Remote Sensing and the Landslide Hazard and Risk Assessment

This repository is a collection of basic scripts — explained in English and Spanish — for analysing landslides using space-based data in Google Earth Engine (GGE). It aims to facilitate working with big data in the cloud as an alternative to using desktop software

Landslide in northern Nepal in August 2014. Image: NASA

“According to the United Nations Office for Disaster Risk Reduction (UNDRR), landslides are a a “variety of processes that result in the downward and outward movement of slope-forming materials, including rock, soil, artificial fill, or a combination of these. The materials may move by falling, toppling, sliding, spreading, or flowing.”

Landslides are a geological hazard that can cause extreme damage to infrastructure and loss of life. They can be defined as the failure of a slope that leads to a variety of ground movements including rockfalls and debris flows. Landslides can be induced by a number of extreme weather or geological conditions such as flooding, volcanic eruptions and earthquakes. Severe earthquakes frequently generate widespread landslides, which can cause damage to roads and cut off rivers. For some earthquakes, the resulting landslides cause damage that is greater than the initial ground motion. Volcanic eruptions can cause large slope failures while extreme weather events such as typhoons can cause flooding and destabilization of the ground, in turn inducing landslides.

What is satellite-based information about landslides used for?

Earth observation technologies are also being increasingly utilized in disaster response situations, for example to direct logistical and emergency support to areas affected by landslides and map the damage they have caused in order to plan for recovery. Mapping of risk areas and monitoring of landslides can be conducted using satellite and airborne imaging platforms, with new methodologies constantly being developed.

Cloud computing in landslides monitoring

The landslides detection using space-based data in these cloud platforms has changed the way to see the risk and management disaster. It aims to facilitate working with big data in the cloud as an alternative to using desktop software.

In order to select the most appropriate data types and processing techniques to apply in reducing landslide risk and responding to disasters, it is necessary to understand the effects of landslides on the environment in a particular area. In addition, the selection of optimal technology needs to consider the integration of observation strategies into current risk management strategies within different communities. Landslide event and susceptibility mapping methods are diverse and often vary by the landscape, finances available and the purpose of the mapping.

General description of the algorithm

The landslide and mass removal processes that occurred in the southern area of the state of Oaxaca in 2020 were analyzed for the area of La Soledad using Sentinel-2 optical imagery, radar image analysis and digital terrain models (DTM). A specific algorithm was developed to establish a model for automatic extraction of the traces of the ground movements by using the Bare Soil Index (BSI) at two different times. This was reduced to a temporal composition (BSI1, NIR, BSI2). The results allow us to extract the shapes of the landslides in the terrain and to calculate their direction of movement. It was observed that there is a close relationship between most of the landslide processes on slopes in front of the areas detected by radar. The methodology proposed allows extracting and characterizing mass clearance processes from Sentinel-2 high-resolution satellite images.

The Bare Soil Index (BSI) is a numerical indicator that combines blue, red, near-infrared and short wave infrared spectral bands to capture soil variations. These spectral bands are used in a normalized manner. The short wave infrared and the red spectral bands are used to quantify the soil mineral composition, while the blue and the near-infrared spectral bands are used to enhance the presence of vegetation. BSI can be used in numerous remote sensing applications such as soil mapping, crop identification (in combination with NDVI), etc. To calculate the BSI, the following formulas (one for each satellite are used):

Interpretation and description of images

Landslides using space-based data in Google Earth Engine (GGE)

The index calculation is applied to the blue and red channels. It shows all vegetation in green and the potential slippery ground in blue. This can be useful for soil mapping as it indicates to the user were to do remote sensing analysis on the bare soil; where crops have been harvested or where they are not growing; and the location of landslides or the extent of erosion in non-vegetated areas. Unfortunately, it also highlights certain buildings, making areas of bare soil difficult to separate from houses. It should be noted that the result depends on the vegetation and agriculture of the season.

The different tests carried out showed that the BSI temporal and the B3 band reflectance are sufficient to identify the traces of the mass removal processes. In the case of the index, the BSI alone does not make it possible to characterize the features investigated that do not correspond solely to an absence of active vegetation, especially since the Sentinel-2 image has a cloud cover and a reactivation of the vegetation was observed locally. Therefore, the temporal values of the BSI need to be taken into account.

This image compares the results of the BSI index before and after the slide on the Google Earth platform

Considerations for selecting appropriate tools

There are a number of factors to take into account in consideration of which specific tools to use for landslide mapping and response. Though ground-based techniques are usually of higher resolution, satellite data can have a number of advantages in terms of coverage, accessibility and long-term monitoring.

Cost is a big factor to take into account in selecting a particular technique. There are three types of costs associated with remote sensing; the cost of the input data, the cost of processing and the additional costs for satellite tasking for rapid response. Additional costs for passive space-borne data during disasters can be large, but during major disasters, particular satellite images can be obtained for free by institutions enrolled in international initiatives such as the ‘International Charter for Space and Major Disasters’, the COPERNICUS Emergency Management Service and Sentinel Asia.

Comparison before after flood event caused by strong rainfalls derivated ETA hurricane at 7 November. We used TerraSAR-X data with medium (ScanSAR mode) and very high resolution (TanDEM-X-1m). Until. The flooded area covered almost all throughout Tabasco State in Mexico

How can I access relevant data?

The availability of relevant data will be dependent on the stage of the disaster cycle being mapped, the region in question and the finances of stakeholders. Higher-resolution data may not be available, and temporal resolution may vary widely.
In the case of landslide risk mapping, orthographic photos, aerial imagery and satellite data may be available from the geological survey of a region or country. In the case of a disaster taking place, a number of organizations will make relevant satellite data and crowdsourcing information available for mapping of landslide locations, such as the Pacific Disaster Center, German Aerospace Center, Copernicus Emergency Management Service, and the International Charter on Space and Major Disasters will be activated. The USGS also operates a Hazard Data Distribution System which compiles satellite imagery for the area in which the disaster has occurred.
For rapid response to disastrous events, there can be additional costs associated with satellite programming, in comparison to operationally acquired datasets.

For more information you can visit:

Thanks to Norma Davila, Juan Carlos Villagran, and Radu Botez.



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