State-of-the-Art: DTM Generation Using Airborne LIDAR Data.

Ziyue Chen,Bingbo Gao,Bernard Devereux

doi:10.3390/s17010150

Abstract

Digital terrain model (DTM) generation is the fundamental application of airborne Lidar data. In past decades, a large body of studies has been conducted to present and experiment a variety of DTM generation methods. Although great progress has been made, DTM generation, especially DTM generation in specific terrain situations, remains challenging. This research introduces the general principles of DTM generation and reviews diverse mainstream DTM generation methods. In accordance with the filtering strategy, these methods are classified into six categories: surface-based adjustment; morphology-based filtering, triangulated irregular network (TIN)-based refinement, segmentation and classification, statistical analysis and multi-scale comparison. Typical methods for each category are briefly introduced and the merits and limitations of each category are discussed accordingly. Despite different categories of filtering strategies, these DTM generation methods present similar difficulties when implemented in sharply changing terrain, areas with dense non-ground features and complicated landscapes. This paper suggests that the fusion of multi-sources and integration of different methods can be effective ways for improving the performance of DTM generation.

Highlights

In past decades, the processing and applications of airborne light detection and ranging (Lidar) (Light detection and ranging) data have been increasingly studied
This study aims to conduct a comprehensive review of existing Digital terrain model (DTM) generation methods and provides useful information for scholars to choose, implement and improve DTM generation methods
The results proved that the cloth simulation filter (CSF) achieved satisfactory accuracy in most terrain and the post-processing significantly enhances the performance of CSF in steep terrain

Summary

Introduction

The processing and applications of airborne Lidar (Light detection and ranging) data have been increasingly studied. The entire point cloud can be transformed to a digital surface model (DSM) whilst the ground points can be transformed into a DTM ( “DTM” is a frequently used term in specific papers, the term “DEM” (digital elevation model) is sometimes employed by researchers to define the surface created using ground points. This terminology strategy may be a potential risk to a broad readership. DEM is a more generic term that could represent DTM, DSM, or any other elevation

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