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Mapping Earthquake Deformation in Taiwan With ENVI

Cherie Tyrrell

Unlocking Critical Insights With ENVI® Tools

Taiwan sits at the junction of major tectonic plates and regularly experiences powerful earthquakes. Understanding how the ground moves during these events is essential for disaster preparedness, public safety, and building community resilience. But traditional approaches like field mapping and single-sensor analysis often fall short, especially when displacements are large, terrain is remote, or dense vegetation obscures the surface.

 

That’s where ENVI® and the ENVI Ecosystem come in.

Professor Yu-Ting Kuo and his team in the Department of the Earth and Environmental Sciences at National Chung-Cheng University used ENVI to process high-resolution satellite imagery and aerial photography from major Taiwanese earthquakes, including:

 

  1. The 1999 Chi-Chi earthquake

  2. The 2018 Hualien earthquake

  3. The 2022 Chihshang earthquake sequence

 

Each of these events features different rupture styles and fault geometries, demanding tools that are both flexible and robust.

With ENVI, the team was able to:

  • Co-register pre- and post-event imagery with high precision

  • Run optical correlation to measure two-dimensional ground displacements

  • Produce detailed deformation maps across entire rupture zones

ENVI streamlines these complex, image-processing steps into repeatable, documented workflows, delivering reliable results even in places where other methods struggle.

The Power of ENVI Deep Learning

One of the key advantages of ENVI today is its integrated deep learning capabilities. Rather than writing custom code, users can work through guided workflows that apply advanced AI methods to geospatial problems.

This horizontal coseismic displacement map (UTM Zone 51N) referenced to the footwall was derived from correlating 12 aerial photo pairs using a 128 × 128 m correlation window with 16 m sampling. Panels (a) and (b) show the east–west and north–south displacement components, respectively. The continuous blue line marks the Chelungpu fault surface rupture (CLPF) from the Chi-Chi earthquake, the dashed blue line marks the Ailiao fold scarp (ALF), and the black triangle (AF23) denotes a GPS station at Tsaotun. (From Kuo et al., 2014)

In earthquake applications, ENVI Deep Learning enables researchers to:

 

  • Automatically identify features such as fault ruptures, scarps, and subtle surface changes.

  • Separate true ground motion from noise caused by vegetation, shadows, or sensor artifacts.

  • Scale analysis across large regions quickly, so teams can move from local case studies to regional insight.

 

With ENVI Deep Learning, scientists don’t just measure displacement, they can also detect, classify, and monitor change with precision and confidence, accelerating time from data to understanding.

Professor Kuo notes that the ease of use and performance of ENVI have been central to his team’s success.

 

“It is worth noting that the success of this approach is due in large part to the robust, user-friendly software tools employed… the software’s performance and extensive toolset allowed us to efficiently derive high-quality displacement maps even with large volumes of data.”

 

This perspective underscores how ENVI’s integrated workflows not only improve scientific output but also make advanced geospatial research more accessible to universities and research groups worldwide.

Extending ENVI With IDL®

 

Many customers start with ENVI’s ready-to-use tools and then go further using IDL (Interactive Data Language) to tailor workflows to their unique missions.

 

In Taiwan and elsewhere, researchers have:

  • Automated processing by scripting ENVI routines in IDL to batch-process large image collections.

  • Integrated third-party tools such as COSI-Corr into ENVI workflows, then deployed those workflows across teams and systems.

  • Customize algorithms for specialized challenges like multi-fault rupture kinematics, thematic change, detection, or combined optical/SAR analysis.

 

The ENVI+IDL combination means teams don’t have to choose between out-of-the-box capability and sophisticated custom science, they can have both in a single, connected environment.

The east–west (left) and north–south (right) components of the coseismic displacement field, obtained by optical image correlation using multiscale window sizes (38.4 m and 19.2 m) with a 4.8 m sampling resolution, are shown. Gray rectangles indicate the locations of profiles 01, 21, and 43. Hexagon symbols mark GPS sites, the red line traces the Milun fault, and the purple circle marks an observed surface rupture. (From Kuo et al., 2019)

From Case Study to Global Impact

For Taiwan, these ENVI-driven studies provided first-of-their-kind detailed maps of earthquake deformation, revealing how slip was distributed along faults, how urban areas were affected, and how adjacent faults interact. The ability to rapidly and accurately measure displacement has significant implications for seismic hazard assessments, disaster response, and long-term infrastructure and community resilience planning.

Results of COSI-Corr from Sentinel-2 image pairs (acquired 23 August 2022 and 22 September 2022) showing (a) north–south offsets, (b) east–west offsets (not used in the inversion), and (c) signal-to-noise ratio (SNR). The N–S offsets exhibit a sharp displacement discontinuity in the north and a diffuse boundary in the south, whereas the E–W offsets show no clear offset pattern across the fault due to rugged topography on both sides of the Longitudinal Valley. (From Tang et al., 2023)

But the impact goes well beyond Tawain. The same ENVI ecosystem that supports earthquake deformation studies can also power:

  • Landslide and subsidence monitoring

  • Critical infrastructure and transportation corridor assessments

  • Environmental and coastal change detection

  • Agricultural applications such as crop health and food security

Why NV5? The Taiwan case studies highlight what sets NV5 apart:

  • ENVI for advanced image processing and analytics

  • ENVI Deep Learning for integrated AI at the desktop and enterprise level

  • IDL for automation, customization, and integration with external tools and data sources

By combining powerful built-in tools, modern deep learning methods, and the flexibility of IDL, ENVI empowers researchers worldwide to transform raw imagery into actionable science, quickly, consistently, and at scale. Whether you’re mapping earthquake deformation, monitoring environmental change, or supporting national security missions, NV5 software helps you see more, understand faster, and make better decisions.

 

Interested in learning how ENVI and the ENVI ecosystem can support your mission? Reach out to our team (GeospatialInfo@NV5.com) to explore options for research institutions, government agencies, and commercial organizations.

 

This blog is based on an article written by Prof. Kuo about his experience using ENVI+IDL+SARscape and COSI-Corr (Developed by Caltech and integrated in ENVI) to analyze earthquakes and surface deformation. You can read his original article here.

 

Comparing Amplitude and Coherence Time Series With ICEYE US GTR Data and ENVI SARscape Comparing Amplitude and Coherence Time Series With ICEYE US GTR Data and ENVI SARscape