Journal
INTERNATIONAL JOURNAL OF REMOTE SENSING
Volume 35, Issue 9, Pages 3135-3149Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1080/01431161.2014.903440
Keywords
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Funding
- Research Council of Norway [192263]
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Remote sensing plays an important role within the field of forest inventory. Airborne laser scanning (ALS) has become an effective tool for acquiring forest inventory data. In most ALS-based forest inventories, accurately positioned field plots are used in the process of relating ALS data to field-observed biophysical properties. The geo-referencing of these field plots is typically carried out by means of differential global navigation satellite systems (dGNSS), and often relies on logging times of 15-20 min to ensure adequate accuracy under different forest conditions. Terrestrial laser scanning (TLS) has been proposed as a possible tool for collection of field data in forest inventories and can facilitate rapid acquisition of these data. In the present study, a novel method for co-registration of TLS and ALS data by posterior analysis of remote-sensing data - rather than using dGNSS - was proposed and then tested on 71 plots in a boreal forest. The method relies on an initial position obtained with a recreational-grade GPS receiver, in addition to analysis of the ALS and TLS data. First, individual tree positions were derived from the remote-sensing data. A search algorithm was then used to find the best match for the TLS-derived trees among the ALS-derived trees within a search area, defined relative to the initial position. The accuracy of co-registration was assessed by comparison with an accurately measured reference position. With a search radius of 25m and using low-density ALS data (0.7 points m(-2)), 82% and 51% of the TLS scans were co-registered with positional errors within 1m and 0.5m, respectively. By using ALS data of medium density (7.5 points m(-2)), 87% and 78% of the scans were co-registered with errors within 1m and 0.5m of the reference position, respectively. These results are promising and the method can facilitate rapid acquisition and geo-referencing of field data. Robust methods to identify and handle erroneous matches are, however, required before it is suitable for operational use.
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