{"accessLevel": "public", "bureauCode": ["010:12"], "contactPoint": {"@type": "vcard:Contact", "fn": "Paula M Burgi", "hasEmail": "mailto:pburgi@usgs.gov"}, "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 Kahramanmara\u015f, T\u00fcrkiye 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).\nDataset Descriptions (dataset abbreviation in parentheses):\nRed band difference (Reddiff)\nPurpose: 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.\nSource: Harmonized Sentinel-2 Level-2A Surface Reflectance (Drusch and others, 2012)\nComputation: Reddiff = Redpost \u2013 Redpre, where Red is the Sentinel-2 Red band (665nm), the \u201cpost\u201d subscript indicates the post-event image, the \u201cpre\u201d subscript indicates the pre-event image, and the \u201cdiff\u201d subscript indicates that the resulting difference between pre- and post-event. \nInput Image Dates: \n2022-05-17 and 2023-05-02 \n2022-07-14 and 2023-07-14\nSuggested visualization: Diverging color scheme; suggested range: -1500 to 1500\nGuide 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. \nNormalized Difference Vegetation Index [NDVI] difference (NDVIdiff)\nPurpose: Captures vegetation loss often associated with slope failures in vegetated terrain.\nSource: Harmonized Sentinel-2 Level-2A Surface Reflectance (Drusch and others, 2012)\nComputation: NDVIdiff = NDVIpost \u2013 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 \u201cpost\u201d subscript indicates the post-event image, the \u201cpre\u201d subscript indicates the pre-event image, and the \u201cdiff\u201d subscript indicates that the resulting difference between pre- and post-event. \nInput Image Dates: \n2022-05-17 and 2023-05-02 \n2022-07-14 and 2023-07-14\nSuggested visualization: Diverging color scheme; suggested range: -0.5 to 0\nGuide for user interpretation: Negative values often indicate vegetation damage or loss. \nOptical pixel offset (PXO)\nPurpose: 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).\nSource: Harmonized Sentinel-2 Level-2A Surface Reflectance (Drusch and others, 2012) \nProducts:\nPXOEW: East-West displacement (meters; +East, \u2013West)\nPXONS: North-South displacement (meters; +South, \u2013North)\nPXOcorr: Cross-correlation quality (0\u2013255, 0=low correlation and 255=high correlation)\nProcessing Tools: MicMac (mm3d MM2DPosSism, default parameters) (Rupnik and others, 2017)\nInput Image Dates: \n2022-05-17 and 2023-05-02 \n2022-07-14 and 2023-07-14\nSuggested visualization: Diverging color scale; pixel masking recommended for PXO_corr < 200\nGuide 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.  \nInSAR post-seismic velocity (InSAR)\nPurpose: Captures cm-scale surface motion post-earthquake related to slow landslide reactivation.\nSource: Sentinel-1 L1 SLC data (Torres and others, 2012)\nProducts: \nSecular velocity (cm/yr) in the satellite line-of-sight\nAverage coherence (0-1, 0=random noise and 1=no noise). \nProcessing tools:\nUnwrapped, co-registered interferograms generated using ISCE2 (Rosen and others, 2012) (parameters: range looks = 4, azimuth looks = 1, filter strength = 0, SRTM 30m DEM)\nSecular velocity derived from time series generated using MintPy (default parameters) (Yunjun and others, 2019)\nInput image paths/dates: \nAscending path 116: 2023-02-28 \u2013 2023-05-23 (7 adjacent interferograms)\nDescending path 21: 2023-02-10 \u2013 2023-05-17 (7 adjacent interferograms)\nSuggested visualization: Diverging color scheme; suggested range: -0.4 to 0.4 cm/yr. Pixel masking recommended for average coherence < 0.5.\nGuide 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. \nNote 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. \nFinally, 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.\nList of Files\nReadme.txt \nNIRP_turkiyeEQ_files.zip: \nReddiff_20220517_20230502.tif\nReddiff_20220714_20230714.tif\nNDVIdiff_20220517_20230502.tif\nNDVIdiff_20220714_20230714.tif\nPXOEW_20220517_20230502.tif\nPXONS_20220517_20230502.tif\nPXOcorr_20220517_20230502.tif\nPXOEW_20220714_20230714.tif\nPXONS_20220714_20230714.tif\nPXOcorr_20220714_20230714.tif\nInSAR_20230228_20230523.tif\nInSARcorr_20230228_20230523.tif\nInSAR_20230210_20230517.tif\nInSARcorr_20230210_20230517.tif\nDisclaimer\nAny use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government\nReferences\nDrusch, 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\u2019s Optical High-Resolution Mission for GMES Operational Services: Remote Sensing of Environment, v. 120, p. 25\u201336, https://doi.org/10.1016/j.rse.2011.11.026.\nRosen, 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.\nRupnik, E., Daakir, M., and Pierrot Deseilligny, M., 2017, MicMac \u2013 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.\nTorres, 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\u2019Abbate, M., Croci, R., Pietropaolo, A.,  Huchler, M., and Rostan, F., 2012, GMES Sentinel-1 mission: Remote Sensing of Environment, v. 120, p. 9\u201324. https://doi.org/10.1016/j.rse.2011.05.028.\nYunjun, 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.\nPurpose\nThese data were produced to support USGS earthquake and landslide response activities following the February 6, 2023, Kahramanmara\u015f, T\u00fcrkiye earthquake sequence and to provide remote sensing products for use in scientific analyses of earthquake-triggered ground failure. \nRights\nUnless otherwise stated, all data, metadata and related materials are considered to satisfy the quality standards relative to the purpose for which the data were collected. Although these data and associated metadata have been reviewed for accuracy and completeness and approved for release by the U.S. Geological Survey (USGS), no warranty expressed or implied is made regarding the display or utility for other purposes, nor on all computer systems, nor shall the act of distribution constitute any such warranty.", "distribution": [{"@type": "dcat:Distribution", "accessURL": "https://doi.org/10.5066/P1CQG4BN", "description": "Landing page for access to the data", "format": "XML", "mediaType": "application/http", "title": "Digital Data"}, {"@type": "dcat:Distribution", "description": "The metadata original format", "downloadURL": "https://data.usgs.gov/datacatalog/metadata/USGS.68a37279d4be021d0f8b5b4d.xml", "format": "XML", "mediaType": "text/xml", "title": "Original Metadata"}], "identifier": "http://datainventory.doi.gov/id/dataset/USGS_68a37279d4be021d0f8b5b4d", "keyword": ["Earthquake-triggered landslides", "February 2023 Kahramanmara\u015f, T\u00fcrkiye earthquake sequence", "InSAR", "Optical remote sensing", "Pixel offsets", "Remote sensing", "USGS:68a37279d4be021d0f8b5b4d", "geographic information systems", "geoscientificInformation", "landslide susceptibility assessment", "landslides", "slope processes", "topography"], "modified": "2025-09-19T00:00:00Z", "publisher": {"@type": "org:Organization", "name": "U.S. Geological Survey"}, "spatial": "37.7214, 37.7301, 38.6487, 38.1794", "theme": ["geospatial"], "title": "Remote Sensing Change Detection and Deformation Datasets Imaging Landslides Triggered by the February 2023 Kahramanmara\u015f, T\u00fcrkiye earthquake sequence"}