Extracting ridge and valley lines in mountainous areas from airborne lidar data by utilizing line feature strength

Abstract

Background Digital elevation models (DEMs) are important in many fields, such as geomatics and water conservation in mountainous areas. Geomorphic feature lines are necessary for topography interpolation and computation from DEMs. Methods Instead of a parameter space, we propose a novel automatic extraction of geomorphic feature lines in the feature space from discrete airborne light detection and ranging (LiDAR) data by the tensor voting method (TVM), which was originally developed for image data. A tensor field for discrete airborne LiDAR points was first established, and then, utilizing the TVM, a new geometric feature metric of data, the line feature strength, was captured. A practical line-growing method based on the local maxima line feature strength is proposed in this study. Results Compared with general line growing, which is based on a certain threshold, our line growing method is quite effective, particularly for the extraction of primary and minor ridge and valley lines in mountainous areas. Conclusions The method presented in this paper is fast and automated, and can furnish operators with a wealth of detailed information about minor line features. This enables the extraction of ridge and valley lines that are tailored to specific requirements. Undoubtedly, the method developed here can be generalized to a large amount of LiDAR data.