{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T06:15:45Z","timestamp":1772950545778,"version":"3.50.1"},"reference-count":113,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2022,7,30]],"date-time":"2022-07-30T00:00:00Z","timestamp":1659139200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Open Access Publication Fund of the University of Wuerzburg"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Arctic permafrost coasts become increasingly vulnerable due to environmental drivers such as the reduced sea-ice extent and duration as well as the thawing of permafrost itself. A continuous quantification of the erosion process on large to circum-Arctic scales is required to fully assess the extent and understand the consequences of eroding permafrost coastlines. This study presents a novel approach to quantify annual Arctic coastal erosion and build-up rates based on Sentinel-1 (S1) Synthetic Aperture RADAR (SAR) backscatter data, in combination with Deep Learning (DL) and Change Vector Analysis (CVA). The methodology includes the generation of a high-quality Arctic coastline product via DL, which acted as a reference for quantifying coastal erosion and build-up rates from annual median and standard deviation (sd) backscatter images via CVA. The analysis was applied on ten test sites distributed across the Arctic and covering about 1038 km of coastline. Results revealed maximum erosion rates of up to 160 m for some areas and an average erosion rate of 4.37 m across all test sites within a three-year temporal window from 2017 to 2020. The observed erosion rates within the framework of this study agree with findings published in the previous literature. The proposed methods and data can be applied on large scales and, prospectively, even for the entire Arctic. The generated products may be used for quantifying the loss of frozen ground, estimating the release of stored organic material, and can act as a basis for further related studies in Arctic coastal environments.<\/jats:p>","DOI":"10.3390\/rs14153656","type":"journal-article","created":{"date-parts":[[2022,8,1]],"date-time":"2022-08-01T04:04:00Z","timestamp":1659326640000},"page":"3656","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Automated Extraction of Annual Erosion Rates for Arctic Permafrost Coasts Using Sentinel-1, Deep Learning, and Change Vector Analysis"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5065-0966","authenticated-orcid":false,"given":"Marius","family":"Philipp","sequence":"first","affiliation":[{"name":"Department of Remote Sensing, Institute of Geography and Geology, University of Wuerzburg, D-97074 Wuerzburg, Germany"},{"name":"German Remote Sensing Data Center (DFD), German Aerospace Center (DLR), Muenchner Strasse 20, D-82234 Wessling, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5733-7136","authenticated-orcid":false,"given":"Andreas","family":"Dietz","sequence":"additional","affiliation":[{"name":"German Remote Sensing Data Center (DFD), German Aerospace Center (DLR), Muenchner Strasse 20, D-82234 Wessling, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6626-3052","authenticated-orcid":false,"given":"Tobias","family":"Ullmann","sequence":"additional","affiliation":[{"name":"Department of Physical Geography, Institute of Geography and Geology, University of Wuerzburg, D-97074 Wuerzburg, Germany"}]},{"given":"Claudia","family":"Kuenzer","sequence":"additional","affiliation":[{"name":"Department of Remote Sensing, Institute of Geography and Geology, University of Wuerzburg, D-97074 Wuerzburg, Germany"},{"name":"German Remote Sensing Data Center (DFD), German Aerospace Center (DLR), Muenchner Strasse 20, D-82234 Wessling, Germany"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"627","DOI":"10.1038\/ngeo2234","article-title":"Recent Arctic amplification and extreme mid-latitude weather","volume":"7","author":"Cohen","year":"2014","journal-title":"Nat. Geosci."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.gloplacha.2011.03.004","article-title":"Processes and impacts of Arctic amplification: A research synthesis","volume":"77","author":"Serreze","year":"2011","journal-title":"Glob. Planet. Chang."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"979","DOI":"10.3390\/rs8120979","article-title":"Land cover mapping in northern high latitude permafrost regions with satellite data: Achievements and remaining challenges","volume":"8","author":"Bartsch","year":"2016","journal-title":"Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.rse.2017.05.021","article-title":"Progress in space-borne studies of permafrost for climate science: Towards a multi-ECV approach","volume":"203","author":"Trofaier","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_5","unstructured":"Van Everdingen, R.O., and Association, I.P. (2005). Multi-Language Glossary of Permafrost and Related Ground-Ice Terms in Chinese, English, French, German, Icelandic, Italian, Norwegian, Polish, Romanian, Russian, Spanish, and Swedish, Arctic Institution of North America University of Calgary. Available online: https:\/\/globalcryospherewatch.org\/reference\/glossary_docs\/Glossary_of_Permafrost_and_Ground-Ice_IPA_2005.pdf."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1002\/ppp.689","article-title":"Permafrost thermal state in the polar Northern Hemisphere during the international polar year 2007\u20132009: A synthesis","volume":"21","author":"Romanovsky","year":"2010","journal-title":"Permafr. Periglac. Process."},{"key":"ref_7","unstructured":"P\u00f6rtner, H.O., Roberts, D.C., Masson-Delmotte, V., Zhai, P., Tignor, M., Poloczanska, E., Mintenbeck, K., Nicolai, M., Okem, A., and Petzold, J. (2019). IPCC Special Report on the Ocean and Cryosphere in a Changing Climate, IPCC Intergovernmental Panel on Climate Change (IPCC), Cambridge University Press."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41467-018-08240-4","article-title":"Permafrost is warming at a global scale","volume":"10","author":"Biskaborn","year":"2019","journal-title":"Nat. Commun."},{"key":"ref_9","unstructured":"Jones, B.M., Irrgang, A.M., Farquharson, L.M., Lantuit, H., Whalen, D., Ogorodov, S., Grigoriev, M., Tweedie, C., Gibbs, A.E., and Strzelecki, M.C. (2020). Coastal Permafrost Erosion. Arct. Rep. Card, 15."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"383","DOI":"10.1007\/s12237-010-9362-6","article-title":"The Arctic coastal dynamics database: A new classification scheme and statistics on Arctic permafrost coastlines","volume":"35","author":"Lantuit","year":"2012","journal-title":"Estuaries Coasts"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1038\/s43017-021-00232-1","article-title":"Drivers, dynamics and impacts of changing Arctic coasts","volume":"3","author":"Irrgang","year":"2022","journal-title":"Nat. Rev. Earth Environ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"115001","DOI":"10.1088\/1748-9326\/aae471","article-title":"A decade of remotely sensed observations highlight complex processes linked to coastal permafrost bluff erosion in the Arctic","volume":"13","author":"Jones","year":"2018","journal-title":"Environ. Res. Lett."},{"key":"ref_13","unstructured":"Forbes, D.L. (2011). State of the Arctic Coast 2010: Scientific Review and Outlook, International Arctic Science Committee, Land-Ocean Interactions in the Coastal Zone, Arctic Monitoring and Assessment Programme, International Permafrost Association, Helmholtz-Zentrum."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"324","DOI":"10.1002\/ppp.1914","article-title":"Remote sensing of landscape change in permafrost regions","volume":"27","author":"Jorgenson","year":"2016","journal-title":"Permafr. Periglac. Process."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1038\/nclimate3188","article-title":"Collapsing arctic coastlines","volume":"7","author":"Fritz","year":"2017","journal-title":"Nat. Clim. Chang."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"900","DOI":"10.1007\/s12237-015-0046-0","article-title":"Erosion and flooding\u2014Threats to coastal infrastructure in the Arctic: A case study from Herschel Island, Yukon Territory, Canada","volume":"39","author":"Radosavljevic","year":"2016","journal-title":"Estuaries Coasts"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"406","DOI":"10.1002\/2017JG004166","article-title":"Coastal erosion of permafrost soils along the Yukon Coastal Plain and fluxes of organic carbon to the Canadian Beaufort Sea","volume":"123","author":"Couture","year":"2018","journal-title":"J. Geophys. Res. Biogeosci."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1038\/s43017-021-00230-3","article-title":"Permafrost carbon emissions in a changing Arctic","volume":"3","author":"Miner","year":"2022","journal-title":"Nat. Rev. Earth Environ."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1038\/nature14338","article-title":"Climate change and the permafrost carbon feedback","volume":"520","author":"Schuur","year":"2015","journal-title":"Nature"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"034014","DOI":"10.1088\/1748-9326\/11\/3\/034014","article-title":"Biomass offsets little or none of permafrost carbon release from soils, streams, and wildfire: An expert assessment","volume":"11","author":"Abbott","year":"2016","journal-title":"Environ. Res. Lett."},{"key":"ref_21","unstructured":"University of Maryland Center for Environmental Science (2020, September 01). IAN Symbol Libraries. Available online: https:\/\/ian.umces.edu\/symbols\/."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"e2019RG000652","DOI":"10.1029\/2019RG000652","article-title":"Space-Based Observations for Understanding Changes in the Arctic-Boreal Zone","volume":"58","author":"Duncan","year":"2020","journal-title":"Rev. Geophys."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Westermann, S., Duguay, C.R., Grosse, G., and K\u00e4\u00e4b, A. (2015). Remote sensing of permafrost and frozen ground. Remote Sensing of the Cryosphere, John Wiley & Sons, Ltd.. Chapter 13.","DOI":"10.1002\/9781118368909.ch13"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"527","DOI":"10.5194\/nhess-5-527-2005","article-title":"Remote sensing of glacier- and permafrost-related hazards in high mountains: An overview","volume":"5","author":"Huggel","year":"2005","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1002\/ppp.619","article-title":"Remote sensing of permafrost-related problems and hazards","volume":"19","year":"2008","journal-title":"Permafr. Periglac. Process."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"143","DOI":"10.3389\/fenvs.2020.00143","article-title":"Feasibility study for the application of Synthetic Aperture Radar for coastal erosion rate quantification across the Arctic","volume":"8","author":"Bartsch","year":"2020","journal-title":"Front. Environ. Sci."},{"key":"ref_27","unstructured":"Brown, J., Ferrians, O., Heginbottom, J., and Melnikov, E. (2002). Circum-Arctic Map of Permafrost and Ground-Ice Conditions, Version 2, National Snow and Ice Data Center."},{"key":"ref_28","unstructured":"Natural Earth (2020, August 28). Natural Earth I With Shaded Relief and Water. Available online: https:\/\/www.naturalearthdata.com\/downloads\/10m-raster-data\/10m-natural-earth-1\/."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1080\/789610186","article-title":"Application of satellite remote sensing techniques to frozen ground studies","volume":"28","author":"Zhang","year":"2004","journal-title":"Polar Geogr."},{"key":"ref_30","unstructured":"European Space Agency (2022, January 14). Sentinel-2 User Handbook. Available online: https:\/\/sentinels.copernicus.eu\/documents\/247904\/685211\/Sentinel-2_User_Handbook."},{"key":"ref_31","unstructured":"ESA Communications (2022, January 14). Sentinel-1: ESA\u2019s Radar Observatory Mission for GMES Operational Services. Available online: https:\/\/sentinel.esa.int\/documents\/247904\/349449\/S1_SP-1322_1.pdf."},{"key":"ref_32","unstructured":"Google Developers (2022, January 14). Sentinel-1 Algorithms. Available online: https:\/\/developers.google.com\/earth-engine\/guides\/sentinel1."},{"key":"ref_33","unstructured":"Mutlu, E. (2021, April 11). What Is Robustness in Statistics? A Brief Intro to Robust Estimators. Available online: https:\/\/towardsdatascience.com\/what-is-robustness-in-statistics-a-brief-intro-to-robust-estimators-e926d74d1609."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"9431","DOI":"10.3390\/rs70709431","article-title":"Sentinel-1A product geolocation accuracy: Commissioning phase results","volume":"7","author":"Schubert","year":"2015","journal-title":"Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Schubert, A., Miranda, N., Geudtner, D., and Small, D. (2017). Sentinel-1A\/B combined product geolocation accuracy. Remote Sens., 9.","DOI":"10.3390\/rs9060607"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1109\/MGRS.2017.2762307","article-title":"Deep learning in remote sensing: A comprehensive review and list of resources","volume":"5","author":"Zhu","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"3954","DOI":"10.1109\/JSTARS.2018.2833382","article-title":"DeepUNet: A deep fully convolutional network for pixel-level sea-land segmentation","volume":"11","author":"Li","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1109\/LGRS.2016.2637439","article-title":"SeNet: Structured edge network for sea\u2013land segmentation","volume":"14","author":"Cheng","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Baumhoer, C.A., Dietz, A.J., Kneisel, C., and Kuenzer, C. (2019). Automated extraction of antarctic glacier and ice shelf fronts from sentinel-1 imagery using deep learning. Remote Sens., 11.","DOI":"10.3390\/rs11212529"},{"key":"ref_40","first-page":"1","article-title":"Driving Forces of Circum-Antarctic Glacier and Ice Shelf Front Retreat over the Last Two Decades","volume":"2020","author":"Baumhoer","year":"2020","journal-title":"Cryosphere Discuss."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"2357","DOI":"10.5194\/tc-15-2357-2021","article-title":"Environmental drivers of circum-Antarctic glacier and ice shelf front retreat over the last two decades","volume":"15","author":"Baumhoer","year":"2021","journal-title":"Cryosphere"},{"key":"ref_42","first-page":"4300514","article-title":"HED-UNet: Combined Segmentation and Edge Detection for Monitoring the Antarctic Coastline","volume":"60","author":"Heidler","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation. International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer International Publishing.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_44","unstructured":"Bishop, C.M., and Nasrabadi, N.M. (2006). Pattern Recognition and Machine Learning, Springer."},{"key":"ref_45","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_46","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (July, January 26). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_47","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (July, January 26). Rethinking the inception architecture for computer vision. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Ioffe, S., Vanhoucke, V., and Alemi, A.A. (2017, January 4\u20139). Inception-v4, inception-resnet and the impact of residual connections on learning. Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, San Francisco, CA, USA.","DOI":"10.1609\/aaai.v31i1.11231"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Xie, S., Girshick, R., Doll\u00e1r, P., Tu, Z., and He, K. (2017, January 21\u201326). Aggregated residual transformations for deep neural networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.634"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K.Q. (2017, January 21\u201326). Densely connected convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2018, January 18\u201323). Squeeze-and-excitation networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","article-title":"Imagenet large scale visual recognition challenge","volume":"115","author":"Russakovsky","year":"2015","journal-title":"Int. J. Comput. Vis."},{"key":"ref_53","unstructured":"OpenStreetMap Contributors (2022, March 01). Planet Dump Retrieved from https:\/\/planet.osm.org. Available online: https:\/\/www.openstreetmap.org."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"8741","DOI":"10.1029\/96JB00104","article-title":"A global, self-consistent, hierarchical, high-resolution shoreline database","volume":"101","author":"Wessel","year":"1996","journal-title":"J. Geophys. Res. Solid Earth"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1111\/j.1654-1103.2005.tb02365.x","article-title":"The circumpolar Arctic vegetation map","volume":"16","author":"Walker","year":"2005","journal-title":"J. Veg. Sci."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1109\/LGRS.2010.2068537","article-title":"Change vector analysis in posterior probability space: A new method for land cover change detection","volume":"8","author":"Chen","year":"2010","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_57","unstructured":"Wegmann, M., Leutner, B., and Dech, S. (2016). Remote Sensing and GIS for Ecologists: Using Open Source Software, Pelagic Publishing Ltd."},{"key":"ref_58","first-page":"7","article-title":"Coastal Lagoons","volume":"1","author":"Barnes","year":"1980","journal-title":"CUP Archive"},{"key":"ref_59","unstructured":"Cohen, D., Lee, T.B., and Sklar, D. (2004). Precalculus: A Problems-Oriented Approach, Cengage Learning."},{"key":"ref_60","unstructured":"Malila, W.A. (1980). Change Vector Analysis: An Approach for Detecting Forest Changes with Landsat, LARS Symposia, Purdue University."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1007\/s10661-007-0031-6","article-title":"Change vector analysis to categorise land cover change processes using the tasselled cap as biophysical indicator","volume":"145","author":"Siwe","year":"2008","journal-title":"Environ. Monit. Assess."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"5951","DOI":"10.1007\/s10661-014-3831-5","article-title":"A change vector analysis technique for monitoring land cover changes in Copsa Mica, Romania, in the period 1985\u20132011","volume":"186","author":"Vorovencii","year":"2014","journal-title":"Environ. Monit. Assess."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"1069","DOI":"10.1080\/01431160600868482","article-title":"Sensitivity of change vector analysis to land cover change in an arid ecosystem","volume":"28","author":"Flores","year":"2007","journal-title":"Int. J. Remote Sens."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"7439","DOI":"10.1080\/01431161.2019.1579390","article-title":"Near real-time deforestation detection in Malaysia and Indonesia using change vector analysis with three sensors","volume":"40","author":"Perbet","year":"2019","journal-title":"Int. J. Remote Sens."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Huang, C., Zan, X., Yang, X., and Zhang, S. (2016, January 10\u201315). Surface water change detection using change vector analysis. Proceedings of the 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, China.","DOI":"10.1109\/IGARSS.2016.7729732"},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Dewi, R.S., Bijker, W., and Stein, A. (2017). Change vector analysis to monitor the changes in fuzzy shorelines. Remote Sens., 9.","DOI":"10.3390\/rs9020147"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1080\/2150704X.2012.699201","article-title":"MODIS-based change vector analysis for assessing wetland dynamics in Southern Africa","volume":"4","author":"Landmann","year":"2013","journal-title":"Remote Sens. Lett."},{"key":"ref_68","unstructured":"Powers, D.M. (2020). Evaluation: From precision, recall and F-measure to ROC, informedness, markedness and correlation. arXiv."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"861","DOI":"10.1016\/j.patrec.2005.10.010","article-title":"An introduction to ROC analysis","volume":"27","author":"Fawcett","year":"2006","journal-title":"Pattern Recognit. Lett."},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Taha, A.A., and Hanbury, A. (2015). Metrics for evaluating 3D medical image segmentation: Analysis, selection, and tool. BMC Med Imaging, 15.","DOI":"10.1186\/s12880-015-0068-x"},{"key":"ref_71","first-page":"51","article-title":"Seasonal and multi-year surface displacements measured by DInSAR in a High Arctic permafrost environment","volume":"64","author":"Rudy","year":"2018","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/2150704X.2016.1225170","article-title":"Seasonal deformation features on Qinghai-Tibet railway observed using time-series InSAR technique with high-resolution TerraSAR-X images","volume":"8","author":"Wang","year":"2017","journal-title":"Remote Sens. Lett."},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Strozzi, T., Antonova, S., G\u00fcnther, F., M\u00e4tzler, E., Vieira, G., Wegm\u00fcller, U., Westermann, S., and Bartsch, A. (2018). Sentinel-1 SAR interferometry for surface deformation monitoring in low-land permafrost areas. Remote Sens., 10.","DOI":"10.3390\/rs10091360"},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Antonova, S., Sudhaus, H., Strozzi, T., Zwieback, S., K\u00e4\u00e4b, A., Heim, B., Langer, M., Bornemann, N., and Boike, J. (2018). Thaw subsidence of a yedoma landscape in northern Siberia, measured in situ and estimated from TerraSAR-X interferometry. Remote Sens., 10.","DOI":"10.3390\/rs10040494"},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41467-018-05457-1","article-title":"Wildfire as a major driver of recent permafrost thaw in boreal peatlands","volume":"9","author":"Gibson","year":"2018","journal-title":"Nat. Commun."},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Zhou, Z., Liu, L., Jiang, L., Feng, W., and Samsonov, S.V. (2019). Using long-term SAR backscatter data to monitor post-fire vegetation recovery in tundra environment. Remote Sens., 11.","DOI":"10.3390\/rs11192230"},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Brown, D., Jorgenson, M.T., Kielland, K., Verbyla, D.L., Prakash, A., and Koch, J.C. (2016). Landscape effects of wildfire on permafrost distribution in interior Alaska derived from remote sensing. Remote Sens., 8.","DOI":"10.3390\/rs8080654"},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"025001","DOI":"10.1088\/1748-9326\/aaf06f","article-title":"Investigating lake-area dynamics across a permafrost-thaw spectrum using airborne electromagnetic surveys and remote sensing time-series data in Yukon Flats, Alaska","volume":"14","author":"Rey","year":"2019","journal-title":"Environ. Res. Lett."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1002\/ppp.1986","article-title":"Thermokarst pond dynamics in subarctic environment monitoring with radar remote sensing","volume":"29","author":"Wang","year":"2018","journal-title":"Permafr. Periglac. Process."},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Nitze, I., Grosse, G., Jones, B.M., Arp, C.D., Ulrich, M., Fedorov, A., and Veremeeva, A. (2017). Landsat-based trend analysis of lake dynamics across northern permafrost regions. Remote Sens., 9.","DOI":"10.3390\/rs9070640"},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"621","DOI":"10.3390\/rs6010621","article-title":"Temporal behavior of lake size-distribution in a thawing permafrost landscape in northwestern Siberia","volume":"6","author":"Karlsson","year":"2014","journal-title":"Remote Sens."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1016\/j.geomorph.2016.02.014","article-title":"Coastal erosion and mass wasting along the Canadian Beaufort Sea based on annual airborne LiDAR elevation data","volume":"293","author":"Obu","year":"2017","journal-title":"Geomorphology"},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1016\/j.geomorph.2006.07.040","article-title":"Fifty years of coastal erosion and retrogressive thaw slump activity on Herschel Island, southern Beaufort Sea, Yukon Territory, Canada","volume":"95","author":"Lantuit","year":"2008","journal-title":"Geomorphology"},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"034009","DOI":"10.1088\/1748-9326\/7\/3\/034009","article-title":"Large methane emission upon spring thaw from natural wetlands in the northern permafrost region","volume":"7","author":"Song","year":"2012","journal-title":"Environ. Res. Lett."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"075001","DOI":"10.1088\/1748-9326\/9\/7\/075001","article-title":"Surface water inundation in the boreal-Arctic: Potential impacts on regional methane emissions","volume":"9","author":"Watts","year":"2014","journal-title":"Environ. Res. Lett."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"679","DOI":"10.1038\/ngeo2795","article-title":"Methane emissions proportional to permafrost carbon thawed in Arctic lakes since the 1950s","volume":"9","author":"Anthony","year":"2016","journal-title":"Nat. Geosci."},{"key":"ref_87","first-page":"1","article-title":"21st-century modeled permafrost carbon emissions accelerated by abrupt thaw beneath lakes","volume":"9","author":"Anthony","year":"2018","journal-title":"Nat. Commun."},{"key":"ref_88","unstructured":"Ulaby, F.T., Moore, R.K., and Fung, A.K. (1982). Microwave Remote Sensing: Active and Passive, Volume II: Radar Remote Sensing and Surface Scattering and Emission Theory, Artech House."},{"key":"ref_89","doi-asserted-by":"crossref","unstructured":"Richards, J.A. (2009). Remote Sensing with Imaging Radar, Springer.","DOI":"10.1007\/978-3-642-02020-9"},{"key":"ref_90","unstructured":"Lighthill, M.J., and Lighthill, J. (2001). Waves in Fluids, Cambridge University Press."},{"key":"ref_91","first-page":"1245924","article-title":"PLANET: Improved convolutional neural networks with image enhancement for image classification","volume":"2020","author":"Tang","year":"2020","journal-title":"Math. Probl. Eng."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"1856","DOI":"10.1109\/TMI.2019.2959609","article-title":"Unet++: Redesigning skip connections to exploit multiscale features in image segmentation","volume":"39","author":"Zhou","year":"2019","journal-title":"IEEE Trans. Med Imaging"},{"key":"ref_93","doi-asserted-by":"crossref","unstructured":"Heaton, J. (April, January 30). An empirical analysis of feature engineering for predictive modeling. Proceedings of the SoutheastCon 2016, Norfolk, VA, USA.","DOI":"10.1109\/SECON.2016.7506650"},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_95","doi-asserted-by":"crossref","unstructured":"Philipp, M., Dietz, A., Buchelt, S., and Kuenzer, C. (2021). Trends in Satellite Earth Observation for Permafrost Related Analyses\u2014A Review. Remote Sens., 13.","DOI":"10.3390\/rs13061217"},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"111534","DOI":"10.1016\/j.rse.2019.111534","article-title":"Using deep learning to map retrogressive thaw slumps in the Beiluhe region (Tibetan Plateau) from CubeSat images","volume":"237","author":"Huang","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_97","doi-asserted-by":"crossref","unstructured":"Zhang, W., Witharana, C., Liljedahl, A.K., and Kanevskiy, M. (2018). Deep convolutional neural networks for automated characterization of arctic ice-wedge polygons in very high spatial resolution aerial imagery. Remote Sens., 10.","DOI":"10.3390\/rs10091487"},{"key":"ref_98","doi-asserted-by":"crossref","unstructured":"Zhang, W., Liljedahl, A.K., Kanevskiy, M., Epstein, H.E., Jones, B.M., Jorgenson, M.T., and Kent, K. (2020). Transferability of the Deep Learning Mask R-CNN Model for Automated Mapping of Ice-Wedge Polygons in High-Resolution Satellite and UAV Images. Remote Sens., 12.","DOI":"10.3390\/rs12071085"},{"key":"ref_99","doi-asserted-by":"crossref","unstructured":"Langford, Z.L., Kumar, J., Hoffman, F.M., Breen, A.L., and Iversen, C.M. (2019). Arctic vegetation mapping using unsupervised training datasets and convolutional neural networks. Remote Sens., 11.","DOI":"10.3390\/rs11010069"},{"key":"ref_100","doi-asserted-by":"crossref","unstructured":"Bartsch, A., Pointner, G., Ingeman-Nielsen, T., and Lu, W. (2020). Towards Circumpolar Mapping of Arctic Settlements and Infrastructure Based on Sentinel-1 and Sentinel-2. Remote Sens., 12.","DOI":"10.3390\/rs12152368"},{"key":"ref_101","unstructured":"Langaas, S. (1995). Completeness of the Digital Chart of the World (DCW) Database, UNEP\/GRID-Arendal."},{"key":"ref_102","unstructured":"Alaska Geobotany Center (2022, February 10). Circumpolar Arctic Coastline and Treeline Boundary. Available online: http:\/\/www.arcticatlas.org\/maps\/themes\/cp\/cpcoast."},{"key":"ref_103","unstructured":"Wessel, P. (2022, February 10). GSHHG\u2014A Global Self-Consistent, Hierarchical, High-Resolution Geography Database. Available online: https:\/\/www.soest.hawaii.edu\/pwessel\/gshhg\/."},{"key":"ref_104","unstructured":"Bennett, J. (2010). OpenStreetMap, Packt Publishing Ltd."},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40965-019-0067-x","article-title":"OpenStreetMap history for intrinsic quality assessment: Is OSM up-to-date?","volume":"4","author":"Minghini","year":"2019","journal-title":"Open Geospat. Data, Softw. Stand."},{"key":"ref_106","doi-asserted-by":"crossref","unstructured":"Wang, J., Li, D., Cao, W., Lou, X., Shi, A., and Zhang, H. (2022). Remote Sensing Analysis of Erosion in Arctic Coastal Areas of Alaska and Eastern Siberia. Remote Sens., 14.","DOI":"10.3390\/rs14030589"},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"4297","DOI":"10.5194\/bg-10-4297-2013","article-title":"Short-and long-term thermo-erosion of ice-rich permafrost coasts in the Laptev Sea region","volume":"10","author":"Overduin","year":"2013","journal-title":"Biogeosciences"},{"key":"ref_108","unstructured":"European Space Agency (2022, February 10). Observation Scenario Archive. Available online: https:\/\/sentinels.copernicus.eu\/web\/sentinel\/missions\/sentinel-1\/observation-scenario\/archive."},{"key":"ref_109","unstructured":"Alaska Satellite Facility (2022, February 10). Sentinel-1\u2014Acquisition Maps. Available online: https:\/\/asf.alaska.edu\/data-sets\/sar-data-sets\/sentinel-1\/sentinel-1-acquisition-maps\/."},{"key":"ref_110","unstructured":"European Space Agency (2022, February 10). Copernicus Sentinel-1B Anomaly. Available online: https:\/\/sentinels.copernicus.eu\/web\/sentinel\/-\/copernicus-sentinel-1b-anomaly."},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"401","DOI":"10.1080\/07038992.2015.1104633","article-title":"Overview of the RADARSAT constellation mission","volume":"41","author":"Thompson","year":"2015","journal-title":"Can. J. Remote Sens."},{"key":"ref_112","doi-asserted-by":"crossref","unstructured":"Banks, S., Millard, K., Behnamian, A., White, L., Ullmann, T., Charbonneau, F., Chen, Z., Wang, H., Pasher, J., and Duffe, J. (2017). Contributions of actual and simulated satellite SAR data for substrate type differentiation and shoreline mapping in the Canadian arctic. Remote Sens., 9.","DOI":"10.3390\/rs9121206"},{"key":"ref_113","doi-asserted-by":"crossref","unstructured":"Jones, B.M., Arp, C.D., Jorgenson, M.T., Hinkel, K.M., Schmutz, J.A., and Flint, P.L. (2009). Increase in the rate and uniformity of coastline erosion in Arctic Alaska. Geophys. Res. Lett., 36.","DOI":"10.1029\/2008GL036205"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/15\/3656\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:59:41Z","timestamp":1760140781000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/15\/3656"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,30]]},"references-count":113,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2022,8]]}},"alternative-id":["rs14153656"],"URL":"https:\/\/doi.org\/10.3390\/rs14153656","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,7,30]]}}}