- Article
(1814 KB) - Metadata XML
- March 31, 2023
- |
- Home
- |
- Sitemap
- |
- The ISPRS Foundation
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Articles | Volume XLII-2/W16
https://doi.org/10.5194/isprs-archives-XLII-2-W16-91-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/isprs-archives-XLII-2-W16-91-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
17 Sep 2019
BLOCK ADJUSTMENT OF LARGE-SCALE HIGH-RESOLUTION OPTICAL SATELLITE IMAGERY WITHOUT GCPS BASED ON THE GPU
Q. Fu
College of surveying and Geo-informatics, Tongji University, Shanghai 200092, China
State Key Laboratory of Disaster Reduction in Civil Engineering, Tongji University, Shanghai 200092, China
School of electronics and information engineering, Jinggangshan University, Jiangxi Ji’an 343009, China
S. Liu
College of surveying and Geo-informatics, Tongji University, Shanghai 200092, China
State Key Laboratory of Disaster Reduction in Civil Engineering, Tongji University, Shanghai 200092, China
X. Tong
College of surveying and Geo-informatics, Tongji University, Shanghai 200092, China
State Key Laboratory of Disaster Reduction in Civil Engineering, Tongji University, Shanghai 200092, China
H. Wang
College of surveying and Geo-informatics, Tongji University, Shanghai 200092, China
State Key Laboratory of Disaster Reduction in Civil Engineering, Tongji University, Shanghai 200092, China
Related authors
No articles found.
Yuelong Xiao, Qunming Wang, Xiaohua Tong, and Peter Atkinson
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2022-415, https://doi.org/10.5194/essd-2022-415, 2023
Revised manuscript under review for ESSD
Short summary
Short summary
A model suitable for large-scale forest age mapping was developed to estimate young forest ages across China in 2020 at a spatial resolution of 30 m, based on 436,967 Landsat tiles across China from 1990 to 2020. The overall accuracy was 90.28 %. The produced forest age map provides a basic dataset for research on the forest carbon cycle and forest ecosystem services, and important guidance for government departments.
H. Zhang, B. Xie, S. Liu, R. Ding, Z. Ye, and X. Tong
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B1-2022, 79–84, https://doi.org/10.5194/isprs-archives-XLIII-B1-2022-79-2022, https://doi.org/10.5194/isprs-archives-XLIII-B1-2022-79-2022, 2022
Q. Xu, H. Xie, Y. Sun, X. Liu, Y. Guo, P. Huang, B. Li, and X. Tong
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2022, 309–314, https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-309-2022, https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-309-2022, 2022
A. Zhao, Y. Cheng, D. Lv, M. Xia, R. Li, L. An, S. Liu, and Y. Tian
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2022, 805–811, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-805-2022, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-805-2022, 2022
H. Zhang, Y. Shang, X. Tong, J. Chen, W. Ma, M. Li, Y. Lu, and H. Chen
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2022, 619–625, https://doi.org/10.5194/isprs-annals-V-3-2022-619-2022, https://doi.org/10.5194/isprs-annals-V-3-2022-619-2022, 2022
S. Luo, Y. Cheng, Z. Li, Y. Wang, K. Wang, X. Wang, G. Qiao, W. Ye, Y. Li, M. Xia, X. Yuan, Y. Tian, X. Tong, and R. Li
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2021, 491–496, https://doi.org/10.5194/isprs-archives-XLIII-B3-2021-491-2021, https://doi.org/10.5194/isprs-archives-XLIII-B3-2021-491-2021, 2021
Y. Gong, H. Xie, X. Tong, Y. Jin, X. Xv, and Q. Wang
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B4-2020, 103–108, https://doi.org/10.5194/isprs-archives-XLIII-B4-2020-103-2020, https://doi.org/10.5194/isprs-archives-XLIII-B4-2020-103-2020, 2020
H. Zhang, S. Liu, Z. Ye, X. Tong, H. Xie, S. Zheng, and Q. Du
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B1-2020, 149–155, https://doi.org/10.5194/isprs-archives-XLIII-B1-2020-149-2020, https://doi.org/10.5194/isprs-archives-XLIII-B1-2020-149-2020, 2020
Z. Ye, Y. Xu, C. Wei, X. Tong, and U. Stilla
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-1-2020, 157–163, https://doi.org/10.5194/isprs-annals-V-1-2020-157-2020, https://doi.org/10.5194/isprs-annals-V-1-2020-157-2020, 2020
Y. Lu, J. Zhang, X. Tong, X. Lu, W. Han, H. Zhang, H. Zhao, and X. Liu
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2020, 141–148, https://doi.org/10.5194/isprs-annals-V-3-2020-141-2020, https://doi.org/10.5194/isprs-annals-V-3-2020-141-2020, 2020
Y. Wang, X. Tong, H. Xie, M. Jiang, Y. Huang, S. Liu, X. Xu, Q. Du, Q. Wang, and C. Wang
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2020, 603–608, https://doi.org/10.5194/isprs-annals-V-3-2020-603-2020, https://doi.org/10.5194/isprs-annals-V-3-2020-603-2020, 2020
S. Gao, Z. Ye, C. Wei, X. Liu, and X. Tong
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2-W7, 33–38, https://doi.org/10.5194/isprs-annals-IV-2-W7-33-2019, https://doi.org/10.5194/isprs-annals-IV-2-W7-33-2019, 2019
Y. Lu, J. Zhang, X. Tong, W. Han, and H. Zhao
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W13, 1243–1247, https://doi.org/10.5194/isprs-archives-XLII-2-W13-1243-2019, https://doi.org/10.5194/isprs-archives-XLII-2-W13-1243-2019, 2019
Y. Cheng, X. Li, G. Qiao, W. Ye, Y. Huang, Y. Li, K. Wang, Y. Tian, X. Tong, and R. Li
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W13, 1735–1739, https://doi.org/10.5194/isprs-archives-XLII-2-W13-1735-2019, https://doi.org/10.5194/isprs-archives-XLII-2-W13-1735-2019, 2019
R. Li, D. Lv, H. Xiao, S. Liu, Y. Cheng, G. Hai, and X. Tong
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W13, 1759–1763, https://doi.org/10.5194/isprs-archives-XLII-2-W13-1759-2019, https://doi.org/10.5194/isprs-archives-XLII-2-W13-1759-2019, 2019
R. Li, H. Xie, Y. Tian, W. Du, J. Chen, G. Hai, S. Zhang, and X. Tong
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W13, 1765–1769, https://doi.org/10.5194/isprs-archives-XLII-2-W13-1765-2019, https://doi.org/10.5194/isprs-archives-XLII-2-W13-1765-2019, 2019
Z. Ye, Y. Xu, L. Hoegner, X. Tong, and U. Stilla
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W13, 147–153, https://doi.org/10.5194/isprs-archives-XLII-2-W13-147-2019, https://doi.org/10.5194/isprs-archives-XLII-2-W13-147-2019, 2019
Y. Tian, S. Zhang, W. Du, J. Chen, H. Xie, X. Tong, and R. Li
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 1657–1660, https://doi.org/10.5194/isprs-archives-XLII-3-1657-2018, https://doi.org/10.5194/isprs-archives-XLII-3-1657-2018, 2018
Wenping Song, Shijie Liu, Xiaohua Tong, Changling Niu, Zhen Ye, Han Zhang, and Yanmin Jin
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-3, 193–196, https://doi.org/10.5194/isprs-annals-IV-3-193-2018, https://doi.org/10.5194/isprs-annals-IV-3-193-2018, 2018
Xin Zhang, Shijie Liu, Haifeng Yu, Xiaohua Tong, and Guoman Huang
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-3, 267–271, https://doi.org/10.5194/isprs-annals-IV-3-267-2018, https://doi.org/10.5194/isprs-annals-IV-3-267-2018, 2018
W. Du, L. Chen, H. Xie, G. Hai, S. Zhang, and X. Tong
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W7, 1513–1516, https://doi.org/10.5194/isprs-archives-XLII-2-W7-1513-2017, https://doi.org/10.5194/isprs-archives-XLII-2-W7-1513-2017, 2017
G. Hai, H. Xie, J. Chen, L. Chen, R. Li, and X. Tong
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W7, 1517–1520, https://doi.org/10.5194/isprs-archives-XLII-2-W7-1517-2017, https://doi.org/10.5194/isprs-archives-XLII-2-W7-1517-2017, 2017
M. Xia, G. Tang, Y. Tian, W. Ye, R. Li, and X. Tong
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W7, 1569–1573, https://doi.org/10.5194/isprs-archives-XLII-2-W7-1569-2017, https://doi.org/10.5194/isprs-archives-XLII-2-W7-1569-2017, 2017
H. Xiao, S. Liu, R. Li, and X. Tong
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W7, 1575–1577, https://doi.org/10.5194/isprs-archives-XLII-2-W7-1575-2017, https://doi.org/10.5194/isprs-archives-XLII-2-W7-1575-2017, 2017
Rongxing Li, Haifeng Xiao, Shijie Liu, and Xiaohua Tong
The Cryosphere Discuss., https://doi.org/10.5194/tc-2017-178, https://doi.org/10.5194/tc-2017-178, 2017
Revised manuscript not accepted
Short summary
Short summary
Fracturing in the RFIS was slightly increased, particularly at its front, from 2003 to 2015. They do not seem to suggest an immediate significant impact on the stability of the shelf. However, with the rapid changes and 3D measurements of Rifts 1 and 2, the most active activities occurred at the front of the FIS from 2001 to 2016. A potential upcoming major calving event in FIS is estimated to occur in 2051. The stability of the ice shelf, particularly Rifts 1 and 2, should be closely monitored.
C. Guo, X. Tong, S. Liu, S. Liu, X. Lu, P. Chen, Y. Jin, and H. Xie
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3-W1, 49–53, https://doi.org/10.5194/isprs-archives-XLII-3-W1-49-2017, https://doi.org/10.5194/isprs-archives-XLII-3-W1-49-2017, 2017
W. Zhao, X. Tong, H. Xie, Y. Jin, S. Liu, D. Wu, X. Liu, L. Guo, and Q. Zhou
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3-W1, 213–218, https://doi.org/10.5194/isprs-archives-XLII-3-W1-213-2017, https://doi.org/10.5194/isprs-archives-XLII-3-W1-213-2017, 2017
Q. Zhou, X. Tong, S. Liu, X. Lu, S. Liu, P. Chen, Y. Jin, and H. Xie
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3-W1, 219–224, https://doi.org/10.5194/isprs-archives-XLII-3-W1-219-2017, https://doi.org/10.5194/isprs-archives-XLII-3-W1-219-2017, 2017
W. Cao, X. H. Tong, S. C. Liu, and D. Wang
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B8, 25–31, https://doi.org/10.5194/isprs-archives-XLI-B8-25-2016, https://doi.org/10.5194/isprs-archives-XLI-B8-25-2016, 2016
T. Feng, Z. Hong, Q. Fu, S. Ma, X. Jie, H. Wu, C. Jiang, and X. Tong
Nat. Hazards Earth Syst. Sci., 14, 2165–2178, https://doi.org/10.5194/nhess-14-2165-2014, https://doi.org/10.5194/nhess-14-2165-2014, 2014
The International Society for Photogrammetry and Remote Sensing is a non-governmental organization devoted to the development of international cooperation for the advancement of photogrammetry and remote sensing and their applications. The Society operates without any discrimination on grounds of race, religion, nationality, or political philosophy.
Useful Links
Useful External Links
Our Contact
c/o
Leibniz University Hannover
Institute of Photogrammetry and GeoInformation
Nienburger Str. 1
D-30167 Hannover
GERMANY
Email: isprs-sg@isprs.org