# Assessment and Prediction of Impact of Flight Configuration Factors on UAS-Based Photogrammetric Survey Accuracy

^{*}

## Abstract

**:**

## 1. Introduction

- (1)
- flight height,
- (2)
- flight overlap,
- (3)
- the quantity of GCPs,
- (4)
- the focal length of the camera lens, and
- (5)
- the average image quality of each image dataset.

- (1)
- Evaluation of five main influence factors of UAS-based photogrammetric surveying and their significance level using the MR method.
- (2)
- MR modeling for prediction of the UAS-based photogrammetric accuracy with different flight configurations.

## 2. Background

#### 2.1. Flight Heights

#### 2.2. Image Overlap

#### 2.3. GCP Quantities and Distribution

_{X}, RMSE

_{Y}and RMSE

_{XY}mean ± standard deviation values were reached for 15 GCPs: 3.3 ± 0.346, 3.2 ± 0.441, 4.6 ± 0.340 and 4.5 ± 0.169 cm, respectively. Similar conclusions were derived for vertical accuracy: lower RMSE

_{Z}mean ± standard deviation values were reached for 15 and 20 GCPs: 5.8 ± 1.21 cm and 4.7 ± 0.860 cm, respectively. The generated accuracies from 15 GCPs had no noticeable difference.

#### 2.4. Georeferencing Methods

_{1}), and GNSS-supported Sensor Orientation (GNSS-SO) using PPK dual-frequency carrier-phase GNSS data (GNSS-SO

_{2}). The results GNSS-SO

_{2}produced the highest accuracy while the ISO method generated the lowest accuracy. A custom-built multi-sensor system for direct georeferencing was proposed by [1] enabled georeferencing to be performed without access to the mapping area. This system combined GNSS receiver with Real-Time Kinematic (RTK) and inertial navigation system (INS) to achieve high accuracy. This system was tested using 30 test points and reported using RMSE. The results showed that this system with micro or light UASs could ensure centimeter-level object accuracy. The highest accuracies in horizontal and vertical directions were 0.012 and 0.020 m using six well-distributed GCPs and the indirect georeferencing method.

#### 2.5. Multiple Factors

_{X}, RMSE

_{Y}, RMSE

_{XY}, and RMSE

_{Z}values equal to 0.038, 0.035, 0.053, and 0.049 m, respectively

## 3. Methodology

#### 3.1. Experimental Design

#### 3.2. Data Collection

- Flight Heights: 40 m (131 ft), 50 m (164 ft), 60 m (197 ft), and 70 m (229 ft)
- Image Overlap: 50%, 60%, 70%, 80%, and 90%
- Focal Length: 17 mm and 25 mm

#### 3.3. Data Processing

#### 3.4. Data Analysis

#### 3.4.1. Spatial Data Analysis

_{X}), northing RMSE (RMSE

_{Y}), horizontal RMSE (RMSE

_{R}), and vertical RMSE (RMSE

_{Z}). These values were computed as follows:

_{i}

_{(ortho),}and Y

_{i}

_{(ortho)}are the X (easting) and Y (northing) coordinates, respectively for the ith CPs as measured in the orthophoto; Z

_{i}

_{(DEM)}is the Z (height) coordinate for the ith checkpoint as measured in the Digital Elevation Model (DEM); and X

_{i}

_{(ortho)}, Y

_{i}

_{(ortho)}, and Z

_{i}

_{(DEM)}are the X, Y, and Z coordinates for the ith checkpoint as surveyed in the field.

#### 3.4.2. Statistical Analysis

_{R}, RMSEz, and pixels in ground units (GSD) were used to evaluate the model accuracy in statistical analysis. The MR analysis was used to quantitatively analyze the relationships among all the influence factors and outcomes to further identify the level of significance of influence factors on horizontal and vertical accuracies. Moreover, a prediction model was constructed to predict the RMSE in horizontal and vertical directions. The reason for choosing the MR method was that all the input and outcome variables were quantitative variables, and there were multiple input variables.

_{R}and RMSEz) were checked and considered. The reason to check the relationship between influence factors was to observe if there was an interaction existing. Interaction presents a particular type of non-linear relationship, which means the influence of an independent variable on the dependent variable varies at different values of another independent variable in the model. The reason to check the distribution of the dependent variables was to identify if the dependent variables followed a normal distribution. Although it was not required that the distribution be a normal distribution, it could eliminate the harmful effects and develop more accurate MR prediction models using a transformation when the distribution was very skewed.

_{R}and RMSEz. IBM SPSS Statistics (a statistical software suite developed by IBM for data management, advanced analytics, multivariate analysis, business intelligence, and criminal investigation. Long produced by SPSS Inc) and R-Studio (an integrated development environment for R, a programming language for statistical computing and graphics) were used to conduct the statistical analysis. The MR models were computed using the following regression equation for RMSE

_{R}and RMSEz, separately, for each flight configuration:

_{R}and RMSEz, or the value of transformed RMSE

_{R}and RMSEz, respectively, in this case. m represents the independent variables, which are influence factors in this paper, and m ranges from 1 to 5 since we have five influence factors. ${x}_{m}$ is the input variable of independent variables. ${\beta}_{0}$ is the intercept of $y$ (a constant). ${\beta}_{m}$ is the regression coefficient. ϵ means the model’s error term, also known as the residuals, which is negligible.

_{R}and RMSEz of 160 flight configurations and ${x}_{m}$ are known values. With them, ${\beta}_{m}$ can be computed followed by ${\beta}_{0}$. With these two coefficients, ‘y’s for RMSE

_{R}and RMSEz that are expected accuracies can be computed for newly collected datasets and are presented in the next section.

#### 3.5. Validation

#### Data Collection

## 4. Results and Discussion

#### 4.1. Influence and Significance of Five Influence Factors

_{Z}, RMSE

_{R}, average errors in pixels in the horizontal direction (average GSDR), and average errors in pixels in the vertical direction (average GSDZ) for the five influence factors. Average GSD was calculated through divided the mean RSME in (m) by average GSD. As can be seen in the subfigures for all influence factors, the mean of RMSE

_{Z}and average GSDZ are more sensitive to changes in influence factors than the mean of RMSE

_{R}and average GSDR. In other words, the influence factors have a stronger influence on vertical accuracy than horizontal accuracy.

_{Z}and average GSDZ than that of RMSE

_{R}and average GSDR. Using a shorter focal length of the camera lens produced higher accuracies in both horizontal and vertical directions. Figure 9b shows that the mean of RMSE

_{R}is more susceptible to the change in flight height than RMSE

_{Z}. A lower flight height yielded a higher accuracy in the horizontal direction. However, a higher flight height yielded a high vertical accuracy.

_{R}using more than eight GCPs. Additionally, Figure 9e shows that the datasets with higher image qualities tend to yield higher accuracies in both directions, although there are some variations.

_{Z}and RMSE

_{R}. The overlap and the GCP quantity have significant impacts with a 95% confidence level on RMSE

_{Z}since the p-values are smaller than 0.05. Compared to other factors, the image overlap has a substantial impact with the 95% confidence level on RMSE

_{R}since the p-values are smaller than 0.05. The focal length, flight height, and average image quality have a low significant influence on both RMSE

_{Z}and RMSE

_{R}. Both p-values of constant ${\beta}_{0}$ for RMSE

_{Z}and RMSE

_{R}are smaller than 0.05, which means it is essential to be included in the MR model to guarantee that the mean of residuals can be zero.

_{Z}and RMSE

_{R}. The X-axis shows the values of an influence factor. The Y-axis shows the values of estimated marginal means of the other influence factor. The estimated marginal mean represents the average response (RMSE values) of the influence factor on the Y-axis for each level of the influence factor on the X-axis.

_{Z}and RMSE

_{R}for influence factors are not parallel. That means the impact of one influence factor on RMSE

_{Z}and RMSE

_{R}is affected by varying the other influence factor.

_{z}are very similar, which indicates that the influence of change in focal length on different quantities of GCPs is not notably different.

#### 4.2. MR Prediction Model Development and Validation

#### 4.2.1. MR Model Development

_{Z}and RMSE

_{R}of the 160 flight combinations are right-skewed (the values of skewness of RMSE

_{Z}and RMSE

_{R}are 0.918 and 2.801, respectively). To improve the MR prediction model fitness, the distributions of RMSE

_{Z}and RMSE

_{R}need to be transformed to normal distributions, which can be performed by applying a logarithmic transformation. The following equations show the logarithmic transformation:

_{Z}and lgRMSE

_{R}are −0.024 and 1.098, separately, which means the new distributions are closer to a normal distribution. Thus, the MR prediction models are established using the values of lgRMSE

_{Z}, lgRMSE

_{R}, influence factors, and the interactions between the influence factors. By plugging these values into Equations (5) and (6), the following MR prediction models are developed for vertical and horizontal directions (${y}_{z}$ and ${y}_{r}$):

_{Z,}${y}_{ZR}$ is the estimated value of lgRMSE

_{R}. ${x}_{i1}$ is the value of focal length; ${x}_{i2}$ is the value of flight height; ${x}_{i3}$ is the value of image overlap; ${x}_{i4}$ is the value of the GCP quantity; and ${x}_{i5}$ is the value of average image quality. –4.234 and –1.353 are the constant (${\beta}_{0}$ of the MR prediction models). The rest of the constants are the parameters (${\beta}_{m}$) of influence factors.

#### 4.2.2. MR Prediction Models Applied to Test Site

_{Z}and lgRMZE

_{R}for flight missions 1 and 2. The predicted lgRMSE

_{Z}and lgRMZE

_{R}for flight mission 1 are −2.753 and −1.577, respectively. The predicted lgRMSE

_{Z}and lgRMZE

_{R}for flight mission 2 are −1.534 and −1.643, respectively. Then, an exponential function with the base of 10 is used to convert the values of predicted lgRMSE

_{Z}and lgRMZE

_{R}to the values of predicted RMSE

_{Z}and predicted RMSE

_{R}. The following are the exponential function for predicted RMSE

_{Z}and predicted RMSE

_{R.}

_{Z}from Pix4DMapper and the predicted RMSE

_{Z}from the MR model for all flight missions are 0.3 cm and 0.7 cm, respectively. The differences between RMSE

_{R}from Pix4DMapper and predicted RMSE

_{R}from the MR model for all flight missions are from 0.2 cm to 0.5 cm. The differences between pixel error in the Z direction from Pix4DMapper and predicted MR model for all flight missions are 0.06 GSD and 0.49 GSD, respectively. The differences between pixel error in the R direction from Pix4DMapper and the predicted MR model for all flights are from 0.13 GSD to 0.3 GSD. Table 8 shows the Prediction Error Rate and Accuracy of the Butner UAS Test Site from Pix4DMapper and MR Models. The error rates of the RMSE

_{Z}are 16.67% and 27.59%, which leads to the prediction accuracies of the MR prediction model being 72.41% and 83.33%, respectively. The MR prediction model has a lower error rate when estimating horizontal accuracy. The error rates of the RMSE

_{Z}are 7.69% and 8.7%, and the prediction accuracies of the MR prediction model are 92.31% and 91.30%, respectively. The following equations show the error rate and prediction accuracy.

#### 4.3. Practical Implications

#### 4.4. Limitations and Future Work

## 5. Conclusions

_{Z}and RMSE

_{R}based on site conditions on a facility site. Moreover, the developed MR prediction models were validated using other datasets collected from the Butner site with s similar terrain type. The validation result shows the difference in the accuracy between MR prediction models and Pix4DMapper is less than 0.007 m, which proves the MR prediction models can be used to estimate the horizontal and vertical accuracies based on flight configurations and site conditions at a facility site with open space at a 72% prediction accuracy. The findings of this study can help surveyors better design flight configurations given different site conditions and constraints. Furthermore, the findings of this research can provide a basic understanding of what levels of accuracy could be achieved using different flight configurations. Moreover, agencies such as state DOTs can generate their prediction models based on their data and sets of equipment used on the interesting sites using our proposed method, especially on some repeated similar scenarios, such as construction progress monitoring.

## Author Contributions

## Funding

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

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**Figure 5.**DJI Inspire II UAS: (

**a**) DJI Inspire II drone, (

**b**) DJI Zenmuse X5S camera and an Olympus M.Zuiko lens.

**Figure 9.**Mean of RMSE

_{Z}, RMSE

_{R}, and average GSD for Five Influence Factors: (

**a**) Focal Length; (

**b**) Flight Heights; (

**c**) Image Overlaps; (

**d**) GCP Quantities; and (

**e**) Average Image Quality.

Number of Influence Factors | Factors | Authors | Research Description | Highest MAE/RMSE Horizontal | Highest MAE/RMSE Vertical |
---|---|---|---|---|---|

Single Influence Factors | Flight Height | [27] | Evaluate the influence of flight height and area coverage orientations on the DSM and orthophoto accuracies for flood damage assessment. | N/A (consistently lower than 0.05 m, i.e., not more than 1–2 pixels) | N/A |

[30] | Provide a solution of data collection and processing of UAS application in complex forest environment | N/A | N/A | ||

GCP Quantity and Distribution | [10] | Assess the influence of numbers of GCPs on DSM accuracy. | N/A | 0.057 m | |

[19] | Propose an algorithm to calculate the sparse point cloud roughness using associated angular interval. | N/A | N/A | ||

[28] | Evaluate the impact of GCP quantities on UAS-based photogrammetry DSM and orthoimage accuracies. | 0.053 m | 0.049 m | ||

[35] | Assess the influence of different grades of tree covers and GCP quantities and distributions on UAS-based point cloud in forest areas. | 0.031 m | 0.058 m | ||

[2] | Evaluate the influence of additional GCPs on spatial accuracy when AAT is applied for georeferencing. | N/A | N/A | ||

[38] | Identify the GCP quantities and distributions to generate a high accuracy for a corridor-shaped site. | 0.027 m | 0.055 m | ||

[39] | Evaluate the effect of the location and quantity of GCPs on UAS-based DSMs in Glaciers. | N/A | N/A | ||

[40] | Evaluate the impact of GCP quantities and distributions on UAS-based photogrammetry DSM and orthoimage accuracies. | 0.035 m | 0.048 m | ||

[41] | Provide a solution about the optimal GCP quantity to generate high precision 3D models. | N/A | N/A | ||

[42] | Provide information of the optimal GCP deployment for dam structures and high-rise structures. | 0.057 m | 0.012 m | ||

[43] | Analyze the influence of the quantities and numbers of GCPs on 3D model accuracy. | N/A | N/A | ||

[44] | Analyze the influence of GCP quantities on UAS photogrammetric mapping accuracy using RTK-GNSS system. | N/A | 0.003 m | ||

[45] | Analyze 3D model and DSM accuracies to determine the optimal GCP quantities in various terrain types. | 0.044 m | 0.036 m | ||

Camera Setting | [32] | Analyze the influence of photogrammetric process elements on the quality of UAS-based photogrammetric accuracy to identify artificial lighting at night | N/A | N/A | |

[34] | Evaluate the influence of camera sensor types and configurations and SfM processing tools on UAS mapping accuracy. | N/A | N/A | ||

[46] | Analyze the influence of the ground control quality and quantity on DEM accuracy using a Monte Carlo Method. | N/A | N/A | ||

[47] | Generate a larger virtual image from five head cameras | 2.13 pixels | N/A | ||

[48] | Investigate three issues of corridor aerial image block, including: focal length error, a gradually varied focal length, and rolling shutter effects. | 0.007 m | 0.008 m | ||

Image Acquisition | [29] | Evaluate the impact of image parameters on the close-range UAS-based photogrammetric inspection accuracy. | N/A | N/A | |

[49] | Evaluate the impact of image formats and levels of JPEG compression in UAS-based photogrammetric accuracy. | N/A | N/A | ||

[50] | Evaluate the influence of low-height UAS photogrammetry systems on stable images, data processing and accuracy. | N/A | 0.059 m | ||

Georeferencing Methods | [1] | Introduces a custom-built multi-sensor system for direct georeferencing. | 0.012 m | 0.020 m | |

[14] | Evaluate the impact of GNSS receivers of techniques features and working modes on positioning accuracy. | N/A | N/A | ||

[15] | Evaluate the geometric accuracy of using four different georeferencing techniques | 0.023 m | 0.03 m | ||

[33] | Evaluate the quality of photogrammetric models and DTMs using PPK and RTK modes in coastline areas. | N/A | 0.016 m | ||

[51] | Analyze the impact of UAS blocks and georeferencing methods on accuracy and repeatability. | 0.016 m | 0.014 m | ||

[52] | Evaluate the influence of image block orientation methods on the accuracy of estimated forest attributes, especially the plot mean tree height. | N/A | N/A | ||

[53] | Analyze the influence of different UAS platforms on positional and within-model accuracies without GCPs. | N/A | N/A | ||

[54] | Provide operational guidelines and best practices of direct georeferencing methods on positional accuracy. | N/A | 0.019 m | ||

[55] | Assess the influence of GNSS with PPK on the UAS-based accuracy in building surveying applications. | N/A | 0.01 m | ||

[56] | Assess the influence of RTK/PPK on geospatial accuracies of photogrammetric products in forest areas. | 0.003 m | 0.006 m | ||

Flight Height and Image Acquisition | [11] | Provide scientific evidence of the impact of flight height, image overlap, and image resolution on forest area reconstruction. | N/A | N/A | |

Multiple Influence Factors | GCP Quantity and Distribution and Georeferencing Methods | [12] | Evaluate the impact of cross flight patterns, GCP distributions, and RTK-GNSS on camera self-calibration and bundle block adjustment quality. | N/A | N/A |

Camera Setting and Image Acquisition | [31] | Evaluate the impact of image resolution, camera type, and side overlap on predicted biomass model accuracy. | N/A | N/A | |

Flight Height and GCP Quantity and Distribution and Image Acquisition | [57] | Evaluate the impact of flight heights, terrain types, and GCP quantities on DSM and orthoimage accuracy in UAS-based photogrammetry | 0.000169 m | 0.047 m | |

[58] | Evaluate the impact of flight height, image overlap, GCPs quantities and distribution, and time of survey on snow depth measurement. | N/A | N/A | ||

[59] | Analyze the impact of flight heights and quantities and distribution of GCPs on survey error. | N/A | N/A | ||

GCP Quantity and Distribution and Camera Setting | [60] | Evaluate the influence of camera calibration methods as well as quantities and distributions of GCPs on UAS photogrammetry accuracy. | 1.3 mm | 5.1 mm | |

Flight Height and GCP Quantity and Distribution and Camera Setting | [61] | Assess the impact of flight height, image overlap, GCP quantities, and construction site conditions on measurement accuracy. | N/A | 0.085 m |

Flight No. | Focal Length (mm) | Flight Height (m) | Overlap (%) | Average Image Quality | No. of Image |
---|---|---|---|---|---|

1 | 25 | 40 | 90 | 0.88 | 539 |

2 | 25 | 40 | 80 | 0.23 | 161 |

3 | 25 | 40 | 70 | 0.30 | 94 |

4 | 25 | 40 | 60 | 0.22 | 57 |

5 | 25 | 40 | 50 | 0.96 | 48 |

6 | 25 | 50 | 90 | 0.29 | 473 |

7 | 25 | 50 | 80 | 0.90 | 156 |

8 | 25 | 50 | 70 | 0.24 | 64 |

9 | 25 | 50 | 60 | 0.63 | 48 |

10 | 25 | 50 | 50 | 0.25 | 39 |

11 | 25 | 60 | 90 | 0.60 | 391 |

12 | 25 | 60 | 80 | 0.34 | 86 |

13 | 25 | 60 | 70 | 0.95 | 47 |

14 | 25 | 60 | 60 | 0.28 | 30 |

15 | 25 | 60 | 50 | 0.18 | 22 |

16 | 25 | 70 | 90 | 0.32 | 171 |

17 | 25 | 70 | 80 | 0.40 | 98 |

18 | 25 | 70 | 70 | 0.31 | 39 |

19 | 25 | 70 | 60 | 0.33 | 30 |

20 | 25 | 70 | 50 | 0.58 | 20 |

21 | 17 | 40 | 90 | 0.49 | 345 |

22 | 17 | 40 | 80 | 1.01 | 148 |

23 | 17 | 40 | 70 | 0.48 | 55 |

24 | 17 | 40 | 60 | 1.01 | 46 |

25 | 17 | 40 | 50 | 0.63 | 35 |

26 | 17 | 50 | 90 | 0.92 | 321 |

27 | 17 | 50 | 80 | 0.37 | 77 |

28 | 17 | 50 | 70 | 0.37 | 48 |

29 | 17 | 50 | 60 | 0.63 | 31 |

30 | 17 | 50 | 50 | 0.63 | 21 |

31 | 17 | 60 | 90 | 0.43 | 226 |

32 | 17 | 60 | 80 | 0.63 | 85 |

33 | 17 | 60 | 70 | 0.65 | 48 |

34 | 17 | 60 | 60 | 0.92 | 28 |

35 | 17 | 60 | 50 | 0.51 | 23 |

36 | 17 | 70 | 90 | 0.49 | 120 |

37 | 17 | 70 | 80 | 0.40 | 75 |

38 | 17 | 70 | 70 | 0.50 | 30 |

39 | 17 | 70 | 60 | 0.39 | 30 |

40 | 17 | 70 | 50 | 0.42 | 20 |

Processing Step | Parameters | Value |
---|---|---|

Alignment | Key points Image Scale | Full |

Image Scale for Alignment | Original Size | |

Matching Image Pairs | Aerial Grid or Corridor | |

Calibration | Targeted Number of Key points | Automatic |

Calibration Method | Standard | |

Camera Optimization | Internal Parameters Optimization | All |

External Parameters Optimization | All | |

Dense Point Cloud Generation | Image Scale for Point Cloud Densification | Original Size with Multiscale |

Point Density | High | |

Minimum Number of Match | 3 |

Processing Step | Parameters | Value |
---|---|---|

Processing Setting | Colorization | Colorize Scans |

Find Targets | Find Checkerboards | |

Registration | Automatic Registration | Target Based |

Optimization and Verify | Cloud to Cloud |

Flight Mission | Flight Height (m) | Overlap (%) | Focal Length (mm) | GCP Quantities | Average Image Quality | No. of Images | GSD (cm) |
---|---|---|---|---|---|---|---|

1 | 116 | 90 | 25 | 13 | 0.85 | 684 | 1.6 |

2 | 86 | 80 | 17 | 12 | 0.48 | 280 | 1.65 |

3 | 116 | 90 | 25 | 10 | 0.85 | 684 | 1.6 |

4 | 86 | 70 | 17 | 12 | 0.48 | 280 | 1.65 |

5 | 86 | 70 | 17 | 10 | 0.48 | 140 | 1.65 |

p-Value for RMSE_{Z} | p-Value for RMSE_{R} | |
---|---|---|

Constant ${\beta}_{0}$ | 0.009 | <0.001 |

Focal Length | 0.773 | 0.057 |

Flight Height | 0.438 | 0.367 |

Image Overlap | 0.015 | <0.001 |

GCP Quantity | 0.027 | 0.126 |

Average Image Quality | 0.427 | 0.103 |

Flight Mission | RMSE_{Z} from Pix4D (cm) | Z Direction Pixel Error from Pix4D | RMSE_{R} from Pix4D (cm) | R Direction Pixel Error from Pix4D | Predicted RMSE_{Z} from MR Model (cm) | Predicted Z Direction Pixel Error from MR Model | Predicted RMSE_{R} from MR Model (cm) | Predicted R Direction Pixel Error from MR Model |
---|---|---|---|---|---|---|---|---|

1 | 2.1 | 1.27 GSD | 2.4 | 1.50 GSD | 1.8 | 1.13 GSD | 2.6 | 1.63 GSD |

2 | 2.1 | 1.27 GSD | 2.5 | 1.52 GSD | 2.9 | 1.76 GSD | 2.3 | 1.39 GSD |

3 | 2.7 | 1.69 GSD | 2.9 | 1.81 GSD | 2.6 | 1.63 GSD | 3.1 | 1.94 GSD |

4 | 3.2 | 1.94 GSD | 3.1 | 1.88 GSD | 3.4 | 2.06 GSD | 2.6 | 1.58 GSD |

5 | 3.5 | 2.12 GSD | 3.3 | 2.00 GSD | 3.0 | 1.82 GSD | 2.8 | 1.70 GSD |

RMSE_{Z} Error Rate | RMSE_{R} Error Rate | Prediction Accuracy | Prediction Accuracy |
---|---|---|---|

16.67 | 7.69 | 83.33 | 92.31 |

27.59 | 8.70 | 72.41 | 91.30 |

3.70 | 6.90 | 96.3 | 93.1 |

6.25 | 16.13 | 93.75 | 83.87 |

14.29 | 15.15 | 85.71 | 84.85 |

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## Share and Cite

**MDPI and ACS Style**

Liu, Y.; Han, K.; Rasdorf, W. Assessment and Prediction of Impact of Flight Configuration Factors on UAS-Based Photogrammetric Survey Accuracy. *Remote Sens.* **2022**, *14*, 4119.
https://doi.org/10.3390/rs14164119

**AMA Style**

Liu Y, Han K, Rasdorf W. Assessment and Prediction of Impact of Flight Configuration Factors on UAS-Based Photogrammetric Survey Accuracy. *Remote Sensing*. 2022; 14(16):4119.
https://doi.org/10.3390/rs14164119

**Chicago/Turabian Style**

Liu, Yajie, Kevin Han, and William Rasdorf. 2022. "Assessment and Prediction of Impact of Flight Configuration Factors on UAS-Based Photogrammetric Survey Accuracy" *Remote Sensing* 14, no. 16: 4119.
https://doi.org/10.3390/rs14164119