IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 52, NO. 2, FEBRUARY 2014 1197 TerraSAR-X Stereo Radargrammetry and Airborne Scanning LiDAR Height Metrics in Imputation of Forest Aboveground Biomass and Stem Volume Mikko Vastaranta, Markus Holopainen, Mika Karjalainen, Ville Kankare, Juha Hyyppä, and Sanna Kaasalainen Abstract— Our objective is to evaluate the boreal forest aboveground biomass (AGB) and stem volume (VOL) impu- tation accuracy when scanning LiDAR or TerraSAR-X stereo radargrammetry-derived point-height metrics are used as pre- dictors in the nearest neighbor imputation approach. Treewise measured field plots are used as reference data in the AGB and VOL imputations and accuracy evaluations. The digital terrain model (DTM) that is produced by the National Land Survey of Finland is used to obtain aboveground elevation values for the TerraSAR-X stereo radargrammetry. The DTM that is used (i.e., grid size 2 m) is derived from LiDAR surveys with an average point density of ∼ 0.5 points/m 2 . The respective DTM and point data are used in LiDAR imputations of AGB and VOL. The relative root mean square errors (RMSEs) for AGB and VOL are 29.9% (41.3 t/ha) and 30.2% (78.1 m 3 /ha) when using TerraSAR-X stereo radargrammetry metrics. The respective LiDAR estimation accuracy values are 21.9% (32.3 t/ha) and 24.8% (64.2 m 3 /ha). LiDAR imputations are clearly more accurate than imputations that are made by using TerraSAR-X stereo radargrammetry metrics. However, the dif- ference between imputation accuracies of LiDAR- and TerraSAR X-based features are smaller than in any previous study in which LiDAR and different types of synthetic aperture radar materials are compared in the variable predictions regarding forests. We conclude that TerraSAR X stereo radargrammetry is a promising remote-sensing technique for large forest-area AGB and VOL mapping and monitoring when an accurate LiDAR-based DTM is available. Index Terms— Forestry, laser scanning, mapping. I. I NTRODUCTION C URRENTLY, the amount of forest-bound carbon is a salient climate political issue on the global level. A major Manuscript received January 11, 2012; revised January 25, 2013; accepted February 9, 2013. Date of publication March 28, 2013; date of current version December 12, 2013. This work was supported by the Academy of Finland through the projects Science and Technology Toward Precision Forestry and New Techniques in Active Remote Sensing: Hyperspectral Laser in Envi- ronmental Change Detection. The TerraSAR-X SAR images were acquired through the German Aerospace Center (DLR) prelaunch Announcement of Opportunity scientific project (LAN-0049). M. Vastaranta, M. Holopainen, and V. Kankare are with the Department of Forest Sciences, University of Helsinki, Helsinki 00014, Finland (e-mail: mikko.vastaranta@helsinki.fi; markus.holopainen@helsinki.fi; ville.kankare@helsinki.fi). M. Karjalainen, J. Hyyppä, and S. Kaasalainen are with the Finnish Geodetic Institute, Masala 02430, Finland (e-mail: mika.karjalainen@fgi.fi; juha.hyyppa@fgi.fi; sanna.kaasalainen@fgi.fi). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/TGRS.2013.2248370 portion of the total store of forest carbon consists of the growing stock’s carbon reserves. Thus, one of the greatest challenges that forest inventory research currently faces is how to effectively and accurately map and monitor forest biomass. Recent knowledge of forest biomass and its variations is often based on ground measurements and coarse- or medium- resolution satellite images. Therefore, the accuracy of biomass mapping, especially at the regional or local level (e.g., a forest stand), should be improved. Airborne scanning light detection and ranging (from here, LiDAR) is becoming a standard technique in the mapping and monitoring of forest resources. LiDAR data can be used to estimate a variety of forest inventory attributes with a high level of accuracy (e.g., [1]). Using LiDAR-based forest inven- tory, it has become possible to achieve at least the same level of accuracy as that found in the conventional standwise field inventory approach that is used in forest management planning. For overviews using airborne LiDAR in forest mapping and monitoring, see [1]–[4]. LiDAR is also a promising technique that can be used for efficient and accurate aboveground biomass (AGB) detec- tion, because of its capacity for the direct measurement of vegetation 3-D structures or tree and stand variables. AGB strongly correlates with canopy height which, in turn, can be accurately determined by LiDAR-based methodologies. As the canopy height, AGB, and carbon pools are functionally related, canopy height is a critical parameter in the terrestrial carbon cycle [5]. LiDAR measurements also provide an excellent starting point for deriving AGB models. It is likely that LiDAR applications enhance the accuracy of current AGB estimation means at all levels, from single-tree to nationwide/global forest inventory applications. Popescu et al. [6], Popescu [7], van Aardt et al. [8], and Zhao et al. [9] showed that AGB can be estimated with high accuracy by using LiDAR metrics. The leaf area index, which is also used as a measure of AGB [10] and is successfully mapped with LiDAR, uses ground calibration data [11], [12]. AGB changes that are caused by logging practices, growth, or forest damage can also be detected by using multitemporal LiDAR datasets (e.g., [13]–[15]). LiDAR is carried out at relatively low altitudes, usually from 0.5 to 3 km, which therefore makes it relatively expensive per unit area. Other remotely sensed data will still be needed, especially when updated information is required on an annual 0196-2892 © 2013 IEEE