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

Landslide susceptibility mapping using remotely sensed soil moisture

Slope stability analysis using remotely sensed data is routinely conducted throughout the world. This study focuses on rainfall induced landslides and the use of AMSRE and TRMM satellite data to develop susceptibility maps that can be used to forecast landslides. This research established the first relationships among soil moisture derived from AMSR-E, precipitation from TRMM and major landslide events, respectively, in California, U.S., Leyte, Philippines and, Dhading, Nepal. Each of the three study regions had slope movements when soil moisture was high and rainfall occurred and clearly indicates a strong relationship among landslide events, remotely sensed soil moisture and rainfall. A slope stability model is used to develop susceptibility maps for a California site under a range of conditions. Results suggest that the AMSR-E and TRMM satellite data, coupled with land-surface model estimates, are viable for enhancing rainfall induced slope stability analysis at regional or global scales. © 2008 IEEE

Landslide susceptibility mapping through enhanced dynamic slope stability analysis using earth observing satellite measurements

Landslides are common throughout the world and can be triggered by earthquakes, volcanoes, floods, and heavy continuous rainfall in mountainous regions. For most types of slope failure, soil moisture plays a critical role because increased pore water pressure reduces the soil strength and increases stress. The combined effect of soil moisture in unsaturated soil layers and pore water pressure in saturated soil layers is critical to accurately predict landslides. However, dynamic in-situ soil moisture profiles are rarely measured on regional or global scales. The dynamic soil moisture can be estimated by a soil vegetation atmosphere transfer (SVAT) model or satellite. While a SVAT model can estimate soil moisture profile, satellite estimates are limited to the upper thin surface (0-5 cm). However, considering the complex database needed for a SVAT model, satellite data can be obtained quickly and may produce promising results in less data-rich regions at the global scale. While no previous landslide studies have used remotely-sensed soil moisture data, Advanced Microwave Scanning Radiometer (AMSR-E) has the potential to be useful in characterizing soil moisture profiles. First this study investigated relationships among landslides, AMSR-E soil moisture and Tropical Rainfall Measuring Mission (TRMM) in landslide prone regions of California, U.S., Leyte, Philippines and Dhading, Nepal. Then, a modified infinite slope stability model was developed and applied at Cleveland Corral, California, US and Dhading Nepal, using variable infiltration capacity (VIC-3L) soil moisture and AMSR-E soil moisture to develop dynamic landslide susceptibility maps at regional scale. Results show a strong relationship among remotely sensed soil moisture, rainfall and landslide events. Results also show a modified infinite slope stability model that directly includes vadose zone soil moisture can produce promising landslide susceptibility maps at regional scale using either VIC-3L or AMSR-E soil moisture. Vadose zone soil moisture has a significant role in shallow slope failure. Results show promising agreement between the susceptible area predicted by the model and the actual slope movements and slope failures observed in the study region. This model is quite reasonable to use in shallow slope stability analysis at a regional or global scale

Soil Moisture Estimation for landslide monitoring: A new approach using multi-temporal Synthetic Aperture RADAR data

This study explores the utility of the Spotlight2 X-band Synthetic Aperture Radar product developed by the Italian Space Agency for use in multi-temporal estimation of soil moisture in a landslide monitoring context, using a time series of monthly images of the Hollin Hill Landslide Observatory – North Yorkshire, UK. The study shows the complexity of surface soil moisture at an active landslide, using high resolution in situ soil moisture data. This in situ data is also used for ground truthing the soil moisture estimations from the SAR data. The study shows the limitations of inter-and intra-sensor calibration within the Cosmo-SkyMed array and contextualises this problem within the current research climate where SAR imagery is increasingly being created using multi-satellite constellation, while being used, increasingly, by environmental scientists rather than remote sensing specialists

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

LANDSLIDE SUSCEPTIBILITY MODELLING UNDER ENVIRONMENTAL CHANGES: A CASE STUDY OF CAMERON HIGHLANDS, MALAYSIA

Modeling landslide susceptibility usually does not include multi temporal factors, e.g. rainfall, especially for medium scale. Landslide occurrences in Cameron Highlands, in particular, and in Peninsular Malaysia, in general, tend to increase during the peak times of monsoonal rainfall. Due to the lack of high spatial resolution of rainfall data, Normalized Different Vegetation Index (NDVI), soil wetness, and LST (Land Surface Temperature) were selected as replacement of multi temporal rainfall data. This research investigated their roles in landslide susceptibility modeling. In doing so, four Landsat 7 Enhanced Multi Temporal Plus (ETM+) images acquired during two peak times of rainy and dry seasons were used to derive multi temporal NDVI, soil wetness, and LST. Topographic, geology, and soil maps were used to derive ‘static’ factors namely slope, slope aspect, curvature, elevation, road network, river/lake, lithology, soil geology lineament maps. Landslide map was used to derive weighting system based on spatial relationship between landslide occurrences and landslide factor using bivariate statistical method. A non-statistical weighting system was also used for comparison purpose. Different scenarios of data processing were applied to allow evaluation on the roles of multi temporal factors in landslide susceptibility modeling in terms of the accuracy of the landslide susceptibility maps (LSMs), the appropriate weighting system of the models, the applicability of the model, the ability to confirm the relation between landslide occurrences and rainfall. The results show that the average accuracy of LSMs produced by the developed models with inclusion of multi temporal factors is 49.1% on the overall. Addition of LST tends to improve the accuracy of LSMs. NDVI can be a suitable replacement for rainfall data since it can explain the relation between landslides occurrences and rainfall cycle. Statistical-based weighting system produced more accurate LSMs than non-statistical-based one and is applicable for landslide susceptibility modeling elsewhere. Significant causative factors were proven to produce more accurate LSMs

LANDSLIDE SUSCEPTIBILITY MODELLING UNDER ENVIRONMENTAL CHANGES: A CASE STUDY OF CAMERON HIGHLANDS, MALAYSIA

Modeling landslide susceptibility usually does not include multi temporal factors, e.g. rainfall, especially for medium scale. Landslide occurrences in Cameron Highlands, in particular, and in Peninsular Malaysia, in general, tend to increase during the peak times of monsoonal rainfall. Due to the lack of high spatial resolution of rainfall data, Normalized Different Vegetation Index (NDVI), soil wetness, and LST (Land Surface Temperature) were selected as replacement of multi temporal rainfall data. This research investigated their roles in landslide susceptibility modeling. In doing so, four Landsat 7 Enhanced Multi Temporal Plus (ETM+) images acquired during two peak times of rainy and dry seasons were used to derive multi temporal NDVI, soil wetness, and LST. Topographic, geology, and soil maps were used to derive ‘static’ factors namely slope, slope aspect, curvature, elevation, road network, river/lake, lithology, soil geology lineament maps. Landslide map was used to derive weighting system based on spatial relationship between landslide occurrences and landslide factor using bivariate statistical method. A non-statistical weighting system was also used for comparison purpose. Different scenarios of data processing were applied to allow evaluation on the roles of multi temporal factors in landslide susceptibility modeling in terms of the accuracy of the landslide susceptibility maps (LSMs), the appropriate weighting system of the models, the applicability of the model, the ability to confirm the relation between landslide occurrences and rainfall. The results show that the average accuracy of LSMs produced by the developed models with inclusion of multi temporal factors is 49.1% on the overall. Addition of LST tends to improve the accuracy of LSMs. NDVI can be a suitable replacement for rainfall data since it can explain the relation between landslides occurrences and rainfall cycle. Statistical-based weighting system produced more accurate LSMs than non-statistical-based one and is applicable for landslide susceptibility modeling elsewhere. Significant causative factors were proven to produce more accurate LSMs