Algorithm for improving the co-registration of LiDAR-derived digital canopy height models and field data

Abstract

Improving trees location under LiDAR-derived digital canopy height models (DCMs) is of great interest as discrepancies between both dataset influence the accuracy of the estimations of forest attributes. A method is proposed for the co-registration of LiDAR-derived DCMs with local field positional measurements under a dense tree canopy. This approach consists of two main stages: (1) the assessment of the match between the LiDAR-derived digital terrain model and topographic surveying measurements when shifting the coordinates around a measured position; and (2) a comparison between the field height of selected trees and the LiDAR-derived DCM. Satisfactory results were obtained from geo-referencing field data and LiDAR models for characterizing the forest structure in heterogeneous Pinus sylvestris stands. Closure error of topographic surveying was 17.7 cm, and GPS accuracy to 95 % probability was below 10 cm, thus considerably lower than the resolution of the LiDAR models (1 m-pixel). The best co-location for field trees and LiDAR models provided a coefficient of determination of 0.56 between field-measured tree heights and LiDAR-derived DCM values.