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Remote Sens. 2014, 6(4), 3227-3246; doi:10.3390/rs6043227

Prediction of Forest Stand Attributes Using TerraSAR-X Stereo Imagery

1
Department of Forest Sciences, University of Helsinki, Helsinki FI-00014, Finland
2
Centre of Excellence in Laser Scanning Research, Finnish Geodetic Institute, Masala FI-02431, Finland
3
Department of Remote Sensing and Photogrammetry, Finnish Geodetic Institute, Masala FI-02431, Finland
4
Arbonaut Oy Ltd., Latokartanontie 7 A, Helsinki FI-00700, Finland
*
Author to whom correspondence should be addressed.
Received: 12 January 2014 / Revised: 13 March 2014 / Accepted: 4 April 2014 / Published: 10 April 2014
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Abstract

Consistent, detailed and up-to-date forest resource information is required for allocation of forestry activities and national and international reporting obligations. We evaluated the forest stand attribute prediction accuracy when radargrammetry was used to derive height information from TerraSAR-X stereo imagery. Radargrammetric elevations were normalized to heights above ground using an airborne laser scanning (ALS)-derived digital terrain model (DTM). Derived height metrics were used as predictors in the most similar neighbor (MSN) estimation approach. In total, 207 field measured plots were used in MSN estimation, and the obtained results were validated using 94 stands with an average area of 4.1 ha. The relative root mean square errors for Lorey’s height, basal area, stem volume, and above-ground biomass were 6.7% (1.1 m), 12.0% (2.9 m2/ha), 16.3% (31.1 m3/ha), and 16.1% (15.6 t/ha). Although the prediction accuracies were promising, it should be noted that the predictions included bias. The respective biases were −4.6% (−0.7 m), −6.4% (−1.6 m2/ha), −9.3% (−17.8 m3/ha), and −9.5% (−9.1 t/ha). With detailed DTM, TerraSAR-X stereo radargrammetry-derived forest information appears to be suitable for providing consistent forest resource information over large areas. View Full-Text
Keywords: remote sensing; GIS; forestry; airborne laser scanning; radargrammetry; forest management planning; forest inventory remote sensing; GIS; forestry; airborne laser scanning; radargrammetry; forest management planning; forest inventory
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Vastaranta, M.; Niemi, M.; Karjalainen, M.; Peuhkurinen, J.; Kankare, V.; Hyyppä, J.; Holopainen, M. Prediction of Forest Stand Attributes Using TerraSAR-X Stereo Imagery. Remote Sens. 2014, 6, 3227-3246.

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