Remote Sensing Approaches and Related Techniques to Map and Study Landslides

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 of Natural Hazards

Each year, natural hazards such as earthquakes, cyclones, flooding, landslides, wildfires, avalanches, volcanic eruption, extreme temperatures, storm surges, drought, etc., result in widespread loss of life, livelihood, and critical infrastructure globally. With the unprecedented growth of the human population, largescale development activities, and changes to the natural environment, the frequency and intensity of extreme natural events and consequent impacts are expected to increase in the future.Technological interventions provide essential provisions for the prevention and mitigation of natural hazards. The data obtained through remote sensing systems with varied spatial, spectral, and temporal resolutions particularly provide prospects for furthering knowledge on spatiotemporal patterns and forecasting of natural hazards. The collection of data using earth observation systems has been valuable for alleviating the adverse effects of natural hazards, especially with their near real-time capabilities for tracking extreme natural events. Remote sensing systems from different platforms also serve as an important decision-support tool for devising response strategies, coordinating rescue operations, and making damage and loss estimations.With these in mind, this book seeks original contributions to the advanced applications of remote sensing and geographic information systems (GIS) techniques in understanding various dimensions of natural hazards through new theory, data products, and robust approaches

Multi-Temporal X-Band Radar Interferometry Using Corner Reflectors: Application and Validation at the Corvara Landslide (Dolomites, Italy)

From the wide range of methods available to landslide researchers and practitioners for monitoring ground displacements, remote sensing techniques have increased in popularity. Radar interferometry methods with their ability to record movements in the order of millimeters have been more frequently applied in recent years. Multi-temporal interferometry can assist in monitoring landslides on the regional and slope scale and thereby assist in assessing related hazards and risks. Our study focuses on the Corvara landslides in the Italian Alps, a complex earthflow with spatially varying displacement patterns. We used radar imagery provided by the COSMO-SkyMed constellation and carried out a validation of the derived time-series data with differential GPS data. Movement rates were assessed using the Permanent Scatterers based Multi-Temporal Interferometry applied to 16 artificial Corner Reflectors installed on the source, track and accumulation zones of the landslide. The overall movement trends were well covered by Permanent Scatterers based Multi-Temporal Interferometry, however, fast acceleration phases and movements along the satellite track could not be assessed with adequate accuracy due to intrinsic limitations of the technique. Overall, despite the intrinsic limitations, Multi-Temporal Interferometry proved to be a promising method to monitor landslides characterized by a linear and relatively slow movement rates

The State of Remote Sensing Capabilities of Cascading Hazards Over High Mountain Asia

Cascading hazard processes refer to a primary trigger such as heavy rainfall, seismic activity, or snow melt, followed by a chain or web of consequences that can cause subsequent hazards influenced by a complex array of preconditions and vulnerabilities. These interact in multiple ways and can have tremendous impacts on populations proximate to or downstream of these initial triggers. High Mountain Asia (HMA) is extremely vulnerable to cascading hazard processes given the tectonic, geomorphologic, and climatic setting of the region, particularly as it relates to glacial lakes. Given the limitations of in situ surveys in steep and often inaccessible terrain, remote sensing data are a valuable resource for better understanding and quantifying these processes. The present work provides a survey of cascading hazard processes impacting HMA and how these can be characterized using remote sensing sources. We discuss how remote sensing products can be used to address these process chains, citing several examples of cascading hazard scenarios across HMA. This work also provides a perspective on the current gaps and challenges, community needs, and view forward toward improved characterization of evolving hazards and risk across HMA

ANALYZING THE LIFE-CYCLE OF UNSTABLE SLOPES USING APPLIED REMOTE SENSING WITHIN AN ASSET MANAGEMENT FRAMEWORK

An asset management framework provides a methodology for monitoring and maintaining assets, which include anthropogenic infrastructure (e.g., dams, embankments, and retaining structures) and natural geological features (e.g., soil and rock slopes). It is imperative that these assets operate efficiently, effectively, safely, and at a high standard since many assets are located along transportation corridors (highways, railways, and waterways) and can cause severe damage if compromised. Assets built on or around regions prone to natural hazards are at an increased risk of deterioration and failure. The objective of this study is to utilize remote sensing techniques such as InSAR, LiDAR, and optical photogrammetry to identify assets, assess past and current conditions, and perform long-term monitoring in transportation corridors and urbanized areas prone to natural hazards. Provided are examples of remote sensing techniques successfully applied to various asset management procedures: the characterization of rock slopes (Chapter 2), identification of potentially hazardous slopes along a railroad corridor (Chapter 3), monitoring subsidence rates of buildings in San Pedro, California (Chapter 4), and mapping displacement rates on dams in India (Chapter 5) and California (Chapter 6). A demonstration of how InSAR can be used to map slow landslides (those with a displacement rate \u3c 16 mm/year and may be undetectable without sensitive instrumentation) and update the California Landslide Inventory on the Palos Verdes Peninsula is provided in Chapter 7. Long-term landslide monitoring using optical photogrammetry, GPS, and InSAR measurements is also used to map landslide activity at three orders of magnitude (meter to millimeter scales) in Chapter 8. Remote sensing has proven to be an effective tool at measuring ground deformation, which is an implicit indicator of how geotechnical asset condition changes (e.g., deteriorates) over time. Incorporating these techniques into a geotechnical asset management framework will provide greater spatial and temporal data for preventative approaches towards natural hazards

Detection and mapping of small-scale and slow-moving landslides from very high resolution optical satellite data

Small slope failures are often ignored because of their perceived less severe impact. Although they may have small velocity, small slope failures can cause damages to facilities such roads and pipelines. The main objective of this research is to utilise very high resolution Pleiades-1 data to extract surface features and identify surface deformations susceptible to small slope failures. An algorithm was developed using object-based image analysis (OBIA), Pleiades-1 imagery, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Digital Elevation Model (GDEM) and Real Time Kinematic-Global Positioning System (RTK-GPS) data. Using the OBIA algorithm four different object attribute parameters namely spectral, textural, spatial and topographic characteristics were applied in a rule-based classification, for semi-automated detection of small translational landslides. The developed OBIA algorithm was further modified by using Pleiades-1 imagery, Nearest Neighbors (k-NN) and Support Vector Machine (SVM) techniques in example-based classification for the detection of small landslides, with focus on the effects of the training samples size and type on the results of the classification. The horizontal displacement of the landslides was investigated based on sub-pixel image correlation method using Pleiades-1 images and Shuttle Radar Topographic Mission (SRTM). Kalman filtering method and RTK-GPS observations from TUSAGA-Aktif Global Navigation Satellite System (GNSS) Network in Turkey were utilised to formulate kinematic analysis model for the landslides. The developed algorithms were validated in Kutlugün test site in Northeastern Turkey. In the rule-based classification results, a total of 123 small landslides covering a total area of approximately 413.332 m2 were detected. The size of landslides detected varied between 0.747 and 7.469 m2. The detected landslides yielded user’s accuracy of 81.8%, producer’s accuracy of 80.6%, quality percentage of 82% and computed kappa index of 0.87. In the small landslides detection using the example-based classification, the SVM method had higher producer accuracy (85.9%), user accuracy (89.4%) and kappa index (0.82) compared to the k-NN algorithm that had producer accuracy (83.1%), user accuracy (86.0%) and kappa index (0.80). A total of 128 small landslides were detected using k-NN algorithm, while a total of 134 landslides were detected using SVM algorithm. The displacement results from RTK-GPS measurements varied from 2.77 mm to 24.87 mm in 6 months, while the velocities varied from 0.80 mm to 8.28 mm/6 month. The displacements from optical image correlation agreed well with RTK-GPS results and provided a more uniform movement pattern than could be derived solely using the RTK-GPS measurements. The landslide movements are dominantly toward the north direction. These trends agree with the results of previous study in the area. The main contributions of this research include – development of a comprehensive metrics to quantify the attribute parameters of small landslides, derivation of susceptibility and inventory maps for small landslides, and the design of an early warning system for small slope failures on highway infrastructures. The results of this research will add to the increasing applications of Pleiades-1 image in landslide investigations

Landslides

Landslides - Investigation and Monitoring offers a comprehensive overview of recent developments in the field of mass movements and landslide hazards. Chapter authors use in situ measurements, modeling, and remotely sensed data and methods to study landslides. This book provides a thorough overview of the latest efforts by international researchers on landslides and opens new possible research directions for further novel developments

An approach for remote landslide mapping, South Nahanni Watershed, Northwest Territories, Canada.

This thesis presents two cost-effective techniques for landslide mapping in large, remote regions. The first technique uses ASTER satellite imagery to characterize and determine landslide distribution for part of the South Nahanni watershed. Results obtained from this study confirm that ASTER images are suitable for regional-scale landslide mapping. The second technique involved the creation of landslide susceptibility models for debris flow and rock/debris slides using logistic regression analysis. Cross validation confirmed the models' success. The debris flow model performed best whereas the rock/debris slide model was only moderately successful. Taken together, the two methods developed in this thesis provide a means to conduct a preliminary landslide investigation in large, remote regions or in developing countries where data are limited or site investigation is not possible. Maps produced from this analysis can be used to gain information on areas susceptible to landslides and to target key areas remotely before conducting field investigations. --P. ii.The original print copy of this thesis may be available here: http://wizard.unbc.ca/record=b175477

Remote sensing technology applications in forestry and REDD+

Advances in close-range and remote sensing technologies are driving innovations in forest resource assessments and monitoring on varying scales. Data acquired with airborne and spaceborne platforms provide high(er) spatial resolution, more frequent coverage, and more spectral information. Recent developments in ground-based sensors have advanced 3D measurements, low-cost permanent systems, and community-based monitoring of forests. The UNFCCC REDD+ mechanism has advanced the remote sensing community and the development of forest geospatial products that can be used by countries for the international reporting and national forest monitoring. However, an urgent need remains to better understand the options and limitations of remote and close-range sensing techniques in the field of forest degradation and forest change. Therefore, we invite scientists working on remote sensing technologies, close-range sensing, and field data to contribute to this Special Issue. Topics of interest include: (1) novel remote sensing applications that can meet the needs of forest resource information and REDD+ MRV, (2) case studies of applying remote sensing data for REDD+ MRV, (3) timeseries algorithms and methodologies for forest resource assessment on different spatial scales varying from the tree to the national level, and (4) novel close-range sensing applications that can support sustainable forestry and REDD+ MRV. We particularly welcome submissions on data fusion