- Article
(656 KB) - Metadata XML
- December 22, 2024
- |
- Home
- |
- Sitemap
- |
- The ISPRS Foundation
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Articles | Volume XLII-3
https://doi.org/10.5194/isprs-archives-XLII-3-1879-2018
© Author(s) 2018. 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-3-1879-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
30 Apr 2018
| 30 Apr 2018
DISASTER MONITORING AND EMERGENCY RESPONSE SERVICES IN CHINA
J. Wu
Faculty of Geographical Sciences, Beijing Normal University, Beijing, China
X. Han
Faculty of Geographical Sciences, Beijing Normal University, Beijing, China
Y. Zhou
Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, China
P. Yue
Wuhan University, China
X. Wang
Institute of Earthquake Forecasting, China Earthquake Administration, China
J. Lu
China Institute of Water Resources and Hydropower Research, China
Faculty of Geographical Sciences, Beijing Normal University, Beijing, China
J. Li
Faculty of Geographical Sciences, Beijing Normal University, Beijing, China
Faculty of Geographical Sciences, Beijing Normal University, Beijing, China
F. Wang
Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, China
X. Li
China Institute of Water Resources and Hydropower Research, China
J. Fan
National Satellite Meteorological Center, China Meteorological Administration, China
Related authors
A. Gong, J. Li, Y. Yang, Y. Chen, T. Zeng, J. Wu, J. Li, Y. Chen, H. Tang, and J. Yue
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 1207–1212, https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-1207-2020, https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-1207-2020, 2020
X. Han and J. Wu
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 481–483, https://doi.org/10.5194/isprs-archives-XLII-3-481-2018, https://doi.org/10.5194/isprs-archives-XLII-3-481-2018, 2018
J. Yang, J. Wu, H. Zhou, and X. Han
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 2073–2077, https://doi.org/10.5194/isprs-archives-XLII-3-2073-2018, https://doi.org/10.5194/isprs-archives-XLII-3-2073-2018, 2018
X. Mo, J. J. Wu, Q. Wang, and H. Zhou
Nat. Hazards Earth Syst. Sci., 16, 469–482, https://doi.org/10.5194/nhess-16-469-2016, https://doi.org/10.5194/nhess-16-469-2016, 2016
Short summary
Short summary
GRACE provided a new perspective on the anomaly monitoring of water resources. In 2003–2013, TWS in China generally followed variations in annual precipitation; it decreased in northern basins and increased in the south. In the North China Plain, human activities might have interfered with TWS's response to climate; it kept decreasing until 2012. With grid-scaled TWS, small regions with significant increasing trends were also identified in Hoh Xil and the headstream region of the Yellow River.
Kaifeng Peng, Beibei Si, Weiguo Jiang, Meihong Ma, and Xuejun Wang
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-468, https://doi.org/10.5194/essd-2024-468, 2024
Preprint under review for ESSD
Short summary
Short summary
We produced the 10-m China River Extent Dataset (CRED), achieving an overall accuracy exceeding 96.8 %, Data comparisons with existing river-related datasets shown the reliability and superiority of CRED. The spatial-temporal changes of China’s river from 2016 to 2023 were revealed, such as the decreased rivers most distributed in Southeast China, while the increased rivers most located in Central and Northeast China. The CRED can support applications related to river protection sustainable use.
Peng Yue, Kaixuan Wang, Hanwen Xu, Jianya Gong, and Longgang Xiang
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., X-4-2024, 441–446, https://doi.org/10.5194/isprs-annals-X-4-2024-441-2024, https://doi.org/10.5194/isprs-annals-X-4-2024-441-2024, 2024
Xianyuan Zhang, Longgang Xiang, Peng Yue, Jianya Gong, and Huayi Wu
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., X-4-2024, 453–459, https://doi.org/10.5194/isprs-annals-X-4-2024-453-2024, https://doi.org/10.5194/isprs-annals-X-4-2024-453-2024, 2024
Zeping Liu, Hong Tang, Lin Feng, and Siqing Lyu
Earth Syst. Sci. Data, 15, 3547–3572, https://doi.org/10.5194/essd-15-3547-2023, https://doi.org/10.5194/essd-15-3547-2023, 2023
Short summary
Short summary
Large-scale maps of building rooftop area (BRA) are crucial for addressing policy decisions and sustainable development. In this paper, we propose a deep-learning method for high-resolution BRA mapping (2.5 m) from Sentinel-2 imagery (10 m). The resulting China building rooftop area dataset (CBRA) is the first multi-annual (2016–2021) and high-resolution (2.5 m) BRA dataset in China. Cross-comparisons show that the CBRA achieves the best performance in capturing the spatiotemporal information.
Ming Wang, Liangcun Jiang, Peng Yue, Dayu Yu, and Tianyu Tuo
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-73, https://doi.org/10.5194/essd-2023-73, 2023
Preprint withdrawn
Short summary
Short summary
We present a large-scale Flame and Smoke Detection Dataset (FASDD) covering complex and varied fire scenarios. FASDD contains fire, smoke, and confusing non-fire/non-smoke images acquired at different distances (near and far), different scenes (indoor and outdoor), different light intensities (day and night), and from various platforms (surveillance cameras, drones, and satellites). To the best of our knowledge, FASDD is currently the most versatile and comprehensive dataset for fire detection.
Ming Wang, Liangcun Jiang, Peng Yue, Dayu Yu, and Tianyu Tuo
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2022-394, https://doi.org/10.5194/essd-2022-394, 2022
Preprint withdrawn
Short summary
Short summary
We present a large-scale Flame and Smoke Detection Dataset (FASDD) covering complex and varied fire scenarios. FASDD contains fire, smoke, and confusing non-fire/non-smoke images acquired at different distances (near and far), different scenes (indoor and outdoor), different light intensities (day and night), and from various sensors (surveillance cameras, UAV, and satellites). To the best of our knowledge, it is the largest multimodal dataset for deep learning based fire detection.
Weijie Zou, Yi Zhou, Shixin Wang, Futao Wang, Litao Wang, Qing Zhao, Wenliang Liu, Jinfeng Zhu, Yibing Xiong, Zhenqing Wang, and Gang Qin
Nat. Hazards Earth Syst. Sci., 22, 2081–2097, https://doi.org/10.5194/nhess-22-2081-2022, https://doi.org/10.5194/nhess-22-2081-2022, 2022
Short summary
Short summary
Landslide dams are secondary disasters caused by landslides, which can cause great damage to mountains. We have proposed a procedure to calculate the key parameters of these dams that uses only a single remote-sensing image and a pre-landslide DEM combined with landslide theory. The core of this study is a modeling problem. We have found the bridge between the theory of landslide dams and the requirements of disaster relief.
Jin Chen, Hong Tang, and Wenkai Chen
Nat. Hazards Earth Syst. Sci., 20, 3117–3134, https://doi.org/10.5194/nhess-20-3117-2020, https://doi.org/10.5194/nhess-20-3117-2020, 2020
Short summary
Short summary
The spatial and temporal distribution characteristics of aftershocks around the fault are analyzed according to the stress changes after the main earthquake. The model can be used to predict the multi-timescale anisotropy distribution of aftershocks fairly. The finite fault model of the main earthquake is used in the construction of the prediction model. The model is a deep neural network; the inputs are the stress components of each point; and the output is the probability of an aftershock.
W. Qu, Y. Yao, Z. Pang, J. Lu, K. Yang, X. Li, L. Li, W. Jiang, J. Fu, and T. Lei
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 745–749, https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-745-2020, https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-745-2020, 2020
A. Gong, J. Li, Y. Yang, Y. Chen, T. Zeng, J. Wu, J. Li, Y. Chen, H. Tang, and J. Yue
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 1207–1212, https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-1207-2020, https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-1207-2020, 2020
T. J. Lei, R. R. Xu, J. H. Cheng, W. L. Song, W. Jiang, W. Qu, J. X. Lu, and S. Li
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B1-2020, 471–478, https://doi.org/10.5194/isprs-archives-XLIII-B1-2020-471-2020, https://doi.org/10.5194/isprs-archives-XLIII-B1-2020-471-2020, 2020
W. Jiang, Y. Ni, Z. Pang, G. He, J. Fu, J. Lu, K. Yang, T. Long, and T. Lei
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2020, 33–38, https://doi.org/10.5194/isprs-annals-V-3-2020-33-2020, https://doi.org/10.5194/isprs-annals-V-3-2020-33-2020, 2020
Z. Pang, X. Qin, W. Jiang, J. Fu, K. Yang, J. Lu, W. Qu, L. Li, and X. Li
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2020, 395–399, https://doi.org/10.5194/isprs-annals-V-3-2020-395-2020, https://doi.org/10.5194/isprs-annals-V-3-2020-395-2020, 2020
J. Fu, M. Wang, Z. Pang, W. Jiang, J. Lu, K. Yang, L. Li, X. Li, and W. Qu
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2020, 739–744, https://doi.org/10.5194/isprs-annals-V-3-2020-739-2020, https://doi.org/10.5194/isprs-annals-V-3-2020-739-2020, 2020
H. J. Wang, Y. Zhou, S. X. Wang, F. T. Wang, and Q. Zhao
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3-W10, 37–43, https://doi.org/10.5194/isprs-archives-XLII-3-W10-37-2020, https://doi.org/10.5194/isprs-archives-XLII-3-W10-37-2020, 2020
Q. Hu, Y. Zhou, S. X. Wang, F. T. Wang, and H. J. Wang
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3-W10, 125–131, https://doi.org/10.5194/isprs-archives-XLII-3-W10-125-2020, https://doi.org/10.5194/isprs-archives-XLII-3-W10-125-2020, 2020
M. Chen, X. Wang, A. Dou, and X. Wu
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 161–165, https://doi.org/10.5194/isprs-archives-XLII-3-161-2018, https://doi.org/10.5194/isprs-archives-XLII-3-161-2018, 2018
L. P. Cui, X. P. Wang, A. X. Dou, and X. Ding
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 239–244, https://doi.org/10.5194/isprs-archives-XLII-3-239-2018, https://doi.org/10.5194/isprs-archives-XLII-3-239-2018, 2018
X. Han and J. Wu
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 481–483, https://doi.org/10.5194/isprs-archives-XLII-3-481-2018, https://doi.org/10.5194/isprs-archives-XLII-3-481-2018, 2018
S. Li and H. Tang
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 929–936, https://doi.org/10.5194/isprs-archives-XLII-3-929-2018, https://doi.org/10.5194/isprs-archives-XLII-3-929-2018, 2018
X. N. Niu, H. Tang, and L. X. Wu
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 1327–1331, https://doi.org/10.5194/isprs-archives-XLII-3-1327-2018, https://doi.org/10.5194/isprs-archives-XLII-3-1327-2018, 2018
W. Qu, N. Hu, J. Fu, J. Lu, H. Lu, T. Lei, Z. Pang, X. Li, and L. Li
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 1461–1465, https://doi.org/10.5194/isprs-archives-XLII-3-1461-2018, https://doi.org/10.5194/isprs-archives-XLII-3-1461-2018, 2018
S. Wang, B. Yang, Y. Zhou, F. Wang, R. Zhang, and Q. Zhao
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 1765–1772, https://doi.org/10.5194/isprs-archives-XLII-3-1765-2018, https://doi.org/10.5194/isprs-archives-XLII-3-1765-2018, 2018
S. Wang, X. Wang, A. Dou, X. Yuan, L. Ding, and X. Ding
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 1773–1776, https://doi.org/10.5194/isprs-archives-XLII-3-1773-2018, https://doi.org/10.5194/isprs-archives-XLII-3-1773-2018, 2018
X. Wu, X. Wang, X. Yuan, M. Chen, and A. Dou
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 1895–1899, https://doi.org/10.5194/isprs-archives-XLII-3-1895-2018, https://doi.org/10.5194/isprs-archives-XLII-3-1895-2018, 2018
J. Yang, J. Wu, H. Zhou, and X. Han
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 2073–2077, https://doi.org/10.5194/isprs-archives-XLII-3-2073-2018, https://doi.org/10.5194/isprs-archives-XLII-3-2073-2018, 2018
X. Yuan, X. Wang, X. Ding, X. Wu, A. Dou, and S. Wang
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 2191–2193, https://doi.org/10.5194/isprs-archives-XLII-3-2191-2018, https://doi.org/10.5194/isprs-archives-XLII-3-2191-2018, 2018
A. X. Dou, X. X. Yuan, X. Q. Wang, and Z. M. Li
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B8, 51–54, https://doi.org/10.5194/isprs-archives-XLI-B8-51-2016, https://doi.org/10.5194/isprs-archives-XLI-B8-51-2016, 2016
Z. Chen, W. G. Jiang, Z. H. Tang, and K. Jia
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B3, 849–852, https://doi.org/10.5194/isprs-archives-XLI-B3-849-2016, https://doi.org/10.5194/isprs-archives-XLI-B3-849-2016, 2016
Xi Chen, Jing Li, Yunfei Zhang, and Liangliang Tao
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., III-7, 125–132, https://doi.org/10.5194/isprs-annals-III-7-125-2016, https://doi.org/10.5194/isprs-annals-III-7-125-2016, 2016
X. N. Niu, X. J. Zhai, H. Tang, and L. X. Wu
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B1, 475–481, https://doi.org/10.5194/isprs-archives-XLI-B1-475-2016, https://doi.org/10.5194/isprs-archives-XLI-B1-475-2016, 2016
X. Mo, J. J. Wu, Q. Wang, and H. Zhou
Nat. Hazards Earth Syst. Sci., 16, 469–482, https://doi.org/10.5194/nhess-16-469-2016, https://doi.org/10.5194/nhess-16-469-2016, 2016
Short summary
Short summary
GRACE provided a new perspective on the anomaly monitoring of water resources. In 2003–2013, TWS in China generally followed variations in annual precipitation; it decreased in northern basins and increased in the south. In the North China Plain, human activities might have interfered with TWS's response to climate; it kept decreasing until 2012. With grid-scaled TWS, small regions with significant increasing trends were also identified in Hoh Xil and the headstream region of the Yellow River.
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