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Open AccessArticle

Forest Variable Estimation Using Radargrammetric Processing of TerraSAR-X Images in Boreal Forests

Department of Forest Resource Management, Swedish University of Agricultural Sciences, SE-901 83 Umeå, Sweden
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Remote Sens. 2014, 6(3), 2084-2107; https://doi.org/10.3390/rs6032084
Received: 23 December 2013 / Revised: 17 February 2014 / Accepted: 24 February 2014 / Published: 7 March 2014
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°N) and Remningstorp (58°N), 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 8–16°. Based on the results in this study, radargrammetry appears to be a promising potential remote sensing technique for future forest applications. View Full-Text
Keywords: radargrammetry; forestry; X-band; stereogrammetric methods; SAR; TerraSAR-X radargrammetry; forestry; X-band; stereogrammetric methods; SAR; TerraSAR-X
MDPI and ACS Style

Persson, H.; Fransson, J.E. Forest Variable Estimation Using Radargrammetric Processing of TerraSAR-X Images in Boreal Forests. Remote Sens. 2014, 6, 2084-2107.

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Remote Sens., EISSN 2072-4292, Published by MDPI AG
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