{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T08:22:22Z","timestamp":1769761342426,"version":"3.49.0"},"reference-count":77,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2021,11,25]],"date-time":"2021-11-25T00:00:00Z","timestamp":1637798400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004359","name":"Swedish Research Council","doi-asserted-by":"publisher","award":["2018-04516"],"award-info":[{"award-number":["2018-04516"]}],"id":[{"id":"10.13039\/501100004359","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000780","name":"European Union","doi-asserted-by":"publisher","award":["H2020 773421"],"award-info":[{"award-number":["H2020 773421"]}],"id":[{"id":"10.13039\/501100000780","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Arctic tundra landscapes are highly complex and are rapidly changing due to the warming climate. Datasets that document the spatial and temporal variability of the landscape are needed to monitor the rapid changes. Synthetic Aperture Radar (SAR) imagery is specifically suitable for monitoring the Arctic, as SAR, unlike optical remote sensing, can provide time series regardless of weather and illumination conditions. This study examines the potential of seasonal backscatter mechanisms in Arctic tundra environments for improving land cover classification purposes by using a time series of HH\/HV TerraSAR-X (TSX) imagery. A Random Forest (RF) classification was applied on multi-temporal Sigma Nought intensity and multi-temporal Kennaugh matrix element data. The backscatter analysis revealed clear differences in the polarimetric response of water, soil, and vegetation, while backscatter signal variations within different vegetation classes were more nuanced. The RF models showed that land cover classes could be distinguished with 92.4% accuracy for the Kennaugh element data, compared to 57.7% accuracy for the Sigma Nought intensity data. Texture predictors, while improving the classification accuracy on the one hand, degraded the spatial resolution of the land cover product. The Kennaugh elements derived from TSX winter acquisitions were most important for the RF model, followed by the Kennaugh elements derived from summer and autumn acquisitions. The results of this study demonstrate that multi-temporal Kennaugh elements derived from dual-polarized X-band imagery are a powerful tool for Arctic tundra land cover mapping.<\/jats:p>","DOI":"10.3390\/rs13234780","type":"journal-article","created":{"date-parts":[[2021,12,1]],"date-time":"2021-12-01T01:45:02Z","timestamp":1638323102000},"page":"4780","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Arctic Tundra Land Cover Classification on the Beaufort Coast Using the Kennaugh Element Framework on Dual-Polarimetric TerraSAR-X Imagery"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8723-3832","authenticated-orcid":false,"given":"Willeke","family":"A\u2019Campo","sequence":"first","affiliation":[{"name":"Department of Physical Geography, Stockholm University, 10691 Stockholm, Sweden"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3737-7931","authenticated-orcid":false,"given":"Annett","family":"Bartsch","sequence":"additional","affiliation":[{"name":"b.geos, 2100 Korneuburg, Austria"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8913-0930","authenticated-orcid":false,"given":"Achim","family":"Roth","sequence":"additional","affiliation":[{"name":"German Aerospace Center (DLR), German Remote Sensing Data Center (DFD), 82234 Wessling, Germany"}]},{"given":"Anna","family":"Wendleder","sequence":"additional","affiliation":[{"name":"German Aerospace Center (DLR), German Remote Sensing Data Center (DFD), 82234 Wessling, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7473-1039","authenticated-orcid":false,"given":"Victoria S.","family":"Martin","sequence":"additional","affiliation":[{"name":"Centre for Microbiology and Environmental Systems Science, Division of Terrestrial Ecosystem Research, University of Vienna, 1030 Wien, Austria"}]},{"given":"Luca","family":"Durstewitz","sequence":"additional","affiliation":[{"name":"Department of Physical Geography, Stockholm University, 10691 Stockholm, Sweden"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4315-3587","authenticated-orcid":false,"given":"Rachele","family":"Lodi","sequence":"additional","affiliation":[{"name":"National Research Council, Institute of Polar Science (ISP-CNR), 30172 Venezia, Italy"},{"name":"Department of Environmental Sciences, Informatics and Statistics, University Ca\u2019 Foscari of Venice, 30172 Venezia, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7047-4848","authenticated-orcid":false,"given":"Julia","family":"Wagner","sequence":"additional","affiliation":[{"name":"Department of Physical Geography, Stockholm University, 10691 Stockholm, Sweden"},{"name":"Bolin Centre for Climate Research, Stockholm University, 10691 Stockholm, Sweden"}]},{"given":"Gustaf","family":"Hugelius","sequence":"additional","affiliation":[{"name":"Department of Physical Geography, Stockholm University, 10691 Stockholm, Sweden"},{"name":"Bolin Centre for Climate Research, Stockholm University, 10691 Stockholm, Sweden"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,25]]},"reference":[{"key":"ref_1","unstructured":"(2020, May 02). Surface Air Temperature, Available online: https:\/\/www.arctic.noaa.gov\/Report-Card\/Report-Card-2018\/ArtMID\/7878\/ArticleID\/783\/Surface-Air-Temperature."},{"key":"ref_2","unstructured":"Core Writing Team, IPCC, Pachauri, B.K., and Meyer, L.A. (2014). Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press. [1st ed.]."},{"key":"ref_3","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_4","doi-asserted-by":"crossref","unstructured":"Hugelius, G. (2012). Spatial upscaling using thematic maps: An analysis of uncertainties in permafrost soil carbon estimates: Errors in estimates of soil carbon. Glob. Biogeochem. Cycles, 26.","DOI":"10.1029\/2011GB004154"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Stettner, S., Lantuit, H., Heim, B., Eppler, J., Roth, A., Bartsch, A., and Rabus, B. (2018). TerraSAR-X time series fill a gap in spaceborne snowmelt monitoring of small Arctic catchments a case study on Qikiqtaruk (Herschel Island), Canada. Remote Sens., 10.","DOI":"10.3390\/rs10071155"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"9410","DOI":"10.3390\/rs70709410","article-title":"Potential of C and X band SAR for shrub growth monitoring in sub-Arctic environments","volume":"7","author":"Duguay","year":"2015","journal-title":"Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Finlayson, C.M., Milton, G.R., Prentice, R.C., and Davidson, N.C. (2016). Arctic Peatlands. The Wetland Book, Springer.","DOI":"10.1007\/978-94-007-6173-5"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2345","DOI":"10.1038\/s41598-018-20692-8","article-title":"Reduced arctic tundra productivity linked with landform and climate change interactions","volume":"8","author":"Lara","year":"2018","journal-title":"Sci. Rep."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1038\/s41558-019-0688-1","article-title":"Complexity revealed in the greening of the Arctic","volume":"10","author":"Kerby","year":"2020","journal-title":"Nat. Clim. Chang."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"5453","DOI":"10.5194\/bg-13-5453-2016","article-title":"Can C-band synthetic aperture radar be used to estimate soil organic carbon storage in tundra?","volume":"13","author":"Bartsch","year":"2016","journal-title":"Biogeosciences"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Duguay, Y., Bernier, M., L\u00e9vesque, E., and Domine, F. (2016). Land cover classification in SubArctic regions using fully polarimetric RADARSAT-2 data. Remote Sens., 8.","DOI":"10.3390\/rs8090697"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"180058","DOI":"10.1038\/sdata.2018.58","article-title":"Tundra landform and vegetation productivity trend maps for the Arctic Coastal Plain of northern Alaska","volume":"5","author":"Lara","year":"2018","journal-title":"Sci. Data"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Stettner, S., Beamish, A., Bartsch, A., Heim, B., Grosse, G., Roth, A., and Lantuit, H. (2017). Monitoring inter- and intra-seasonal dynamics of rapidly degrading ice-rich permafrost riverbanks in the Lena Delta with TerraSAR-X time series. Remote Sens., 10.","DOI":"10.3390\/rs10010051"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Ullmann, T., Banks, S.N., Schmitt, A., and Jagdhuber, T. (2017). Scattering Characteristics of X-, C- and L-Band PolSAR Data Examined for the Tundra Environment of the Tuktoyaktuk Peninsula, Canada. Appl. Sci., 7.","DOI":"10.3390\/app7060595"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Banks, S., Ullmann, T., Roth, A., Schmitt, A., Dech, S., and King, D. (May, January 29). Classification of Arctic Coastal land covers with polarimetric SAR data. Proceedings of the 2013 IEEE Radar Conference (RadarCon13), Ottawa, ON, Canada.","DOI":"10.1109\/RADAR.2013.6586059"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1016\/j.rse.2016.05.003","article-title":"Spatio-temporal variability of X-band radar backscatter and coherence over the Lena River Delta, Siberia","volume":"182","author":"Antonova","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.isprsjprs.2017.05.010","article-title":"Random forest wetland classification using ALOS-2 L-band, RADARSAT-2 C-band, and TerraSAR-X imagery","volume":"130","author":"Mahdianpari","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Widhalm, B., Bartsch, A., Roth, A., and Leibman, M. (2018, January 22\u201327). Classification of Tundra Regions with Polarimetric Terrasar-X Data. Proceedings of the 2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain.","DOI":"10.1109\/IGARSS.2018.8518283"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.isprsjprs.2018.03.026","article-title":"Mapping permafrost landscape features using object-based image classification of multi-temporal SAR images","volume":"141","author":"Wang","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1762","DOI":"10.1111\/gcb.12822","article-title":"Observing terrestrial ecosystems and the carbon cycle from space","volume":"21","author":"Schimel","year":"2015","journal-title":"Glob. Chang. Biol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1175","DOI":"10.1080\/01431161.2018.1524176","article-title":"Predicting aboveground biomass in Arctic landscapes using very high spatial resolution satellite imagery and field sampling","volume":"40","author":"Juutinen","year":"2019","journal-title":"Int. J. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1038\/nclimate2919","article-title":"Combining satellite data for better tropical forest monitoring","volume":"6","author":"Reiche","year":"2016","journal-title":"Nat. Clim. Chang."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Ullmann, T., Schmitt, A., and Jagdhuber, T. (2016). Two Component Decomposition of Dual Polarimetric HH\/VV SAR Data: Case Study for the Tundra Environment of the Mackenzie Delta Region, Canada. Remote Sens., 8.","DOI":"10.3390\/rs8121027"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"111515","DOI":"10.1016\/j.rse.2019.111515","article-title":"Feasibility of tundra vegetation height retrieval from Sentinel-1 and Sentinel-2 data","volume":"237","author":"Bartsch","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"2134","DOI":"10.3390\/rs6032134","article-title":"Artificial Neural Network Modeling of High Arctic Phytomass Using Synthetic Aperture Radar and Multispectral Data","volume":"6","author":"Collingwood","year":"2014","journal-title":"Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"483","DOI":"10.5194\/tc-11-483-2017","article-title":"Active-layer thickness estimation from X-band SAR backscatter intensity","volume":"11","author":"Widhalm","year":"2017","journal-title":"Cryosphere"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2018\/5712046","article-title":"Spatiotemporal Variability of Arctic Soil Moisture Detected from High-Resolution RADARSAT-2 SAR Data","volume":"2018","author":"Collingwood","year":"2018","journal-title":"Adv. Meteorol."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"3190","DOI":"10.1029\/2018JF004658","article-title":"Evaluation of a MetOp ASCAT-Derived Surface Soil Moisture Product in Tundra Environments","volume":"123","author":"Heim","year":"2018","journal-title":"J. Geophys. Res. Earth Surf."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"111257","DOI":"10.1016\/j.rse.2019.111257","article-title":"Vegetation\u2013soil moisture coupling metrics from dual-polarization microwave radiometry using regularization","volume":"231","author":"Zwieback","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Cloude, S. (2010). Polarisation: Applications in Remote Sensing, Oxford University Press. [1st ed.].","DOI":"10.1093\/acprof:oso\/9780199569731.001.0001"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"963","DOI":"10.1109\/36.673687","article-title":"A three-component scattering model for polarimetric SAR data","volume":"36","author":"Freeman","year":"1998","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"498","DOI":"10.1109\/36.485127","article-title":"A review of target decomposition theores in radar polarimetry","volume":"34","author":"Cloude","year":"1996","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1080\/07038992.2014.979487","article-title":"Characterizing Scattering Behaviour and Assessing Potential for Classification of Arctic Shore and Near-Shore Land Covers with Fine Quad-Pol RADARSAT-2 Data","volume":"40","author":"Banks","year":"2014","journal-title":"Can. J. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1699","DOI":"10.1109\/TGRS.2005.852084","article-title":"Four-component scattering model for polarimetric SAR image decomposition","volume":"43","author":"Yamaguchi","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Wohlfart, C., Winkler, K., Wendleder, A., and Roth, A. (2018). TerraSAR-X and Wetlands: A Review. Remote Sens., 10.","DOI":"10.3390\/rs10060916"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1016\/j.isprsjprs.2015.01.007","article-title":"The Kennaugh element framework for multi-scale, multi-polarized, multi-temporal and multi-frequency SAR image preparation","volume":"102","author":"Schmitt","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Moser, L., Schmitt, A., Wendleder, A., and Roth, A. (2016). Monitoring of the Lac Bam Wetland Extent Using Dual-Polarized X-Band SAR Data. Remote Sens., 8.","DOI":"10.3390\/rs8040302"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1036","DOI":"10.3390\/w5031036","article-title":"Wetland Monitoring Using the Curvelet-Based Change Detection Method on Polarimetric SAR Imagery","volume":"5","author":"Schmitt","year":"2013","journal-title":"Water"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Bartsch, A., Pointner, G., Bergstedt, H., Widhalm, B., Wendleder, A., and Roth, A. (2021, January 11\u201316). Utility of Polarizations Available from Sentinel-1 for Tundra Mapping. Proceedings of the 2021 IEEE International Geoscience and Remote Sensing Symposium, Brussels, Belgium.","DOI":"10.1109\/IGARSS47720.2021.9553993"},{"key":"ref_40","unstructured":"Lara, M.J. (2020, February 20). SNAP Data Portal, 2017. [Dataset]. Available online: http:\/\/ckan.snap.uaf.edu\/dataset\/alaskan-arctic-coastal-plain-polygonal-tundra-geomorphology-map\/."},{"key":"ref_41","unstructured":"Bartsch, A., Widhalm, B., Pointner, G., Ermokhina, K., Leibman, M., and Heim, B. (2019). Landcover Derived from Sentinel-1 and Sentinel-2 Satellite data (2015\u20132018) for subarctic and Arctic Environments. Zentralanstalt f\u00fcr Meteorologie und Geodynamik, Wien. PANGAEA."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"725","DOI":"10.1016\/j.catena.2016.07.048","article-title":"Landscape controls and vertical variability of soil organic carbon storage in permafrost-affected soils of the Lena River Delta","volume":"147","author":"Siewert","year":"2016","journal-title":"CATENA"},{"key":"ref_43","unstructured":"Inuvialuit Regional Corporation (2020, May 26). Inuvialuit Land Administration: Inuvialuit Settlement Region. Available online: https:\/\/www.irc.inuvialuit.com\/inuvialuit-land-administration."},{"key":"ref_44","unstructured":"Environment and Climate Change Canada (2020, February 20). Historical Data: Climate Station Komakuk Beach. 2020. [Dataset]. Available online: https:\/\/climate.weather.gc.ca\/."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"180214","DOI":"10.1038\/sdata.2018.214","article-title":"Present and future K\u00f6ppen-Geiger climate classification maps at 1-km resolution","volume":"5","author":"Beck","year":"2018","journal-title":"Sci. Data"},{"key":"ref_46","unstructured":"Rampton, V.N. (2019, July 15). Quarternary Geology, Yukon Coastal Plain Yukon Territory-Northwest Territories. Geological Survey of Canada (GSC) MAP 1503A 1:250,000 Scale. 1982. [Dataset], Available online: http:\/\/data.geology.gov.yk.ca\/Compilation\/20#InfoTab."},{"key":"ref_47","unstructured":"Overduin, P., and Obu, J. (2020, May 08). Permafrost in the Northern Hemisphere. 2019. [Dataset]. Available online: https:\/\/news.grida.no\/new-map-shows-extent-of-permafrost-in-northern-hemisphere."},{"key":"ref_48","unstructured":"CAVM TEAM (2020, February 20). Circumpolar Arctic Vegetation Map. (1:7,500,000 scale), Conservation of Arctic Flora and Fauna (CAFF) Map No. 1.0 U.S. Fish and Wildlife Service, Anchorage, Alaska. 2003. [Dataset]. Available online: http:\/\/www.arcticatlas.org\/maps\/themes\/cp\/."},{"key":"ref_49","unstructured":"Porter, C., Morin, P., Howat, I., Noh, M.J., Bates, B., Peterman, K., Keesey, S., Schlenk, M., Gardiner, J., and Tomko, K. (2019, October 03). ArcticDEM, 2018. Harvard Dataverse, V1. [Dataset]. Available online: https:\/\/www.pgc.umn.edu\/data\/arcticdem\/."},{"key":"ref_50","unstructured":"Digital Globe, I. (2019, July 12). WorldView-3 Image, 2019. [Dataset]. Available online: http:\/\/www.digitalglobe.com."},{"key":"ref_51","unstructured":"German Aerospace Center (2013). TerraSAR-X Ground Segment Basic Product Specification Document, German Aerospace Center. In: TX-GS-DD-3302."},{"key":"ref_52","unstructured":"Airbus (2015). TerraSAR-X Image Product Guide: Basic and Enhanced Radar Satellite Imagery, Airbus. In: OP00xxxxxxxxxx."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Ulaby, F., and Long, D. (2014). Microwave Radar and Radiometric Remote Sensing, University of Michigan Press. [1st ed.].","DOI":"10.3998\/0472119356"},{"key":"ref_54","unstructured":"Digital Globe, I. (2020, May 08). Tools & Resources. Available online: http:\/\/www.digitalglobe.com\/resources#resource-table-section."},{"key":"ref_55","unstructured":"Schoeneberger, P.J., Wysocki, D.A., and Benham, E.C. (2012). Soil Survey Staff, Field Book for Describing and Sampling Soils, Version 3.0."},{"key":"ref_56","unstructured":"Beaudette, D., Roudier, P., and Brown, A. (2020, February 01). aqp: Algorithms for Quantitative Pedology, Available online: https:\/\/cran.r-project.org\/package=aqp."},{"key":"ref_57","unstructured":"R Core Team, R. (2020, February 01). R: A Language and Environment for Statistical Computing [Computer Software]. R Foundation for Statistical Computing v3.6.3. Available online: https:\/\/www.R-project.org\/."},{"key":"ref_58","unstructured":"Airbus (2014). Radiometric Calibration of TerraSAR-X Data: Beta Naught and Sigma Naught Coefficient Calculation, Airbus. In: TSXX-ITD-TN-0049."},{"key":"ref_59","unstructured":"SNAP (2019, November 01). SNAP\u2014ESA Sentinel Application Platform (Version 8.0.0), 2019 [Computer Software]. Available online: http:\/\/step.esa.int."},{"key":"ref_60","unstructured":"Lee, J.S., and Pottier, E. (2009). Polarimetric Radar Imaging: From Basics to Applications, CRC Press. [1st ed.]. Optical Science and Engineering."},{"key":"ref_61","unstructured":"Exelis Visual Information Solutions (2020, January 15). ENVI\u2014Exelis Visual Information Solutions (Version 5.2.2) [Computer Software]. Boulder, Colorado. Available online: https:\/\/www.l3harrisgeospatial.com\/Software-Technology\/ENVI."},{"key":"ref_62","unstructured":"Exelis Visual Information Solutions (2020, January 15). FLAASH \u00a9 Background. Boulder, Colorado: Exelis Visual Information Solutions. Available online: https:\/\/www.l3harrisgeospatial.com\/docs\/backgroundflaash.html."},{"key":"ref_63","first-page":"7","article-title":"Application Of Different Pan-Sharpening Methods On WorldView-3 Images","volume":"11","author":"Belfiore","year":"2016","journal-title":"ARPN J. Eng. Appl. Sci."},{"key":"ref_64","first-page":"309","article-title":"Monitoring vegetation systems in the Great Plains with ERTS","volume":"1","author":"Rouse","year":"1973","journal-title":"Proc. Third Earth Resour. Technol.-Satell.- Symp."},{"key":"ref_65","unstructured":"Baatz, M., and Sch\u00e4pe, A. (2000). Multiresolution segmentation\u2014An optimization approach for high quality multi-scale image segmentation. Angewandte Geographische Informationsverarbeitung XII, Wichmann Verlag."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1016\/j.isprsjprs.2003.10.002","article-title":"Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information","volume":"58","author":"Benz","year":"2004","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"van Iersel, W., Straatsma, M., Middelkoop, H., and Addink, E. (2018). Multitemporal Classification of River Floodplain Vegetation Using Time Series of UAV Images. Remote Sens., 10.","DOI":"10.3390\/rs10071144"},{"key":"ref_68","unstructured":"Ramsar Convention Secretariat (2010). Wetland inventory: A Ramsar framework for wetland inventory and ecological character description. Ramsar Handbooks for the Wise Use of Wetlands, Ramsar Convention Secretariat. [4th ed.]."},{"key":"ref_69","unstructured":"Canada Committee on Ecological (Biophysical) Land Classification and the National Wetlands Working Group (1997). The Canadian Wetland Classification System, Wetlands Research Branch, University of Waterloo. [2nd ed.]. OCLC: 43464321."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1109\/TSMC.1973.4309314","article-title":"Textural Features for Image Classification","volume":"SMC-3","author":"Haralick","year":"1973","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_72","unstructured":"Breiman, L., Cutler, A., Liaw, A., and Wiener, M. (2020, February 01). randomForest: Breiman and Cutler\u2019s Random Forests for Classification and Regression, Available online: https:\/\/cran.r-project.org\/package=randomForest."},{"key":"ref_73","unstructured":"Kuhn, M. (2020, February 01). Caret: Classification and Regression Training, Available online: https:\/\/cran.r-project.org\/package=caret."},{"key":"ref_74","unstructured":"Diaz-Uriarte, R. (2020, February 01). varSelRF: Variable Selection Using Random Forests, Available online: https:\/\/cran.r-project.org\/package=varSelRF."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.rse.2014.02.015","article-title":"Good practices for estimating area and assessing accuracy of land change","volume":"148","author":"Olofsson","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_76","unstructured":"A\u2019Campo, W., Bartsch, A., Roth, A., Wendleder, A., Durstewitz, L., Lodi, R., Martin, V.S., Wagner, J., and Hugelius, G. (2021). Raster land cover product derived from TerraSAR-X imagery for the Komakuk Beach study site on the Beaufort Coast, Canada. PANGAEA."},{"key":"ref_77","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"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/23\/4780\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:35:48Z","timestamp":1760168148000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/23\/4780"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,25]]},"references-count":77,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2021,12]]}},"alternative-id":["rs13234780"],"URL":"https:\/\/doi.org\/10.3390\/rs13234780","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,11,25]]}}}