- Article
(2492 KB) - Metadata XML
- November 08, 2024
- |
- Home
- |
- Sitemap
- |
- The ISPRS Foundation
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Articles | Volume XLII-2/W13
https://doi.org/10.5194/isprs-archives-XLII-2-W13-1243-2019
© 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-W13-1243-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
05 Jun 2019
| 05 Jun 2019
CLASSIFICATION ACCURACY ASSESSMENT FOR REGIONAL VECTOR DATA PRODUCT BASED ON SPATIAL SAMPLING: A CASE STUDY OF JAPAN
Y. Lu
School of Surveying and Geo-Informatics, Tongji University, Shanghai, 200092, China
National Quality Inspection and Testing Center For Surveying and Mapping Products, Beijing, 100830, China
J. Zhang
National Quality Inspection and Testing Center For Surveying and Mapping Products, Beijing, 100830, China
X. Tong
School of Surveying and Geo-Informatics, Tongji University, Shanghai, 200092, China
W. Han
National Quality Inspection and Testing Center For Surveying and Mapping Products, Beijing, 100830, China
H. Zhao
National Quality Inspection and Testing Center For Surveying and Mapping Products, Beijing, 100830, China
Related authors
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
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
Jiarui Cao, Rong Huang, Zhen Ye, Yusheng Xu, and Xiaohua Tong
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-3-2024, 51–56, https://doi.org/10.5194/isprs-archives-XLVIII-3-2024-51-2024, https://doi.org/10.5194/isprs-archives-XLVIII-3-2024-51-2024, 2024
Binbin Li, Huan Xie, Shijie Liu, Zhen Ye, Zhonghua Hong, Qihao Weng, Yuan Sun, Qi Xu, and Xiaohua Tong
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-277, https://doi.org/10.5194/essd-2024-277, 2024
Revised manuscript under review for ESSD
Short summary
Short summary
We released a dataset (IC2-GDEM) by directly refining the ASTER GDEM elevation with ICESat-2 altimeter data. It presents a superior elevation quality compared to ASTER GDEM at a global scale. It can promote seamless integration with the historical datasets of ASTER GDEM, which is essential for longitudinal studies of long-term environmental changes, etc. impacts. It can be as a new complementary data source to other DEMsfor leading to more reliable and comprehensive scientific discoveries.
C. Chen, X. Wang, H. Zhao, F. Luo, and H. Li
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-1-W2-2023, 1973–1978, https://doi.org/10.5194/isprs-archives-XLVIII-1-W2-2023-1973-2023, https://doi.org/10.5194/isprs-archives-XLVIII-1-W2-2023-1973-2023, 2023
S. Xu, R. Huang, Y. Xu, Z. Ye, H. Xie, and X. Tong
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-1-W2-2023, 771–776, https://doi.org/10.5194/isprs-archives-XLVIII-1-W2-2023-771-2023, https://doi.org/10.5194/isprs-archives-XLVIII-1-W2-2023-771-2023, 2023
W. Mao, H. Zhao, W. Han, H. Tu, and W. Gao
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-1-W2-2023, 1453–1458, https://doi.org/10.5194/isprs-archives-XLVIII-1-W2-2023-1453-2023, https://doi.org/10.5194/isprs-archives-XLVIII-1-W2-2023-1453-2023, 2023
Yuelong Xiao, Qunming Wang, Xiaohua Tong, and Peter M. Atkinson
Earth Syst. Sci. Data, 15, 3365–3386, https://doi.org/10.5194/essd-15-3365-2023, https://doi.org/10.5194/essd-15-3365-2023, 2023
Short summary
Short summary
Forest age is closely related to forest production, carbon cycles, and other ecosystem services. Existing stand age products in China derived from remote-sensing images are of a coarse spatial resolution and are not suitable for applications at the regional scale. Here, we mapped young forest ages across China at an unprecedented fine spatial resolution of 30 m. The overall accuracy (OA) of the generated map of young forest stand ages across China was 90.28 %.
J. Shen, W. Han, L. Zhang, H. Li, and L. Lyu
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B4-2022, 627–632, https://doi.org/10.5194/isprs-archives-XLIII-B4-2022-627-2022, https://doi.org/10.5194/isprs-archives-XLIII-B4-2022-627-2022, 2022
Y. Xu, J. Zhang, X. Wang, H. Zhao, C. Chen, and W. Mao
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2022, 1249–1254, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-1249-2022, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-1249-2022, 2022
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
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
H. T. Zhao, W. C. Gao, C. F. Jing, and X. F. Li
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B4-2021, 327–332, https://doi.org/10.5194/isprs-archives-XLIII-B4-2021-327-2021, https://doi.org/10.5194/isprs-archives-XLIII-B4-2021-327-2021, 2021
C. X. Chen, H. Zhang, K. Jiang, H. T. Zhao, W. Xie, C. He, and S. Yin
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2021, 749–754, https://doi.org/10.5194/isprs-archives-XLIII-B3-2021-749-2021, https://doi.org/10.5194/isprs-archives-XLIII-B3-2021-749-2021, 2021
S. Yin, H. T. Zhao, C. X. Chen, B. Q. Yao, and J. Zhou
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2021, 805–809, https://doi.org/10.5194/isprs-archives-XLIII-B3-2021-805-2021, https://doi.org/10.5194/isprs-archives-XLIII-B3-2021-805-2021, 2021
H. T. Zhao, J. Zhou, C. F. Jing, and X. F. Li
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2021, 811–816, https://doi.org/10.5194/isprs-archives-XLIII-B3-2021-811-2021, https://doi.org/10.5194/isprs-archives-XLIII-B3-2021-811-2021, 2021
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
W. C. Gao, H. T. Zhao, W. J. Mao, S. Yin, and Z. B. Tian
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 1327–1331, https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-1327-2020, https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-1327-2020, 2020
J. Guo, J. X. Zhang, H. T. Zhao, C. Li, J. Zhou, H. J. Tu, and Y. Zhao
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 1333–1338, https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-1333-2020, https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-1333-2020, 2020
Y. Zhao, H. T. Zhao, J. Guo, H. J. Tu, and S. Yin
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 1411–1416, https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-1411-2020, https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-1411-2020, 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
Q. Fu, S. Liu, X. Tong, and H. Wang
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W16, 91–94, https://doi.org/10.5194/isprs-archives-XLII-2-W16-91-2019, https://doi.org/10.5194/isprs-archives-XLII-2-W16-91-2019, 2019
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
X. Zhang, J. Zhang, L. Zhang, and J. Liu
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W13, 1281–1286, https://doi.org/10.5194/isprs-archives-XLII-2-W13-1281-2019, https://doi.org/10.5194/isprs-archives-XLII-2-W13-1281-2019, 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
W. Lu, J. Zhang, G. Xue, and C. Wang
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 1203–1208, https://doi.org/10.5194/isprs-archives-XLII-3-1203-2018, https://doi.org/10.5194/isprs-archives-XLII-3-1203-2018, 2018
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
H. Ni, X. G. Lin, and J. X. Zhang
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W7, 837–843, https://doi.org/10.5194/isprs-archives-XLII-2-W7-837-2017, https://doi.org/10.5194/isprs-archives-XLII-2-W7-837-2017, 2017
H. Ni, X. G. Lin, and J. X. Zhang
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W7, 277–283, https://doi.org/10.5194/isprs-archives-XLII-2-W7-277-2017, https://doi.org/10.5194/isprs-archives-XLII-2-W7-277-2017, 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