| 光谱学与光谱分析 |
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| Advance in the Study of the Powdered Weathering Profile of Sandstone on China Yungang Grottoes Based on VIS/NIR Hyperspectral Imaging |
| ZHOU Xiao1, GAO Feng1, 2, ZHANG Ai-wu3, ZHOU Ke-chao2 |
1. Chinese Academy of Culture Heritage, Beijing 100029, China
2. State Key Laboratory of Powder Metallurgy, Central South University, Changsha 410083, China
3. Ministry of Education Key Laboratory on 3D Information Acquisition & Application; Capital Normal University，Beijing 100048, China |
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Abstract Yungang Grottoes were built in the mid-5th century A.D., and named as a UNESCO World Heritage site in 2001. Most of the grottoes were built on the feldspathic quartz sandstones. They were seriously damaged due to the environmental impact. The main form of the weathering is the powdered weathering. The weathering conditions are generally characterized by electrical sounding, penetration resistance, molecular spectroscopy, etc. However, although these methods can give good results about the weathering conditions for a specified sample or site, they are not suitable for providing a global profile of the weathering conditions. The present paper provides a method for effectively and roundly assessing the overall powdered weathering conditions of the Yungang Grottoes based on hyperspectral imaging. Powdered weathering could change the structure and granularity of the sandstone, and thus change the spectral reflectance of the sandstone surface. Based on the hyperspectral data collected from 400 nm to 1 000 nm and normalized by log residuals method, the powdered weathering conditions of the sandstones were classified into strong weathering and weak weathering. The weathering profile was also mapped in the Envi platform. The mapping images were verified using the measured hyperspectal data of the columns in front of the 9th and 10th grottoes as the examples. The mapping images were substantially fitted to the real observations, showing that hyperspectral imaging can be used to estimate the overall powdered weathering of the sandstones.
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Received: 2011-05-17
Accepted: 2011-09-15
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Corresponding Authors:
ZHOU Xiao
E-mail: xiao1978cn@yahoo.com.cn
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[1] HUANG Ke-zhong, ZHONG Shi-hang(黄克忠，钟世航). Sciences of Conservation and Archaeology(文物保护与考古科学), 1989, (1): 28.
[2] SUN Jin-zhong, CHEN Xiang, YUAN Jia-bei(孙进忠, 陈 祥, 袁加贝). Science & Technology Review(科技导报)，2006, 24: 19.
[3] YUAN Jing-hu(苑静虎). Dunhuang Research(敦煌研究), 1989, (3): 94.
[4] WANG Jin-di, ZHANG Li-xin, LIU Qin-huo, et al(王锦地, 张立新, 柳钦火,等). Spectral Database System of Typical Objects in China(中国典型地物波谱知识库). Beijing：Science Press(北京：科学出版社), 2009. 37.
[5] Green A A, Craig M D. Proc. AIS Workshop, JPL Publications 85-41, Jet Propulsion Laboratory, Pasadena, California, April 8-10, 1985. 111.
[6] GAN Fu-ping, WANG Run-sheng, MA Ai-nai(甘甫平, 王润生, 马蔼乃). Earth Science Frontiers(地学前缘)，2003, 10(2): 445.
[7] Huguenin R L, Jones J L. J. of Geophysical Research, 1986, 91: 9587.
[8] GUO Lei, CHANG Wei-wei, FU Chao-yang(郭 雷, 常威威, 付朝阳). Journal of Astronautics(宇航学报), 2011, 32(2): 374.
[9] Kruse F A, Lefkoff A B. Remote Sensing Reviews, 1993, 8: 3.
[10] Datong Ministry of Environmental Protection(大同市环保局). Environmental Monitoring Report on Yungang Grottoes(云冈石窟环境监测报告), 1999.
[11] Kim M S, Chen Y R, Mehl P M. Transactions of ASAE, 2001, 44(3): 721.
[12] Ellis D I, Broadhurst D, Kell D B, et al. Appl Environ. Microbiol., 2002, 68: 2822.
[13] Kemper T, Sommer S. Environmental Science and Technology, 2002, 36(12): 2742.
[14] Lü Feng-jun, HAO Yue-sheng, WANG Juan(吕凤军, 郝跃生, 王 娟). Journal of Jilin University(Earth Science Edition(吉林大学学报·地球科学版)，2011, 41(1): 316.
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