A1 Refereed original research article in a scientific journal
Prediction of Forest Stand Attributes Using TerraSAR-X Stereo Imagery
Authors: Vastaranta, Mikko; Niemi, Mikko; Karjalainen, Mika; Peuhkurinen, Jussi; Kankare, Ville; Hyyppä, Juha; Holopainen, Markus
Publisher: MDPI
Publishing place: BASEL
Publication year: 2014
Journal: Remote Sensing
Journal name in source: REMOTE SENSING
Journal acronym: REMOTE SENS-BASEL
Volume: 6
Issue: 4
First page : 3227
Last page: 3246
Number of pages: 20
eISSN: 2072-4292
DOI: https://doi.org/10.3390/rs6043227
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 m(2)/ha), 16.3% (31.1 m(3)/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 m(2)/ha), -9.3% (-17.8 m(3)/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.
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 m(2)/ha), 16.3% (31.1 m(3)/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 m(2)/ha), -9.3% (-17.8 m(3)/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.
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