DEM Generation from GF-7 Satellite Stereo Imagery Assisted by Space-Borne LiDAR and Its Application to Active Tectonics
Abstract
: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
2.5. Ancillary Data
3. Methods
3.1. Extraction of GCPs from ICESat-2/ATLAS Data
3.2. Extraction of DEMs from GF-7 Stereo Images
- 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.
3.3. Measurement of the Horizontal and Vertical Offsets
3.4. Validation of Accuracy
4. Results and Discussion
4.1. Validation of the Accuracy and a Comparison of the GF-7 DEMs
4.2. Observations of the Offset Based on GF-7 DEM
4.3. Comparison of the Measurements of the Vertical Offsets
4.4. Comparison of the Measurements of the Horizontal Offset
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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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 × 1038 |
4 | |h_te_best_fit−dem_h| < 50 |
5 | terrain_slope < 0.05 |
6 | |
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 | DEMMethod1 | DEMMethod2 | DEMMethod3 | ||||||
---|---|---|---|---|---|---|---|---|---|
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 |
DEM | GF-7 DEMMethod1 | GF-7 DEMMethod2 | GF-7 DEMMethod3 | ||||||
---|---|---|---|---|---|---|---|---|---|
R2 | Bias | RMSE | R2 | Bias | RMSE | R2 | 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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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
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 StyleZhu, 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