{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T10:16:32Z","timestamp":1771668992214,"version":"3.50.1"},"reference-count":64,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2021,1,22]],"date-time":"2021-01-22T00:00:00Z","timestamp":1611273600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The purpose of this study was to develop methods to localize forest windstorm damages, assess their severity and estimate the total damaged area using space-borne SAR data. The development of the methods is the first step towards an operational system for near-real-time windstorm damage monitoring, with a latency of only a few days after the storm event in the best case. Windstorm detection using SAR data is not trivial, particularly at C-band. It can be expected that a large-area and severe windstorm damage may affect backscatter similar to clear cutting operation, that is, decrease the backscatter intensity, while a small area damage may increase the backscatter of the neighboring area, due to various scattering mechanisms. The remaining debris and temporal variation in the weather conditions and possible freeze\u2013thaw transitions also affect observed backscatter changes. Three candidate windstorm detection methods were suggested, based on the improved k-nn method, multinomial logistic regression and support vector machine classification. The approaches use multitemporal ESA Sentinel-1 C-band SAR data and were evaluated in Southern Finland using wind damage data from the summer 2017, together with 27 Sentinel-1 scenes acquired in 2017 and other geo-referenced data. The stands correctly predicted severity category corresponded to 79% of the number of the stands in the validation data, and already 75% when only one Sentinel-1 scene after the damage was used. Thus, the damaged forests can potentially be localized with proposed tools within less than one week after the storm damage. In this study, the achieved latency was only two days. Our preliminary results also indicate that the damages can be localized even without separate training data.<\/jats:p>","DOI":"10.3390\/rs13030383","type":"journal-article","created":{"date-parts":[[2021,1,22]],"date-time":"2021-01-22T11:13:53Z","timestamp":1611314033000},"page":"383","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Detection of Forest Windstorm Damages with Multitemporal SAR Data\u2014A Case Study: Finland"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5715-8912","authenticated-orcid":false,"given":"Erkki","family":"Tomppo","sequence":"first","affiliation":[{"name":"Department of Electronics and Nanoengineering, Aalto University, P.O. Box 11000, 00076 Aalto, Finland"},{"name":"Department of Forest Sciences, University of Helsinki, Latokartanonkaari 7, P.O. Box 27, 00014 Helsinki, Finland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ghasem","family":"Ronoud","sequence":"additional","affiliation":[{"name":"Department of Electronics and Nanoengineering, Aalto University, P.O. Box 11000, 00076 Aalto, Finland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8576-404X","authenticated-orcid":false,"given":"Oleg","family":"Antropov","sequence":"additional","affiliation":[{"name":"VTT Technical Research Centre of Finland, P.O. Box 1000, 00076 Espoo, Finland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3206-4872","authenticated-orcid":false,"given":"Harri","family":"Hyt\u00f6nen","sequence":"additional","affiliation":[{"name":"Finnish Forest Centre, Kauppakatu 19 B, 40100 Jyv\u00e4skyl\u00e4, Finland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jaan","family":"Praks","sequence":"additional","affiliation":[{"name":"Department of Electronics and Nanoengineering, Aalto University, P.O. Box 11000, 00076 Aalto, Finland"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,1,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"46397","DOI":"10.1038\/srep46397","article-title":"Increasing large scale windstorm damage in Western, Central and Northern European forests, 1951\u20132010","volume":"7","author":"Gregow","year":"2017","journal-title":"Sci. Rep."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.agrformet.2009.08.010","article-title":"Increasing storm damage to forests in Switzerland from 1858 to 2007","volume":"150","author":"Usbeck","year":"2010","journal-title":"Agric. For. Meteorol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2170","DOI":"10.1109\/TGRS.2002.804913","article-title":"Detection of storm-damaged forested areas using airborne CARABAS-II VHF SAR image data","volume":"40","author":"Fransson","year":"2002","journal-title":"IEEE Trans. Geosci. Remote. Sens."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Fransson, J.E.S., Pantze, A., Eriksson, L.E.B., Soja, M.J., and Santoro, M. (2010, January 25\u201330). Mapping of wind-thrown forests using satellite SAR images. Proceedings of the 2010 IEEE International Geoscience and Remote Sensing Symposium, Honolulu, HI, USA.","DOI":"10.1109\/IGARSS.2010.5654183"},{"key":"ref_5","unstructured":"Gardiner, B., Blennow, K., Carnus, J.M., Fleischer, P., Ingemarson, F., Landmann, G., Lindner, M., Marzano, M., Nicoll, B., and Orazio, C. (2013). Destructive Storms in European Forests: Past and Forthcoming Impacts, European Forest Institue, Atlantic European Regional Office\u2014EFIAtalantic. Final Report to European Commission\u2014DG Environment."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Eriksson, L.E.B., Fransson, J.E.S., Soja, M.J., and Santoro, M. (2012, January 22\u201327). Backscatter signatures of wind-thrown forest in satellite SAR images. Proceedings of the 2012 IEEE International Geoscience and Remote Sensing Symposium, Munich, Germany.","DOI":"10.1109\/IGARSS.2012.6352732"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Frolking, S., Palace, M.W., Clark, D.B., Chambers, J.Q., Shugart, H.H., and Hurtt, G.C. (2009). Forest disturbance and recovery: A general review in the context of spaceborne remote sensing of impacts on aboveground biomass and canopy structure. J. Geophys. Res. Biogeosci., 114.","DOI":"10.1029\/2008JG000911"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"R\u00fcetschi, M., Small, D., and Waser, L.T. (2019). Rapid Detection of Windthrows Using Sentinel-1 C-Band SAR Data. Remote Sens., 11.","DOI":"10.3390\/rs11020115"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.rse.2011.05.028","article-title":"GMES Sentinel-1 mission","volume":"120","author":"Torres","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_10","unstructured":"GFOI (2014). Integrating Remote-Sensing and Ground-Based Observations for Estimation of Emissions and Removals of Greenhouse Gases in Forests: Methods and Guidance from the Global Forest Observations Initiative, Group on Earth Observations."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Antropov, O., Rauste, Y., H\u00e4me, T., and Praks, J. (2017). Polarimetric ALOS PALSAR Time Series in Mapping Biomass of Boreal Forests. Remote Sens., 9.","DOI":"10.3390\/rs9100999"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1016\/j.rse.2017.05.003","article-title":"Coverage of high biomass forests by the ESA BIOMASS mission under defense restrictions","volume":"196","author":"Carreiras","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Kellogg, K., Hoffman, P., Standley, S., Shaffer, S., Rosen, P., Edelstein, W., Dunn, C., Baker, C., Barela, P., and Shen, Y. (2020, January 7\u201314). NASA-ISRO Synthetic Aperture Radar (NISAR) Mission. Proceedings of the 2020 IEEE Aerospace Conference, Big Sky, MT, USA.","DOI":"10.1109\/AERO47225.2020.9172638"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Antropov, O., Praks, J., Kauppinen, M., Laurila, P., Ignatenko, V., and Modrzewski, R. (June, January 28). Assessment of Operational Microsatellite Based SAR for Earth Observation Applications. Proceedings of the 2018 2nd URSI Atlantic Radio Science Meeting, AT-RASC 2018, Meloneras, Spain.","DOI":"10.23919\/URSI-AT-RASC.2018.8471324"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Ignatenko, V., Laurila, P., Radius, A., Lamentowski, L., Antropov, O., and Muff, D. (October, January 26). ICEYE Microsatellite SAR Constellation Status Update: Evaluation of first commercial imaging modes. Proceedings of the 2020 IEEE International Geoscience and Remote Sensing Symposium.","DOI":"10.1109\/IGARSS39084.2020.9324531"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1109\/MGRS.2015.2437353","article-title":"Tandem-L: A Highly Innovative Bistatic SAR Mission for Global Observation of Dynamic Processes on the Earth\u2019s Surface","volume":"3","author":"Moreira","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Torres, R., and Davidson, M. (August, January 28). Overview of Copernicus SAR Space Component and its Evolution. Proceedings of the IGARSS 2019\u20142019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan.","DOI":"10.1109\/IGARSS.2019.8899134"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2419","DOI":"10.1080\/014311698214811","article-title":"The sensitivity of SAR backscatter to forest windthrow gaps","volume":"19","author":"Green","year":"1998","journal-title":"Int. J. Remote Sens."},{"key":"ref_19","first-page":"28","article-title":"Mapping forest damage caused by the 1999 lothar storm in Jura (France), using SAR interferometry","volume":"12","author":"Dwyer","year":"2000","journal-title":"Earth Obs. Q."},{"key":"ref_20","unstructured":"Ulander, L.M.H., Smith, G., Eriksson, L., Folkesson, K., Fransson, J.E.S., Gustavsson, A., Hallberg, B., Joyce, S., Magnusson, M., and Olsson, H. (2005, January 29). Mapping of wind-thrown forests in Southern Sweden using space- and airborne SAR. Proceedings of the 2005 IEEE International Geoscience and Remote Sensing Symposium, 2005, IGARSS\u201905, Seoul, Korea."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Thiele, A., Boldt, M., and Hinz, S. (2012, January 22\u201327). Automated detection of storm damage in forest areas by analyzing TerraSAR-X data. Proceedings of the 2012 IEEE International Geoscience and Remote Sensing Symposium, Munich, Germany.","DOI":"10.1109\/IGARSS.2012.6351205"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"700","DOI":"10.1016\/j.rse.2018.03.009","article-title":"Detection of windthrows and insect outbreaks by L-band SAR: A case study in the Bavarian Forest National Park","volume":"209","author":"Tanase","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Tomppo, E., Antropov, O., and Praks, J. (2019). Boreal Forest Snow Damage Mapping Using Multi-Temporal Sentinel-1 Data. Remote Sens., 11.","DOI":"10.3390\/rs11040384"},{"key":"ref_24","unstructured":"Rauste, Y., Antropov, O., Hame, T., Ramminger, G., Gomez, S., and Seifert, F.M. (2013, January 9\u201313). Mapping Selective Logging in Tropical Forest with Space-Borne SAR Data. Proceedings of the ESA Living Planet Symposium, Edinburgh, UK."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Antropov, O., Rauste, Y., Seifert, F.M., and H\u00e4me, T. (2015, January 26\u201331). Selective logging of tropical forests observed using L- and C-band SAR satellite data. Proceedings of the 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Milan, Italy.","DOI":"10.1109\/IGARSS.2015.7326669"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Hoekman, D., Kooij, B., Qui\u00f1ones, M., Vellekoop, S., Carolita, I., Budhiman, S., Arief, R., and Roswintiarti, O. (2020). Wide-Area Near-Real-Time Monitoring of Tropical Forest Degradation and Deforestation Using Sentinel-1. Remote Sens., 12.","DOI":"10.3390\/rs12193263"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Hethcoat, M., Carreiras, J., Edwards, D., Bryant, R., and Quegan, S. (2020). Detecting tropical selective logging with SAR data requires a time series approach. bioRxiv.","DOI":"10.1101\/2020.03.31.018606"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Antropov, O., Rauste, Y., Praks, J., Seifert, F., and H\u00e4me, T. (2020). Mapping forest disturbance due to selective logging in the Congo Basin with RADARSAT-2 time series, submitted.","DOI":"10.3390\/rs13040740"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Fransson, J.E.S., Magnusson, M., Olsson, H., Eriksson, L.E.B., Sandberg, G., Smith-Jonforsen, G., and Ulander, L.M.H. (2007, January 23\u201328). Detection of forest changes using ALOS PALSAR satellite images. Proceedings of the 2007 IEEE International Geoscience and Remote Sensing Symposium, Barcelona, Spain.","DOI":"10.1109\/IGARSS.2007.4423308"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"618","DOI":"10.1109\/JSTARS.2010.2048201","article-title":"Clear-Cut Detection in Swedish Boreal Forest Using Multi-Temporal ALOS PALSAR Backscatter Data","volume":"3","author":"Santoro","year":"2010","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_31","unstructured":"Rauste, Y., Antropov, O., Mutanen, T., and H\u00e4me, T. (2016, January 9\u201313). On Clear-Cut Mapping with Time-Series of Sentinel-1 Data in Boreal Forest. Proceedings of the Living Planet Symposium 2016, Prague, Czech Republic."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Olesk, A., Voormansik, K., P\u00f5hjala, M., and Noorma, M. (2015, January 1\u20134). Forest change detection from Sentinel-1 and ALOS-2 satellite images. Proceedings of the 2015 IEEE 5th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR), Singapore.","DOI":"10.1109\/APSAR.2015.7306263"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Tanase, M., Ismail, I., Lowell, K., Karyanto, O., and Santoro, M. (2015). Detecting and quantifying forest change: The potential of existing C- and X-band radar datasets. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0131079"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1016\/j.rse.2013.08.050","article-title":"Change detection of boreal forest using bi-temporal ALOS PALSAR backscatter data","volume":"155","author":"Pantze","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_35","unstructured":"Finnish Meterological Institute (2021, January 05). Windspeed Observations. Available online: https:\/\/en.ilmatieteenlaitos.fi\/."},{"key":"ref_36","unstructured":"Tomppo, E., Haakana, M., Katila, M., and Per\u00e4saari, J. (2008). Multi-Source National Forest Inventory\u2014Methods and Applications. Managing Forest Ecosystems, Springer."},{"key":"ref_37","unstructured":"M\u00e4kisara, K., Katila, M., and Per\u00e4saari, J. (2019). The Multi-Source National Forest Inventory of Finland\u2014Methods and Results 2017, Publications of the Natural Resources Institute Finland."},{"key":"ref_38","unstructured":"LUKE Natural Resources Institute Finland (2019). MS-NFI Products from Year 2017, LUKE Natural Resources Institute Finland. Available online: https:\/\/kartta.luke.fi\/index-en.html."},{"key":"ref_39","unstructured":"Land Survey Finland (2018). Elevation Model 10 m. Maps and Spatial Data, Land Survey Finland. Available online: https:\/\/www.maanmittauslaitos.fi\/en\/maps-and-spatial-data\/expert-users\/product-descriptions\/elevation-model-10-m."},{"key":"ref_40","unstructured":"European Space Agency (ESA) (2020). SNAP-ESA Sentinel Application Platform v7.0.0, European Space Agency (ESA)."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"GDAL\/OGR Contributors (2020, December 01). GDAL\/OGR Geospatial Data Abstraction Software Library. Open Source Geospatial Foundation. Available online: https:\/\/gdal.org\/.","DOI":"10.22224\/gistbok\/2020.4.1"},{"key":"ref_42","unstructured":"R Core Team (2020). R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing."},{"key":"ref_43","unstructured":"(2020, December 01). GNU Fortran Project. GNU Compiler Collection, Available online: https:\/\/gcc.gnu.org\/."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1109\/TPAMI.1980.4766999","article-title":"Image Segmentation with Directed Trees","volume":"PAMI-2","author":"Narendra","year":"1980","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_45","first-page":"52","article-title":"Segmentation of Spot and Landsat satellite imagery","volume":"13","author":"Parmes","year":"1992","journal-title":"Photogramm. J. Finl."},{"key":"ref_46","first-page":"253","article-title":"An application of a segmentation method to the forest stand delineation and estimation of stand variates from satellite images","volume":"Volume 1","author":"Tomppo","year":"1987","journal-title":"Image Analyses, Proceedings of the 5th Scandinavian Conference on Image Analysis, Stockholm, Sweden, June 1987"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2004.04.003","article-title":"Using coarse scale forest variables as ancillary information and weighting of variables in k-NN estimation: A genetic algorithm approach","volume":"92","author":"Tomppo","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"500","DOI":"10.1016\/j.rse.2008.05.021","article-title":"Predicting categorical forest variables using an improved k-Nearest Neighbour estimator and Landsat imagery","volume":"113","author":"Tomppo","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"282","DOI":"10.1016\/j.rse.2016.02.001","article-title":"A meta-analysis and review of the literature on the k-Nearest Neighbors technique for forestry applications that use remotely sensed data","volume":"176","author":"Chirici","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1982","DOI":"10.1016\/j.rse.2007.03.032","article-title":"Combining national forest inventory field plots and remote sensing data for forest databases","volume":"112","author":"Tomppo","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"725","DOI":"10.1080\/01431160110040323","article-title":"An assessment of support vector machines for land cover classification","volume":"23","author":"Huang","year":"2002","journal-title":"Int. J. Remote Sens."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1822","DOI":"10.1109\/TGRS.2008.916201","article-title":"Kernel-Based Framework for Multitemporal and Multisource Remote Sensing Data Classification and Change Detection","volume":"46","year":"2008","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1007","DOI":"10.1080\/01431160512331314083","article-title":"Support vector machines for classification in remote sensing","volume":"26","author":"Pal","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1109\/JSTARS.2013.2262926","article-title":"A Kernel-Based Feature Selection Method for SVM With RBF Kernel for Hyperspectral Image Classification","volume":"7","author":"Kuo","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/BF00994018","article-title":"Support-Vector Networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Vapnik, V.N. (1995). The Nature of Statistical Learning Theory, Springer.","DOI":"10.1007\/978-1-4757-2440-0"},{"key":"ref_57","unstructured":"Cochran, W.G. (1977). Sampling Techniques, Wiley. [3rd ed.]."},{"key":"ref_58","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_59","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1109\/36.739154","article-title":"Retrieval of biomass in boreal forests from multitemporal ERS-1 and JERS-1 SAR images","volume":"37","author":"Kurvonen","year":"1999","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Ge, S., Tomppo, E., Rauste, Y., Praks, J., McRoberts, R., Gu, H., Su, W., and Antropov, O. (2020). Hypertemporal Sentinel-1 data in boreal forest growing stock prediction, submitted.","DOI":"10.1101\/2021.09.02.458789"},{"key":"ref_61","first-page":"49","article-title":"Seasonal Variations of Vegetation Cover Scattering Properties","volume":"11","author":"Liudmila","year":"2019","journal-title":"Radiolocation"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"535","DOI":"10.1109\/JSTARS.2019.2958847","article-title":"Sentinel-1 InSAR Coherence for Land Cover Mapping: A Comparison of Multiple Feature-Based Classifiers","volume":"13","author":"Jacob","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"6404","DOI":"10.1109\/TGRS.2013.2296533","article-title":"TanDEM-X Pol-InSAR Performance for Forest Height Estimation","volume":"52","author":"Kugler","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Olesk, A., Praks, J., Antropov, O., Zalite, K., Arum\u00e4e, T., and Voormansik, K. (2016). Interferometric SAR Coherence Models for Characterization of Hemiboreal Forests Using TanDEM-X Data. Remote Sens., 8.","DOI":"10.3390\/rs8090700"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/3\/383\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:14:14Z","timestamp":1760159654000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/3\/383"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,22]]},"references-count":64,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2021,2]]}},"alternative-id":["rs13030383"],"URL":"https:\/\/doi.org\/10.3390\/rs13030383","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1,22]]}}}