RegisTree: a registration algorithm to enhance forest inventory plot georeferencing

Abstract

& Key message The accuracy of remote sensing-based models of forest attributes could be improved by controlling the spatial registration of field and remote sensing data. We have demonstrated the potential of an algorithm matching plotlevel field tree positions with lidar canopy height models and derived local maxima to achieve a precise registration automatically. & Context The accuracy of remote sensing-based estimates of forest parameters depends on the quality of the spatial registration of the training data. & Aims This study introduces an algorithm called RegisTree to correct field plot positions by matching a spatialized field tree height map with lidar canopy height models (CHMs). & Methods RegisTree is based on a point (field positions) to surface (CHM) adjustment approach modified to ensure that at least one field tree position corresponds to CHM local maxima. & Results RegisTree has been validated with respect to positioning errors and the performance of lidar-derived estimation of plot volume. Overall, RegisTree enabled to register field plots surveyed in a range of forest conditions with a precision of 1.5 m (± 1.23 m), but a higher performance for conifer plots, and a limited efficiency in homogeneous stands, having similar heights. Improved plot positions were found to have a limited impact on volume predictions under the range of tested conditions, with a gain up to 1.3%. & Conclusion RegisTree could be used to improve the forest plot position from field surveys collected with low-grade GPS and to contribute to the development of processing chains of 3D remote sensing-based models of forest parameters.

Point cloud registration method for maize plants based on conical surface fitting—ICP

“…Colaco et al 13 utilized K-means and Alpha-shape algorithm to register and reconstruct citrus. Fadili et al 14 introduced a RegisTree algorithm to improve the quality of spatial registration of forests. The above algorithms have their characteristics, but the most widely used registration method is ICP algorithm.…”

Improving the Accuracy of Timber Volume and Basal Area Prediction in Heterogeneously Structured and Mixed Forests by Automated Co-Registration of Forest Inventory Plots and Remote Sensing Data

“…Even though GNSS and dGNSS devices are now used in most forest inventories to measure the sampling location exactly, positional errors of > 10 m are common due to erroneous instrument handling or a weak reference signal. The accuracy of statistical regression models that are derived from that position decreases with increasing positional error (Dorigo et al, 2010;Fadili et al, 2019;Hernández-Stefanoni et al, 2018). Therefore, predictions made with these models also have a greater error.…”