-2009
  88. 88. Corominas J, Copons R, Vilaplana JM, Altimir J, Amigó J. Integrated landslide susceptibility analysis and hazard assessment in the principality of Andorra. Natural Hazards. 2003;30:421-435
  89. 89. van Westen CJ, Terlien MT. An approach towards deterministic landslide hazard analysis in GIS. A case study from Manizales (Colombia). Earth Surface Processes and Landforms. 1996;21:853-868
  90. 90. Huabin W, Gangjun L, Weiya X, Gonghui W. GIS-based landslide hazard assessment: An overview. Progress in Physical Geography. 2005;29(4):548-567. DOI: 10.1191/0309133305pp462ra
  91. 91. Nadim F, Kjeksta O. Assessment of global high-risk landslide disaster hotspots. In: Sassa K, Canuti P, editors. Landslides disaster risk reduction. Berlin: Springer; 2009. pp. 213-221
  92. 92. Mantovani F, Soeters R, van Westen CJ. Remote sensing techniques for landslide studies and hazard zonation in Europe. Geomorphology. 1996;15:213-225. DOI: 10.1016/0169-555X(95)00071-C
  93. 93. Ray RL, Jacobs JM. Relationships among remotely sensed soil moisture, precipitation and landslide events. Natural Hazards. 2007;43:211-222. DOI: 10.1007/s11069-006-9095-9
  94. 94. Savvaidis PD. Existing landslide monitoring systems and techniques. In: Proceedings of the Conference from Stars to Earth and Culture; In Honor of the Memory of Professor Alexandros Tsioumis. Thessaloniki, Greece; 2003. pp. 242-258
  95. 95. Maček M, Petkovšek A, Majes B, Mikoš M. Landslide monitoring techniques database. In: Sassa K et al., editors. Landslide Science for a Safer Geoenvironment. Vol. 1. Heidelberg: Springer; 2014. pp. 193-197. DOI: 10.1007/978-3-319-04999-1_24
  96. 96. Kääb A. Photogrammetry for early recognition of high mountain hazards: New techniques and applications. Physics and Chemistry of the Earth, Part B: Hydrology, Oceans and Atmosphere. 2000;25(9):765-770. DOI: 10.1016/S1464-1909(00)00099-X
  97. 97. Sun L, Muller J-P. Evaluation of the use of sub-pixel offset tracking techniques to monitor landslides in densely vegetated steeply sloped areas. Remote Sensing. 2016;8:659. DOI: 10.3390/rs8080659
  98. 98. van Westen CJ, Getahun FL. Analyzing the evolution of the Tessina landslide using aerial photographs and digital elevation models. Geomorphology. 2003;1347:1-13. DOI: 10.1016/S0169-555X(03)00057-6
  99. 99. Qi S, Zou Y, Wu F, Yan C, Fan J, Zang M, et al. A recognition and geological model of a deep-seated ancient landslide at a reservoir under construction. Remote Sensing. 2017;9:383
  100. 100. Schlögel R, Thiebes B, Mulas M, Cuozzo G, Notarnicola C, Schneiderbauer S, et al. Multi-temporal X-band radar interferometry using corner reflectors: Application and validation at the Corvara landslide (Dolomites, Italy). Remote Sensing. 2017;9:739
  101. 101. Yang Z, Li Z, Zhu J, Preusse A, Yi H, Hu J, et al. Retrieving 3-D large displacements of mining areas from a single amplitude pair of SAR using offset tracking. Remote Sensing. 2017;9:338
  102. 102. Mondini A. Measures of spatial autocorrelation changes in multitemporal SAR images for event landslides detection. Remote Sensing. 2017;9:554
  103. 103. Chen T, Trinder JC, Niu R. Object-oriented landslide mapping using ZY-3 satellite imagery, random Forest and mathematical morphology, for the three-gorges reservoir, China. Remote Sensing. 2017;9:333
  104. 104. Bivic RL, Allemand P, Quiquerez A, Delacourt C. Potential and limitation of SPOT-5 Ortho-image correlation to investigate the Cinematics of landslides: The example of “Mare à Poule d’Eau” (Réunion, France). Remote Sensing. 2017;9:106
  105. 105. Kamps M, Bouten W, Seijmonsbergen A. LiDAR and orthophoto synergy to optimize object-based landscape change: Analysis of an active landslide. Remote Sensing. 2017;9:805
  106. 106. Luo L, Ma W, Zhang Z, Zhuang Y, Zhang Y, Yang J, et al. Freeze/thaw-induced deformation monitoring and assessment of the slope in permafrost based on terrestrial laser scanner and GNSS. Remote Sensing. 2017;9:198
  107. 107. Ren Z, Zhang Z, Yin J. Erosion associated with seismically-induced landslides in the middle Longmen Shan region, eastern Tibetan plateau, China. Remote Sensing. 2017;9:864
  108. 108. Du Y, Xu Q , Zhang L, Feng G, Li Z, Chen R, et al. Recent landslide movement in Tsaoling, Taiwan tracked by TerraSAR-X/TanDEM-X DEM time series. Remote Sensing. 2017;9:353
  109. 109. Talubo JP, Jacildo AJ, Espaldon MV, Acosta LA, et al. Vulnerability to rainfall-induced landslide of three communities in Infanta, Quezon, Philippines. International Journal of Sciences: Basic and Applied Research (IJSBAR). 2015;23(1):138-166
  110. 110. Gorsevski PV, Brown KM, Panter K, Onasch CM, et al. Landslide detection and susceptibility mapping using LiDAR and an artificial neural network approach: A case study in the Cuyahoga Valley National Park, Ohio. Landslides. 2016;13:467-484. DOI: 10.1007/s10346-015-0587-0
  111. 111. Lahousse T, Chang K, Lin Y. Landslide mapping with multi-scale object-based image analysis – A case study in the Baichi watershed, Taiwan. Natural Hazards and Earth System Sciences. 2011;11:2715-2726. DOI: 10.5194/nhess-11-2715-2011
  112. 112. Razak K, Santangelo M, Van Westen CJ, Straatsma M, de Jong SM. Generating an optimal DTM from airborne laser scanning data for landslide mapping in a tropical forest environment. Geomorphology. 2013;190:112-125. DOI: 10.1016/j.geomorph.2013.02.021
  113. 113. Shahabi H, Hashim M. Landslide susceptibility mapping using GIS-based statistical models and remote sensing data in tropical environment. Scientific Reports. 2015;5:9899. DOI: 10.1038/srep09899
  114. 114. Gariano SL, Petrucci O, Guzzetti F. Changes in the occurrence of rainfall-induced landslides in Calabria, southern Italy, in the 20th century. Natural Hazards and Earth System Sciences. 2015;15:2313-2330. DOI: 10.5194/nhess-15-2313-2015
  115. 115. Bonì R, Bordoni M, Colombo A, Lanteri L, Meisina C. Landslide state of activity maps by combining multi-temporal A-DInSAR (LAMBDA). Remote Sensing of Environment. 2018;217:172-190
  116. 116. Bardi F, Raspini F, Frodella W, Lombardi L, et al. Monitoring the rapid-moving reactivation of earth flows by means of GB-InSAR: The April 2013 Capriglio landslide (Northern Appennines, Italy). Remote Sensing. 2017;9:165. DOI: 10.3390/rs9020165
  117. 117. Bianchini S, Cigna F, Righini G, et al. Landslide hotspot mapping by means of persistent scatterer interferometry. Environment and Earth Science. 2012;67:1155-1172. DOI: 10.1007/s12665-012-1559-5
  118. 118. Bianchini S, Herrera G, Mateos RM, Notti D, et al. Landslide activity maps generation by means of persistent scatterer interferometry. Remote Sensing. 2013;5(12):6198-6222. DOI: 10.3390/rs5126198
  119. 119. Righini G, Pancioli V, Casagli N. Updating landslide inventory maps using persistent Scatterer interferometry (PSI). International Journal of Remote Sensing. 2012;33(7):2068-2096. DOI: 10.1080/01431161.2011.605087
  120. 120. Cigna F, Bianchini S, Casagli N. How to assess landslide activity and intensity with persistent scatterer interferometry (PSI): The PSI-based matrix approach. Landslides. 2013;10(3):267-283
  121. 121. Lu P, Catani V, Casagli N. Quantitative hazard and risk assessment for slow-moving landslides from persistent scatterer interferometry. Landslides. 2014;11(4):685-696
  122. 122. Martire D, Tessitore S, Brancato D, Ciminelli MG, et al. Landslide detection integrated system (LaDIS) based on in-situ and satellite SAR interferometry data. Catena. 2016;137:406-421
  123. 123. Batista J, Julien P. Remotely sensed survey of landslide clusters: Case study of Itaoca, Brazil. Journal of South American Earth Sciences. 2019;92:145-150. DOI: 10.1016/j.jsames.2019.02.021
  124. 124. Hayati N, Niemeier W, Sadarviana V. Ground deformation in the Ciloto landslides area revealed by multi-temporal InSAR. Geosciences. 2020;101:56. DOI: 10.3390/geosciences10050156
  125. 125. Bravo-Carvajal I, Ricardo G, Luis P-B, Irasema A-A. Landslide Susceptibility Mapping Using Remote Sensing and GIS. Nueva Colombia, Chiapas. Mexico: Springer; 2014. DOI: 10.1007/978-3-319-05050-8_63
  126. 126. Khan H, Shafique M, Khan M, Bacha M, et al. Landslide susceptibility assessment using frequency ratio, a case study of northern Pakistan. Egyptian Journal of Remote Sensing and Space Sciences. 2019;22:11-24. DOI: 10.1016/j.ejrs.2018.03.004
  127. 127. Shroder JF, Weihs BJ. Geomorphology of the lake Shewa landslide dam, Badakhshan, Afghanistan, using remote sensing data. Geografiska Annaler. Series A, Physical Geography. 2010;92(4):469-483. DOI: 10.1111/j.1468-0459.2010.00408.x
  128. 128. Das S, Raja D. Susceptibility analysis of landslide in Chittagong City Corporation area, Bangladesh. International Journal of Environment. 2015;4:157-181. DOI: 10.3126/ije.v4i2.12635
  129. 129. Singhroy V. Satellite remote sensing application for landslide detection and monitoring. In: Sassa K, Canuti P, editors. Landslides disaster risk reduction. Berlin: Springer; 2009. pp. 143-158

Written By

Ram L. Ray, Maurizio Lazzari and Tolulope Olutimehin

Submitted: February 3rd, 2020 Reviewed: August 21st, 2020 Published: September 23rd, 2020