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

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## Abstract

**:**

## 1. Introduction

## 2. Study Area and Topographic Characterization

^{2}and is characterized by point bar and swale series (Figure 1b, Figure 2a), which has remained as a consequence of the continuous lateral movement of the former Tisza River bed. It was selected due to its diverse environment. The widths of these landforms are various, mostly ranging between 10 and 30 m, but there are some narrower ones (3–5 m), and some are really well spread, with a width of more than one hundred meters. Differences in terrain height in most of the cases are less than one meter (0.3–1 m) between the point bars and swales situated next to each other. The different morphology of the two landforms—point bars are positive, swales are negative forms—cause essential discrepancies: e.g., the ground water level is closer to the surface in the swales due to their concave shapes, and also precipitation and snowmelt run off from the concave form and gather here (Figure 1b). Besides, they have differences in their granulometric composition, with swales having finer sediments that also slow down the infiltration of the water [46]. All these features provide a higher percentage of moisture in swales, which support denser vegetation (that in some cases becomes impervious) and afford good conditions for aquatic vegetation (reed, sedge, etc.). In contrast, the vegetation density of point bars is relatively sparse compared to swales, except when they lie in a relatively lower part of the floodplain, because in this case their surface can be also covered by dense reeds. In Figure 2b,c, we highlight the pattern of the vegetation as it is shown in a portion of the 3D view of a point bar and swale series. The density of the vegetation points is higher and that of the ground points is lower in the case of the swales. In our previous work [50] we also quantified this fact.

## 3. Materials and Methods

#### 3.1. Aerial LiDAR Dataset

#### 3.2. Data Preparation

#### 3.2.1. Neighborhood Distance-Based Filter

#### 3.2.2. Surface Distance-Based Filter

#### 3.2.3. Ground Point Classification

#### 3.3. DTM Generation

#### 3.4. Validation and Statistics Analyses

_{0}was that there was no difference between the number of points per square meter of the fluvial forms.

- -
- df: degree of freedom
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- F: F-statistic
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- p: significance
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- pmc: Monte Carlo simulation based p-value (significance)
- -
- Q: Q-statistic for 2-way ANOVA (analysis of variance)
- -
- r: Spearman correlation
- -
- W: Wilcoxon test statistic
- -
- z: z-score

## 4. Results

#### 4.1. Number of Points and Accuracy

#### 4.2. Effect of Noise Reduction and the CSF Parameters

^{−4}; p < 0.001). In the following step, we analyzed the effects of CSF parameters, the cloth sizes, and thresholds (Figure 4). The smallest differences were obtained using the point clouds with noise reduction and the cloth size of 5, and a threshold of 0.2 occurred in the models (the difference was −0.08 m in relation to the reference). In the case of the original point cloud, these parameters provided the worst model, having the poorest accuracy (−0.12 m).

#### 4.3. Consequences of the Interpolation Algorithms

^{−5}; p < 0.001; Figure 8). The LI usually resulted in models with the largest differences, and the NA interpolation was the most accurate. Although TT, TD, and TH were not the most accurate, there was another important issue to be considered: the narrower range of data. The most accurate model, based on the median difference, was the one with the NA interpolation with a cloth size of 5 and a threshold of 0.2; however, this model had the highest outliers in a positive direction. The TT, TD, and TH models were the models with minimal differences in medians (and insignificant differences based on the post hoc test).

#### 4.4. Effect of the Resolution on the Accuracy

#### 4.5. Effects of the Noise Reduction and the Ground Point Classification on the Fluvial Forms

^{2}values in each model except in two cases (both distance- and neighborhood-based noise reduction models with the cloth size of 5 and the threshold of 0.2). Accordingly, the difference was significant (W = 140; z = 2.373; p

_{MC}= 0.015).

^{2}for point bars and 1.90 points/m

^{2}for swales (Figure 10). Furthermore, the best CSF parameters were in accordance with the previous results, and the fewest points, the cloth size of 5, and the threshold of 0.2 resulted in the highest accuracy. The noise reduction was efficient, and, although the difference between them was slight (less than 0.01 m), the neighborhood-related filtering was the most effective. Resolution had a significant effect on the accuracy, but the difference was only 0.009–0.011 m between the 1 and 2 m geometric resolution models. Generally, the accuracy of the swales was always below that of the point bars by 0.057–0.069 m.

## 5. Discussion

^{2}; furthermore, calculating with multiple echoes, it can even reach 10 points/m

^{2}[50]. Accordingly, a finer cloth size would have been reasonable, the recommendation of the developer is one-third of the point spacing (http://ramm.bnu.edu.cn/researchers/wumingzhang/english/default_contributions.htm), but according to the mean difference between the two settings, the 5 m cloth was 0.012 m better (for the neighborhood-related filter (Figure 4)). The second CSF parameter is the threshold. A threshold value of 0.5 was suggested by [29], but this was not the best setting in our case. 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. A distance-based filter resulted in the least points, whereas the dataset of the neighborhood-based filter was the best input for the interpolation (Table 2).

^{2}was significantly smaller (5.88 vs. 4.91). NDVI differed significantly (F = 1567, p < 0.001), which was a relevant background factor which influenced the model accuracy when investigating these fluvial forms: removing the dense vegetation from the surface cannot be as accurate for swales as point bars.

## 6. Conclusions

## Supplementary Materials

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 1.**(

**a**) The location of the study area; (

**b**) a photo from the studying area showing point bar and swale series, captured by drone by Csaba Albert Tóth, 2017.

**Figure 2.**The characteristics of the study area. (

**a**) It is characterized by a well-developed point bar and swale series; (

**b**) the widths of bars and swales are various; (

**c**) the point cloud of the study (exaggeration: 4) reflects back that swales usually have denser vegetation than point bars.

**Figure 3.**The workflow of the analysis (full factorial analysis in all possible combinations, altogether there were 180 models).

**Figure 4.**Difference between ground reference and the modeled values by noise filtering methods (d: surface distance-based, n: neighborhood distance-based filtering, o: original point cloud) and cloth simulation filter (CSF) settings (cloth size | threshold).

**Figure 5.**Mean differences (m) between the cloth size (

**a**) and the threshold (

**b**) parameters according to the noise filtering (d: surface distance-based, n: neighborhood distance-based filtering, o: original point cloud).

**Figure 6.**Boxplots of model accuracies ordered by medians for 1 m resolution (o: original database, d: distance-based noise filter, n: neighborhood-based noise filter; first number: cloth size parameter; second number: threshold parameter; LI: linear interpolation, NA: natural neighbor interpolation, TD: terrain dataset with natural neighbor interpolation; TH: terrain dataset with thinning and natural neighbor interpolation; TT: topo to raster interpolation).

**Figure 7.**The best and the worst models according to the range of medians and the differences between them: (

**a**) o|2|0.2|LI: o: original database; first number: cloth size parameter (2); second number: threshold parameter (0.2); LI: linear interpolation; (

**b**) n|5|0.2|NA: n: neighborhood-based noise filter; first number: cloth size parameter (5); second number: threshold parameter (0.2); NA: natural neighbor interpolation. (

**c**) The difference between o|2|0.2|LI and n|5|0.2|NA terrain models.

**Figure 8.**Mean differences by interpolation types (LI: linear interpolation; NA: natural neighbor interpolation; TD: terrain dataset with natural neighbor interpolation; TH: terrain dataset with thinning and natural neighbor interpolation; TT: topo to raster interpolation; error bars: 95% confidence intervals; insignificant differences: where error bars intersected the 0 value, this is shown by the vertical dashed line).

**Figure 9.**Interaction plot of resolution and noise filtering (

**a**) and resolution and interpolation techniques (

**b**) (LI: linear interpolation; NA: natural neighbor interpolation; TD: terrain dataset with natural neighbor interpolation; TH: terrain dataset with thinning and natural neighbor interpolation; TT: topo to raster interpolation; d: surface distance-based; n: neighborhood distance-based filtering; o: original point cloud).

**Figure 10.**The effect of the noise reduction and the ground point classification on the point bars and swales.

Parameters | Value |
---|---|

Designed point density | 4 pts/m^{2} |

Average accuracy (horizontal and vertical) | ±0.15 m |

Overlap | 30–60% |

Pulse repetition rate | 270 kHz |

Registration | discrete return |

Laser wavelength | 1550 nm |

AGL height | 688 m |

Extent of the surveyed area | 126 ha |

**Table 2.**Accuracies as reflected in the noise reduction and CSF parameters (o: original LAS dataset; d: distance-based filter with island detection; n: neighborhood-based filter; CS: cloth size; Thd: threshold; SD: standard deviation).

Filtering Method | Noise Reduction | CSF Parameters (CS; Thd) | Point Number | Accuracy (mean ± SD; m) |
---|---|---|---|---|

Original point cloud | - | - | 10,120,880 | −0.15 ± 0.17 |

Noise filter | d | - | 8,718,994 | −0.13 ± 0.15 |

n | - | 10,073,485 | −0.12 ± 0.15 | |

Ground point filter | d | 2; 1 | 6,943,468 | −0.15 ± 0.17 |

d | 2; 0.2 | 5,199,607 | −0.12 ± 0.13 | |

d | 2; 0.5 | 6,375,149 | −0.14 ± 0.14 | |

d | 5; 1 | 6,875,994 | −0.15 ± 0.17 | |

d | 5; 0.2 | 3,720,552 | −0.09 ± 0.16 | |

d | 5; 0.5 | 5,905,299 | −0.13 ± 0.14 | |

n | 2; 1 | 8,050,253 | −0.14 ± 0.16 | |

n | 2; 0.2 | 5,958,207 | −0.12 ± 0.13 | |

n | 2; 0.5 | 7,395,394 | −0.14 ± 0.14 | |

n | 5; 1 | 7,971,242 | −0.14 ± 0.16 | |

n | 5; 0.2 | 4,246,638 | −0.09 ± 0.16 | |

n | 5; 0.5 | 6,842,756 | −0.13 ± 0.14 | |

o | 2; 1 | 8,293,970 | −0.15 ± 0.18 | |

o | 2; 0.2 | 6,999,426 | −0.15 ± 0.16 | |

o | 2; 0.5 | 7,837,259 | −0.15 ± 0.16 | |

o | 5; 1 | 8,287,750 | −0.15 ± 0.18 | |

o | 5; 0.2 | 6,729,067 | −0.16 ± 0.16 | |

o | 5; 0.5 | 7,781,547 | −0.15 ± 0.16 |

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Szabó, Z.; Tóth, C.A.; Holb, I.; Szabó, S. Aerial Laser Scanning Data as a Source of Terrain Modeling in a Fluvial Environment: Biasing Factors of Terrain Height Accuracy. *Sensors* **2020**, *20*, 2063.
https://doi.org/10.3390/s20072063

**AMA Style**

Szabó Z, Tóth CA, Holb I, Szabó S. Aerial Laser Scanning Data as a Source of Terrain Modeling in a Fluvial Environment: Biasing Factors of Terrain Height Accuracy. *Sensors*. 2020; 20(7):2063.
https://doi.org/10.3390/s20072063

**Chicago/Turabian Style**

Szabó, Zsuzsanna, Csaba Albert Tóth, Imre Holb, and Szilárd Szabó. 2020. "Aerial Laser Scanning Data as a Source of Terrain Modeling in a Fluvial Environment: Biasing Factors of Terrain Height Accuracy" *Sensors* 20, no. 7: 2063.
https://doi.org/10.3390/s20072063