# DEM Generation from GF-7 Satellite Stereo Imagery Assisted by Space-Borne LiDAR and Its Application to Active Tectonics

^{1}

^{2}

^{3}

^{4}

^{5}

^{*}

## Abstract

**:**

_{Method1}, GF-7 DEM

_{Method2}, and GF-7 DEM

_{Method3}, respectively, and were verified by the airborne LiDAR data in the Hasishan section of the Haiyuan fault. Second, the capability of the GF-7 DEMs for identifying active faults, fault scarps, and horizontal offsets was evaluated. Finally, 8 vertical and 13 horizontal offsets were measured based on three different GF-7 DEMs, and airborne LiDAR data were used to verify the measurements’ accuracies. The results indicated that the accuracy of GF-7 DEM

_{Method1}was the worst and that of GF-7 DEM

_{Method3}was superior to that of GF-7 DEM

_{Method2}. The GF-7 DEMs could effectively identify the apparent fault scarps and horizontal offsets. The RMSE values of the vertical offsets measured based on GF-7 DEM

_{Method1}, GF-7 DEM

_{Method2}, and GF-7 DEM

_{Method3}were 0.55 m, 0.55 m, and 0.41 m, respectively. The horizontal offsets yielded RMSE values of 3.98 m, 2.52 m, and 1.37 m, respectively. These findings demonstrated that vertical and horizontal offsets could be accurately measured using the DEMs generated from GF-7 stereo images. Meanwhile, our study indicated that the GCPs derived from ICESat-2 data could be utilized to improve the accuracies of the GF-7 DEM, and the measurements of vertical and horizontal offsets.

## 1. Introduction

## 2. Study Site and Materials

#### 2.1. Study Site

#### 2.2. GF-7 Data

#### 2.3. ICESat-2/ATLAS Data

#### 2.4. Airborne LiDAR Data

^{2}. The triangulated irregular network (TIN) interpolation algorithm was utilized to generate DEM products with a spatial resolution of 1 m based on the cloud points. The coordinate system of the produced DEM was the WGS84 universal transverse Mercator (UTM) projection system. The accuracy of the airborne LiDAR DEM’s horizontal and vertical elevation was 0.3 m and 0.2 m, respectively [3,31]. On the basis of the airborne LiDAR-derived DEM, 20 checkpoints with apparent marks were selected to evaluate the horizontal and vertical accuracies of the DEMs extracted from the GF-7 stereo images (Figure 2a). In addition, 8 positions were selected for measurements of the vertical offset and 13 for measurements of the horizontal offset (Figure 2b).

#### 2.5. Ancillary Data

## 3. Methods

#### 3.1. Extraction of GCPs from ICESat-2/ATLAS Data

#### 3.2. Extraction of DEMs from GF-7 Stereo Images

_{1}, p

_{2}, p

_{3}, and p

_{4}are the RPCs; ($\Delta c$, $\Delta r$) are the compensation values of the image coordinates; and a

_{i}, b

_{i}(i = 0, 1, 2) are the affine transformation coefficients.

- Block adjustment without GCPs. This method was essential for selecting some tie points in the GF-7 images and calculating the object space coordinates for each pair of GF-7 stereo pixels, utilizing the space-forward intersection. The final object space coordinate of the tie point was the average coordinate of the tie points between each pair of GF-7 stereo pixels. These tie points were utilized as the virtual GCPs in the block adjustment process.
- Block adjustment with the aid of geographic information system (GIS) data. This method utilized the existing GIS data (such as digital orthophoto maps (DOM) and DEMs) to assist the block adjustment of stereo images. First, a large number of cognominal points were obtained through the automatic registration of the GF-7 satellite images and the existing DOM. Second, the DOM and DEM were used to obtain these cognominal points’ horizontal and elevation coordinates, respectively. Finally, these cognominal points were taken as the control points. In addition to the basic geographic information data (the DOM and DEM), public geographic information data can also be utilized. The most commonly used public geographic information data are Google Earth images and SRTM DEM [21,22,23].
- Block adjustment with GCPs. This method used high-precision GCPs to constrain the elevation values of the forward intersection of the GF-7 stereo images. The most commonly used GCPs can be obtained from space-borne LiDAR data, such as ICESat-1/GLAS and ICESat-2/ATLAS.

_{Method1}, GF-7 DEM

_{Method2}, and GF-7 DEM

_{Method3}, respectively. The coordinate system of these generated GF-7 DEMs was WGS 84.

#### 3.3. Measurement of the Horizontal and Vertical Offsets

_{1}and h

_{2}are the fault scarp heights extracted from the fitting line of the hanging wall (L1) and the footwall (L2), respectively; z

_{i}is the DEM’s elevation value at the location x

_{i}; ${z}_{L{1}_{i}}$ is the fitted value of the elevation of the hanging wall’s fitting line (L1) at location x

_{i}; and ${z}_{L{2}_{i}}$ is the fitted value of the elevation value of the footwall’s fitting line (L2) at location x

_{i}.

#### 3.4. Validation of Accuracy

^{2}, Equation (5)), bias (Equation (6)), and the root mean square error (RMSE, Equation (7)).

_{i}is the i

_{th}horizontal or vertical offset value extracted from the airborne LiDAR-derived DEM, x

_{i}is the i

_{th}horizontal or vertical offset value extracted from the GF-7 DEM, $\overline{y}$ is the average value of the horizontal or vertical offsets extracted from the airborne LiDAR-derived DEM, and n is the number of horizontal or vertical offsets.

## 4. Results and Discussion

#### 4.1. Validation of the Accuracy and a Comparison of the GF-7 DEMs

_{Method1}were extremely large, namely 178.24 m, 198.90 m, and 110.58 m in the X, Y, and Z directions. The ΔX values gradually increased while the ΔZ values decreased with an increase in the number of checkpoints along the fault. The GF-7 DEM generated by [28] using the block adjustment method without GCPs yielded a plane positioning accuracy of 4.26 m and an elevation accuracy of 8 m. However, the plane positioning and elevation errors of GF-7 DEM

_{Method1}exceeded 100 m. The reason may be that the GF-7 stereo images used in this paper were mainly collected during the initial operation phase. At this stage, the geometric calibration of GF-7 in orbit was not yet mature, leading to relatively large errors in the horizontal and vertical positioning. The obtained accuracies of GF-7 DEM

_{Method2}and GF-7 DEM

_{Method3}were superior to that obtained with DEM

_{Method1}. The RMSE values of GF-7 DEM

_{Method3}in the X, Y, and Z directions were 1.38 m, 1.73 m, and 1.35 m, respectively. This indicates that the use of GCPs extracted from ICESat-2/ATLAS data significantly improved the accuracy of the generated GF-7 DEMs.

_{Method1}point clouds mainly ranged from −10 m to 10 m, with a bias of 1.2 m and a standard deviation (STD) of 3.23 m. In contrast to GF-7 DEM

_{Method1}, GF-7 DEM

_{Method2}and GF-7 DEM

_{Method3}yielded lower biases of –1.03 m and 0.03 m, and lower STD values of 2.05 m and 1.54 m, respectively. The average difference in the elevation of 0.03 m indicated that GF-7 DEM

_{Method3}was highly consistent with the airborne LiDAR data, demonstrating that the use of GCPs extracted from ICESat-2/ATLAS data can decrease the differences in elevation between GF-7 DEM and airborne LiDAR data, and improve the accuracy of the elevation of GF-7 DEMs.

_{Method1}and GF-7 DEM

_{Method3}were superior to GF-7 DEM

_{Method2}for reproducing the slope of the terrain.

#### 4.2. Observations of the Offset Based on GF-7 DEM

#### 4.3. Comparison of the Measurements of the Vertical Offsets

_{Method1}, GF-7 DEM

_{Method2}, and GF-7 DEM

_{Method3}. The GF-7 DEMs could reconstruct the fault profile shape with a scarp height larger than 2 m, but significant errors could be observed in the fault profile shape reconstructed by the GF-7 DEMs for small vertical scarps, such as V6 and V8. Figure 13 represents the error scatterplot between the vertical offsets extracted from the airborne LiDAR-derived DEM and the three different GF-7 DEMs. The bias and RMSE values of the vertical offsets measured by GF-7 DEM

_{Method1}were –0.32 m and 0.55 m. Compared with the significant error in the elevation of GF-7 DEM

_{Method1}(RMSE = 110.58 m), the accuracy of the vertical offsets measured by GF-7 DEM

_{Method1}improved significantly, indicating that the absolute elevation error of GF-7 DEM

_{Method1}was large while the relative elevation error was small. The RMSE and bias values of the vertical offsets measured by GF-7 DEM

_{Method2}were the same as or smaller than those of GF-7 DEM

_{Method1}. Among these three GF-7 DEMs, the accuracy of the vertical offsets measured by GF-7 DEM

_{Method3}is the highest, with a bias value of –0.19 m and an RMSE value of 0.41 m. This means that the GCPs extracted from ICESat-2/ATLAS data can slightly improve the accuracy of the measurements of the vertical offsets.

#### 4.4. Comparison of the Measurements of the Horizontal Offset

_{Method1}, GF-7 DEM

_{Method2}, and GF-7 DEM

_{Method3}. The horizontal offsets extracted by the GF-7 DEMs were compatible with those extracted by the airborne LiDAR-derived DEM. Table 5 lists the R

^{2}, bias, and RMSE values of the horizontal offsets extracted by the airborne LiDAR-derived DEM and the GF-7 DEMs. The errors in the horizontal offsets measured by GF-7 DEM

_{Method1}were the highest, with a bias value of –1.80 m and an RMSE value of 3.98 m. The possible reason is that GF-7 DEM

_{Method1}had a specific deformation (the horizontal positioning errors in different locations were not constant). Compared with GF-7 DEM

_{Method1}and GF-7 DEM

_{Method2}, the measured horizontal offsets obtained with GF-7 DEM

_{Method3}yielded the highest accuracy, with bias and the RMSE values of –0.81 m and 1.37 m, respectively. In other words, the GCPs extracted from ICESat-2/ATLAS data can improve the accuracy of the measurements of horizontal offsets.

## 5. Conclusions

## Author Contributions

## Funding

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

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**Figure 1.**(

**a**) Tectonic map of the area around the Haiyuan fault. (

**b**) The Hasishan section of the Haiyuan fault and the coverage areas of the GF-7 stereo images.

**Figure 2.**The distribution of (

**a**) the checkpoints and (

**b**) the positions for measurement of the vertical and horizontal offset.

**Figure 5.**Measurement of the horizontal offsets using LaDiCaoz_v2.1 software. (

**a**) The faults (light blue), fault-parallel profile lines (red and blue), and the longitudinal tracking line (yellow) were drawn on the basis of the DEM. (

**b**) Dislocation recovery results of the fault. (

**c**) The red and blue topographic profile lines were projected onto the fault plane based on the orientation of the gully, and the red and blue profile lines were matched through horizontal movement, vertical movement, and stretching adjustment. (

**d**) Evaluation of the matching results of the horizontal offsets.

**Figure 6.**Schematic diagram of the measurement results and errors of the vertical offset [37].

**Figure 8.**The spatial distribution and histogram of the difference in elevation between the GF-7 and airborne LiDAR point clouds: (

**a**,

**d**) GF-7 DEM

_{Method1}; (

**b**,

**e**) GF-7 DEM

_{Method2}; (

**c**,

**f**) for GF-7 DEM

_{Method3}.

**Figure 9.**The distribution of the slope of the terrain derived from (

**a**) the airborne LiDAR DEM, (

**b**) GF-7 DEM

_{Method1}, (

**c**) GF-7 DEM

_{Method2}, and (

**d**) GF-7 DEM

_{Method3}.

**Figure 10.**The histogram of the difference in the slope between the airborne LiDAR DEM and (

**a**) GF-7 DEM

_{Method1}, (

**b**) GF-7 DEM

_{Method2}, and (

**c**) GF-7 DEM

_{Method3}.

**Figure 11.**The fault features identified by the airborne LiDAR-derived DEM with a 1 m resolution (

**a**,

**c**,

**e**,

**g**) and GF-7 DEM with a 2 m resolution (

**b**,

**d**,

**f**,

**h**). The blue triangles represent the faults identified.

**Figure 12.**Vertical offsets of V1–V8 measured by the airborne LiDAR-derived DEM, GF-7 DEM

_{Method1}, GF-7 DEM

_{Method2}, and GF-7 DEM

_{Method3}.

**Figure 13.**Error scatterplot between the vertical offsets measured by the airborne LiDAR-DEM and (

**a**) GF-7 DEM

_{Method1}; (

**b**) GF-7 DEM

_{Method2}; (

**c**) GF-7 DEM

_{Method3}.

**Figure 14.**Horizontal offsets and errors of H1–H13 measured by the airborne LiDAR DEM, GF-7 DEM

_{Method1}, GF-7 DEM

_{Method2}and GF-7 DEM

_{Method3}.

Number | Sensor | Product Level | Product ID | Acquisition Time | Cloud Ratio | Coordinate System |
---|---|---|---|---|---|---|

1 | Dual-line-array camera | LEVEL1A | 11181 | 4 December 2019 | 5% | WGS 84 |

2 | Dual-line-array camera | LEVEL1A | 11182 | 4 December 2019 | 5% | WGS 84 |

3 | Dual-line-array camera | LEVEL1A | 51458 | 11 February 2020 | 5% | WGS 84 |

4 | Dual-line-array camera | LEVEL1A | 264266 | 7 December 2020 | 1% | WGS 84 |

Space-Borne LiDAR | ICESat-2/ATLAS |
---|---|

Product | ATL08 |

Version | Version 5 |

Vertical datum | WGS 84 ellipsoid |

Terrain parameters | h_te_best_fit |

Location parameters | latitude, longitude |

Other parameters | atlas_beam_type: dummy indicating strong beams or weak beams |

cloud_flag_atm: cloud confidence flag | |

dem_h: the elevation of the terrain of the reference DEM | |

h_te_skew: the skewness of the heights of the ground photons | |

h_te_uncertainty: uncertainty of the mean terrain height for the 100 m segment | |

n_ca_photons: the number of canopy photons within the 100 m segment | |

n_te_photons: the number of ground photons within the 100 m segment | |

n_toc_photons: the number of top of canopy photons within the 100 m segment | |

night_flag: dummy indicating the data acquisition time, 0 = day, 1 = night | |

segment_landcover: land cover surface type classification, where 60 represents bare, sparse vegetation | |

subset_te_flag: quality flag | |

terrain_slope: the along-track terrain slope of each 100 m segment |

Steps | Filter Criteria |
---|---|

1 | night_flag = 1 |

2 | atlas_beam_type = strong |

3 | h_te_uncertainty < 3.4028235 × 10^{38} |

4 | |h_te_best_fit−dem_h| < 50 |

5 | terrain_slope < 0.05 |

6 | $\mathrm{n}\_\mathrm{te}\_\mathrm{photons}50\mathrm{and}\frac{\mathrm{n}\_\mathrm{te}\_\mathrm{photons}}{\mathrm{n}\_\mathrm{te}\_\mathrm{photons}+\mathrm{n}\_\mathrm{ca}\_\mathrm{photons}+\mathrm{n}\_\mathrm{toc}\_\mathrm{photons}}0.5$ |

7 | h_te_uncertainty < 327.6 |

8 | h_te_skew < 6.03 |

9 | Five flags of subset_te_flag greater than −1 with the middle three flags equal to 1 |

10 | cloud_flag_atm <= 2 |

11 | segment_landcover = 60 |

12 | The distances between ATL08 points should be larger than 500 m |

ID | DEM_{Method1} | DEM_{Method2} | DEM_{Method3} | ||||||
---|---|---|---|---|---|---|---|---|---|

ΔX (m) | ΔY (m) | ΔZ (m) | ΔX (m) | ΔY (m) | ΔZ (m) | ΔX (m) | ΔY (m) | ΔZ (m) | |

1 | 149.77 | −201.53 | −130.55 | 4.69 | −0.21 | 5.24 | 0.50 | 1.20 | 1.16 |

2 | 148.46 | −200.34 | −130.39 | 0.90 | 0.18 | 6.04 | 0.40 | 2.21 | 2.55 |

3 | 157.04 | −201.51 | −129.07 | 3.20 | 0.63 | 1.43 | 0.40 | 0.73 | −2.20 |

4 | 158.32 | −199.87 | −128.46 | 0.37 | −0.38 | 1.46 | 0.18 | 1.63 | −2.50 |

5 | 163.89 | −204.72 | −130.49 | 7.68 | −2.97 | −2.14 | 2.42 | −3.42 | −1.01 |

6 | 159.40 | −202.56 | −127.09 | 2.14 | −3.10 | −0.76 | −2.03 | −0.38 | −0.26 |

7 | 163.83 | −200.53 | −124.44 | 2.26 | −0.81 | −0.13 | 0.06 | −0.12 | −0.75 |

8 | 168.24 | −198.69 | −121.77 | 3.03 | 0.42 | −0.46 | 0.45 | 3.60 | −0.53 |

9 | 174.50 | −201.82 | −115.70 | 2.61 | −0.33 | 2.04 | −0.02 | 0.90 | −1.24 |

10 | 178.16 | −202.59 | −111.41 | 2.25 | −3.15 | 1.57 | 0.13 | 0.47 | −1.72 |

11 | 179.35 | −200.34 | −108.97 | 1.16 | −2.41 | 1.76 | −0.67 | 0.20 | −0.55 |

12 | 182.20 | −204.23 | −105.27 | −0.58 | −7.23 | 1.34 | −3.45 | −4.60 | −1.84 |

13 | 188.54 | −197.85 | −99.99 | −0.01 | −1.92 | 1.36 | −2.13 | 0.59 | −1.46 |

14 | 193.19 | −196.84 | −92.74 | −1.29 | −2.35 | 1.74 | −1.07 | −0.95 | −1.10 |

15 | 194.25 | −197.83 | −92.07 | −1.29 | −2.53 | 1.69 | −2.35 | −0.83 | −1.26 |

16 | 195.00 | −195.72 | −91.87 | 0.05 | −0.09 | 1.59 | −1.59 | 0.88 | −1.03 |

17 | 194.57 | −192.76 | −89.07 | 1.65 | −0.99 | 2.96 | 0.19 | 0.23 | −0.82 |

18 | 199.60 | −192.95 | −85.52 | 0.66 | 0.14 | 0.39 | −0.33 | 0.90 | 0.76 |

19 | 198.63 | −193.20 | −86.36 | −0.75 | −0.04 | −0.54 | −1.03 | 0.16 | −0.17 |

20 | 201.13 | −191.41 | −82.87 | 5.06 | 4.16 | 1.15 | −0.27 | 0.05 | 0.49 |

RMSE | 178.24 | 198.90 | 110.58 | 2.80 | 2.47 | 2.30 | 1.38 | 1.73 | 1.35 |

**Table 5.**Statistics of the horizontal offsets measured by the airborne LiDAR-derived DEM and the GF-7 DEMs.

DEM | GF-7 DEM_{Method1} | GF-7 DEM_{Method2} | GF-7 DEM_{Method3} | ||||||
---|---|---|---|---|---|---|---|---|---|

R^{2} | Bias | RMSE | R^{2} | Bias | RMSE | R^{2} | Bias | RMSE | |

Statistics | 0.99 | –1.80 m | 3.98 m | 1.00 | −0.98 m | 2.52 m | 1.00 | –0.81 m | 1.37 m |

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## Share and Cite

**MDPI and ACS Style**

Zhu, X.; Ren, Z.; Nie, S.; Bao, G.; Ha, G.; Bai, M.; Liang, P.
DEM Generation from GF-7 Satellite Stereo Imagery Assisted by Space-Borne LiDAR and Its Application to Active Tectonics. *Remote Sens.* **2023**, *15*, 1480.
https://doi.org/10.3390/rs15061480

**AMA Style**

Zhu X, Ren Z, Nie S, Bao G, Ha G, Bai M, Liang P.
DEM Generation from GF-7 Satellite Stereo Imagery Assisted by Space-Borne LiDAR and Its Application to Active Tectonics. *Remote Sensing*. 2023; 15(6):1480.
https://doi.org/10.3390/rs15061480

**Chicago/Turabian Style**

Zhu, Xiaoxiao, Zhikun Ren, Sheng Nie, Guodong Bao, Guanghao Ha, Mingkun Bai, and Peng Liang.
2023. "DEM Generation from GF-7 Satellite Stereo Imagery Assisted by Space-Borne LiDAR and Its Application to Active Tectonics" *Remote Sensing* 15, no. 6: 1480.
https://doi.org/10.3390/rs15061480