An application-oriented automated approach for co-registration of forest inventory and airborne laser scanning data

Abstract

Airborne laser scanning (ALS) data can be used for downscaling point-based forest inventory (FI) measurements to obtain spatially distributed estimates of forest parameters at a more detailed, local scale. Such downscaling algorithms usually consist of a direct coupling between selected FI parameters and ALS data collected at the field sampling locations. Thus, precise co-registration between FI and ALS data is an essential preprocessing step to obtain accurate predictive relationships. This paper presents a new, automated co-registration approach that searches iteratively for the best match between an ALS-based canopy height model and the tree positions and heights measured during the FI. While the basic principle of the algorithm applies to various types of FI sampling configurations, the co-registration approach was developed specifically to take into account the tree selection criterion posed by angle count sampling. The angle count sampling method only includes trees that at a given distance from the sample plot centre have a minimum required diameter at breast height (DBH). This tree selection criterion leads to maximum plot radii and number of inventoried trees that strongly vary from sample plot to sample plot. In the automated co-registration procedure, several criteria (e.g. the occurrence of more than one spatial cluster of minimum residuals and a predominance of deciduous trees in a sample plot) were used to detect possible uncertain solutions and to reduce post-processing efforts by an image operator. Model calibration and validation were based on national forest inventory (NFI) and ALS data from the Austrian federal state of Vorarlberg. Transferability and robustness of the approach was verified using an independent local FI. The results show that 68% of the NFI sample plots and 74% of the local FI plots could be automatically co-registered to a location at a distance of less than 5.0 m from the reference location. The maximum difference of 5.0 m used for marking a solution as correct was based on the relatively small influence that deviations of up to this value have on ALS-based predictions of biophysical forest variables at a stand level. The quality flagging criteria adopted were very successful in identifying uncertain solutions; only one out of 153 co-registered sample plots with a deviation from the reference data set greater than 5.0 m was not identified as uncertain. Applying the automatically co-registered sample plots in calibration of a growing stock model provided estimates that were clearly superior to those obtained with the original plot positions and even slightly outperformed those based on manual co-registration. As the algorithm developed will be part of an operational processing chain for Austrian NFI data, it has a high practical relevance.