Comparison of data reduction algorithms for LiDAR‐derived digital terrain model generalisation

Abstract

A digital terrain model (DTM) is a three-dimensional representation of the terrain relief created from discrete points related to each other through their elevations. New technologies such as satellite remote sensing, airborne laser scanning and radar interferometry are efficient methods for constructing high-quality DTMs in a cost-effective manner. The accuracy of a DTM is influenced by a number of factors, including the accuracy, density and spatial distribution of elevation points, the terrain surface characteristics, etc. In this paper, direct comparisons of absolute and relative vertical accuracies are made between data reduction algorithms for the generalisation of DTM extracted from airborne Light Detection and Ranging (LiDAR) data. The absolute vertical accuracies are presented in terms of the mean error (ME), the mean absolute error (MAE) and the root mean square error (RMSE) and the relative vertical accuracies are characterised as per cent slope over Mount St Helens in southwest Washington State. The results show that LiDAR datasets can be reduced to 50 per cent density level by a uniform data reduction algorithm using triangulation with a linear interpolation method for the generalisation of DTM while still maintaining the quality of the original data.

Aerial Laser Scanning Data as a Source of Terrain Modeling in a Fluvial Environment: Biasing Factors of Terrain Height Accuracy

“…A smaller value of 0.2 resulted in more accurate models with all interpolation methods. We confirmed that, generally speaking, lesser points provided better input for DTM generation, which agrees with the results of [68,69,71]. However, lesser points did not mean the least points.…”

“…In previous works, e.g., [19,28,[67][68][69], it was found that noise reduction during preprocessing yielded a better digital terrain model, but on the other hand this procedure could sometimes reduce the accuracy of the model, as [70] found in their study. We had a hypothesis that the noise reduction of the point cloud results in more accurate models in our case, and we pointed out that different noise reduction techniques can have a significant effect on the input data which are the basis of the next stage of the process, i.e., ground point classification.…”

Rapid mapping application of vegetated terraces based on high resolution airborne LiDAR

“…The matter of input data density was, already discussed by several authors (e.g. Liu et al 2007;Yilmaz and Uysal 2016) when dealing with morphological description of landscape. The availability of sensors able to provide High Resolution Topography (HRT) is of great help in this field of research (Passalacqua et al 2015).…”

Using terrestrial LiDAR to measure water erosion on stony plots under simulated rainfall

“…Bare surface soil behind rock elements is difficult to sample when scanned by a single TLS scan, resulting in missing data (Becker et al, 2009;Wang et al, 2011). However, increasing the number of scan positions will increase data acquisition efforts, data storage and computational time (Yilmaz and Uysal, 2016). To minimize the amount of missing data due to occlusion, it is necessary to collect scans from different angles, and then merge them to attain a full terrain representation (Perroy et al, 2010;Liu et al, 2017).…”

“…Hence the scans complement each other to minimize the data occlusions. However, increasing the number of scan positions will increase data acquisition efforts, data storage and computational time (Yilmaz and Uysal, 2016). The number of scan positions should be designed to minimize the efforts needed to acquire and process data while minimizing occlusions in order to ensure the preservation of the important terrain features without sacrificing the level of accuracy (Soudarissanane et al, 2011;Shen et al, 2016).…”