DOI: 10.5066/p1cqg4bn
title: Remote Sensing Change Detection and Deformation Datasets Imaging Landslides Triggered by the February 2023 Kahramanmaraş, Türkiye Earthquake Sequence

description: This data release contains optical and radar-based remote sensing products used to assess ground failure and surface deformation triggered by the February 6, 2023, magnitude 7.8 and magnitude 7.5 Kahramanmaraş, Türkiye earthquake doublet and subsequent aftershocks. The datasets span the northern portion of the earthquake rupture zone and were produced to assist with the U.S. Geological Survey (USGS) field response to this event in June 2023, and to more completely detect and map the landslides that occurred. The four included data products, described in detail below, include four different ways to detect change using multispectral surface reflectance and synthetic aperture radar images. Each product contributes to a multi-sensor approach for detecting different types of earthquake-triggered ground failure. The products differ in sensitivity to environmental conditions, ground cover, and failure styles (for example incoherent landslides, coherent slope deformation, or post-seismic creep).


Dataset Descriptions (dataset abbreviation in parentheses)

Red band difference (Reddiff)


Purpose: Highlights surface change due to removal of vegetation or exposure of fresh rock/soil faces, which often has higher red reflectivity, typically associated with incoherent landslides in semi-arid to arid environments.
Source: Harmonized Sentinel-2 MSI Level-2A Surface Reflectance (Drusch and others, 2012)
Computation: Reddiff = Redpost – Redpre, where Red is the Sentinel-2 Red band (665nm), the “post” subscript indicates the post-event image, the “pre” subscript indicates the pre-event image, and the “diff” subscript indicates that the resulting difference between pre- and post-event.
Input Image Dates
	
2022-05-17 and 2023-05-02
2022-07-14 and 2023-07-14


Suggested visualization: Diverging color scheme; suggested range: -1500 to 1500
Guide for user interpretation: Positive values often indicate newly exposed bedrock/soil. Other large positive and negative signals are present in the data, including signals related to cloud cover and vegetation differences.


Normalized Difference Vegetation Index [NDVI] difference (NDVIdiff)


Purpose: Captures vegetation loss often associated with slope failures in vegetated terrain.
Source: Harmonized Sentinel-2 MSI Level-2A Surface Reflectance (Drusch and others, 2012)
Computation: NDVIdiff = NDVIpost – NDVIpre, where Red is the Sentinel-2 Red band (665nm), NIR is the Sentinel-2 Near-infrared band (834nm), and NDVI = (NIR - Red) / (NIR + Red). The “post” subscript indicates the post-event image, the “pre” subscript indicates the pre-event image, and the “diff” subscript indicates that the resulting difference between pre- and post-event.
Input Image Dates
	
2022-05-17 and 2023-05-02
2022-07-14 and 2023-07-14


Suggested visualization: Diverging color scheme; suggested range: -0.5 to 0
Guide for user interpretation: Negative values often indicate vegetation damage or loss.


Optical pixel offset (PXO)

Purpose: Measures ground displacement (typically 1s to 10s of meters) related to coherent landslides (i.e., landslides that move as an intact mass, generally maintaining their shape and structure).
Source: Harmonized Sentinel-2 MSI Level-2A Surface Reflectance (Drusch and others, 2012)
Products:
	
PXOEW: East-West displacement (meters; +East, –West)
PXONS: North-South displacement (meters; +South, –North)
PXOcorr: Cross-correlation quality (0–255, 0=low correlation and 255=high correlation)


Processing Tools: MicMac (mm3d MM2DPosSism, default parameters) (Rupnik and others, 2017)
Input Image Dates
	
2022-05-17 and 2023-05-02
2022-07-14 and 2023-07-14


Suggested visualization: Diverging color scale; pixel masking recommended for PXO_corr < 200
Guide for user interpretation: Landslides detected by PXO are generally characterized by coherent deformation patterns that exhibit a sharp contrast from the background values in the surrounding region and are consistent with downslope gravitational movement. 


InSAR post-seismic velocity (InSAR)

Purpose: Captures cm-scale surface motion post-earthquake related to slow landslide reactivation.
Source: Sentinel-1 L1 SLC data (Torres and others, 2012)
Products:
	
Secular velocity (cm/yr) in the satellite line-of-sight
Average coherence (0-1, 0=random noise and 1=no noise).


Processing tools:
	
Unwrapped, co-registered interferograms generated using ISCE2 (Rosen and others, 2012) (parameters: range looks = 4, azimuth looks = 1, filter strength = 0, SRTM 30m DEM)
Secular velocity derived from time series generated using MintPy (default parameters) (Yunjun and others, 2019)


Input image paths/dates:
	
Ascending path 116: 2023-02-28 – 2023-05-23 (7 adjacent interferograms)
Descending path 21: 2023-02-10 – 2023-05-17 (7 adjacent interferograms)


Suggested visualization: Diverging color scheme; suggested range: -0.4 to 0.4 cm/yr. Pixel masking recommended for average coherence < 0.5.
Guide for user interpretation: Landslides detected by InSAR are generally characterized by coherent deformation patterns that exhibit a sharp contrast from the background values in the surrounding region and are consistent with downslope gravitational movement. 


Note that for the optical-based datasets (Reddiff, NDVIdiff, and PXO), we use post-event dates that are months after the earthquake sequence. This is because persistent snow and or cloud cover was present in earlier imagery. Finally, not all changes and deformation signals are related to earthquake-triggered landslides. The most common noise sources come from clouds and cloud shadows, as well as annual vegetation differences related to, for example, agricultural activity and timing of leaf out.  Because the data was acquired using the same viewing geometry and at the same time of year, there is very little noise related to topographic distortion and illumination. In general, landslide-related changes/deformation must exist in both differenced time ranges of a given method, be located on a slope and follow gravitational paths, and not be co-located with common change/deformation sources, such as snow cover, mining, or active agriculture.


List of Files

Readme.txt
NIRP_turkiyeEQ_files.zip:
	
Reddiff_20220517_20230502.tif
Reddiff_20220714_20230714.tif
NDVIdiff_20220517_20230502.tif
NDVIdiff_20220714_20230714.tif
PXOEW_20220517_20230502.tif
PXONS_20220517_20230502.tif
PXOcorr_20220517_20230502.tif
PXOEW_20220714_20230714.tif
PXONS_20220714_20230714.tif
PXOcorr_20220714_20230714.tif
InSAR_20230228_20230523.tif
InSARcorr_20230228_20230523.tif
InSAR_20230210_20230517.tif
InSARcorr_20230210_20230517.tif




Disclaimer
Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government


References
Drusch, M., Del Bello, U., Carlier, S., Colin, O., Fernandez, V., Gascon, F., Hoersch, B., Isola, C., Laberinti, P., Martimort, P., Meygret, A., Spoto, F., Sy, O., Marchese, F., and Bargellini, P., 2012, Sentinel-2: ESA’s Optical High-Resolution Mission for GMES Operational Services: Remote Sensing of Environment, v. 120, p. 25–36, https://doi.org/10.1016/j.rse.2011.11.026.
Rosen, P. A., Gurrola, E., Sacco, G. F., and Zebker, H. A., 2012, The InSAR scientific computing environment: EUSAR 2012, 9th European Conference on Synthetic Aperture Radar, Nuremberg, Germany.
Rupnik, E., Daakir, M., and Pierrot Deseilligny, M., 2017, MicMac – a free, open-source solution for photogrammetry. Open Geospatial Data, Software and Standards, v. 2, no. 14. https://doi.org/10.1186/s40965-017-0027-2.
Torres, R., Snoeij, P., Geudtner, D., Bibby, D., Davidson, M., Attema, E., Potin, P., Rommen, B., Floury, N., Brown, M., Traver, I. N., Deghaye, P., Duesmann, B., Rosich, B., Miranda, N., Bruno, C., L’Abbate, M., Croci, R., Pietropaolo, A.,  Huchler, M., and Rostan, F., 2012, GMES Sentinel-1 mission: Remote Sensing of Environment, v. 120, p. 9–24. https://doi.org/10.1016/j.rse.2011.05.028.
Yunjun, Z., Fattahi, H., and Amelung, F, 2019, Small baseline InSAR time series analysis: Unwrapping error correction and noise reduction: Computers & Geosciences, v. 133, no. 104331. https://doi.org/10.1016/j.cageo.2019.104331.