{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T00:59:52Z","timestamp":1776387592997,"version":"3.51.2"},"reference-count":107,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2021,2,22]],"date-time":"2021-02-22T00:00:00Z","timestamp":1613952000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004955","name":"\u00d6sterreichische Forschungsf\u00f6rderungsgesellschaft","doi-asserted-by":"publisher","award":["873674"],"award-info":[{"award-number":["873674"]}],"id":[{"id":"10.13039\/501100004955","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012650","name":"Technische Universit\u00e4t Wien Bibliothek","doi-asserted-by":"publisher","award":["-"],"award-info":[{"award-number":["-"]}],"id":[{"id":"10.13039\/501100012650","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>With the increasing occurrence of forest fires in the mid-latitudes and the alpine region, fire risk assessments become important in these regions. Fuel assessments involve the collection of information on forest structure as, e.g., the stand height or the stand density. The potential of airborne laser scanning (ALS) to provide accurate forest structure information has been demonstrated in several studies. Yet, flight acquisitions at the state level are carried out in intervals of typically five to ten years in Central Europe, which often makes the information outdated. The Sentinel-1 (S-1) synthetic aperture radar mission provides freely accessible earth observation (EO) data with short revisit times of 6 days. Forest structure information derived from this data source could, therefore, be used to update the respective ALS descriptors. In our study, we investigated the potential of S-1 time series to derive stand height and fractional cover, which is a measure of the stand density, over a temperate deciduous forest in Austria. A random forest (RF) model was used for this task, which was trained using ALS-derived forest structure parameters from 2018. The comparison of the estimated mean stand height from S-1 time series with the ALS derived stand height shows a root mean square error (RMSE) of 4.76 m and a bias of 0.09 m on a 100 m cell size, while fractional cover can be retrieved with an RMSE of 0.08 and a bias of 0.0. However, the predictions reveal a tendency to underestimate stand height and fractional cover for high-growing stands and dense areas, respectively. The stratified selection of the training set, which we investigated in order to achieve a more homogeneous distribution of the metrics for training, mitigates the underestimation tendency to some degree, yet, cannot fully eliminate it. We subsequently applied the trained model to S-1 time series of 2017 and 2019, respectively. The computed difference between the predictions suggests that large decreases in the forest height structure in this two-year interval become apparent from our RF-model, while inter-annual forest growth cannot be measured. The spatial patterns of the predicted forest height, however, are similar for both years (Pearson\u2019s R = 0.89). Therefore, we consider that S-1 time series in combination with machine learning techniques can be applied for the derivation of forest structure information in an operational way.<\/jats:p>","DOI":"10.3390\/rs13040798","type":"journal-article","created":{"date-parts":[[2021,2,22]],"date-time":"2021-02-22T20:42:51Z","timestamp":1614026571000},"page":"798","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["Potential of Sentinel-1 C-Band Time Series to Derive Structural Parameters of Temperate Deciduous Forests"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2138-8038","authenticated-orcid":false,"given":"Moritz","family":"Bruggisser","sequence":"first","affiliation":[{"name":"Department of Geodesy and Geoinformation, TU Wien, 1040 Vienna, Austria"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8054-7572","authenticated-orcid":false,"given":"Wouter","family":"Dorigo","sequence":"additional","affiliation":[{"name":"Department of Geodesy and Geoinformation, TU Wien, 1040 Vienna, Austria"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4738-211X","authenticated-orcid":false,"given":"Alena","family":"Dost\u00e1lov\u00e1","sequence":"additional","affiliation":[{"name":"Department of Geodesy and Geoinformation, TU Wien, 1040 Vienna, Austria"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6063-7239","authenticated-orcid":false,"given":"Markus","family":"Hollaus","sequence":"additional","affiliation":[{"name":"Department of Geodesy and Geoinformation, TU Wien, 1040 Vienna, Austria"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5701-9509","authenticated-orcid":false,"given":"Claudio","family":"Navacchi","sequence":"additional","affiliation":[{"name":"Department of Geodesy and Geoinformation, TU Wien, 1040 Vienna, Austria"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4742-8648","authenticated-orcid":false,"given":"Stefan","family":"Schlaffer","sequence":"additional","affiliation":[{"name":"Department of Geodesy and Geoinformation, TU Wien, 1040 Vienna, Austria"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2348-7929","authenticated-orcid":false,"given":"Norbert","family":"Pfeifer","sequence":"additional","affiliation":[{"name":"Department of Geodesy and Geoinformation, TU Wien, 1040 Vienna, Austria"}]}],"member":"1968","published-online":{"date-parts":[[2021,2,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"619","DOI":"10.1111\/geb.12440","article-title":"A new global burned area product for climate assessment of fire impacts","volume":"25","author":"Chuvieco","year":"2016","journal-title":"Glob. Ecol. Biogeogr."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1002\/jgrg.20042","article-title":"Analysis of daily, monthly, and annual burned area using the fourth-generation global fire emissions database (GFED4)","volume":"118","author":"Giglio","year":"2013","journal-title":"J. Geophys. Res. Biogeosci."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"FFR2-1","DOI":"10.1029\/2001JD000461","article-title":"Emissions of carbon dioxide, carbon monoxide, and methane from boreal forest fires in 1998","volume":"107","author":"Kasischke","year":"2002","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"11707","DOI":"10.5194\/acp-10-11707-2010","article-title":"Global fire emissions and the contribution of deforestation, savanna, forest, agricultural, and peat fires (1997\u20132009)","volume":"10","author":"Randerson","year":"2010","journal-title":"Atmos. Chem. Phys."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"566","DOI":"10.1134\/S1024856015060184","article-title":"Forest fires in Siberia and the Far East: Emissions and atmospheric transport of black carbon to the Arctic","volume":"28","author":"Vinogradova","year":"2015","journal-title":"Atmos. Ocean. Opt."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"21-1","DOI":"10.1029\/2001GB001466","article-title":"Interannual growth rate variations of atmospheric CO2 and its \u03b413C, H2, CH4, and CO between 1992 and 1999 linked to biomass burning","volume":"16","author":"Langenfelds","year":"2002","journal-title":"Glob. Biogeochem. Cycles"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Simpson, I.J., Rowland, F.S., Meinardi, S., and Blake, D.R. (2006). Influence of biomass burning during recent fluctuations in the slow growth of global tropospheric methane. Geophys. Res. Lett., 33.","DOI":"10.1029\/2006GL027330"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"11770","DOI":"10.1073\/pnas.1607171113","article-title":"Impact of anthropogenic climate change on wildfire across western US forests","volume":"113","author":"Abatzoglou","year":"2016","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"101151","DOI":"10.1016\/j.ecoinf.2020.101151","article-title":"Towards an integrated forest fire danger assessment system for the European Alps","volume":"60","author":"Vacik","year":"2020","journal-title":"Ecol. Inform."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.agrformet.2012.08.011","article-title":"Large-scale weather types, forest fire danger, and wildfire occurrence in the Alps","volume":"168","author":"Wastl","year":"2013","journal-title":"Agric. For. Meteorol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"903","DOI":"10.3390\/f6040903","article-title":"Anomalies of the Austrian forest fire regime in comparison with other Alpine countries: A research note","volume":"6","author":"Vacik","year":"2015","journal-title":"Forests"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"393","DOI":"10.1007\/s00704-013-0839-7","article-title":"Selecting the best performing fire weather indices for Austrian ecoregions","volume":"114","author":"Arpaci","year":"2013","journal-title":"Theor. Appl. Climatol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/ncomms8537","article-title":"Climate-induced variations in global wildfire danger from 1979 to 2013","volume":"6","author":"Jolly","year":"2015","journal-title":"Nat. Commun."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"258","DOI":"10.1016\/j.apgeog.2014.05.015","article-title":"Using multi variate data mining techniques for estimating fire susceptibility of Tyrolean forests","volume":"53","author":"Arpaci","year":"2014","journal-title":"Appl. Geogr."},{"key":"ref_15","first-page":"1","article-title":"Characterisation of forest fires in Austria","volume":"128","author":"Vacik","year":"2011","journal-title":"Austrian J. For. Sci."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1007\/s00704-012-0653-7","article-title":"Analysis of lightning-induced forest fires in Austria","volume":"111","author":"Vacik","year":"2013","journal-title":"Theor. Appl. Climatol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.ecolmodel.2008.11.017","article-title":"Development of a framework for fire risk assessment using remote sensing and geographic information system technologies","volume":"221","author":"Chuvieco","year":"2010","journal-title":"Ecol. Model."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Chuvieco, E., Allg\u00f6wer, B., and Salas, J. (2003). Integration of physical and human factors in fire danger assessment. Wildland Fire Danger Estimation and Mapping: The Role of Remote Sensing Data, World Scientific.","DOI":"10.1142\/9789812791177"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1369","DOI":"10.1016\/j.rse.2011.01.017","article-title":"Multispectral and LiDAR data fusion for fuel type mapping using Support Vector Machine and decision rules","volume":"115","author":"Chuvieco","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Domingo, D., de la Riva, J., Lamelas, M.T., Garc\u00eda-Mart\u00edn, A., Ibarra, P., Echeverr\u00eda, M., and Hoffr\u00e9n, R. (2020). Fuel Type Classification Using Airborne Laser Scanning and Sentinel 2 Data in Mediterranean Forest Affected by Wildfires. Remote Sens., 12.","DOI":"10.3390\/rs12213660"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1016\/S0034-4257(03)00140-8","article-title":"Identifying species of individual trees using airborne laser scanner","volume":"90","author":"Holmgren","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_22","unstructured":"Hollaus, M., M\u00fccke, W., H\u00f6fle, B., Dorigo, W., Pfeifer, N., Wagner, W., Bauerhansl, C., and Regner, B. (2009, January 14\u201316). Tree species classification based on full-waveform airborne laser scanning data. Proceedings of the SilviLaser, College Station, TX, USA."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"319","DOI":"10.14214\/sf.156","article-title":"Tree species classification using airborne LiDAR\u2013effects of stand and tree parameters, downsizing of training set, intensity normalization, and sensor type","volume":"44","author":"Korpela","year":"2010","journal-title":"Silva Fenn."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1016\/j.isprsjprs.2008.12.004","article-title":"Mapping the understorey of deciduous woodland from leaf-on and leaf-off airborne LiDAR data: A case study in lowland Britain","volume":"64","author":"Hill","year":"2009","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1403","DOI":"10.1016\/j.rse.2010.01.023","article-title":"Discrimination of vegetation strata in a multi-layered Mediterranean forest ecosystem using height and intensity information derived from airborne laser scanning","volume":"114","author":"Morsdorf","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"4146","DOI":"10.3390\/f6114146","article-title":"Towards automated characterization of canopy layering in mixed temperate forests using airborne laser scanning","volume":"6","author":"Leiterer","year":"2015","journal-title":"Forests"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"400","DOI":"10.1016\/j.rse.2018.08.033","article-title":"Characterizing understory vegetation in Mediterranean forests using full-waveform airborne laser scanning data","volume":"217","author":"Tompalski","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1080\/02827580600700353","article-title":"A comparative study of the use of laser scanner data and field measurements in the prediction of crown height in boreal forests","volume":"21","author":"Maltamo","year":"2006","journal-title":"Scand. J. For. Res."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1213","DOI":"10.1080\/01431160903380615","article-title":"Estimating crown base height for Scots pine by means of the 3D geometry of airborne laser scanning data","volume":"31","author":"Vauhkonen","year":"2010","journal-title":"Int. J. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"767","DOI":"10.1016\/j.rse.2007.06.011","article-title":"A voxel-based lidar method for estimating crown base height for deciduous and pine trees","volume":"112","author":"Popescu","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1016\/j.rse.2004.05.013","article-title":"LIDAR-based geometric reconstruction of boreal type forest stands at single tree level for forest and wildland fire management","volume":"92","author":"Morsdorf","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_32","first-page":"138","article-title":"3D Point clouds for forestry applications","volume":"103","author":"Hollaus","year":"2015","journal-title":"\u00d6sterreichische Z. Vermess. Geoinf. VGI"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"111520","DOI":"10.1016\/j.rse.2019.111520","article-title":"Modelling canopy gap probability, foliage projective cover and crown projective cover from airborne lidar metrics in Australian forests and woodlands","volume":"237","author":"Fisher","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1396","DOI":"10.1109\/36.718844","article-title":"Toward consistent regional-to-global-scale vegetation characterization using orbital SAR systems","volume":"36","author":"Kellndorfer","year":"1998","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"7738","DOI":"10.1080\/01431161.2018.1479788","article-title":"Annual seasonality in Sentinel-1 signal for forest mapping and forest type classification","volume":"39","author":"Wagner","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"R\u00fcetschi, M., Schaepman, M.E., and Small, D. (2018). Using multitemporal sentinel-1 c-band backscatter to monitor phenology and classify deciduous and coniferous forests in northern switzerland. Remote Sens., 10.","DOI":"10.3390\/rs10010055"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"252","DOI":"10.3390\/f6010252","article-title":"Combining lidar and synthetic aperture radar data to estimate forest biomass: Status and prospects","volume":"6","author":"Kaasalainen","year":"2015","journal-title":"Forests"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"339","DOI":"10.1016\/j.rse.2004.07.017","article-title":"Vegetation height estimation from shuttle radar topography mission and national elevation datasets","volume":"93","author":"Kellndorfer","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"3831","DOI":"10.1109\/TGRS.2012.2185803","article-title":"LIDAR-aided SAR interferometry studies in boreal forest: Scattering phase center and extinction coefficient at X-and L-band","volume":"50","author":"Praks","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"770","DOI":"10.1109\/TGRS.2018.2860590","article-title":"Generation of Large-Scale Moderate-Resolution Forest Height Mosaic With Spaceborne Repeat-Pass SAR Interferometry and Lidar","volume":"57","author":"Lei","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Siqueira, P., Hensley, S., Chapman, B., and Ahmed, R. (2008, January 7\u201311). Combining lidar and InSAR observations over the harvard and duke forests for making wide area maps of vegetation height. Proceedings of the IGARSS 2008\u20142008 IEEE International Geoscience and Remote Sensing Symposium, Boston, MA, USA.","DOI":"10.1109\/IGARSS.2008.4780148"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"68","DOI":"10.5589\/m10-025","article-title":"Deriving forest monitoring variables from X-band InSAR SRTM height","volume":"36","author":"Solberg","year":"2010","journal-title":"Can. J. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"646","DOI":"10.1109\/LGRS.2014.2354551","article-title":"Estimation of forest height and canopy density from a single InSAR correlation coefficient","volume":"12","author":"Soja","year":"2014","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"15933","DOI":"10.3390\/rs71215809","article-title":"Comparison of laser and stereo optical, SAR and InSAR point clouds from air-and space-borne sources in the retrieval of forest inventory attributes","volume":"7","author":"Yu","year":"2015","journal-title":"Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"388","DOI":"10.1016\/j.rse.2005.09.020","article-title":"Integration of radar and Landsat-derived foliage projected cover for woody regrowth mapping, Queensland, Australia","volume":"100","author":"Lucas","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Morin, D., Planells, M., Guyon, D., Villard, L., Mermoz, S., Bouvet, A., Thevenon, H., Dejoux, J.F., Le Toan, T., and Dedieu, G. (2019). Estimation and mapping of forest structure parameters from open access satellite images: Development of a generic method with a study case on coniferous plantation. Remote Sens., 11.","DOI":"10.3390\/rs11111275"},{"key":"ref_47","first-page":"102163","article-title":"High-resolution mapping of forest canopy height using machine learning by coupling ICESat-2 LiDAR with Sentinel-1, Sentinel-2 and Landsat-8 data","volume":"92","author":"Li","year":"2020","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1016\/j.rse.2015.06.013","article-title":"Assessment of the mapping of fractional woody cover in southern African savannas using multi-temporal and polarimetric ALOS PALSAR L-band images","volume":"166","author":"Urbazaev","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Dost\u00e1lov\u00e1, A., Lang, M., Ivanovs, J., Waser, L.T., and Wagner, W. (2021). European Wide Forest Classification Based on Sentinel-1 Data. Remote Sens., 13.","DOI":"10.3390\/rs13030337"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"6","DOI":"10.4102\/koedoe.v62i1.1621","article-title":"Woody cover mapping in the savanna ecosystem of the Kruger National Park using Sentinel-1 C-Band time series data","volume":"62","author":"Urban","year":"2020","journal-title":"Koedoe"},{"key":"ref_51","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_52","doi-asserted-by":"crossref","first-page":"338","DOI":"10.1016\/j.rse.2011.10.008","article-title":"Prediction of plot-level forest variables using TerraSAR-X stereo SAR data","volume":"117","author":"Karjalainen","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Solimini, D. (2016). Understanding Earth Observation, Springer International Publishing.","DOI":"10.1007\/978-3-319-25633-7"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"725","DOI":"10.1016\/j.rse.2009.11.002","article-title":"Fusion of LiDAR and imagery for estimating forest canopy fuels","volume":"114","author":"Erdody","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"224","DOI":"10.1071\/WF13086","article-title":"Estimation of forest structure and canopy fuel parameters from small-footprint full-waveform LiDAR data","volume":"23","author":"Hermosilla","year":"2014","journal-title":"Int. J. Wildland Fire"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1093\/forestry\/72.1.59","article-title":"Assessing forest canopies and understorey illumination: Canopy closure, canopy cover and other measures","volume":"72","author":"Jennings","year":"1999","journal-title":"Forestry"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1065","DOI":"10.1016\/j.rse.2010.12.011","article-title":"Airborne discrete-return LIDAR data in the estimation of vertical canopy cover, angular canopy closure and leaf area index","volume":"115","author":"Korhonen","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.foreco.2007.09.045","article-title":"Bark beetles, fuels, fires and implications for forest management in the Intermountain West","volume":"254","author":"Jenkins","year":"2008","journal-title":"For. Ecol. Manag."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1016\/j.agrformet.2004.02.005","article-title":"Estimation of leaf area index and covered ground from airborne laser scanner (Lidar) in two contrasting forests","volume":"124","author":"Valladares","year":"2004","journal-title":"Agric. For. Meteorol."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.rse.2006.04.019","article-title":"Estimation of LAI and fractional cover from small footprint airborne laser scanning data based on gap fraction","volume":"104","author":"Morsdorf","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"364","DOI":"10.1016\/j.rse.2006.03.001","article-title":"Mapping defoliation during a severe insect attack on Scots pine using airborne laser scanning","volume":"102","author":"Solberg","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_62","unstructured":"Holmgren, J., Johansson, F., Olofsson, K., Olsson, H., and Glimsk\u00e4r, A. (2008, January 17\u201319). Estimation of crown coverage using airborne laser scanning. Proceedings of the SilviLaser 2008, Scotland, UK."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"5574","DOI":"10.3390\/rs5115574","article-title":"Model-based biomass estimation of a hemi-boreal forest from multitemporal TanDEM-X acquisitions","volume":"5","author":"Askne","year":"2013","journal-title":"Remote Sens."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.rse.2013.07.036","article-title":"Monitoring spruce volume and biomass with InSAR data from TanDEM-X","volume":"139","author":"Solberg","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_65","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_66","doi-asserted-by":"crossref","first-page":"3608","DOI":"10.1109\/JSTARS.2015.2431646","article-title":"TanDEM-X Pol-InSAR inversion for mangrove canopy height estimation","volume":"8","author":"Lee","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Richards, J.A. (2009). Remote Sensing with Imaging Radar, Springer. Signals and Communication Technology.","DOI":"10.1007\/978-3-642-02020-9"},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Immitzer, M., Neuwirth, M., B\u00f6ck, S., Brenner, H., Vuolo, F., and Atzberger, C. (2019). Optimal input features for tree species classification in Central Europe based on multi-temporal Sentinel-2 data. Remote Sens., 11.","DOI":"10.3390\/rs11222599"},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Lawrence, M., McRoberts, R.E., Tomppo, E., Gschwantner, T., and Gabler, K. (2010). Comparisons of National Forest Inventories. National Forest Inventories, Springer.","DOI":"10.1007\/978-90-481-3233-1_2"},{"key":"ref_70","unstructured":"RIEGL (2019). VQ-1560i Datasheet, RIEGL Laser Measurement Systems GmbH."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1016\/j.cageo.2014.07.005","article-title":"Optimisation of global grids for high-resolution remote sensing data","volume":"72","author":"Sabel","year":"2014","journal-title":"Comput. Geosci."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1016\/j.compenvurbsys.2013.11.002","article-title":"OPALS\u2013A framework for Airborne Laser Scanning data analysis","volume":"45","author":"Pfeifer","year":"2014","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_73","unstructured":"Hollaus, M., Mandlburger, G., Pfeifer, N., and M\u00fccke, W. (2010, January 1\u20133). Land cover dependent derivation of digital surface models from airborne laser scanning data. Proceedings of the ISPRS Commission III Symposium PCV 2010, IAPRS Volume XXXVIII Part 3A, Saint-Mand\u00e9, France."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"403","DOI":"10.1109\/36.134089","article-title":"Relating forest biomass to SAR data","volume":"30","author":"Beaudoin","year":"1992","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"388","DOI":"10.1109\/36.295053","article-title":"Mapping biomass of a northern forest using multifrequency SAR data","volume":"32","author":"Ranson","year":"1994","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Vreugdenhil, M., Wagner, W., Bauer-Marschallinger, B., Pfeil, I., Teubner, I., R\u00fcdiger, C., and Strauss, P. (2018). Sensitivity of Sentinel-1 backscatter to vegetation dynamics: An Austrian case study. Remote Sens., 10.","DOI":"10.3390\/rs10091396"},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1016\/j.rse.2006.08.004","article-title":"Properties of ERS-1\/2 coherence in the Siberian boreal forest and implications for stem volume retrieval","volume":"106","author":"Santoro","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1016\/j.rse.2014.12.012","article-title":"Tandem-X interferometry in the prediction of forest inventory attributes in managed boreal forests","volume":"159","author":"Karila","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"306","DOI":"10.1080\/02827581.2016.1220618","article-title":"Comparison between TanDEM-X-and ALS-based estimation of aboveground biomass and tree height in boreal forests","volume":"32","author":"Persson","year":"2017","journal-title":"Scand. J. For. Res."},{"key":"ref_80","unstructured":"Dost\u00e1lov\u00e1, A., Milenkovic, M., Hollaus, M., and Wagner, W. (2016, January 9\u201313). Influence of Forest Structure on the Sentinel-1 Backscatter Variation-Analysis with Full-Waveform LiDAR Data. Proceedings of the Living Planet Symposium, Prague, Czech Republic."},{"key":"ref_81","unstructured":"Small, D., Miranda, N., Ewen, T., and Jonas, T. (2013, January 9\u201313). Reliably flattened radar backscatter for wet snow mapping from wide-swath sensors. Proceedings of the ESA Living Planet Symposium, Edinburgh, UK."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"848","DOI":"10.1109\/JSTARS.2012.2186791","article-title":"Improving PolSAR land cover classification with radiometric correction of the coherency matrix","volume":"5","author":"Atwood","year":"2012","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"3081","DOI":"10.1109\/TGRS.2011.2120616","article-title":"Flattening gamma: Radiometric terrain correction for SAR imagery","volume":"49","author":"Small","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_84","first-page":"227","article-title":"Forest area derivation from sentinel-1 data","volume":"3","author":"Hollaus","year":"2016","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_85","unstructured":"ACube (2021, February 01). Austrian Data Cube. An EODC Service for the Austrian Earth Observation User Community. Available online: https:\/\/acube.eodc.eu\/."},{"key":"ref_86","unstructured":"Naeimi, V., Elefante, S., Cao, S., Wagner, W., Dostalova, A., and Bauer-Marschallinger, B. (2016, January 3\u20136). Geophysical parameters retrieval from Sentinel-1 SAR data: A case study for high performance computing at EODC. Proceedings of the 24th High Performance Computing Symposium, Pasadena, CA, USA."},{"key":"ref_87","unstructured":"SNAP (2021, February 01). Sentinel Application Platform. Available online: https:\/\/step.esa.int\/main\/toolboxes\/snap\/."},{"key":"ref_88","unstructured":"Geoland (2021, February 01). Digitales Gel\u00e4ndemodell (DGM) \u00d6sterreich. Available online: https:\/\/www.data.gv.at\/katalog\/dataset\/dgm."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"601","DOI":"10.1109\/36.581975","article-title":"The Use of ERS-1 SAR Data in Snow Melt Monitoring","volume":"35","author":"Koskinen","year":"1997","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_90","unstructured":"R Core Team (2019). R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing."},{"key":"ref_91","unstructured":"Hijmans, R.J. (2021, February 01). Raster: Geographic Data Analysis and Modeling. R package version 3.3-13. Available online: https:\/\/CRAN.R-project.org\/package=raste."},{"key":"ref_92","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_93","doi-asserted-by":"crossref","first-page":"6668","DOI":"10.1080\/01431161.2012.693969","article-title":"Evaluation of most similar neighbour and random forest methods for imputing forest inventory variables using data from target and auxiliary stands","volume":"33","author":"Latifi","year":"2012","journal-title":"Int. J. Remote Sens."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v077.i01","article-title":"ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R","volume":"77","author":"Wright","year":"2017","journal-title":"J. Stat. Softw."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"407","DOI":"10.1016\/j.rse.2005.10.019","article-title":"Empirical relationships between AIRSAR backscatter and LiDAR-derived forest biomass, Queensland, Australia","volume":"100","author":"Lucas","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"466","DOI":"10.1016\/j.rse.2012.05.029","article-title":"Mapping forest aboveground biomass in the Northeastern United States with ALOS PALSAR dual-polarization L-band","volume":"124","author":"Cartus","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.rse.2014.01.029","article-title":"Biomass assessment in the Cameroon savanna using ALOS PALSAR data","volume":"155","author":"Mermoz","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1016\/j.rse.2016.10.018","article-title":"Combining Tandem-X InSAR and simulated GEDI lidar observations for forest structure mapping","volume":"187","author":"Qi","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1109\/TGRS.1995.8746014","article-title":"Repeat-pass SAR interferometry over forested terrain","volume":"33","author":"Hagberg","year":"1995","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1109\/36.551931","article-title":"C-band repeat-pass interferometric SAR observations of the forest","volume":"35","author":"Askne","year":"1997","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1109\/36.551930","article-title":"Retrieval of vegetation parameters with SAR interferometry","volume":"35","author":"Werner","year":"1997","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_102","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_103","doi-asserted-by":"crossref","first-page":"644","DOI":"10.1111\/gcb.13841","article-title":"Spatial evaluation of Indonesia\u2019s 2015 fire-affected area and estimated carbon emissions using Sentinel-1","volume":"24","author":"Lohberger","year":"2018","journal-title":"Glob. Chang. Biol."},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1016\/j.rse.2017.10.034","article-title":"Improving near-real time deforestation monitoring in tropical dry forests by combining dense Sentinel-1 time series with Landsat and ALOS-2 PALSAR-2","volume":"204","author":"Reiche","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_105","doi-asserted-by":"crossref","unstructured":"Bouvet, A., Mermoz, S., Ball\u00e8re, M., Koleck, T., and Le Toan, T. (2018). Use of the SAR shadowing effect for deforestation detection with Sentinel-1 time series. Remote Sens., 10.","DOI":"10.3390\/rs10081250"},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"1119","DOI":"10.1080\/01431169008955084","article-title":"The effect of changing environmental conditions on microwave signatures of forest ecosystems: Preliminary results of the March 1988 Alaskan aircraft SAR experiment","volume":"11","author":"Way","year":"1990","journal-title":"Int. J. Remote Sens."},{"key":"ref_107","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"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/4\/798\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:27:39Z","timestamp":1760160459000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/4\/798"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,2,22]]},"references-count":107,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2021,2]]}},"alternative-id":["rs13040798"],"URL":"https:\/\/doi.org\/10.3390\/rs13040798","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,2,22]]}}}