{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,16]],"date-time":"2025-10-16T06:53:37Z","timestamp":1760597617367,"version":"build-2065373602"},"reference-count":40,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2014,3,7]],"date-time":"2014-03-07T00:00:00Z","timestamp":1394150400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The last decade has seen launches of radar satellite missions operating in  X-band with the sensors acquiring images with spatial resolutions on the order of 1 m. This study uses digital surface models (DSMs) extracted from stereo synthetic aperture radar images and a reference airborne laser scanning digital terrain model to calculate the  above-ground biomass and tree height. The resulting values are compared to in situ data. Analyses were undertaken at the Swedish test sites Krycklan (64\u00b0N) and Remningstorp (58\u00b0N), which have different site conditions. The results showed that, for 459 forest stands in Remningstorp, biomass estimation at the stand level could be performed with 22.9% relative root mean square error, while the height estimation showed 9.4%. Many factors influenced the results and it was found that the topography has a significant effect on the generated DSMs and should therefore be taken into consideration when the stand level mean slope is above four degrees. Different tree species did not have a major effect on the models during leaf-on conditions. Moreover, correct estimation within young forest stands was problematic. The intersection angles resulting in the best results were in the range  8\u201316\u00b0. Based on the results in this study, radargrammetry appears to be a promising potential remote sensing technique for future forest applications.<\/jats:p>","DOI":"10.3390\/rs6032084","type":"journal-article","created":{"date-parts":[[2014,3,7]],"date-time":"2014-03-07T11:44:38Z","timestamp":1394192678000},"page":"2084-2107","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":35,"title":["Forest Variable Estimation Using Radargrammetric Processing of TerraSAR-X Images in Boreal Forests"],"prefix":"10.3390","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3403-057X","authenticated-orcid":false,"given":"Henrik","family":"Persson","sequence":"first","affiliation":[{"name":"Department of Forest Resource Management, Swedish University of Agricultural Sciences,  SE-901 83 Ume\u00e5, Sweden"}]},{"given":"Johan","family":"Fransson","sequence":"additional","affiliation":[{"name":"Department of Forest Resource Management, Swedish University of Agricultural Sciences,  SE-901 83 Ume\u00e5, Sweden"}]}],"member":"1968","published-online":{"date-parts":[[2014,3,7]]},"reference":[{"key":"ref_1","unstructured":"(2012). 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