Introduction
Landslides are natural disasters that have a significant global implication. They are typically caused by extreme natural events such as heavy rains, volcanic eruptions, and earthquakes, associated with predictive factors that are closely correlated with human activities such as deforestation and intensive land exploitation for agriculture activities. A landslide is defined as "the mobility of a mass of rock, earth, or debris down a slope." They can occur on a variety of terrain types if the right conditions of soil, rock, moisture, and slope are met. Landslides are a gradual phenomenon of the earth's surface geology that serves to redistribute soil and sediments in either sudden collapses or slow gradual slides. Due to the multispectral and textural characteristics, high revisiting cycles, wide-area coverage, and high spatial resolution, radar and optical remote sensing data are increasingly being used to facilitate landslide risk management.
Methodology
Object-Based Image Analysis (OBIA)
Object-Based Image Analysis (OBIA) of optical satellite images can aid in the monitoring and mapping of landslides in the past and present. Due to the large quantity of earth observation data available today, as well as recent advances in computing force and machine intelligence, complex image analysis is now possible. Object-based image analysis is a technique for analyzing digital imagery that was recently emerged in comparison to traditional pixel-based image analysis. OBIA is based on data derived from a collection of similar pixels known as objects or image objects. Image objects, as opposed to single pixels, are identified by a set of properties that can be used during classification. Thus, OBIA provides a strong methodological framework for interpreting complex classes defined by spectral, textual, spatial, contextual, and hierarchical properties. Even with OBIA's benefits, visual interpretation of orthophotos or pixel-based classification approaches is still the most commonly used method for mapping landslides. Because of the unique properties of landslides (e.g., shape) and the improved resolution of available imagery, pixel-based classification techniques produce significant commission and omission errors.
Monitoring of Landslides
The frequency of observation of satellites is limited. As a result, they are used to determine changes in the number of landslides over time. For this purpose, high-resolution satellite data are extremely useful. Though satellite remote sensing has limitations for site-specific monitoring due to a short revisit period, terrestrial remote sensing techniques such as SAR interferometry and terrestrial laser scanners are emerging as viable alternatives. Differential and multi-temporal Synthetic Aperture Radar (SAR) Interferometry, in particular, can provide millimeter-level ground displacement estimates derived from the processing of large stacks of radar satellite images. These methods are extremely accurate and can predict movement to the centimetre level. A terrestrial laser scanner can continuously monitor the density of the joint density pattern and identify areas of new stress buildup.
Satellite remote sensing data can be useful in assisting with detailed landslide inventories mapping, particularly during the prevention and disaster risk reduction phases, as well as during the emergency response phase when the emphasis is on the rapid assessment of the extent of events, the damages caused by the event, and the current ground motion situation and its evolution.
Ground instruments
Ground instruments are used in landslide-prone areas to estimate parameters such as surface and subsurface movement, as well as surface and subsurface water passage. We can obtain real-time data from site-specific monitoring and then fit a slope stability model for predicting the time of occurrence of a landslide. The success of site-specific monitoring may result in an early warning of a landslide, saving lives and property. GPS, piezometer raingauge, extensometer, inclinometer, tiltmeter, and automatic weather station (AWS) are instruments helpful for site-specific monitoring of landslides.
Summary
The physical processes that cause a landslide are critical for an area's landslide hazard zonation to be effective. Satellite remote sensing data is now widely used for thematic layer preparation. DEMs derived from satellite data will now not only provide important parameters like slope, but will also aid in better geovisualization. Furthermore, GIS has made significant contributions to the processes of conducting spatial analyses of landslides.
