Satellite Monitoring of Land Movement: InSAR Remote Sensing Applications and Improvements

     In recent decades and years scientists have enabled the monitoring of ground movements and ground deformation from space via satellites. The method used is interferometric synthetic aperture radar, or InSAR. According to the USGS: “By bouncing signals from a radar satellite off the ground in successive orbits and looking at the differences between the images, interferometric synthetic aperture radar (InSAR) can detect small differences in the distance between its position and the ground as the land surface moves—whether up, down, or sideways. InSAR shows spatial patterns of deformation in remarkable detail and, in combination with ground-based monitoring, gives USGS scientists unprecedented insight into a wide range of earth science processes.” Radar waves are used because they penetrate cloud cover and darkness. The resulting shaded relief maps are known as interferograms, and the processing is known as interferometry. By comparing two radar images of a selected area at two different times, changes in land positions can be detected. Hot colors (red) indicate uplift and cool colors (blue) indicate subsidence. Phase differences in the reflected waves account for the changes as the diagrams below show.

 

     An important application for InSAR is measuring land deformation around active volcanoes where such deformation is frequent and extensive. InSAR is very good at detecting land movement around volcanoes because that movement is mostly vertical and InSAR measures mostly vertical movement, as the interferograms below show. However, in the case of earthquakes, many can have predominantly horizontal movement which renders InSAR less effective. One way to account for this has been demonstrated in California where an extensive network of GPS stations has been deployed to help predict and detect earthquakes, allowing for continuous GPS monitoring. Comparing the continuous GPS with InSAR shows that both can monitor ground movement and also be complementary. The continuous GPS from a Continuous Global Navigation Satellite System (GNSS) can better detect changes in time while InSAR can better detect spatial changes. Different wavelength bands for InSAR can offer better resolution for different terrains, for example, vegetated vs. non-vegetated. The 3rd diagram below shows how it was done in a case study from Croatia published in October 2020. In landslide-prone areas InSAR data is typically combined with higher resolution ground-based land movement remote sensing methods including ground-based interferometric radar, Doppler radar and lidar. It is thought that integrating data from these different sources offers the best protection in high-risk areas.

 

 

     Utilization of parallel processing and cloud-based computing have resulted in faster processing times for InSAR data. This has enabled faster predictive capability.

 

     In summary, modern geodetic measurement techniques include radar interferometry (InSAR), global navigation satellite systems (GNSS), light detection and ranging (LiDAR), close-range photogrammetry (CRP), Robotic Total Station (RTS), and digital levelling. The following table shows the different methods and their applications, geographic focus, and data for geodetic monitoring of land deformation from a series of papers presented in a special 2023 issue of the journal Remote Sensing.

 

 

 

NASA’s Landslide Hazard Assessment for Situational Awareness (LHASA) model

     In 2018 NASA’s Goddard Space Center introduced their Landslide Hazard Assessment for Situational Awareness (LHASA) model that enables the prediction of landslides around the globe. Many people are killed by landslides in susceptible areas. Rain is the biggest trigger of landslides. The LHASA model can examine rain–induced landslide threats anywhere around the globe every 30 minutes. Analysis of long-term data has revealed that landslides are often seasonal in different areas. For example, July and August are the highest risk months for landslides in eastern and southern Asia due to its monsoon season and landslide risk peaks in March in Peru.

     “The map below shows 2,085 reported landslides with fatalities from NASA’s Global Landslide Catalog. The model showed more landslide activity in the Southern Andes, East African Rift Zone, Turkey, and Iran than was previously accounted for in the Global Landslide Catalog.”

 

 

The LHASA model integrates satellite-derived and ground-based recent global precipitation data with susceptibility mapping to predict landslides and can be predictive down to a scale of minutes.

 

NI-SAR Satellite Set for Launch in 2024

     A new InSAR satellite is set to be launched in the 1^st quarter of 2024. It is a collaboration between NASA and the Indian Space Research Organisation (ISRO). It is called NISAR and will be the first remote sensing InSAR satellite to include two radar frequencies, L-band and S-band. The $1.5 billion satellite is expected to detect land movements as small as 1 centimeter when focused on a small area, with a resolution of 5-10 meters over the whole earth, which it can map 4-6 times per month. “It is designed to observe and measure some of the planet's most complex natural processes, including ecosystem disturbances, ice-sheet collapse, and natural hazards such as earthquakes, tsunamis, volcanoes and landslides.” Land subsidence due to groundwater exploitation and compaction due to soft sediments can also be measured by InSAR. Data is expected to be available within a few days for most analyses and within hours when natural disasters occur. In the past, the European Space Agency’s Sentenal-1 constellation of satellites has been a major source of InSAR data.

 

Data Availability: Using Past and Current Data to Protect Assets

     Land movements can be the result of natural processes such as weathering, erosion, and tectonic activity, as well as anthropogenic processes such as mining, construction, and deforestation. Some companies that specialize in remote sensing offer analysis of past and current data to monitor land motion in order to protect assets such as power lines, roads and railways, as well as subsurface assets such as oil, gas, and water pipelines. Satellite data stretched out over the past 10 years is available for analysis. One company, Spottitt, offers such services that include utilizing machine learning to analyze satellite imagery and InSAR for land movement monitoring.

 

Japan Using Satellite Data Processed with AI to Monitor Land Movement to Predict Landslides

     InSAR data requires lots of processing and as mentioned, machine learning and cloud computing have helped to increase processing times. In Japan, Nature Portfolio reports that “by using artificial intelligence (AI) to process data collected by various low-Earth-orbit satellites, the Japanese satellite communications company SKY Perfect JSAT, based in Tokyo, is able to monitor for ground deformation across the entire country, including both cities and regional areas. Accessible via an explorable web portal, the service allows users, such as municipal governments and civil-engineering firms, to keep careful tabs on gradual and sudden ground movements.”

“Raw InSAR data includes observation error, topographic location error, atmospheric delay and other components that require many steps to correct,” adds Takuma Anahara, chief researcher of SKY Perfect JSAT’s Space Intelligence Business Team.”

“SKY Perfect JSAT has turned to AI to overcome this limitation. “We’ve developed an AI-driven statistical analysis and low-computation algorithm that can complete this process and validate the outputs quickly and accurately with minimal expert input,” says Anahara “This allows us to offer the capability at much lower cost across all areas of Japan, not just cities.”

“SKY Perfect JSAT’s InSAR service has been calibrated for urban, rural, and wilderness environments, allowing it to be used for remote landslide monitoring and early warning. The system is already in use by municipal governments, construction companies, airports, etc. The team is now developing the capability further to allow point-by-point monitoring of infrastructure such as bridges and towers, where resolving differential vertical movement is critical.”

“Our InSAR service is a unique and powerful capability that can replace laborious ground-based surveys and extend monitoring to inaccessible areas such as the mountainous regions of Japan,” says Hirata. “At present, we can achieve measurement of changes with accuracy under 10 millimetres, and we’re continually improving our statistical processing to improve this resolution in different environments.”

“This service demonstrates the valuable potential of space observations,” says Hirata. “And it will help reduce uncertainty and increase safety related to landslides and subsidence in Japan.”

Japan is susceptible to landslides due to its degree of mountainous terrain (~75%), high rainfall amounts, and tectonic activity from earthquakes. A July 2021 landslide killed 27 people.

 

References:

An eye in the sky to offer early warning of landslides. An AI-driven, high-resolution satellite monitoring service provides unprecedented coverage and precision for detecting landslides across Japan. Nature Portfolio. October 2023. An eye in the sky to offer early warning of landslides (nature.com)

InSAR—Satellite-based technique captures overall deformation "picture". USGS. Volcano Hazards Program. InSAR—Satellite-based technique captures overall deformation "picture" | U.S. Geological Survey (usgs.gov)

Monitoring Ground Deformation from Space. USGS. 2005-3025 (usgs.gov)

Major Earth Satellite to Track Disasters, Effects of Climate Change. Jet Propulsion Laboratory. March 24, 2021. Major Earth Satellite to Track Disasters, Effects of Climate Change (nasa.gov)

Ensure Assets Safety and Reliability with Spottitt's Risk Monitoring and Assessment. Spottitt. Satellite-Based Land & Asset Displacement Monitoring System (spottitt.com)

Estimating Vertical Land Motion from Remote Sensing and In-Situ Observations in the Dubrovnik Area (Croatia): A Multi-Method Case Study. Marijan Grgić, Josip Bender, and Tomislav Bašić. Remote Sens. 2020, 12(21), 3543. Remote Sensing | Free Full-Text | Estimating Vertical Land Motion from Remote Sensing and In-Situ Observations in the Dubrovnik Area (Croatia): A Multi-Method Case Study (mdpi.com)

Landslide detection, monitoring and prediction with remote-sensing techniques. Nicola Casagli, Emanuele Intrieri, Veronica Tofani, Giovanni Gigli & Federico Raspini. Nature Reviews Earth & Environment volume 4, pages51–64 (2023). January 10. 2023. Landslide detection, monitoring and prediction with remote-sensing techniques | Nature Reviews Earth & Environment

Predicting Landslide Hazards in Near Real-Time. NASA Earth Observatory. April 18, 2018. Predicting Landslide Hazards in Near Real-Time (nasa.gov)

Geodetic Monitoring for Land Deformation. Alex Hay-Man Ng, Linlin Ge , Hsing-Chung Chan, and Zheyuan Du. Remote Sens. 2023, 15(1), 283. January 3, 2023. Remote Sensing | Free Full-Text | Geodetic Monitoring for Land Deformation (mdpi.com)

Continuous, semi-automatic monitoring of ground deformation using Sentinel-1 satellites. Federico Raspini, Silvia Bianchini, Andrea Ciampalini, Matteo Del Soldato, Lorenzo Solari, Fabrizio Novali, Sara Del Conte, Alessio Rucci, Alessandro Ferretti & Nicola Casagli. Scientific Reports volume 8, Article number: 7253 (2018). May 8, 2018. Continuous, semi-automatic monitoring of ground deformation using Sentinel-1 satellites | Scientific Reports (nature.com)

NISAR (satellite). Wikipedia. NISAR (satellite) - Wikipedia