{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T11:53:56Z","timestamp":1773921236006,"version":"3.50.1"},"reference-count":68,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2021,8,3]],"date-time":"2021-08-03T00:00:00Z","timestamp":1627948800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Internal Grant Agency of the Faculty of Environmental Sciences, Czech University of Life Sciences Prague","award":["2020B0022"],"award-info":[{"award-number":["2020B0022"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The availability of global digital elevation models (DEMs) from multiple time points allows their combination for analysing vegetation changes. The combination of models (e.g., SRTM and TanDEM-X) can contain errors, which can, due to their synergistic effects, yield incorrect results. We used a high-resolution LiDAR-derived digital surface model (DSM) to evaluate the accuracy of canopy height estimates of the aforementioned global DEMs. In addition, we subtracted SRTM and TanDEM-X data at 90 and 30 m resolutions, respectively, to detect deforestation caused by bark beetle disturbance and evaluated the associations of their difference with terrain characteristics. The study areas covered three Central European mountain ranges and their surrounding areas: Bohemian Forest, Erzgebirge, and Giant Mountains. We found that vertical bias of SRTM and TanDEM-X, relative to the canopy height, is similar with negative values of up to \u22122.5 m and LE90s below 7.8 m in non-forest areas. In forests, the vertical bias of SRTM and TanDEM-X ranged from \u22120.5 to 4.1 m and LE90s from 7.2 to 11.0 m, respectively. The height differences between SRTM and TanDEM-X show moderate dependence on the slope and its orientation. LE90s for TDX-SRTM differences tended to be smaller for east-facing than for west-facing slopes, and varied, with aspect, by up to 1.5 m in non-forest areas and 3 m in forests, respectively. Finally, subtracting SRTM and NASA DEMs from TanDEM-X and Copernicus DEMs, respectively, successfully identified large areas of deforestation caused by hurricane Kyril in 2007 and a subsequent bark beetle disturbance in the Bohemian Forest. However, local errors in TanDEM-X, associated mainly with forest-covered west-facing slopes, resulted in erroneous identification of deforestation. Therefore, caution is needed when combining SRTM and TanDEM-X data in multitemporal studies in a mountain environment. Still, we can conclude that SRTM and TanDEM-X data represent suitable near global sources for the identification of deforestation in the period between the time points of their acquisition.<\/jats:p>","DOI":"10.3390\/rs13153042","type":"journal-article","created":{"date-parts":[[2021,8,4]],"date-time":"2021-08-04T02:16:07Z","timestamp":1628043367000},"page":"3042","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Use of TanDEM-X and SRTM-C Data for Detection of Deforestation Caused by Bark Beetle in Central European Mountains"],"prefix":"10.3390","volume":"13","author":[{"given":"Kate\u0159ina","family":"Gdulov\u00e1","sequence":"first","affiliation":[{"name":"Department of Spatial Sciences, Faculty of Environmental Sciences, Czech University of Life Sciences Prague, Kam\u00fdck\u00e1 129, 16500 Praha-Suchdol, Czech Republic"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7543-301X","authenticated-orcid":false,"given":"Jana","family":"Mare\u0161ov\u00e1","sequence":"additional","affiliation":[{"name":"Department of Spatial Sciences, Faculty of Environmental Sciences, Czech University of Life Sciences Prague, Kam\u00fdck\u00e1 129, 16500 Praha-Suchdol, Czech Republic"}]},{"given":"Vojt\u011bch","family":"Bart\u00e1k","sequence":"additional","affiliation":[{"name":"Department of Spatial Sciences, Faculty of Environmental Sciences, Czech University of Life Sciences Prague, Kam\u00fdck\u00e1 129, 16500 Praha-Suchdol, Czech Republic"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1305-5476","authenticated-orcid":false,"given":"Marta","family":"Szostak","sequence":"additional","affiliation":[{"name":"Department of Forest Resources Management, Faculty of Forestry, University of Agriculture in Krakow, Al. 29 Listopada 46, 31-425 Krakow, Poland"}]},{"given":"Jaroslav","family":"\u010cervenka","sequence":"additional","affiliation":[{"name":"Department of Nature Protection, \u0160umava National Park administration, Su\u0161ick\u00e1 339, 34192 Ka\u0161persk\u00e9 Hory, Czech Republic"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3194-451X","authenticated-orcid":false,"given":"V\u00edt\u011bzslav","family":"Moudr\u00fd","sequence":"additional","affiliation":[{"name":"Department of Spatial Sciences, Faculty of Environmental Sciences, Czech University of Life Sciences Prague, Kam\u00fdck\u00e1 129, 16500 Praha-Suchdol, Czech Republic"}]}],"member":"1968","published-online":{"date-parts":[[2021,8,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"e2020JF005588","DOI":"10.1029\/2020JF005588","article-title":"Beyond x, y, z (t). Navigating New Landscapes of Science in the Science of Landscapes","volume":"125","author":"Koppes","year":"2020","journal-title":"J. Geophys. Res. Earth Surf."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/j.isprsjprs.2015.10.004","article-title":"Remote Sensing Platforms and Sensors: A Survey","volume":"115","author":"Toth","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Farr, T.G., Rosen, P.A., Caro, E., Crippen, R., Duren, R., Hensley, S., Kobrick, M., Paller, M., Rodriguez, E., and Roth, L. (2007). The Shuttle Radar Topography Mission. Rev. Geophys., 45.","DOI":"10.1029\/2005RG000183"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2522","DOI":"10.1109\/JSTARS.2021.3055546","article-title":"TanDEM-X Long-Term System Performance after 10 Years of Operation","volume":"14","author":"Bojarski","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Hojo, A., Takagi, K., Avtar, R., Tadono, T., and Nakamura, F. (2020). Synthesis of L-Band SAR and Forest Heights Derived from TanDEM-X DEM and 3 Digital Terrain Models for Biomass Mapping. Remote Sens., 12.","DOI":"10.3390\/rs12030349"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2145","DOI":"10.1016\/j.rse.2007.08.025","article-title":"Integrating Landsat TM and SRTM-DEM Derived Variables with Decision Trees for Habitat Classification and Change Detection in Complex Neotropical Environments","volume":"112","author":"Sesnie","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1016\/j.ecolmodel.2017.01.024","article-title":"Should Topographic Metrics Be Considered When Predicting Species Density of Birds on a Large Geographical Scale? A Case of Random Forest Approach","volume":"349","author":"Kosicki","year":"2017","journal-title":"Ecol. Model."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"105044","DOI":"10.1016\/j.jas.2019.105044","article-title":"How Old Are the Towns and Villages in Central Europe? Archaeological Data Reveal the Size of Bias in Dating Obtained from Traditional Historical Sources","volume":"113","author":"Fanta","year":"2020","journal-title":"J. Archaeol. Sci."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"111319","DOI":"10.1016\/j.rse.2019.111319","article-title":"Accuracy Assessment of the TanDEM-X 90 Digital Elevation Model for Selected Floodplain Sites","volume":"232","author":"Hawker","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"477","DOI":"10.1007\/s11069-019-03638-9","article-title":"Flood Hazard Assessment and Mapping of River Swat Using HEC-RAS 2D Model and High-Resolution 12-m TanDEM-X DEM (WorldDEM)","volume":"97","author":"Farooq","year":"2019","journal-title":"Nat. Hazards"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"112132","DOI":"10.1016\/j.rse.2020.112132","article-title":"Automated Estimation of Forest Height and Underlying Topography over a Brazilian Tropical Forest with Single-Baseline Single-Polarization TanDEM-X SAR Interferometry","volume":"252","author":"Lei","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"428","DOI":"10.1016\/j.rse.2006.09.007","article-title":"Quality Assessment of SRTM C- and X-Band Interferometric Data: Implications for the Retrieval of Vegetation Canopy Height","volume":"106","author":"Walker","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_13","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_14","first-page":"101904","article-title":"Canopy Height Estimation with TanDEM-X in Temperate and Boreal Forests","volume":"82","author":"Schlund","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"111833","DOI":"10.1016\/j.rse.2020.111833","article-title":"Comparison of TanDEM-X InSAR Data and High-Density ALS for the Prediction of Forest Inventory Attributes in Plantation Forests with Steep Terrain","volume":"246","author":"Leonardo","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"6976","DOI":"10.1080\/01431161.2020.1752414","article-title":"Comparing the Potential of Stereo Aerial Photographs, Stereo Very High-Resolution Satellite Images, and TanDEM-X for Estimating Forest Height","volume":"41","author":"Ullah","year":"2020","journal-title":"Int. J. Remote Sens."},{"key":"ref_17","first-page":"53","article-title":"Forest Biomass Retrieval Approaches from Earth Observation in Different Biomes","volume":"77","author":"Quegan","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1016\/j.rse.2016.10.022","article-title":"A Nationwide Forest Attribute Map of Sweden Predicted Using Airborne Laser Scanning Data and Field Data from the National Forest Inventory","volume":"194","author":"Nilsson","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"451","DOI":"10.1016\/j.rse.2004.02.001","article-title":"Automatic Detection of Harvested Trees and Determination of Forest Growth Using Airborne Laser Scanning","volume":"90","author":"Yu","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2368","DOI":"10.3390\/rs5052368","article-title":"Quantifying Dynamics in Tropical Peat Swamp Forest Biomass with Multi-Temporal LiDAR Datasets","volume":"5","author":"Englhart","year":"2013","journal-title":"Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1186\/s13021-014-0005-2","article-title":"Forest Biomass Change Estimated from Height Change in Interferometric SAR Height Models","volume":"9","author":"Solberg","year":"2014","journal-title":"Carbon Balance Manag."},{"key":"ref_22","first-page":"202","article-title":"Mapping Boreal Forest Biomass from a SRTM and TanDEM-X Based on Canopy Height Model and Landsat Spectral Indices","volume":"68","author":"Sadeghi","year":"2018","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_23","first-page":"407","article-title":"Satellite Open Data to Monitor Forest Damage Caused by Extreme Climate-Induced Events: A Case Study of the Vaia Storm in Northern Italy","volume":"94","author":"Francini","year":"2021","journal-title":"For. Int. J. For. Res."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Treuhaft, R., Lei, Y., Gon\u00e7alves, F., Keller, M., Santos, J., Neumann, M., and Almeida, A. (2017). Tropical-Forest Structure and Biomass Dynamics from TanDEM-X Radar Interferometry. Forests, 8.","DOI":"10.3390\/f8080277"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Askne, J.I., Persson, H.J., and Ulander, L.M. (2018). Biomass Growth from Multi-Temporal TanDEM-X Interferometric Synthetic Aperture Radar Observations of a Boreal Forest Site. Remote Sens., 10.","DOI":"10.3390\/rs10040603"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Solberg, S., May, J., Bogren, W., Breidenbach, J., Torp, T., and Gizachew, B. (2018). Interferometric SAR DEMs for Forest Change in Uganda 2000\u20132012. Remote Sens., 10.","DOI":"10.3390\/rs10020228"},{"key":"ref_27","first-page":"47","article-title":"Range of Variability of Unmanaged Subalpine Forests","volume":"8","author":"Kulakowski","year":"2004","journal-title":"Forum Wissen"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1016\/j.foreco.2004.07.018","article-title":"Ecology and Management of the Spruce Bark Beetle Ips Typographus\u2014A Review of Recent Research","volume":"202","author":"Wermelinger","year":"2004","journal-title":"For. Ecol. Manag."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Klou\u010dek, T., Kom\u00e1rek, J., Surov\u1ef3, P., Hrach, K., Janata, P., and Va\u0161\u00ed\u010dek, B. (2019). The Use of UAV Mounted Sensors for Precise Detection of Bark Beetle Infestation. Remote Sens., 11.","DOI":"10.3390\/rs11131561"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/j.foreco.2019.03.064","article-title":"Intra-Annual Ips Typographus Outbreak Monitoring Using a Multi-Temporal GIS Analysis Based on Hyperspectral and ALS Data in the Bia\u0142owie\u017ca Forests","volume":"442","author":"Mielcarek","year":"2019","journal-title":"For. Ecol. Manag."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Holzwarth, S., Thonfeld, F., Abdullahi, S., Asam, S., Da Ponte Canova, E., Gessner, U., Huth, J., Kraus, T., Leutner, B., and Kuenzer, C. (2020). Earth Observation Based Monitoring of Forests in Germany: A Review. Remote Sens., 12.","DOI":"10.3390\/rs12213570"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Mina\u0159\u00edk, R., Langhammer, J., and Lendzioch, T. (2020). Automatic Tree Crown Extraction from UAS Multispectral Imagery for the Detection of Bark Beetle Disturbance in Mixed Forests. Remote Sens., 12.","DOI":"10.3390\/rs12244081"},{"key":"ref_33","first-page":"102335","article-title":"Early Detection of Bark Beetle Infestation in Norway Spruce Forests of Central Europe Using Sentinel-2","volume":"100","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1016\/j.foreco.2015.12.023","article-title":"Frequent Severe Natural Disturbances and Non-Equilibrium Landscape Dynamics Shaped the Mountain Spruce Forest in Central Europe","volume":"363","author":"Morrissey","year":"2016","journal-title":"For. Ecol. Manag."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1016\/j.foreco.2010.10.012","article-title":"Factors Affecting the Spatio-Temporal Dispersion of Ips Typographus (L.) in Bavarian Forest National Park: A Long-Term Quantitative Landscape-Level Analysis","volume":"261","author":"Lausch","year":"2011","journal-title":"For. Ecol. Manag."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1038\/nclimate3303","article-title":"Forest Disturbances under Climate Change","volume":"7","author":"Seidl","year":"2017","journal-title":"Nat. Clim. Chang."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"737","DOI":"10.1111\/1365-2745.13502","article-title":"Do Bark Beetle Outbreaks Amplify or Dampen Future Bark Beetle Disturbances in Central Europe?","volume":"109","author":"Sommerfeld","year":"2021","journal-title":"J. Ecol."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"850","DOI":"10.1126\/science.1244693","article-title":"High-Resolution Global Maps of 21st-Century Forest Cover Change","volume":"342","author":"Hansen","year":"2013","journal-title":"Science"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.isprsjprs.2017.08.008","article-title":"Generation and Performance Assessment of the Global TanDEM-X Digital Elevation Model","volume":"132","author":"Rizzoli","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1016\/j.isprsjprs.2018.02.017","article-title":"Accuracy Assessment of the Global TanDEM-X Digital Elevation Model with GPS Data","volume":"139","author":"Wessel","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"(2019). Kramm; Hoffmeister A Relief Dependent Evaluation of Digital Elevation Models on Different Scales for Northern Chile. ISPRS Int. J. Geo-Inf., 8.","DOI":"10.3390\/ijgi8100430"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Podg\u00f3rski, J., Kinnard, C., P\u0119tlicki, M., and Urrutia, R. (2019). Performance Assessment of TanDEM-X DEM for Mountain Glacier Elevation Change Detection. Remote Sens., 11.","DOI":"10.3390\/rs11020187"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Pasquetti, F., Bini, M., and Ciampalini, A. (2019). Accuracy of the TanDEM-X Digital Elevation Model for Coastal Geomorphological Studies in Patagonia (South Argentina). Remote Sens., 11.","DOI":"10.3390\/rs11151767"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"111509","DOI":"10.1016\/j.rse.2019.111509","article-title":"Evaluation of ASTER GDEM2, SRTMv3. 0, ALOS AW3D30 and TanDEM-X DEMs for the Peruvian Andes against Highly Accurate GNSS Ground Control Points and Geomorphological-Hydrological Metrics","volume":"237","author":"Viveen","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/j.isprsjprs.2019.11.015","article-title":"TanDEM-X DEM: Comparative Performance Review Employing LIDAR Data and DSMs","volume":"160","author":"Vassilaki","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"111724","DOI":"10.1016\/j.rse.2020.111724","article-title":"Accuracy Assessment of the Global TanDEM-X Digital Elevation Model in a Mountain Environment","volume":"241","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Uuemaa, E., Ahi, S., Montibeller, B., Muru, M., and Kmoch, A. (2020). Vertical Accuracy of Freely Available Global Digital Elevation Models (ASTER, AW3D30, MERIT, TanDEM-X, SRTM, and NASADEM). Remote Sens., 12.","DOI":"10.3390\/rs12213482"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"893","DOI":"10.1080\/2150704X.2020.1792001","article-title":"Accuracy Validation and Bias Assessment for Various Multi-Sensor Open-Source DEMs in Part of the Karakoram Region","volume":"11","author":"Kumar","year":"2020","journal-title":"Remote Sens. Lett."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"3016","DOI":"10.1109\/JSTARS.2021.3055399","article-title":"Using Kinematic GNSS Data to Assess the Accuracy and Precision of the TanDEM-X DEM Resampled at 1-m Resolution Over the Western Corinth Gulf, Greece","volume":"14","author":"Briole","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_50","first-page":"104","article-title":"Generating Spike-Free Digital Surface Models Using LiDAR Raw Point Clouds: A New Approach for Forestry Applications","volume":"52","author":"Khosravipour","year":"2016","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1672","DOI":"10.1080\/17538947.2020.1791267","article-title":"Sensitivity Analysis of Parameters and Contrasting Performance of Ground Filtering Algorithms with UAV Photogrammetry-Based and LiDAR Point Clouds","volume":"13","author":"Fogl","year":"2020","journal-title":"Int. J. Digit. Earth"},{"key":"ref_52","unstructured":"Wessel, B. (2021, February 18). TanDEM-X Ground Segment DEM Products Specification Document. Report TD-GS-PS-0021. Deutsches Zentrum fur Luft- und Raumfahrt, Oberpfaffenhofen: Wessling, Germany, 2018; Volume 43. Available online: https:\/\/tandemx-science.dlr.de\/pdfs\/TD-GS-PS-0021_DEM-Product-Specification_v3.1.pdf."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1576","DOI":"10.1016\/j.rse.2011.02.017","article-title":"Spatial Structure and Landscape Associations of SRTM Error","volume":"115","author":"Shortridge","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_54","unstructured":"(2021, June 18). Copernicus DEM. Available online: https:\/\/spacedata.copernicus.eu\/documents\/20126\/0\/GEO1988-CopernicusDEM-SPE-002_ProductHandbook_I1.00.pdf."},{"key":"ref_55","first-page":"125","article-title":"Nasadem Global Elevation Model: Methods and Progress","volume":"XLI-B4","author":"Crippen","year":"2016","journal-title":"ISPRS Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_56","unstructured":"Maune, D.F. (2007). Digital Elevation Model Technologies and Applications: The DEM User Manual, American Society for Photogrammetry and Remote Sensing. [2nd ed.]."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"555","DOI":"10.1080\/01431161.2018.1516311","article-title":"Comparison of a Commercial and Home-Assembled Fixed-Wing UAV for Terrain Mapping of a Post-Mining Site under Leaf-off Conditions","volume":"40","author":"Urban","year":"2019","journal-title":"Int. J. Remote Sens."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"398","DOI":"10.1016\/j.isprsjprs.2009.02.003","article-title":"Accuracy Assessment of Digital Elevation Models by Means of Robust Statistical Methods","volume":"64","year":"2009","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"5449","DOI":"10.3390\/rs5115449","article-title":"Detection of Forest Clear-Cuts with Shuttle Radar Topography Mission (SRTM) and Tandem-X InSAR Data","volume":"5","author":"Solberg","year":"2013","journal-title":"Remote Sens."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Tanase, M.A., 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_61","doi-asserted-by":"crossref","first-page":"257","DOI":"10.5194\/essd-12-257-2020","article-title":"A Spatially Explicit Database of Wind Disturbances in European Forests over the Period 2000\u20132018","volume":"12","author":"Forzieri","year":"2020","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"981","DOI":"10.1126\/science.1248753","article-title":"Comment on \u201cHigh-Resolution Global Maps of 21st-Century Forest Cover Change\u201d","volume":"344","author":"Tropek","year":"2014","journal-title":"Science"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/j.cageo.2015.02.003","article-title":"Effects of Spatial Resolution on Slope and Aspect Derivation for Regional-Scale Analysis","volume":"77","author":"Grohmann","year":"2015","journal-title":"Comput. Geosci."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"100987","DOI":"10.1016\/j.ecoinf.2019.100987","article-title":"Potential Pitfalls in Rescaling Digital Terrain Model-Derived Attributes for Ecological Studies","volume":"54","author":"Lecours","year":"2019","journal-title":"Ecol. Inform."},{"key":"ref_65","first-page":"10","article-title":"Modeling Slope in a Geographic Information System","volume":"58","author":"Mattson","year":"2004","journal-title":"J. Ark. Acad. Sci."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Millan, R., Dehecq, A., Trouve, E., Gourmelen, N., and Berthier, E. (2015, January 22\u201324). Elevation Changes and X-Band Ice and Snow Penetration Inferred from TanDEM-X Data of the Mont-Blanc Area. Proceedings of the 2015 8th International Workshop on the Analysis of Multitemporal Remote Sensing Images (Multi-Temp), Annecy, France.","DOI":"10.1109\/Multi-Temp.2015.7245753"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"3870","DOI":"10.1109\/JSTARS.2016.2581482","article-title":"Elevation Changes Inferred from TanDEM-X Data over the Mont-Blanc Area: Impact of the X-Band Interferometric Bias","volume":"9","author":"Dehecq","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"648","DOI":"10.3390\/rs5020648","article-title":"Mapping Tropical Rainforest Canopy Disturbances in 3D by COSMO-SkyMed Spotlight InSAR-Stereo Data to Detect Areas of Forest Degradation","volume":"5","author":"Deutscher","year":"2013","journal-title":"Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/15\/3042\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:39:44Z","timestamp":1760164784000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/15\/3042"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,3]]},"references-count":68,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2021,8]]}},"alternative-id":["rs13153042"],"URL":"https:\/\/doi.org\/10.3390\/rs13153042","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,8,3]]}}}